In recent times, large American technology companies such as Meta Platforms, Salesforce, and Alphabet have initiated a dividend.

It’s easy to imagine that their shareholders would be pleased about it, but this isn’t always the case. Some shareholders are actually disappointed about the dividend announcements. They think that the companies have nowhere else to invest their capital and are thus returning it to their shareholders. In other words, they think that the companies’ growth potential have stalled.

But I see things differently. Dividends are ultimately what we, as shareholders, invest in a company for. Long-term shareholders are here to earn a cash stream from investing in companies. This is akin to building your own business which generates profits which you can cash out and enjoy. As such, dividends are the fruits of our investment.

And just because a company has started paying a dividend does not mean it can’t grow its earnings. Just look at some of the dividend aristocrats that have grown their earnings over a long span of time. There are many companies that can generate high returns on invested capital. This means that they can pay out a high proportion of their earnings as dividends and still continue to grow.

Dividends can compound too

For investors who don’t want to spend the dividend a company is paying, they can put that dividend to use by reinvesting it.

When a company is not paying a dividend, its shareholders have to rely on management to invest the company’s profits. When there’s a dividend, shareholders can invest the dividend in a way that they believe give them the highest risk-adjusted return available. Moreover, a company’s management team may not be the best capital allocators around – in such a case, when the company generates excess cash, management may invest it in a way that does not generate good returns. When a company pays a dividend, shareholders can make their own decisions and do not have to rely on management’s capital allocation skills.

And if you think the company was better off buying back shares, you can simply buy shares of that company with your dividends. This will have a similar effect to share buybacks as it will increase your stake in the company.

What’s the catch?

Dividends have some downsides though. 

Compared to buybacks, reinvesting dividends to buy more shares may be slightly less effective as shareholders may have to pay tax on those dividends. For example, Singapore-based investors who buy US stocks have to pay a 30% withholding tax on all US-company dividends.

The other downside is there’s more work for shareholders. If management was reinvesting prudently and not paying dividends, shareholders wouldn’t need to make a decision. But with dividends, shareholders have to decide where and when to reinvest that dividend. This said, it does give shareholders more options and opens up possibilities of where the dividend can be invested, instead of just relying on management. To me, I would happily take this tradeoff.

Don’t fret

Dividends are good. It’s funny that I even need to say this.

Dividends are the fruits of our investments and are what makes investing in companies so profitable. Without it, we will just be traders of companies, and not investors.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have a vested interest in Alphabet, Meta Platforms, and Salesforce. Holdings are subject to change at any time.

Last month, I published The Latest Thoughts From American Technology Companies On AI (2024 Q1). In it, I shared commentary in earnings conference calls for the first quarter of 2024, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2024’s first quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here

Here they are, in no particular order:

Adobe (NASDAQ: ADBE)

Adobe’s management thinks that creativity is a human trait and that AI assists and amplifies human ingenuity without replacing it

Adobe’s highly differentiated approach to AI is rooted in the belief that creativity is a uniquely human trait and that AI has the power to assist and amplify human ingenuity and enhance productivity.

Adobe’s Firefly generative AI models within its Creative Cloud suite were trained on proprietary data; Adobe’s management has infused AI functionality into its flagship products within the Creative Cloud suite; management has built Adobe Express as an AI-first application; Firefly has generated over 9 billion images since its launch in March 2023 (was 6.5 billion in 2023 Q4); customers are excited about the commercial safety of Firefly; Firefly Services can create significantly more asset variations in a much shorter time, and the speed enables Adobe to monetise Firefly through the volume of content created; Firefly generations in May 2024 was the most generations of any month-to-date; Firefly Services has started to see customer wins; Firefly Services allows users to build customer models and access APIs and these are early in adoption, but customer-interest is better than expected; early Firefly Services usage is on (1) creating multiple variations in the ideation process, (2) creating geography-based variations on ads, (3) assets for community engagement

In Creative Cloud, we’ve invested in training our Firefly family of creative generative AI models with a proprietary data set and delivering AI functionality within our flagship products, including Photoshop, Illustrator, Lightroom, and Premier. We’re reimagining creativity for a broader set of customers by delivering Adobe Express as an AI-first application across the web and mobile surfaces. Since its debut in March 2023, Firefly has been used to generate over 9 billion images across Adobe creative tools…

…This week’s Design Made Easy event, which focused on Express for Business, was another big step forward for us. Companies of all sizes are excited about the integrated power and commercial safety of Firefly, the seamless workflows with Photoshop, Illustrator and Adobe Experience Cloud, and enterprise-grade brand controls that are now part of Express for Business, making it the optimal product for marketing, sales and HR teams to quickly and easily create visual content to share…

… Firefly Services can power the creation of thousands of asset variations in minutes instead of months and at a fraction of the cost. This allows us to monetize the volume of content being created through automation services. The increasing availability of Firefly in Creative Cloud, Express, Firefly Services and the web app is giving us opportunities to access more new users, provide more value to existing users and monetize content automation. These integrations are driving the acceleration of Firefly generations with May seeing the most generations of any month to date…

…On the models, we released Firefly Services. We’ve started to see some customer wins in Firefly Services. So they’re using it for variations, and these are the custom models that we’re creating as well as access to APIs. I would say that’s early in terms of the adoption, but the interest as customers say how they can ingest their data into our models as well as custom models, that’s really ahead of us, and we expect that to continue to grow in Q3 and Q4…

…In terms of what I would say we’re seeing usage of, I think the initial usage of Firefly Services in most companies was all around ideation, how can they create multiple variations of them and in the ideation process really just accelerate that ideation process? Most companies are then starting with as they’re putting it into production, how can they, with the brand assets and the brand guidelines that they have, do this in terms of the variations, whether they be geographic variations or they be just variations. I mean, if you take a step back also, every single ad company right now will tell you that the more variance that you provide, the better your chances are of appropriately getting an uplift for your media spend. So I would say that most companies are starting with creating these variations for geographies. The other one that we see a fair amount of is engaging with their communities. So when they want their communities to have assets that they have blessed for usage within community campaigns, that’s the other place where Firefly Services are being used. And a company has a community portal where the community can come in, take something and then post whether it’s on whatever social media site that you want. 

Adobe’s management has introduced Acrobat AI Assistant, an AI-powered tool for users to have conversations with their documents, within Adobe’s Document Cloud suite; Acrobat AI Assistant features are available as a standalone offer or as an add-on subscription to existing Adobe products; Acrobat AI Assistant for English documents was made generally available in April; management is seeing early success in adoption of Acrobat AI Assistant; Acrobat AI Assistant can be applied to document types beyond PDFs

In Document Cloud, we’re revolutionizing document productivity with Acrobat AI Assistant, an AI-powered conversational engine that can easily be deployed in minutes. This enhances the value of the trillions of PDFs, which hold a significant portion of the world’s information. Acrobat AI Assistant features are now available through an add-on subscription to all Reader and Acrobat enterprise and individual customers across desktop, web and mobile…

…The introduction of Acrobat AI Assistant made generally available in April for English documents marks the beginning of a new era of innovation and efficiency for the approximately 3 trillion PDFs in the world. Acrobat AI Assistant is empowering everyone to shift from reading documents to having conversations with them in order to summarize documents, extract insights, compose presentations and share learnings. AI Assistant is available as a stand-alone offer for use in reader and as an add-on to Acrobat Standard and Pro. We’re seeing early success driving adoption of AI Assistant as part of our commerce flows and remain optimistic about the long-term opportunities…

…Other business highlights include general availability of Acrobat AI Assistant support for document types beyond PDF, meeting transcripts and enterprise requirements. 

The Adobe Experience platform, which is part of the Digital Experience segment, is on track to become a billion-dollar annual revenue business; management has released AEP (Adobe Experience Platform) AI Assistant to improve the productivity of marketing professionals; Adobe is the #1 digital experience platform; customer interest and adoption of AEP AI Assistant is great

At the end of May, we celebrated the 5-year anniversary of Adobe Experience Platform, which we conceived and built from scratch and which is on track to be the next billion-dollar business in our Digital Experience portfolio. We released AEP AI Assistant to enhance the productivity of marketing practitioners through generative AI while expanding access to native AEP applications…

…When we introduced Adobe Experience Platform 5 years ago, it was a revolutionary approach to address customer data and journeys. Today, we’re the #1 digital experience platform and AEP with native apps is well on its way to becoming a billion-dollar business…

…We are excited by the customer interest and adoption of our latest innovations, including AEP AI Assistant, a generative AI-powered conversational interface that empowers practitioners to automate tasks, simulate outcomes and generate new audiences and journeys. For example, customers like General Motors and Hanesbrands have been working with AEP AI Assistant to boost productivity and accelerate time to value while democratizing access to AEP and apps across their organizations…

…When you think about the AEP AI Assistant, it’s doing a couple of things. One, it’s really making it easier for customers to deploy use cases. When you think of use cases that they have around, for example, generating audiences and running campaigns around those audiences, these are things today that require some data engineering. They require the ability to put these audiences together. So they require marketing and IT teams to work together. The AEP AI Assistant is making it much easier for marketers to be able to do it themselves and be able to deploy a lot more use cases.

Adobe’s management’s vision for Adobe Express is to make design easy; the launch of the new Adobe Express app in 2024 Q1 (FY2024 Q2) has been well received, with monthly active users doubling sequentially; management has been deeply integrating AI features into Adobe Express; cumulative exports from Adobe Express has increased by 80% year-on-year in 2024 Q1; management is building Adobe Express to be AI-first; management thinks Adobe Express is leveraging people’s need for AI 

Our vision for Adobe Express is to provide a breakthrough application to make design easy for communicators worldwide, leveraging generative AI and decades of Adobe technology across web and mobile. Our launch of the all-new Express application on iOS and Android earlier this quarter is off to a strong start with monthly active users doubling quarter-over-quarter…

…There’s a lot of buzz with Express here at Adobe coming off the event we just had earlier this week, but it’s really based on the fact that the innovation in Express is on a tear, right? A few months ago, we introduced an all-new Express for the web. This quarter, we introduced an all-new Express for mobile. We introduced Express for Business. We also now have, as we’ve just talked about, been more deeply integrating AI features, whether it’s for imaging generation or Generative Fill or text effects, character animation, design generations, more deeply into the flow for Express. And that combination has led to an incredible set of metrics over the last quarter, in particular, but building throughout the year. Express MAU is growing very quickly. We talked about in the script earlier that MAU on mobile has more than doubled quarter-over-quarter, which is fantastic to see. And cumulative exports, if you look at year-over-year, has grown by over 80%. So really feeling good about sort of the momentum we’re seeing…

…Express that is now in market is built on a brand-new platform, right? And that brand-new platform lays the groundwork for the AI era. And this will be — Express will be the place that anyone can come and create through a combination of conversational and standard inputs. That’s the vision that we have. And I think it’s an opportunity for us to really leap forward in terms of what we can do on the web and mobile at Adobe…

…Express is really being driven by sort of the need for AI and how people are able to describe what they want and get the final output. When David talked about exports, just to clarify, what that means is people who have successfully got what they want to get done, done. And that’s a key measure of how we are doing it, and AI is certainly facilitating and accelerating that.

Adobe GenStudio uses AI to help enterprises transform their content supply chain; enterprise customers view customer experience management and personalisation at scale as key investments to make

We’re now transforming the content supply chain for enterprises with Adobe GenStudio, enabling them to produce content at scale, leveraging generative AI through native integrations with Firefly Services and Adobe Express for Business. Enterprise customers, both B2C and B2B, view customer experience management and personalization at scale as key areas of differentiation, making it a priority investment for Chief Marketing Officers, Chief Information Officers and Chief Digital Officers.

Adobe’s management thinks the biggest opportunity in AI for Adobe is in interfaces, such as performing tasks faster, improving workflows etc; in AI interfaces, management is seeing significant usage in AI Assistant and Photoshop; management believes that (1) the real benefits from disruptive technologies such as AI come when people use interfaces to improve their work, and that (2) in the future, more people will be using these interfaces

I think the biggest opportunity for us and why we’re really excited about GenAI is in the interfaces because that’s the way people derive value, whether it’s in being able to complete their tasks faster, whether it’s be able to do new workflows. And I would say, in that particular space, Acrobat has really seen a significant amount of usage as it relates to AI Assistant and Photoshop…

… And so we’re always convinced that when you have this kind of disruptive technology, the real benefits come when people use interfaces to do whatever task they want to do quicker, faster and when it’s embedded into the workflows that they’re accustomed to because then there isn’t an inertia associated with using it…

…And so net-net, I am absolutely betting on the fact that 5 years from now, there’ll be more people saying, “I’m using creative tools to accomplish what I want,” and there’ll be more marketers saying, “I can now, with the agility that I need, truly deliver a marketing campaign in an audience that’s incredibly more specific than I could in the past.” And that’s Adobe’s job to demonstrate how we are both leading in both those categories and to continue to innovate.

Adobe’s management’s primary focus for generative AI is still on user adoption and proliferation

From the very beginning, we’ve talked to you guys about our primary focus for generative AI is about user adoption and proliferation, right? And that has continued to be the primary thing on our mind.

Adobe’s management thinks there are different routes to monetise AI, such as winning new users, and getting higher ARPU (average revenue per user)

And to your point, there are many different ways that we can monetize this. First is as you think about the growth algorithms that we always have in our head, it always starts with, as Shantanu said, new users, right? And then it’s about getting more value to existing users at higher ARPU, right? So in the context of new users, first and foremost, we want to make sure that everything we’re doing generative AI is embedded in our tools, starting with Express, right?

Adobe has seen strong growth in emerging markets because users need access to the cloud for all of the AI functionality

I mean I think in the prepared remarks, Dan also talked about the strength in emerging markets. And I think the beautiful part about AI is that since they need access to the cloud to get all of the AI functionality, emerging market growth has been really strong for us.

Adobe’s management thinks that they have hit a sweet spot with pricing for generative AI credits in Adobe’s subscription plans for imaging and vector work, but they will need to explore different plans for generative AI credits when it comes to video work

When we think about what we’ve done with imaging and video, we’ve done the right thing by making sure the higher-value paid plans that people don’t have to think about the amount of generative capability. And so there, the balance between for free and trialist users, they’re going to run into the generative capability limits and therefore, have to subscribe. But for the people who actually have imaging and vector needs, that they’re not constantly thinking about generative, I think we actually got it right. To your point, as we move to video, expect to see different plans because those plans will, by necessity, take into account the amount of work that’s required to do video generation. So you’re absolutely right as a sort of framework for you to think about it.

Adobe’s management thinks that there’s a lot of excitement now on AI infrastructure and chips, but the value of AI will need to turn to inference in order for all the investment in AI infrastructure and chips to make sense

It’s fair to say that the interest that exists right now from investors, as it relates to AI, is all associated with the infrastructure and chips and perhaps rightly so because that’s where everybody is creating these models. They’re all trying to train them. And there’s a lot of, I think, deserved excitement associated with that part of where we are in the evolution of generative AI. If the value of AI doesn’t turn to inference and how people are going to use it, then I would say all of that investment would not really reap the benefit in terms of where people are spending the money.

Adobe’s management think it doesn’t matter what AI model is used to generate content – DALL-E, Firefly, Midjourney, or more – because the content ultimately needs to be edited on Adobe’s software; management is building products on Firefly, but they are also happy to leverage on third-party AI models

So Firefly might be better at something. Midjourney might be something at something else. DALL·E might do something else. And the key thing here is that, around this table, we get excited when models innovate. We get excited when Firefly does something amazing. We get excited when third-party models do something because our view, to Shantanu’s point, is that the more content that gets generated out of these models, the more content that needs to be edited, whether it’s color correction, tone matching, transitions, assembling clips or masking compositing images. And the reason for this is that this is not a game where there’s going to be one model. There’s — each model is going to have its own personality, what it generates, what it looks like, how fast it generates, how much it costs when it generates that, and to have some interface layer to help synthesize all of this is important. And so just sort of to note, we’ve said this before but I’ll say it again here, you will see us building our products and tools and services leveraging Firefly for sure, but you’ll also see us leveraging best-of-breed personalities from different models and integrate them all together.

Ultimately, generative AI is going to create more growth in Adobe’s category

[Analyst] Awesome, the message here is that GenAI is going to create more growth in the category. And Shantanu, you did that with the pivot to cloud. You grew the category, so here we go again.

DocuSign (NASDAQ: DOCU)

DocuSign Navigator is a new AI-powered product that allows users to store and manage their entire library of accumulated agreements, including non-DocuSign agreements

Second, DocuSign Navigator allows you to store, manage and analyze the customer’s entire library of accumulated agreements. This includes past agreements signed using DocuSign eSignature as well as non-DocuSign agreements. Navigator leverages AI to transform unstructured agreements into structured data, making it easy to find agreements, quickly access vital information, and gain valuable insights from agreements. 

DocuSign acquired Lexion, an AI-based agreements company, this May; management thinks Lexion can improve Docusign’s Agreement AI and legal workflow; the Lexion acquisition is not for revenue growth, but to integrate the AI technology into DocuSign’s products

AI is central to our platform vision, and we’re thrilled to welcome Lexion to the DocuSign family. Lexion is a proven leader in AI-based agreement technology, which significantly accelerates our IAM platform goals. We maintain a high bar for acquisitions, and Lexion stood out due to its sophisticated AI capabilities, compatible technology architecture, and promising commercial traction with excellent customer feedback, particularly in the legal community…

… With regard to capital allocation, we also closed the Lexion acquisition on May 31…

…In terms of how it adds to DocuSign, I think overall, agreement AI, their extraction quantity and quality where we augment our platform. Another area where I think they’re really market-leading is in legal workflow. So workflow automation for lawyers, for example, if you’re ingesting a third-party agreement, how can you immediately use AI to assess the agreement, understand how terms may deviate from your standard templates and highlight language that you might want to propose as a counter that really accelerates productivity for legal teams. And they’ve done an excellent job with that. So overall, that’s how it fits in…

…We’re not breaking it out just because of its size and materiality. It’s not material to revenue or op margin for us. The overarching message that I would like to send on Lexion is that the purchase of Lexion is about integrating the technology into the DocuSign IAM platform. That opportunity for us, we think, in the long term, can apply to the well over 1 million customers that we have.

MongoDB (NASDAQ: MDB)

MongoDB’s management wants to prioritise investments in using generative AI to modernise legacy relational applications; management has found that generative AI can help with analyzing existing code, converting existing code and building unit and functional tests, resulting in a 50% reduction in effort for app modernisation; management sees a growing list of customers across industries and geographies who want to participate; interest in modernising legacy relational applications is high, but it’s still early days for MongoDB

Second, we are more optimistic about the [ opti-tech ] to accelerate legacy app modernization using AI. This is a large segment of the market that has historically been hard to penetrate. We recently completed the first 2 GenAI powered modernization pilots, demonstrating we can use AI to meaningfully reduce the time, cost and risk of modernizing legacy relational applications. In particular, we see that AI can significantly help with analyzing existing code, converting existing code and building unit and functional tests. Based on our results from our early pilots, we believe that we may be able to reduce the effort needed for app modernization by approximately 50%. We have a growing list of customers across different industries and geos, who want to participate in this program. Consequently, we will be increasing our level of investment in this area…

…We have an existing relational migrated product that allows people to essentially migrate data from legacy relational databases and does the schema mapping for them. The one thing it does not do, which is the most cumbersome and tedious part of the migration is to auto generate or build application code. So when you go from a relational app to an app built on MongoDB, you still have to essentially rewrite the application code. And for many customers, that was the inhibitor for them to migrate more apps because that takes a lot of time and a lot of labor resources. So our app modernization effort is all about or using AI is all about now solving the third leg of that stool, which is being able to reduce the time and cost and effort of rewriting the app code, all the way from analyzing existing code, converting that code to new code and then also building the test suites, both unit tests and functional tests to be able to make sure the new app is obviously operating and functioning the way it should be…

…That’s why customers are getting more excited because the lower you reduce the cost for that migration or the switching costs, the more apps you can then, by definition, migrate. And so that is something that we are very excited about. I will caution you that it’s early days. You should not expect some inflection in the business because of this. 

MongoDB’s management wants to prioritise investments in building an ecosystem for customers to build AI-powered applications because management recognises that there are other critical elements in the AI tech stack beyond MongoDB’s document-based database; management has launched the MongoDB AI Application Program, or MAP, that combines cloud computing providers, model providers, and more; Accenture is the first global systems integrator to join MAP

Third, although still early in terms of customers building production-ready AI apps, we want to capitalize on our inherent technical advantages to become a key component of the emerging AI tech stack…

… Recognizing there are other critical elements of the AI tech stack, we are leveraging partners to build an ecosystem that will make it easier for customers to build AI-powered applications. Earlier this month, we launched the MongoDB AI application Program, or MAP, a first-of-its-kind collaboration that brings together all 3 hyperscalers, foundation model providers, generative AI frameworks, orchestration tools and industry-leading consultancies. With MAP, MongoDB offers customers reference architectures for different AI use cases, prebuilt integrations and expert professional services to help customers get started quickly. Today, we are announcing that Accenture is the first global systems integrator to join MAP and that it will establish a center of excellence focused on MongoDB projects. We will continue to expand the program through additional partnerships and deeper technical integrations.

MongoDB’s document-based database architecture is a meaningful differentiator in AI because AI use cases involve various types of data, which are incompatible with legacy databases; there was a customer who told management that if he were to design a database specifically for AI purposes, it would be exactly like MongoDB

Customers tell us that our document-based architecture is a powerful differentiator in an AI world, the most powerful use cases rely on data of different types and structures such as text, image, audio and video. The flexibility required to handle a variety of different data structures is fundamentally at odds with legacy databases that rely on rigid schemes, which is what makes MongoDB’s document model such a good fit for these AI workloads…

…One customer told us if he had to build a database, it would be designed exactly like MongoDB and so for this new AI era. And so we feel really good about our position. 

A unit with Toyota that is focused on AI and data science migrated to MongoDB Atlas after experiencing reliability issues with its original legacy database system; the Toyota unit now uses MongoDB Atlas for over 150 micro-services and will use MongoDB Atlas as its database of choice for future AI needs

Toyota Connected, an independent Toyota company focused on innovation, AI, data science, and connected intelligence services, migrated to MongoDB Atlas after experiencing reliability issues with the original legacy database system. The team selected MongoDB Atlas for its ease of deployment, reliability and multi-cloud and multi-region capabilities. Toyota Connected now uses Atlas for over 150 micro-services. Their solution benefits from 99.99% uptime with Atlas as a platform for all data, including mission-critical vehicle telematics and location data needed for emergency response services. MongoDB’s Toyota Connected’s database of choice for all future services as they explore vector and AI capabilities, knowing they’ll get the reliability and scalability they need to meet customer needs.

Novo Nordisk is using MongoDB Atlas Vector Search to power its generative AI efforts in producing drug development reports; Novo Nordisk switched from its original relational database when it wasn’t capable of handling complex data and lacked flexibility to keep up with rapid feature development; reports that Novo Nordisk used to take 12 weeks to prepare can now be completed with MongoDB Atlas Vector Search in 10 minutes

By harnessing GenAI with MongoDB Atlas Vector search, Novo Nordisk, one of the world’s leading health care companies is dramatically accelerating how quickly can get new medicines approved and delivered to patients. The team responsible for producing clinical study report turn to Atlas when the original relational database wasn’t capable of handling complex data and lack the flexibility needed to keep up with the rapid feature development. Now with GenAI and the MongoDB Atlas platform, Novo Nordisk gets the mission-critical assurances that needs to run highly regulated applications, enabling them to generate complete reports in 10 minutes rather than 12 weeks. 

MongoDB’s management still sees MongoDB as well-positioned to be a key beneficiary when organisations embed AI into next-gen software applications

Our customers recognize that modernizing legacy applications is no longer optional in the age of AI. And are preparing for a multiyear journey to accomplish that goal. They see MongoDB as a key partner in that journey. We are well positioned to be a key beneficiary as organizations embed AI into the next generation of software applications that transform their business.

MongoDB’s management  believes that MongoDB’s performance in 2024 Q1 was less upbeat than the cloud computing hyperscalers because the hyperscalers’ growth came primarily from reselling GPU (graphic processing unit) capacity for AI training and there’s a lot of demand for AI training at the moment, whereas MongoDB is not seeing AI apps in production at scale, which is where MongoDB is exposed to

In contrast to the hyperscalers, like we believe the bulk of their growth across all 3 hyperscalers was really spent on reselling GPU capacity because there’s a lot of demand for training models. We don’t see a lot of, at least today, a lot of AI apps in production. We see a lot of experimentation, but we’re not seeing AI apps in production at scale. And so I think that’s the delta between the results that the hyperscalers produce versus what we are seeing in our business.

MongoDB’s management  thinks that AI is going to drive a step-fold increase in the number of apps and software created, but it’s going to take time, although the process is happening

I think with AI, you’re going to see a stepfold increase in the number of apps and the number of amount of software that’s being built to run businesses, et cetera. But that’s going to take some time. as with any new adoption cycle, the adoption happens in what people commonly refer to as S curves. And I think we’re going through one of those S curves.

MongoDB’s management sees the possibility of customers’ desire to spend on AI crowding out other software spending, but does not think it is an excuse for MongoDB not meeting new business targets

Is AI essentially crowding out new business? We definitely think that that’s plausible. We definitely see development teams experimenting on AI projects. The technology is changing very, very quickly. But that being said, we don’t see that as a reason for us to not hit our new business targets. And as I said, even though we started slow, we almost caught up at the end of this quarter, and we feel really good about our new business opportunity for the rest of this year. So — so I don’t want to use that as an excuse for us not meeting our new business targets.

Okta (NASDAQ: OKTA)

A new product, Identity Threat Protection with Okta AI, is expected to become generally available soon

We’re also excited about the launch of Identity Threat Protection with Okta AI, which includes powerful features like Universal Logout, which makes it possible to automatically log users out of all of their critical apps when there is a security issue. Think of this as identity threat detection and response for Okta. We expect Identity Threat Protection to become generally available this summer.

Okta’s management does not expect the company’s new products – which includes governance, PAM, Identity Threat Protection with Okta AI, and Identity Security Posture Management – to have material impacts on the company’s financials in FY2025; of the new products, management thinks Identity Threat Protection with Okta AI and Identity Security Posture Management will make impacts first before PAM does

I wouldn’t expect for these newer things that are coming out like posture management or threat protection, I wouldn’t expect it in FY ’25 at all. I probably wouldn’t even think it would impact it in FY ’26 because we’re talking about a $2.5 billion business at this point. It takes a lot of money in any of these products to make a material difference to the overall numbers. So we’re setting these up for the long term…

…How we’re thinking about this internally is that the — I think it will mirror the order of broad enablement. So we’re broadly enabling people in the following order: governance is first, followed by a combination of posture management and identity threat protection, followed by privileged access. So we think that Identity Threat Protection with Okta AI and Identity Security Posture Management, that bundle could pretty quickly have as much of an impact as governance. And then we think the next sequential enablement in the next order of impact will probably be Privileged Access.

Okta’s management is currently not seeing companies changing their software spending plans because they want to invest in AI, although that might change in the future

[Question] There is a shift in the marketplace among the C-suite from fear about the economy to, gee, I need to focus on how I’m going to implement AI. And in that context, there’s uncertainty around the mechanics of what they need to do to secure AI within their organizations. And I guess my question to you is we’re hearing the pipelines of the VAR channels, particularly in security, are extremely robust into the back half of the year. But the uncertainty around AI decision is keeping people from implementing it. So how robust is the pipeline that you’re looking at? And are you, in fact, hearing that from your C-suite customers when you talk to them?

[Answer] What I’ve heard is everyone is figuring out how they can deploy this new wave of technology to their products and services and business and how they can use it for security and how they can use it for innovation. But they’re not at the stage where it’s broadly impacting other plans. It’s more of like a — their planning exercise at this point. I think that might change in the future.

Okta’s management thinks that more companies will invest in AI in the future, and this will be a tailwind for the company because more identity features will be needed; the current AI wave is not impacting spending on Okta at the moment, but might be a boon in the future

My bet is that they’re going to be building new apps. They’re going to be deploying more technology from vendors that are building apps with AI built in, which is going to — all of that’s going to lead to more identity. They’re going to have to log people into their new apps they build. They’re going to have to secure the privileged accounts that are running the infrastructure behind the new apps. They’re going to have to make sure that people in their workforce can get to the apps that are the latest, greatest AI-driven experiences for support or for other parts of the business. So I think that identity is one of these foundational things that’s going to be required whether it’s the AI wave, which is going to be really real and impactful and — or whether it’s whatever comes after that.

[Question] So not impacting spending today but might impact to help it in the future.

[Answer] Yes, yes. That’s how I see it.

Okta’s management sees 2 ways of monetising Okta AI: Through new products, and through making existing products better

Okta AI will be monetized through 2 ways. one will be new products like Identity Threat Protection with Okta AI; and the other way, it will be — it will just make products better. For example, the Identity Security Posture Management, it has a new capability that’s going to be added to that product that’s just going to make it smarter about how it detects service accounts. That Identity Security Posture Management scans a customer’s entire SaaS estate, and says, here are all the things you should look at. You should take — this account needs MFA. This other account is — probably has overly permissive permissions. The challenge there is how does the customer know which of those accounts are service accounts, so they can’t have human biometrics. And we added — we used some AI capability to add that to the scan. So that’s an example of just the product gets better versus Identity Threat Protection is like it’s a whole new product enabled by that.

Salesforce (NYSE: CRM)

Salesforce is managing 250 petabytes of customer data and management thinks this is going to be critical and positions Salesforce for success when Salesforce’s customers move into AI; management thinks that customer data is the critical success factor in AI, not AI models and UIs (user interfaces); management thinks most of the AI models that are being built today, both large and small, are just commodities and will not survive

We’re now managing more than 250 petabytes of data for our customers. This is going to be absolutely critical as they move into artificial intelligence…

…When you look at the power of AI, you realize the models and the UI are not the critical success factors. It’s not critical where the enterprise will transform. There are thousands of these models, some open source and some close source models, some built with billions, some with just a few dollars, most of these will not survive. They’re just commodities now, and it’s not where the intelligence lies. And they don’t know anything about a company’s customer relationships. Each day, hundreds of petabytes of data are created that AI models can use for training and generating output. But the one thing that every enterprise needs to make AI work is their customer data as well as the metadata that describes the data, which provides the attributes and contacts the AI models need to generate accurate, relevant output. And customer data and metadata are the new gold for these enterprises…

…Not every company is as well positioned, as you know, for this artificial intelligence capability of Salesforce is because they just don’t have the data. They may say they have this capability or that capability, this user interface, that model, that whatever, all of these things are quite fungible and are expiring quickly as the technology rapidly moves forward. But the piece that will not expire is the data. The data is the permanent key aspect that, as we’ve said, even in our core marketing, it’s the gold for our customers and their ability to deliver our next capability in their own enterprises.

Salesforce’s management is seeing incredible momentum in Data Cloud, which is Salesforce’s fastest-growing organic and next billion-dollar cloud; Data Cloud’s momentum is powered by the need for customers to free their data from being trapped in thousands of apps and silos; the need to free their data is important if Salesforce’s customers want to embrace AI; Data Cloud was in 25% of Salesforce’s >$1 million deals in 2024 Q1; 2024 Q1 was the second quarter in a row when >1,000 Data Cloud customers were added; in 2024 Q1, 8 trillion records were ingested in Data Cloud, up 42% year-on-year, 2 quadrillion records were processed, up 217% year-on-year, and there were 1 trillion activations, up 33% year-on-year

Many of these customers have a central business and customer data that exists outside of Salesforce that’s trapped in thousands of apps and silos. It’s disconnected. That’s why we’re seeing this incredible momentum with our Data Cloud, our fastest-growing organic, and our next billion-dollar cloud. It’s the first step to becoming an AI enterprise. Data Cloud gives every company a single source of truth and you can securely power AI insights and actions across the entire Customer 360.

Now let me tell you why I’m excited about Data Cloud and why it’s transforming our customers and how it’s preparing them for this next generation of artificial intelligence. Data Cloud was included in 25% of our $1 million-plus deals in the quarter. We added more than 1,000 Data Cloud customers for the second quarter in a row. 8 trillion records were ingested in the Data Cloud in the quarter, up 42% year-over-year, and we processed 2 quadrillion records. That’s a 217% increase compared to last year. Over 1 trillion activations drove customer engagement, which is a 33% increase year-over-year. This incredible growth of data in our system and the level of transactions that we’re able to deliver not just in the core system but especially in data cloud is preparing our customers for this next generation of AI.

Salesforce’s predictive AI, Einstein, is generating hundreds of billions of predictions daily; Salesforce is working with thousands of customers in generative AI use cases through the launch in 2024 Q1 of Einstein Copilot, Prompt Builder,and Einstein Studio; Salesforce has closed hundreds of Einstein Copilot deals since the product’s general availability (GA)

Einstein is generating hundreds of billions of predictions per day, trillions per week. Now we’re working with thousands of customers to power generative AI use cases with our Einstein Copilot, our Prompt Builder, our Einstein Studio, all of which went live in the first quarter, and we’ve closed hundreds of Copilot deals since this incredible technology has gone GA. And in just the last few months, we’re seeing Einstein Copilot develop higher levels of capability. We are absolutely delighted and could not be more excited about the success that we’re seeing with our customers with this great new capability.

Luxury fashion company Saks is using Salesforce’s Einstein 1 Platform in Data Cloud to create AI-powered personal experiences for customers

Saks, a leader in the luxury fashion market, part of Hudson’s Bay, went all-in on Salesforce in the quarter. CEO Marc Metrick is using AI to create more personal experiences for every customer touch point across their company. And with our Einstein 1 Platform in Data Cloud, Saks can unify and activate all its customer data to power trusted AI.

Salesforce is helping FedEx generate savings and accelerate top-line partly with the help of its AI solutions

The Salesforce data and app and AI capabilities generate expense savings. This is the core efficiency while growing and accelerating top line revenue. This is the effectiveness that we’re delivering for FedEx. This efficiency includes next best action for sellers, automated lead nurturing, Slack for workflow management, opportunity scoring, a virtual assistant, AI on unstructured data for delivering content to sales and customer service. And when we think about effectiveness, we see our Journey Builder delivering hyper personalization, integrating customer experiences across service, sales, marketing, the ability to tailor and deliver customer experiences based on a Customer 360 view. When we look at these incredible next generation of capability we’ve delivered at FedEx, gone now are these days of static business rules that leave customers dissatisfied, asking, “Do they not know that I’m a valued customer of FedEx?” Now FedEx has not only the power of the Customer 360 but the power of AI to unlock so much more commercial potential by conducting an orchestra of commercial functions that never played well together before.

Air India is using Data Cloud and Einstein across 550,000 service cases each month to improve its customer experience and deliver more personalised customer service

And with Data Cloud, Air India is unifying Data Cloud across loyalty, reservations, flight systems and data warehouses. They have a single source of truth to handle more than 550,000 service cases each month. And now with Einstein, we’re automatically classifying and summarizing cases and sending that to the right agent who’d recommend the next steps and upgrading in high-value passenger experiences. Even when things happen like a flight delay, our system is able to immediately intervene and provide the right capability to the right customer at the right time. All of that frees up agents to deliver more personal service and create more personal relationships, a more profitable, a more productive, a more efficient Air India, a company that’s using AI to completely transform their capability.

Salesforce’s management is seeing good demand, driven by customers recognising the value of transforming their front-office operations with AI, but buying behaviour among customers is measured (similar to the past 2 years) with the exception of 2023 Q4

We’re seeing good demand as AI technology rapidly evolves and customers recognize the value of transforming into AI enterprises. CEOs and CIOs are excited about the opportunity with data and AI and how it can impact their front-office operations…

…We continue to see the measured buying behavior similar to what we experienced over the past 2 years and with the exception of Q4 where we saw stronger bookings. The momentum we saw in Q4 moderated in Q1 and we saw elongated deal cycles, deal compression and high levels of budget scrutiny.

Siemens used Einstein 1 Commerce to build and launch its AI-powered digital marketplace, named Accelerator Marketplace, in just 6 months

Siemens lacked a centralized destination for customers to easily choose the right products and buy on demand. To simplify the buying experience for customers, Siemens worked with Salesforce to develop and launch its Accelerator Marketplace, an AI-powered digital marketplace built on Einstein 1 Commerce, providing AI-generated product pages, smart recommendations and self-service ordering. And they did it all in just 6 months.

Salesforce is using AI internally with great results; Salesforce has integrated Einstein into Slack and Einstein has already answered 370,000 employee queries in a single quarter; Salesforce’s developers have saved 20,000 hours of coding through the use of AI tools

AI is not just for our customers. As part of our own transformation, we continue to adopt AI inside Salesforce. Under the leadership of our Chief People Officer Nathalie Scardino and our Chief Information Officer Juan Perez, we’ve integrated Einstein right into Slack, helping our employees schedule, plan and summarize meetings and answer employee questions. Einstein has already answered nearly 370,000 employee queries in a single quarter. In our engineering organization, our developers now save more than 20,000 hours of coding each month through the use of our AI tools.

Slack AI was launched in February and it provides recap, summaries and personalized search within Slack; >28 million Slack messages have been summarised by Salesforce’s customers since the launch of Slack AI

We also launched Slack AI in February, an amazing innovation that provides recap, summaries and personalized search right within Slack. I personally have been using it every day to get caught up on the conversations happening in every channel. And we’ve seen great traction with our customers with this product, and our customers have summarized over 28 million Slack messages since its launch in February.

Los Angeles city will use Salesforce’s Government Cloud and other solutions to integrate AI into its software system

And in the public sector, the city of Los Angeles chose Salesforce to modernize how the city’s 4 million residents request city services using its MyLA311 system. The city will use government cloud and other Salesforce solutions to integrate AI assistance into MyLA311 and modernize its own constituent-facing services, giving residents more self-service options and improving service reliability and responsiveness.

Salesforce’s products for SMBs (small and medium businesses), Start and Pro Suite, which both have AI built-in, are building momentum; Salesforce added 2,300 new logos to the products in 2024 Q1

Our new offerings for small and medium businesses, Starter and Pro Suite, which are ready-to-use, simplified solutions, with AI built in, are building momentum. In Q1, we added another 2,300 new logos to these products. Since Starter’s launch last year, we’ve seen customers upgrade to our recently launched Pro Suite and even to our Enterprise and Unlimited editions.

Studies have shown that 75% of the value of generative AI use cases is in the front office of companies; Salesforce is the leader in front-office software, so management thinks this is why – with Data Cloud at the heart – the company is in a good position for growth going forward

We all saw the report from McKinsey, 75% of the value of Gen AI use cases is in the front office. And everybody knows Salesforce is the leader in front-office software. That’s our fundamental premise for our growth going forward. We’re already managing 250 petabytes of data and metadata that’s going to be used to generate this incredible level of intelligence and artificial intelligence capability to deliver for our customers a level of productivity and profitability they’ve just never been able to see before. And at the heart of that is going to be our Data Cloud. 

Salesforce’s management is focused on 2 things at the company: The ongoing financial transformation at Salesforce, and the use of AI

Look, we really are focused on two things in our company. One is this incredible financial transformation that we’ve all gone through with you in the last year. The second one is this incredible transformation to artificial intelligence, which is going to be based on data. 

Salesforce’s management thinks that the relative weakness seen in the software world currently is because of pull-forward in demand from COVID, and not because of crowding out by AI; management thinks AI is a growth driver for software companies

[Question] When we think about this measured buying environment, is there any sort of crowding effect around AI that’s impacting software in your view, meaning when you think about all these companies starting to gear up for this next platform shift, was it just the uncertainty of what they’re going to spend on over the next 6 to 12 months, holding them back perhaps on what their normal sort of pace of spending might be with you all or other enterprise software companies?

[Answer] As we entered the post-pandemic reality, we saw companies who had acquired so much software in that time looked to actually rationalize it, ingest it, integrate it, install it, update it. I mean it’s just a massive amount of software that was put in. And so every enterprise software company kind of has adjusted during end of this post-pandemic environment. So when you look at all of these companies, especially as you saw them report in the last 30 days, they’re all basically saying that same thing in different ways. When you take AI, that has to be our growth driver for future capabilities for these companies. 

Salesforce’s management sees the consumer AI world and the enterprise AI world as having very different needs for the kind of data they use for AI implementations, even though the model architectures are very similar; enterprise AI requires internal datasets from companies

It’s been pretty magical to use OpenAI over the last year, especially in the last release, when I’m really talking to it. And when I think about the incredible engineering effort that OpenAI has done, it’s pretty awesome. They’ve built a great UI. I love talking to the software. They have really strong algorithms or what we call Models, especially their new one, which is their 4o Model. And then they stole data from lots of companies like Time, Dow Jones, New York Times, Reddit. Now they’re all making good, doing agreements with all of us, saying, “We’re sorry,” and paying for it. And they took that data, they normalized it, they delivered a comprehensive data set that they train their model on…

…And then we’ve seen a lot of fast followers with the models. It could be open source models like Llama 3. It could be some proprietary models like Gemini from Google and others. Now there’s thousands and thousands of these models. And if you look on Hugging Face, everybody is a fast follower. And 6 months later, everybody is where everybody else was 6 months ago. And the data, well, a lot of these companies are all thinking they can rip off all this data, too, and they’re all having to pay that price. Okay, that’s the consumer world.

The enterprise world is a little different, right? We have great user interfaces, great apps, all kinds of great technology that our users are using, the millions and millions of users. Then we have the same models, in many cases, or maybe we’ve written some of our own models with our engineers. But then the third piece is the data. And that data is a little bit different. Because in the enterprise, how do you put together these large, fully normalized data sets to deliver this incredible capability, and that is where the magic is going to be. Because for all companies, including ours and others, who want to deploy generative AI internally, it’s not going to be Times Magazine that’s going to give you the intelligence, it’s going to be our customer data and your transaction history and how you’re how your company operates in your workflow and your metadata. And that idea that we can deliver another level of productivity for companies using that architecture is absolutely in front of us. But that idea that we have to do it with the right architecture, that also is in front of us. And I think that while we can say it’s a different kind of architecture, it’s still the same idea that we need a great UI, we need models, but we’re going to need very highly normalized and federated data. And that data needs to be stored somewhere, and it needs to come from somewhere. And that is going to be something that’s going to continue in perpetuity over time as these models and UIs are quite fungible. And we’ll be using different models and different UIs over the years, but we’ll be using the same deep data sources. And I think that is why, when I look at what Salesforce is doing, this is going to be critical for our customers.

Salesforce’s management has seen many instances where software vendors promise customers they can deliver AI magic, only for the customers to come up empty-handed because (1) the vendors did not put in the work – and are unable – to make the customers’ data AI-ready, and (2) there’s no proper UI that’s commonly accessed within the customer

Don’t think that there aren’t a lot of people walking into these companies saying, “Hey, you can do this. You can do that. You can do these other things”. We’ve seen a lot of that in the last 6 to 12 months, and then it turns out that you can’t. “Hey, I can make this happen. I can make that happen. I can pull a rabbit out of the hat in the enterprise for you by doing this, that and the other thing,” and then it doesn’t actually happen. And then what it turns out is you got to do a lot of the hard work to make this AI happen, and that starts with building highly normalized, large-scale, federated, highly available data sources. And then building on top of that the kind of capabilities to deliver it to our customers. I think a common story is, “Hey, oh, yes, I am a provider of a data lake or a data capability. And just by going to that, I’m going to be able to provide all your AI.” But then it turns out that no one in the enterprise actually uses that product. There is no UI that’s commonly accessed. That’s why I’m so excited that Salesforce has Sales Cloud and Service Cloud and Tableau and Slack and all of our amazing products that have these huge numbers of users that use these products every single day in a trusted, scalable way and then connecting that into this new capability.

Veeva Systems (NYSE: VEEV)

Veeva’s management’s strategy with generative AI is to enable customers and partners to develop generative AI solutions that work well with Veeva’s applications; generative AI applications require access to data and Veeva provides the access through solutions such as Direct Data API; Direct Data APi provides data access 100 times faster than traditional APIs; management is seeing customers being appreciate of Veeva’s efforts to allow generative AI applications to work well with its own applications; management thinks that the generative AI applications its customers and partners will develop will be very specific; Veeva’s work on Direct Data API started more than 2 years ago

In these early days as GenAI matures, our strategy is to enable our customers and partners to develop GenAI solutions that work well with Veeva applications through our AI Partner Program and powerful Vault Platform capabilities like the Vault Direct Data API. GenAI applications need access to accurate, secure, and timely data from Vault and our data applications. Released in April, our Direct Data API provides data access up to 100 times faster than traditional APIs…

…In general, customers are appreciative of our strategy to enable a broad range of GenAI use cases and experimentation through their own resources and our partner network…

…In terms of the AI strategy, our strategy is to really enable customers and their partners to develop AI applications because they’re going to be very specific AI applications, GenAI applications for very specific use cases whether it’s field information, pre-call planning, next best action, what have you. They’re going to be very specific applications. That innovation has to come from everywhere. And one of the things it needs is clean data. All of these AI applications need clean, concurrent, fast data. So one of the things we did — started about 2 years ago actually is put in a new API on the Vault platform called the Direct Data API, and that was just released this April. 

Veeva’s management has no plans to develop or acquire generative AI solutions currently, but are open to the idea as they observe how the technology evolves; Veeva’s applications do use AI technology, but not specifically generative AI; customers really trust Veeva, so management wants to move carefully when it comes to Veeva developing generative AI applications

We don’t have plans to develop or acquire GenAI solutions today, but that may change in the coming years as we see how GenAI technology evolves, and we determine which use cases can provide consistent value for the industry. In the meantime, we will continue to add advanced automation to our applications. Some, like TMF Bot and RIM Bot, use AI technology, but generally not GenAI…

… We have that trust. We have to continue to earn that trust. So we don’t really get into things that are too speculative. We definitely don’t overpromise. The trust is the most valuable thing we have. So we’ll be really targeted when we get into an AI application if we do. It will be an area where, hey, that’s a use case that we’re pretty sure that can be solved by GenAI, and there’s not a great partner to do it. Okay. Then we might step in because we do have that trusted position.

Veeva’s management lowered the company’s FY2025 revenue guidance slightly (was previously $2.725 billion – $2.74 billion) because of macro challenges and crowding-out from companies wanting to reallocate resources to AI; management is seeing some deferment of spending on core systems because customers are busy investing in AI, but the deferment creates pent-up demand and it’s not spending that has stopped

For fiscal year 2025, we now expect total revenue between $2.700 and $2.710 billion. This is a roughly $30 million reduction compared to our prior guidance, mostly in the services area. As we have said, the macro environment remains challenging as the industry continues to navigate inflation, higher interest rates, global conflicts, political instability, and the Inflation Reduction Act. There is also some disruption in large enterprises as they work through their plans for AI…

…A little more than a year ago, AI really burst upon the scene with GenAI…

…That caused a lot of pressure in our larger enterprises, on the IT department, “Hey, what are we going to do about GenAI? What’s our strategy as a large pharmaceutical company, biotech about AI?” And that we would land in the IT department of these companies. Now for the smaller — our smaller SMB customers, doesn’t land so much. They have other things to think about, other more pertinent, very stressful things. But in the large companies, with tens of thousands of people, they’re looking for these operational efficiencies that they could potentially get through AI and they have a budget to kind of get ahead of that game. So that — by the word disruption, I meant that through a competing priority into our customers, hey, we had some existing plans. Now this AI, we have to plan for what we’re going to do on that. Where are we going to spend on innovation, on experimentation? Who’s going to do that? What budget would we use, that type of thing. So some of that would take an impact onto us, which is core systems. Now those core systems, when we get that type of impact, it will delay a project, but it won’t stop it because these core systems are things you need. You can delay them, but all that does is create somewhat of a pent-up demand.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Adobe, DocuSign, MongoDB, Okta, Salesforce, and Veeva Systems. Holdings are subject to change at any time.

Investors often use valuation metrics to screen for “cheap” or “undervalued” companies. Identified correctly, undervalued companies will provide better returns than the broader market.

However, we can often fall into the trap of believing a company is cheap if we are overly reliant on using common valuation metrics that may be misinterpreted.

Since I started investing, I have come across numerous value traps and thought it would be useful to put together a short list of common value traps to be avoided.

Low earnings multiple but unstable earnings

This is probably the most obvious value trap. A company that trades at a low earnings multiple – i.e. a company with a market capitalisation that is relatively low compared to its past earnings – may look cheap. But if its earnings are not sustainable, it may become a really bad investment.

Moderna and BioNTech both had massive earnings boosts from selling COVID vaccines in 2021 and 2022. But both have also seen their earnings plummet since. Investors who looked at their historical earnings multiples during the COVID-boom may get the idea that they were cheap, but the COVID-induced profits were not likely to be repeated.

Looking at a company’s historical earnings can give investors some idea of its profitability. But the past does not always mirror the future and investors need to think about a company’s future earnings too.

High but unsustainable dividend yield

A company with a high trailing-twelve-month dividend yield can seem enticing. But it could still end up as a value trap if its dividend is not sustainable. What makes a dividend sustainable? Some good questions to ask include:

• Is the dividend supported by a regular profit stream?
• Is the dividend a one-off special dividend that will not recur?
• Is the dividend payout ratio above 100%?
• Does the company have a predictable and recurring revenue stream?

All of these questions help us to identify if a company is a sustainable dividend payer or simply a dividend value trap.

Lots of cash, but with a cash-burning business

Investors may get attracted to a company that has lots of cash on the balance sheet. The company is even more enticing if its net cash positive is a large percentage of its market capitalisation. But the balance sheet is not an indicator of the quality of a business. 

A company with a lot of cash on the balance sheet can still end up as a value trap if it has a weak business that constantly burns cash. For instance, there are numerous biotech companies in the US stock market that look promising with high net cash balances but that are burning lots of cash to research potential drugs. Although some of these biotechs may end up getting FDA approval for their drugs and become winners, a vast majority of them will end up with unfeasible drugs and a cash balance that has been wiped out after years of unfruitful research.

A management that won’t return cash to shareholders

What’s a company with lots of cash on the balance sheet, a stable and profitable business, trades at low earnings multiple, but management refuses to return cash to shareholders? It is a potential value trap.

There is no point in having a stable and recurring business generating lots of cash but shareholders will never see that cash. This is a common situation in companies listed in Singapore and Japan, where corporations retain too much cash on the balance sheet.

This phenomenon may happen because a company is majority family-owned and the family does not need the cash as dividends. Or the management team may be ultra-conservative and retain cash unnecessarily. Either way, shareholders are left with nothing or have to wait decades to see their cash.

Final word

It does not make sense to invest in a company at a price that’s significantly higher than its intrinsic value. But just searching for companies with low valuation metrics does not mean you will end up with bargains. It pays to recognise the existence of value traps.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently do not have a vested interest in any stocks mentioned. Holdings are subject to change at any time.

The way I see it, artificial intelligence (or AI), really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Both are provided by OpenAI and are software products that use AI to generate art and writing, respectively (and often at astounding quality). Since then, developments in AI have progressed at a breathtaking pace.

With the latest earnings season for the US stock market – for the first quarter of 2024 – coming to its tail-end, I thought it would be useful to collate some of the interesting commentary I’ve come across in earnings conference calls, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here

With that, here are the latest commentary, in no particular order:

Airbnb (NASDAQ: ABNB)

Airbnb has been using AI for a long time and has made a lot of progress in the last 12 months, including (1) a computer vision AI model trained with 100 million photos that allows hosts to organise all their photos by room, which leads to higher conversion rates, (2) an AI-powered feature for hosts to reply guests quickly, and (3) a reservation screening technology

We’ve been using AI for a long time. In the last 12 months, we’ve made a lot of progress. I’ll just give you 3 examples of things we’ve done with AI. We made it easier to host. We have a computer vision model that we trained with 100 million photos, and that allows hosts to — like the AI model to organize all their photos by room. Why would you want to do this? Because this increases conversion rate when you do this. We launched last week AI-powered quick replies for hosts. So basically, predicts the right kind of question or answer for a host to pre-generate to provide to guests. And this has been really helpful. And then we’ve made a really big impact on reducing partners in Airbnb with a reservation screening technology.

Airbnb’s management is going much bigger on generative AI; management thinks the biggest near-term impact generative AI can have on Airbnb’s business is in customer service; management thinks that generative AI in the realm of customer service can benefit Airbnb a lot more than hotels and online travel agents (OTAs); AI can solve difficult customer service challenges for Airbnb

So now we’re going much bigger on generative AI. I think I think we’re going to see the biggest impact is going to be on customer service in the near term. I think more than hotels, probably even more than OTA, Airbnb will benefit from generative AI. And the reason why, it’s just a simple structural reason. We have the most like buried inventory. We don’t have any SKUs, and we’re an incredibly global platform. So it’s a very difficult customer service challenge. But imagine an AI agent that can actually like read a corpus of 1,000 pages of policies and be able to help adjudicate and help a customer service agent help a guest from Germany staying with a host in Japan. It’s a very difficult problem that AI can really supplement. 

Airbnb’s management wants to bring AI capabilities from customer service to search and to the broader experience; the end game is to provide an AI-powered concierge

Over time, we’re going to bring the AI capabilities from customer service to search and to the broader experience. And the end game is to provide basically an AI-powered concierge. 

Alphabet (NASDAQ: GOOG)

Alphabet’s management gave a reminder that Alphabet has been an AI-first company since 2016; Alphabet started building TPUs (tensor processing units) in 2016

We’ve been an AI-first company since 2016, pioneering many of the modern breakthroughs that power AI progress for us and for the industry…

… You can imagine we started building TPUs in 2016, so we’ve definitely been gearing up for a long time.

Alphabet’s management rolled out Gemini 1.5 Pro in February, a foundational AI model which has a breakthrough in long context understanding and multimodal capabilities; Gemini 1.5 Pro has been embraced by developers and enterprise customers in a wide range of use cases

In February, we rolled out Gemini 1.5 Pro, which shows dramatic performance enhancements across a number of dimensions. It includes a breakthrough in long context understanding, achieving the longest context window of any large-scale foundation model yet. Combining this with Gemini’s native multimodal understanding across audio, video, text code and more, it’s highly capable. We are already seeing developers and enterprise customers enthusiastically embrace Gemini 1.5 and use it for a wide range of things.

Alphabet’s management thinks that the company has the best infrastructure for AI; Gemini’s training and inference is done with Alphabet’s custom TPU (tensor processing unit) chips; Google Cloud offers the latest generation of Nvidia GPUs (graphics processing units) and Alphabet’s own TPUs

We have the best infrastructure for the AI era… Our data centers are some of the most high-performing, secure, reliable, and efficient in the world. They’ve been purpose-built for training cutting-edge AI models and designed to achieve unprecedented improvements in efficiency. We have developed new AI models and algorithms that are more than 100x more efficient than they were 18 months ago. Our custom TPUs, now in their fifth generation, are powering the next generation of ambitious AI projects. Gemini was trained on and is served using TPUs…

…We offer an industry-leading portfolio of NVIDIA GPUs along with our TPUs. This includes TPU v5p, which is now generally available and NVIDIA’s latest generation of Blackwell GPUs. 

Alphabet’s management is seeing generative AI cause a shift in what people can do with Search, and they think this will lead to a new stage of growth, similar to the outcomes of prior shifts in Search; Alphabet has been experimenting with SGE (Search Generative Experience) for over a year and the company is now bringing AI overseas to main Search results; Alphabet has served billions of queries with its generative AI features; people who use the AI overviews in Google Search increase their search usage and report higher satisfaction with search results; ads that are above or below SGE results were found by users to be helpful; management is confident that SGE with ads will remain relevant; management thinks that the use of generative AI can help Google answer more complex questions and expand the type of queries it can serve

We have been through technology shifts before, to the web, to mobile, and even to voice technology. Each shift expanded what people can do with Search and led to new growth. We are seeing a similar shift happening now with generative AI. For nearly a year, we’ve been experimenting with SGE in search labs across a wide range of queries. And now we are starting to bring AI overviews to the main Search results page. We are being measured in how we do this, focusing on areas where gen AI can improve the search experience while also prioritizing traffic to websites and merchants. We have already served billions of queries with our generative AI features. It’s enabling people to access new information, to ask questions in new ways and to ask more complex questions. Most notably, based on our testing, we are encouraged that we are seeing an increase in search usage among people who use the new AI overviews as well as increased user satisfaction with the results…

…We shared in March how folks are finding ads either above or below the SGE results helpful. We’re excited to have a solid baseline to keep innovating on and confident in the role SGE, including Ads, will play in delighting users and expanding opportunities to meet user needs…

… I think with generative AI in Search, with our AI overviews, I think we will expand the type of queries we can serve our users. We can answer more complex questions as well as, in general, that all seems to carry over across query categories. Obviously, it’s still early, and we are going to be measured and put user experience at the front, but we are positive about what this transition means…

…On SGE in Search, we are seeing early confirmation of our thesis that this will expand the universe of queries where we are able to really provide people with a mix of actual answers linked to sources across the Web and bring a variety of perspectives, all in an innovative way. 

The cost of producing SGE responses has decreased by 80% from when SGE was first introduced a year ago because of work Alphabet has done on its Gemini models and TPUs

A number of technical breakthroughs are enhancing machine speed and efficiency, including the new family of Gemini models and a new generation of TPUs. For example, since introducing SGE about a year ago, machine costs associated with SGE responses have decreased 80% from when first introduced in Labs driven by hardware, engineering, and technical breakthroughs.

Alphabet’s immense reach – 6 products with >2 billion monthly users each, and 15 products with 0.5 billion users – is helpful in distributing AI to users; management has brought AI features to many Alphabet products

We have 6 products with more than 2 billion monthly users, including 3 billion Android devices. 15 products have 0.5 billion users, and we operate across 100-plus countries. This gives us a lot of opportunities to bring helpful gen AI features and multimodal capabilities to people everywhere and improve their experiences. We have brought many new AI features to Pixel, Photos, Chrome, Messages and more. We are also pleased with the progress we are seeing with Gemini and Gemini Advanced through the Gemini app on Android and the Google app on iOS.

Alphabet’s management thinks the company has a clear path to monetisation of AI services through ads, cloud, and subscriptions; Alphabet introduced Gemini Advance, a subscription service to access the most advanced Gemini model, was introduced in 2024 Q1

We have clear paths to AI monetization through Ads and Cloud as well as subscriptions…

… Our Cloud business continues to grow as we bring the best of Google AI to enterprise customers and organizations around the world. And Google One now has crossed 100 million paid subscribers, and in Q1, we introduced a new AI premium plan with Gemini Advanced.

Established enterprises are using Google Cloud for their AI needs (For example: (1) Discover Financial has begun deploying generative AI tools to its 10,000 call center agents, (2) McDonald’s is using gen AI to enhance its customer and employee experiences, and (3) WPP is integrating with Gemini models); more than 60% of funded generative AI (gen AI) start-ups and nearly 90% of gen AI unicorns are also using Google Cloud; more than 1 million developers are now using Alphabet’s generative AI tools; customers can now also ground their generative AI with Google Search and their own data 

At Google Cloud Next, more than 300 customers and partners spoke about their generative AI successes with Google Cloud, including global brands like Bayer, Cintas, Mercedes-Benz, Walmart and many more…

…Today, more than 60% of funded gen AI start-ups and nearly 90% of gen AI unicorns are Google Cloud customers. And customers like PayPal and Kakao Brain are choosing our infrastructure… 

……On top of our infrastructure, we offer more than 130 models, including our own models, open source models and third-party models. We made Gemini 1.5 Pro available to customers as well as Imagine 2.0 at Cloud Next. And we shared that more than 1 million developers are now using our generative AI across tools, including AI Studio and Vertex AI. We spoke about how customers like Bristol-Myers Squibb and Etsy can quickly and easily build agents and connect them to their existing systems. For example, Discover Financial has begun deploying gen AI-driven tools to its nearly 10,000 call center agents to achieve faster resolution times for customers. Customers can also now ground their gen AI with Google Search and their own data from their enterprise databases and applications. In Workspace, we announced that organizations like Uber, Pepperdine University and PennyMac are using Gemini and Google Workspace, our AI-powered agent that’s built right into Gmail, Docs sheets and more…

…To help McDonald’s build the restaurant of the future, we’re deepening our partnership across cloud and ads. Part of this includes them connecting Google Cloud’s latest hardware and data technologies across restaurants globally and starting to apply Gen AI to enhance its customer and employee experiences. Number two, WPP. At Google Cloud Next, we announced a new collaboration that will redefine marketing through the integration of our Gemini models with WPP Open, WPP’s AI-powered marketing operating system, already used by more than 35,000 of its people and adopted by key clients, including The Coca-Cola Company, L’Oreal and Nestle. We’re just getting started here and excited about the innovation this partnership will unlock. 

Alphabet’s management has AI solutions to help advertisers with predicting ad conversions and to match ads with relevant searches; management thinks Alphabet’s ability to help advertisers find customers and grow their advertising ROI (return on investment) is getting better as the company’s AI models improve

We’ve talked about whole solutions like Smart Bidding use AI to predict future ad conversions and their value in helping businesses stay agile and responsive to rapid shifts in demand and how products like broad match leverage LLMs to match ads to relevant searches and help advertisers respond to what millions of people are searching for…

…As advances accelerate in our underlying AI models, our ability to help businesses find users at speed and scale and drive ROI just keeps getting better.

Alphabet’s management introduced Gemini into Performance Max (PMax) in February and early results show PMax users are 63% more likely to publish a campaign with good or excellent ad strength and those who improve their ad strength on PMax to excellent see a 6% increase in conversions; PMax is available to all US advertisers and is starting to be rolled out internationally

In February, we rolled Gemini into PMax. It’s helping curate and generate text and image assets so businesses can meet PMax asset requirements instantly. This is available to all U.S. advertisers and starting to roll out internationally in English, and early results are encouraging. Advertisers using PMax asset generation are 63% more likely to publish a campaign with good or excellent ad strength. And those who improve their PMX ad strength to excellent see 6% more conversions on average.

Advertisers who use Alphabet’s ACA (automatically created assets) feature that is powered by generative AI see conversions increase by 5%

We’re also driving improved results for businesses opting into automatically created assets, which are supercharged with gen AI. Those adopting ACA see, on average, 5% more conversions at a similar cost per conversion in Search and Performance Max campaigns.

Alphabet’s Demand Gen AI-powered service helps advertisers engage with new and existing customers across Youtube, Shorts, Gmail, and Discover; movie studio Lionsgate tested Demand Gen for a movie’s promotion and saw that it provided an 85% more efficient CPC (cost per click) and 96% more efficient cost per page view compared to social benchmarks; Lionsgate has used Demand Gen for two more films; Alphabet recently introduced new tools in Demand Gen

And then there’s Demand Gen. Advertisers are loving its ability to engage new and existing customers and drive purchase consideration across our most immersive and visual touch points like YouTube, Shorts, Gmail and Discover. Hollywood film and TV studio, Lionsgate, partnered with Horizon Media to test what campaign type will deliver the most ticketing page views for its The Hunger Games: Ballad of Songbirds and Snakes film. Over a 3-week test, demand gen was significantly more efficient versus social benchmarks with an 85% more efficient CPC and 96% more efficient cost per page view. Lionsgate has since rolled out Demand Gen for 2 new titles. We’re also bringing new creative features to demand gen. Earlier this month, we announced new generative image tools to help advertisers create high-quality assets in a few steps with a few simple prompts. This will be a win for up-leveling visual storytelling and testing creative concepts more efficiently.

Google Cloud had 28% revenue growth in 2024 Q1 (was 26% in 2023 Q4), driven by an increasing contribution from AI; management sees the growth of Google Cloud being underpinned by the benefits AI provides for customers, and management wants to invest aggressively in cloud while remaining focused on profitable growth; Alphabet’s big jump capex in 2024 Q1 (was $6.3 billion in 2023 Q1) was mostly for technical infrastructure and reflects management’s confidence in the opportunities offered by AI; management expects Alphabet’s quarterly capex for the rest of 2024 to be similar to what was seen in 2024 Q1; management has no view on 2025 capex at the moment; management sees Google Cloud hitting an inflection point because of AI

Turning to the Google Cloud segment. Revenues were $9.6 billion for the quarter, up 28%, reflecting significant growth in GCP with an increasing contribution from AI and strong Google Workspace growth, primarily driven by increases in average revenue per seat. Google Cloud delivered operating income of $900 million and an operating margin of 9%…

…With respect to Google Cloud, performance in Q1 reflects strong demand for our GCP infrastructure and solutions as well as the contribution from our Workspace productivity tools. The growth we are seeing across Cloud is underpinned by the benefit AI provides for our customers. We continue to invest aggressively while remaining focused on profitable growth…

…With respect to CapEx, our reported CapEx in the first quarter was $12 billion, once again driven overwhelmingly by investment in our technical infrastructure, with the largest component for servers followed by data centers. The significant year-on-year growth in CapEx in recent quarters reflects our confidence in the opportunities offered by AI across our business. Looking ahead, we expect quarterly CapEx throughout the year to be roughly at or above the Q1 level, keeping in mind that the timing of cash payments can cause variability in quarterly reported CapEx…

…And then with respect to 2025, as you said, it’s premature to comment so nothing to add on that…

…On the Cloud side, obviously, it’s definitely a point of inflection overall. I think the AI transformation is making everyone think about their whole stack, and we are engaged in a number of conversations. I think paid AI infrastructure, people are really looking to Vertex AI, given our depth and breadth of model choice, or using Workspace to transform productivity in your workplace, et cetera. So I think the opportunities there are all related to that, both all the work we’ve built up and AI being a point of inflection in terms of driving conversations. I think you’ll see us do it both organically and with a strong partner program as well. So we’ll do it with a combination.

Alphabet’s management thinks the AI transition is a once-in-a-generation opportunity; it’s the first time they think Alphabet can work on AI in a horizontal way

I think the AI transition, I think it’s a once-in-a-generation kind of an opportunity. We’ve definitely been gearing up for this for a long time. You can imagine we started building TPUs in 2016, so we’ve definitely been gearing up for a long time…

… The real opportunities we see is the scale of research and innovation, which we have built up and are going to continue to deliver. I think for the first time, we can work on AI in a horizontal way and it impacts the entire breadth of the company, be it Search, be it YouTube, be it Cloud, be it Waymo and so on. And we see a rapid pace of innovation in that underlying.

Alphabet’s management thinks that, with regards to monetising the opportunity of smartphone-based AI searches, there will be search use-cases that can be fulfilled on-device, but there will be many, many search use-cases that will require the internet

[Question] As users start searching on smartphones and those searches are basically rendered on the model, on the phone, without accessing the web, how do you guys anticipate monetizing some of these smartphone-based behaviors that are kind of run on the edge?

[Answer] If you look at what users are looking for, people are looking for information and an ability to connect with things outside. So I think there will be a set of use cases which you will be able to do on device. But for a lot of what people are looking to do, I think you will need the richness of the cloud, the Web and you have to deliver it to users. So again, to my earlier comments, I think through all these moments, you saw what we have done with Samsung with Circle to Search. I think it gives a new way for people to access Search conveniently wherever they are. And so we view this as a positive way to bring our services to users in a more seamless manner. So I think it’s positive from that perspective. In terms of on-device versus cloud, there will be needs which can be done on-device and we should to help it from a privacy standpoint. But there are many, many things for which people will need to reach out to the cloud. And so I don’t see that as being a big driver in the on-cloud versus off-cloud in any way.

Amazon (NASDAQ: AMZN)

Amazon’s management recently launched a new generative AI tool for third-party sellers to quickly create product detail pages on Amazon using just the sellers’ URL to their websites; more than 100,000 third-party sellers on Amazon are already using at least one of Amazon’s generative AI tools

We’ve recently launched a new generative AI tool that enables sellers to simply provide a URL to their own website, and we automatically create high-quality product detail pages on Amazon. Already, over 100,000 of our selling partners have used one or more of our gen AI tools. 

Amazon’s management is seeing AWS customers being excited about leveraging generative AI to change their customer experiences and businesses; AWS’s AI business is already at a multibillion-dollar revenue rate; AWS AI’s business is driven by a few things, including the fact that many companies are still building their models; management expects more models to be built on AWS over time because of the depth of AI offerings AWS has

Our AWS customers are also quite excited about leveraging gen AI to change the customer experiences and businesses. We see considerable momentum on the AI front where we’ve accumulated a multibillion-dollar revenue run rate already…

… I mentioned we have a multibillion-dollar revenue run rate that we see in AI already, and it’s still relatively early days. And I think that there’s — at a high level, there’s a few things that we’re seeing that’s driving that growth. I think first of all, there are so many companies that are still building their models. And these range from the largest foundational model builders like Anthropic, you mentioned, to every 12 to 18 months or building new models. And those models consume an incredible amount of data with a lot of tokens, and they’re significant to actually go train. And a lot of those are being built on top of AWS, and I expect an increasing amount of those to be built on AWS over time because our operational performance and security and as well as our chips, both what we offer from NVIDIA. But if you take Anthropic, as an example, they’re training their future models on our custom silicon on Trainium. And so I think we’ll have a real opportunity for a lot of those models to run on top of AWS.

Amazon’s management’s framework for thinking about generative AI consists of 3 layers –  the first is the compute layer, the second is LLMs as a service, the third is the applications that run on top of LLMs – and Amazon continues to add capabilities in all 3

You heard me talk about our approach before, and we continue to add capabilities at all 3 layers of the gen AI stack. At the bottom layer, which is for developers and companies building models themselves, we see excitement about our offerings…

…The middle layer of the stack is for developers and companies who prefer not to build models from scratch but rather seek to leverage an existing large language model, or LLM, customize it with their own data and have the easiest and best features available to deploy secure high-quality, low-latency, cost-effective production gen AI apps…

…The top of the stack are the gen AI applications being built. 

Amazon’s management thinks AWS has the broadest selection of Nvidia compute instances but also sees high demand for Amazon’s custom silicon, Trainium and Inferentia, as they provide favourable price performance benefits; larger quantities of Amazon’s latest Trainium chip, Trainium 2, will arrive in 2024 H2 and early 2025; Anthropic’s future models will be trained on Tranium

We have the broadest selection of NVIDIA compute instances around, but demand for our custom silicon, Trainium and Inferentia, is quite high given its favorable price performance benefits relative to available alternatives. Larger quantities of our latest generation Trainium2 is coming in the second half of 2024 and early 2025…

…But if you take Anthropic, as an example, they’re training their future models on our custom silicon on Trainium. 

SageMaker, AWS’s fully-managed machine learning service, has helped (1) Perplexity AI train models 40% faster, (2) Workday reduce inference latency by 80%, and (3) NatWest reduce time to value for AI from 12-18 months to less than 7 months; management is seeing an increasing number of AI model builders standardising on SageMaker

Companies are also starting to talk about the eye-opening results they’re getting using SageMaker. Our managed end-to-end service has been a game changer for developers in preparing their data for AI, managing experiments, training models faster, lowering inference latency, and improving developer productivity. Perplexity.ai trains models 40% faster than SageMaker. Workday reduces inference latency by 80% with SageMaker, and NatWest reduces its time to value for AI from 12 to 18 months to under 7 months using SageMaker. This change is how challenging it is to build your own models, and we see an increasing number of model builders standardizing on SageMaker.

Amazon’s management thinks Amazon Bedrock, a LLM-as-a-service offering, has the broadest selection of LLMs (large language models) for customers in addition to retrieval augmented generation (RAG) and other features; Bedrock offers high-profile LLMs – such as Anthropic’s Claude 3 and Meta’s Llama 3 – in addition to Amazon’s own Titan models; Custom Model Import is a new feature from Bedrock that satisfies a customer request (the ability to import models from SageMaker or elsewhere into Bedrock in a simple manner) that nobody has yet met; management is seeing customers being excited about Custom Model Import; Bedrock has tens of thousands of customers

 This is why we built Amazon Bedrock, which not only has the broadest selection of LLMs available to customers but also unusually compelling model evaluation, retrieval augmented generation, or RAG, to expand model’s knowledge base, guardrails to safeguard what questions applications will answer, agents to complete multistep tasks, and fine-tuning to keep teaching and refining models. Bedrock already has tens of thousands of customers, including adidas, New York Stock Exchange, Pfizer, Ryanair and Toyota. In the last few months, Bedrock’s added Anthropic’s Claude 3 models, the best-performing models in the planet right now; Meta’s Llama 3 models; Mistral’s various models, Cohere’s new models and new first-party Amazon Titan models.

A week ago, Bedrock launched a series of other features, but perhaps most importantly, Custom Model Import. Custom Model Import is a sneaky big launch as it satisfies a customer request we’ve heard frequently and that nobody has yet met. As increasingly more customers are using SageMaker to build their models, they’re wanting to take advantage of all the Bedrock features I mentioned earlier that make it so much easier to build high-quality production-grade gen AI apps. Bedrock Custom Model Import makes it simple to import models from SageMaker or elsewhere into Bedrock before deploying their applications. Customers are excited about this, and as more companies find they’re employing a mix of custom-built models along with leveraging existing LLMs, the prospect of these 2 linchpin services in SageMaker and Bedrock working well together is quite appealing…

…And the primary example we see there is how many companies, tens of thousands of companies, already are building on top of Amazon Bedrock.

Amazon’s management has announced the general availability of Amazon Q, a highly-capable generative AI-powered assistant; Amazon Q helps developers generate code, test code, debug code, and can save developers months of work when moving from older versions of Java to newer ones; Amazon Q has an Agents capability which can autonomously perform a range of tasks, including (1) implementing application features, and (2) parsing a company’s entire data stock to create summaries and surface insights; Amazon Q also has Q Apps, which lets employees describe in natural language what app they want to build on top of internal data; management believes that Q is the most functionally-capable AI-powered assistant for software development and data, as Q outperforms competitors; many companies are already using Amazon Q

And today, we announced the general availability of Amazon Q, the most capable generative AI-powered assistant for software development and leveraging company’s internal data.

On the software development side, Q doesn’t just generate code. It also tests code, debugs coding conflicts, and transforms code from one form to another. Today, developers can save months using Q to move from older versions of Java to newer, more secure and capable ones. In the near future, Q will help developers transform their .NET code as well, helping them move from Windows to Linux.

Q also has a unique capability called Agents, which can autonomously perform a range of tasks, everything from implementing features, documenting, and refactoring code to performing software upgrades. Developers can simply ask Amazon Q to implement an application feature such as asking it to create an add to favorites feature in a social sharing app, and the agent will analyze their existing application code and generate a step-by-step implementation plan, including code changes across multiple files and suggested new functions. Developers can collaborate with the agent to review and iterate on the plan, and then the agent implements it, connecting multiple steps together and applying updates across multiple files, code blocks and test suites. It’s quite handy. On the internal data side, most companies have large troves of internally relevant data that resides in wikis, Internet pages, Salesforce, storage repositories like Amazon S3 and a bevy of other data stores and SaaS apps that are hard to access. It makes answering straightforward questions about company policies, products, business results, code, people, and many other topics hard and frustrating. Q makes this much simpler. You can point Q at all of your enterprise data repositories and it will search all this data, summarize logically, analyze trends, engage in dialogue with customers about this data.

We also introduced today a powerful new capability called Q Apps, which lets employees describe a natural language what apps they want to build on top of this internal data and Q Apps will quickly generate that app. This is going to make it so much easier for internal teams to build useful apps from their own data.

Q is not only the most functionally capable AI-powered assistant for software development and data but also setting the standard for performance. Q has the highest-known score and acceptance rate for code suggestions, outperforms all other publicly benchmarkable competitors and catching security vulnerabilities, and leads all software development assistants on connecting multiple steps together and applying automatic actions. Customers are gravitating to Q, and we already see companies like Brightcove, British Telecom, Datadog, GitLab, GoDaddy, National Australia Bank, NCS, Netsmart, Slam, Smartsheet, Sun Life, Tata Consultancy Services, Toyota, and Wiz using Q, and we’ve only been in beta until today.

Amazon’s management believes that AWS has a meaningful edge in security elements when it comes to generative AI, and this has led to companies moving their AI focus to AWS

I’d also caution folks not to overlook the security and operational performance elements of these gen AI services. It’s less sexy but critically important. Most companies care deeply about the privacy of the data in their AI applications and the reliability of their training and production apps. If you’ve been paying attention to what’s been happening in the last year or so, you can see there are big differences between providers on these dimensions. AWS has a meaningful edge, which is adding to the number of companies moving their AI focus to AWS.

Amazon’s management sees Amazon’s capex increasing meaningfully in 2024 compared to 2023 ($48.4 billion in 2023) because of AWS’s accelerating growth and high demand for generative AI; the capex in 2024 will go mostly towards technology infrastructure; the capex of $14 billion in 2024 Q1 will be the low quarter for the year;

We expect the combination of AWS’ reaccelerating growth and high demand for gen AI to meaningfully increase year-over-year capital expenditures in 2024, which given the way the AWS business model works is a positive sign of the future growth…

…As a reminder, we define these as the combination of CapEx plus equipment finance leases. In 2023, overall capital investments were $48.4 billion…

…We do see, though, on the CapEx side that we will be meaningfully stepping up our CapEx and the majority of that will be in our — to support AWS infrastructure and specifically generative AI efforts…

…We’re talking about CapEx. Right now, in Q1, we had $14 billion of CapEx. We expect that to be the low quarter for the year.

Amazon’s management is very bullish on AWS, as 85% or more of global IT spend remains on-premise, even though AWS is already at at $100 billion-plus revenue run rate; in addition, there’s demand for generative AI, most of which will be created in the next few decades from scratch and on the cloud

We remain very bullish on AWS. We’re at $100 billion-plus annualized revenue run rate, yet 85% or more of the global IT spend remains on-premises. And this is before you even calculate gen AI, most of which will be created over the next 10 to 20 years from scratch and on the cloud. There is a very large opportunity in front of us. 

Amazon’s management thinks the generative AI opportunity is something they have not seen since the cloud or internet

We have a lot of growth in front of us, and that’s before the generative AI opportunity, which I don’t know if any of us have seen a possibility like this in technology in a really long time, for sure, since the cloud, perhaps since the Internet. 

Amazon’s management thinks much more money will be spent on AI inference than on model training; management sees quite a few companies that are building their generative AI applications to do inference on AWS

I think the thing that people sometimes don’t realize is that while we’re in the stage that so many companies are spending money training models, once you get those models into production, which not that many companies have, but when you think about how many generative AI applications will be out there over time, most will end up being in production when you see the significant run rates. You spend much more in inference than you do in training because you train only periodically, but you’re spinning out predictions and inferences all the time. And so we also see quite a few companies that are building their generative AI applications to do inference on top of AWS.

Amazon’s management sees both training and inference being really big drivers for AWS; this is helped by the fact that these AI models will work with companies’ data and the security surrounding the data is important for companies, and AWS has a meaningful edge in security

We see both training and inference being really big drivers on top of AWS. And then you layer on top of that the fact that so many companies, their models and these generative AI applications are going to have their most sensitive assets and data. And it’s going to matter a lot to them what kind of security they get around those applications. And yes, if you just pay attention to what’s been happening over the last year or 2, not all the providers have the same track record. And we have a meaningful edge on the AWS side so that as companies are now getting into the phase of seriously experimenting and then actually deploying these applications to production, people want to run their generative AI on top of AWS.

Apple (NASDAQ: AAPL)

Apple’s management continues to feel bullish about Apple’s opportunity in generative AI; Apple is making significant investments in the area and will be sharing details soon; management thinks Apple has advantages with AI given its unique combination of hardware, software, services, custom silicon (with industry-leading neural engines), and privacy

We continue to feel very bullish about our opportunity in generative AI. We are making significant investments, and we’re looking forward to sharing some very exciting things with our customers soon. We believe in the transformative power and promise of AI, and we believe we have advantages that will differentiate us in this new era, including Apple’s unique combination of seamless hardware, software, and services integration, groundbreaking Apple silicon with our industry-leading neural engines, and our unwavering focus on privacy, which underpins everything we create. 

Apple’s management does not expect Apple’s capex to inflect higher, nor the composition of the capex to change much, even as the company leans into AI

[Question] As Apple leans more into AI and generative AI, should we expect any changes to the historical CapEx cadence that we’ve seen in the last few years of about $10 billion to $11 billion per year? Or any changes to how we may have historically thought about the split between tooling, data center, and facilities?

[Answer]  We are obviously very excited about the opportunity with GenAI. We obviously are pushing very hard on innovation on every front, and we’ve been doing that for many, many years. Just during the last 5 years, we spent more than $100 billion in research and development. As you know, on the CapEx front, we have a bit of a hybrid model where we make some of the investments ourselves. In other cases, we share them with our suppliers and partners. On the manufacturing side, we purchase some of the tools and manufacturing equipment. In some of the cases, our suppliers make the investment. And we do something similar on the data center side. We have our own data center capacity and then we use capacity from third parties. It’s a model that has worked well for us historically, and we plan to continue along the same lines going forward.

Apple’s management will soon share their thoughts on how Apple intends to monetise AI on its devices – but not today

[Question] You’ve obviously mentioned your excitement around generative AI multiple times. I’m just curious how Apple is thinking about the different ways in which you can monetize this technology because, historically, software upgrades haven’t been a big factor in driving product cycles. And so could AI be potentially different?

[Answer] I don’t want to get in front of our announcements obviously. I would just say that we see generative AI as a very key opportunity across our products, and we believe that we have advantages that set us apart there. And we’ll be talking more about it in — as we go through the weeks ahead.

Arista Networks (NYSE: ANET)

Arista Networks’ management sees an addressable market of US$60 billion in client-to-cloud AI networking

Amidst all the network consolidation, Arista is looking to establish ourselves as the pure-play networking innovator, for the next era, addressing at least a $60 billion TAM in data-driven client-to-cloud AI networking.

Arista Networks’ management is pleased with the momentum they are seeing in the company’s customer segments, including the Cloud and AI Titans segment; management is becoming increasingly constructive about hitting their 2025 target of US$750 million in AI revenue; the 2025 target of US$750 million is not a hockey-stick target, but a glide path

We are quite pleased with the momentum across all our 3 sectors: Cloud and AI Titans, Enterprise and Providers. Customer activity is high as Arista continues to impress our customers and prospects with our undeniable focus on quality and innovation…

… A good AI network needs a good data strategy, delivered by our highly differentiated EOS and network data lake architecture. We are, therefore, becoming increasingly constructive about achieving our AI target of $750 million in 2025…

…When you think about the $750 million target that has become more constructive to Jayshree’s prepared remarks, that’s a glide path. So it’s not 0 in ’24, It’s a glide path to ’25. 

Traditional networking discards data as the network changes state, but recent developments in AI show how important it is to gather and store large data sets – this is a problem Arista Networks’ management is solving through the company’s NetDL (Network Data Lake) platform, which streams every piece of network data in real time and archives the full data history

From the inception of networking decades ago, networking has involved rapidly changing data. Data about how the network is operating, which paths through the network our best and how the network is being used. But historically, most of this data was to simply discarded as the network changes state and that which was collected can be difficult to interpret because it lacks context. Network addresses and port numbers by themselves, provide a little insight into what users are doing or experiencing.

Recent developments in AI have proved the value of data. But to take advantage of these breakthroughs, you need to gather and store large data sets, labeled suitably for machine learning. Arista is solving this problem with NetDL, we continually monitor every device, not simply taking snapshots, but rather streaming every network event, every counter, every piece of data in real time, archiving a full history in NetDL. Alongside this device data, we also collect flow data and inbound network telemetry data gathered by our switches. Then we enrich this performance data further with user, service and application layer data from external sources outside the network, enabling us to understand not just how each part of the network is performing, but also which users are using the network for what purposes. And how the network behavior is influencing their experience. NetDL is a foundational part of the EOS stack, enabling advanced functionality across all of our use cases. For example, in AI fabrics, NetDL enables fabric-wide visibility, integrating network data and NIC data to enable operators to identify misconfigurations or misbehaving hosts and pinpoint performance bottlenecks.

Any slowdown in the network when running generative AI training tasks can reduce processor performance by 30% or more

As generative AI training tasks evolve, they are made up of many thousands of individual iterations. Any slowdown due to network and critically impact the application performance, creating inefficient wait stage and idling away processor performance by 30% or more. The time taken to reach coherence known, as job completion time is an important benchmark achieved by building proper scale-out AI networking to improve the utilization of these precious and expensive GPUs. 

A Cloud and AI Titan customer of Arista Networks used the company’s product to build a 24,000 node GPU cluster for complex AI training tasks; Arista Networks’ product offered an improvement of at least 10% on job completion performance across all packet sizes versus InfiniBand; in Arista Networks’ four recent AI Ethernet clusters that was won versus InfiniBand, management is seeing all four projects migrate from trials to pilots; Arista Networks will be connecting thousands of GPUs in the four projects this year and management expects to connect 10,000 to 100,000 GPUs in 2025; ethernet was traditionally considered to have loss properties while InfiniBand was traditionally considered to be lossless, but when ethernet is used in actual GPU clusters, ethernet is 10% faster than Infiniband; management expects improvement in ethernet’s performance relative to Infiniband in the future, driven partly by the Ultra Ethernet Consortium 

In a recent blog from one of our large Cloud and AI Titan customers, Arista was highlighted for building a 24,000 node GPU cluster based on our flagship 7800 AI Spine. This cluster tackles complex AI training tasks that involve a mix of model and data parallelization across thousands of processors and ethernet is proving to offer at least 10% improvement of job completion performance across all packet sizes versus InfiniBand…

…If you recall, in February, I shared with you that we are progressing well in 4 major AI Ethernet clusters, that we won versus InfiniBand recently. In all 4 cases, we are now migrating from trials to pilots, connecting thousands of GPUs this year, and we expect production in the range of 10,000 to 100,000 GPUs in 2025…

…Historically, as you know, when you look at InfiniBand and Ethernet in isolation, there are a lot of advantages of each technology. Traditionally, InfiniBand has been considered lossless and Ethernet is considered to have some loss properties. However, when you actually put a full GPU cluster together along with the optics and everything, and you look at the coherents of the job completion time across all packet sizes, data has shown that and this is data that we have gotten from third parties, including Broadcom, that just about in every packet size in a real-world environment independent of the comparing those technologies, the job completion time of Ethernet was approximately 10% faster. So you can look at these things in silos. You can look at it in a practical cluster and in a practical cluster we are already seeing improvements on Ethernet. Now don’t forget, this is just Ethernet as we know it today. Once we have the Ultra Ethernet Consortium and some of the improvements you’re going to see on packet spring and dynamic load balancing and congestion control, I believe those numbers will get even better. 

Arista Networks’ management is witnessing an inflection of AI networking and expects the trend to continue both in the short and long run; management is seeing ethernet emerging as critical infrastructure for both front-end and back-end AI data centers; AI applications require seamless communication between the front-end (includes CPUs, or central processing units) and back-end (includes GPUs and AI accelerators); management is seeing ethernet at scale becoming the de facto network and premium choice for scaled-out AI training workloads

We are witnessing an inflection of AI networking and expect this to continue throughout the year and decade. Ethernet is emerging as a critical infrastructure across both front-end and back-end AI data centers. AI applications simply cannot work in isolation and demand seamless communication among the compute nodes, consisting of back-end GPUs and AI accelerators and as well as the front end nodes like the CPUs, alongside storage and IP/WAN systems as well…

…Ethernet at scale is becoming the de facto network at premier choice for scale-out AI training workloads.

Arista Networks’ management thinks that visibility on new AI and cloud projects is getting better and has now improved to at least 6 months

In summary, as we continue to set the direction of Arista 2.0 networking, our visibility to new AI and cloud projects is improving and our enterprise and provider activity continues to progress well…

…In the Cloud and AI Titans in November, we were really searching for even 3 months visibility, 6 would have been amazing. Today, I think after a year of tough situations for us where the Cloud Titans were pivoting rather rapidly to AI and not thinking about the Cloud as much. We’re now seeing a more balanced approach where they’re still doing AI, which is exciting, but they’re also expanding their regions on the Cloud. So I would say our visibility has now improved to at least 6 months and maybe it gets longer as time goes by.

Arista Networks’ management still sees Infiniband as the de facto network of choice for AI workloads, but ethernet is gaining ground; management sees ethernet as being the eventual winner against InfiniBand because ethernet has a long history of 50 years that gives it an advantage (Metcalfe’s law) 

And then sometimes we see them, obviously, when they’re pushing InfiniBand, which has been, for most part, the de facto network of choice. You might have heard me say, last year or the year before, I was outside looking into this AI networking. But today, we feel very pleased that we are able to be the scale-out network for NVIDIA’s, GPUs and NICs based on Ethernet.,,

…This InfiniBand topic keeps coming up. And I’d just like to point out that Ethernet is about 50 years old. And over those 50 years, Ethernet has come head-to-head with a bunch of technologies like Token ring, SONET, ATM, FDDI, HIPPI, Scalable Coherent Interconnect, [ Mirrornet ]. And all of these battles have one thing in common. Ethernet won. And the reason why is because of Metcalfe’s law, the value of a network is quadratic in the number of nodes of the interconnect. And so anybody who tries to build something which is not Ethernet, is starting off with a very large quadratic disadvantage. And any temporary advantage they have because of the — some detail of the tech cycle is going to be quickly overwhelmed by the connectivity advantage you have with Ethernet.

Arista Networks’ management does not see Nvidia as a direct competitor for ethernet; management also believes that Arista Networks’ focus and experience are advantages

We don’t see NVIDIA as a direct competitor yet on the Ethernet side. I think it’s 1% of their business. It’s 100% of our business. So we don’t worry about that overlap at all. And we think we’ve got 20 years of founding to now experience to make our Ethernet switching better and better at both on the front end and back end. So we’re very confident that Arista can build the scale up network and work with NVIDIA scale-up GPUs.

Within AI networking, Arista Networks’ management is seeing the first use case emerging to be the build-out of the fastest training workloads and clusters

The first use case that’s emerging for AI networking is, let’s just build the fastest training workloads and clusters. And they’re looking at performance. Power is a huge consideration, the cooling of the GPUs is a huge part of it. You would be surprised to hear a lot of times, it’s just waiting on the facilities and waiting for the infrastructure to be set up, right?

Arista Networks’ management is seeing Tier 2 cloud providers starting to pick up AI initiatives, although the Tier 2 providers are not close to the level the activity as the Cloud Titans

The Tier 2 cloud providers, I want to speak to them for a moment because not only are they strong for us right now, but they are starting to pick up some AI initiatives as well. So they’re not as large as close as the Cloud Titans, but the combination of the Service Providers and the Tier 2 Specialty Providers is also seeing some momentum.

Arista Networks is seeing GPU lead times improve significantly 

The GPU, the number of GPUs, the location of the GPUs, the scale of the GPUs, the locality of these GPUs, should they go with Blackwell should they build with a scale up inside the server or scale out to the network. So the whole center of gravity, what’s nice to watch which is why we’re more constructive on the 2025 numbers is that the GPU lead times have significantly improved, which means more and more of our customers will get more GPUs, which in turn means they can build out to scale our network.

Arista Networks’ management is not seeing any pause in their customers’ investments in GPU clusters and networking just to wait for the delivery of Nvidia’s latest Blackwell AI chips; Arista Networks’ networking products can perform the required networking tasks well regardless of what GPU is used

[Question] I want to go back to AI, the road map and the deployment schedule for Blackwell. So it sounds like it’s a bit slower than maybe initially expected with initial customer delivery late this year. How are you thinking about that in terms of your road map specifically and how that plays into what you’re thinking about ’25 in a little bit more detail. And does that late delivery maybe put a little bit of a pause on maybe some of the cloud spend in the fall of this year as there seems to be somewhat of a technology transition going on towards Blackwell away from the Legacy product?

[Answer] We’re not seeing a pause yet. I don’t think anybody is going to wait for Blackwell necessarily in 2024 because they’re still bringing up their GPU clusters. And how a cluster is divided across multiple tenants, the choice of host, memory, storage architectures, optimizations on the GPU for collective communication, libraries, specific workloads, resilience, visibility, all of that has to be taken into consideration. All this to say, a good scale-out network has to be built, no matter whether you’re connecting to today’s GPUs or future Balckwells. And so they’re not going to pause the network because they’re waiting for Blackwell. they’re going to get ready for the network, whether it connects to a Blackwell or a current H100. So as we see it, the training workloads and the urgency of getting the best job completion time is so important that they’re not going to spare any investments on the network side and the network side can be ready no matter what the GPU is.

ASML (NASDAQ: ASML)

ASML’s management sees no change to the company’s outlook for 2024 from what was mentioned in the 2023 Q4 earnings call, with AI-related applications still driving demand, Memory demand being driven by DRAM technology node transitions to support DDR5 and HBM, and Logic customers digesting capacity additions made in 2023

Looking at the market segments, we see a similar environment as communicated last quarter with demand momentum from AI-related applications. Memory demand is primarily driven by DRAM technology node transitions in support of advanced memories such as DDR5 and HBM. Logic customers continue to digest the significant capacity additions made over the last year — over the past year

ASML’s management sees some macro uncertainties as still being present, but the long-term trends in the company’s business (AI, electrification, energy transition) are intact

There are still some uncertainties. I would say primarily macro uncertainties. That’s still clearly there…

…If you look at the trends in the industry, if you look at, and I’m talking about the cyclicality trends in the industry, so like the utilization going up, inventory downstream being managed to more normal levels. I think it’s pretty clear that the industry is in its upturn and therefore we do believe that by 2024 we’re going to see a recovery. Clearly a recovery of the industry. So then fast forward to 2025. Then what do we find ourselves in? First off, I think we will find ourselves in 2025 in the midst of the upturn. So that’s a positive. Second – and we’ve talked about that many times – the secular trends are really strong. If you look at AI, if you look at electrification, if you look at the energy transition. It’s all very strong, very positive momentum behind it. So the secular trends are very, very strong. That is also something that I think will yield in 2025. Finally, if you just look at all the fab openings that have been indicated by our customers. The recent news on positive outcomes of CHIPS Act money allocation. All of that is very strong, very supportive for new fab openings across the globe. I think by 2025 you will see all three of those coming together. New fab openings, strong secular trends and the industry in the midst of its upturn. So that’s why we’re doing what we’re doing. Which is really preparing for that ramp, for that momentum that we see being built up.

ASML’s management thinks that AI will be driving demand for leading-edge and high-performance compute; AI is itself driven by massive amounts of data and the overlay of smart software over the data; management also thinks that IoT (Internet of Things) will be an area with plenty of AI applications

You’re basically saying what will drive leading-edge, high-performance compute. But you’re absolutely right. I mean, when you think about high-performance compute, and especially in the context of AI, and I’ve said this many, many times before, AI is driven by massive amounts of data and about also understanding the correlation between those data elements and then overlaying that with smart software. But — and I also believe, it’s actually what I’m seeing and what I’m hearing is that IoT in the industrial space will actually be in — will be an area where we will see a lot of AI applications. Well, in order to collect all that data, you need sensors because you’ve got all kinds of examples, whether it’s the car or whether it’s life science, medical equipment, it’s about sensing, and that is really the domain of mainstream semiconductors.

ASML’s management is seeing the software world enjoying 30% to 50% increases in productivity because of the use of AI

And when you think about AI, I mean, some of these examples, and especially in the software space where you see productivity, just the calculated productivity advantages of 30% to 50%, then the value of the next-generation transistor will be huge.

Coupang (NYSE: CPNG)

Coupang’s management is exploring both the company’s own foundational AI models as well as those from third-parties; AI has been a core part of Coupang’s strategy and management has been deploying the technology in many areas of the company’s business; management is excited about the potential of AI, but will be testing its ability to generate returns for the business

On AI, we are exploring, both for us, as you mentioned, foundational models as well as our own. Machine learning and AI continues to be — have been a core part of our strategy. We’ve deployed them in many facets of our business, from supply chain management to same-day logistics. We’re also seeing tremendous potential with large language models in a number of areas from search and ads, to catalog and operations, among others. There’s exciting potential for AI that we see and we see opportunities for it to contribute even more significantly to our business. But like any investment we make, we’ll test and iterate and then invest further only in the cases where we see the greatest potential for return.

Datadog (NASDAQ: DDOG)

Datadog’s management has announced general availability of Bits AI for incident management, where Bits AI can produce auto-generated incident summaries for incident responders

In the MegaGenAI space, we announced general availability of Bits AI for incident management. By using Bits AI for incident management, incident responders get auto-generated incident summaries to quickly understand the context and scope of a complex incident. And users can also enqure Bits AI to ask about related incidents and to form tasks on the fly from incident creation to resolution.

There’s growing interest in AI from Datadog’s customers, and the company’s next-gen AI customers accounted for 3.5% of ARR (was 3% in 2023 Q4); the percentage of ARR from next-gen AI customers is a metric that management thinks will become less relevant over time as AI adoption broadens

We’re also continuing to see more interest in AI from our customers. As a data point, ARR for next GenAI customers was about 3.5% of our total, a strong sign of the growing ecosystem of companies in this area…

…I’m not sure this is a metric we’ll keep bringing up. It was interesting for us to look at this small group of early AI-native companies to get a sense of what might come next in the world of AI. But I think as we — as time goes by and as AI adoption broadens, I think it becomes less and less relevant. 

Datadog has AI integrations that allow customers to pull their AI data into the Datadog platform; around 2,000 customers are already using 1 or more of the AI integrations

To help customers understand AI technologies and bring them into production applications, our AI integrations allow customers to pull their AI data into the Datadog platform. And today, about 2,000 of our customers are using 1 or more of these AI integrations. And we’ve continued to keep up with the rapid innovation in this space. For example, adding a new integration in Q1 with the NVIDIA Triton [indiscernible] server. 

Datadog’s management has announced general availability for Event Management in the cloud service management area; Event Management reduces the volume of alerts and events Datadog’s customers have to deal with; with Event Management, Datadog now has a full AI solution that helps teams automate remediation, proactively prevent outages and reduce the impact of incidents.

In the cloud service management area, we released event management in the general availability. Our customers face increasing complexity at scale, causing the volume of alerts and events to explode, which makes it difficult for teams to identify, prioritize, summarize and route issue to the right responders. Event management addresses this challenge by automatically reducing a massive volume of events and alerts into actionable insights. These are then used to generate tickets, call an incident or trigger an automated remediation. By combining event management with Watchdog, Bits AI and workflow automations, Datadog now provides a full AI solution that helps teams automate remediation, proactively prevent outages and reduce the impact of incidents…

…We just announced in GA, the Event Management product, which is the main missing building block we had for AIOps platform.

Datadog’s Azure business is growing faster than Azure itself, and Datadog’s AI-part of Azure is growing than faster then the AI-part of Azure itself

The hyperscaler that is the most open by is — or transparent by in terms of numbers is Microsoft as they disclose how much of their growth comes from AI more specifically. And I will say that if you compare our business to theirs, the Azure part of our business is growing faster than Azure itself. And the AI-driven part of our Azure business itself is also growing faster than what you see on the on the overall Azure number. So we think we have similar exposure, and we track to the same trends broadly

Datadog’s AI exposure leans toward inferencing and applications in production a lot more than the training of models

I will say also on AI adoption that some of the revenue jumps you might see from the cloud providers might relate to supply of GPUs coming online and a lot of training clusters being provisioned. And those typically won’t generate a lot of new usage for us. We tend to be more correlative with the live applications, production applications and inference workloads that tend to follow after that, and that are more tied to all of these applications going into production. 

Datadog has products for monitoring what AI models are doing, but those products are not in general availability yet; management expects to have more announcements on these monitoring products in the near future; Datadog’s customers that are the most scaled on AI workloads are model providers, and they tend to have their own monitoring infrastructure for the quality of the models; the needs of the model providers for monitoring infrastructure are not representative of the needs of the bulk of the market, but there may still be overlaps in the future if the situation with the cloud hyperscalers is used as a guide

We have products for monitoring, not just the infrastructure, but what the LLMs are doing. Those products are still not in GA, so we’re working with a smaller number of design partners for that. As I think not only these products are maturing, but also the industry around us is maturing and more of these applications are getting into production. You should expect to hear more from us on that topic in the near future. The customers we have that are the most scaled on AI workloads are the model providers themselves, and they tend to have their own infrastructure for monitoring the quality of the models…

…On the tooling, I would say there’s a handful of players that have been building that tooling for a few years for — in a way that’s very specialized to what they do internally. They are not necessarily the representative of the bulk of the market. So in those situations, we’re always careful about overfitting products to a group that might not be the right target customer group in the end in the same way that building infrastructure monitoring for the cloud providers to use internally might not be an exact fit for what the rest of the world needs. That being said, I mean, look, we work a lot with those companies, and they have a number of needs that some of them they can meet internally and some of them, they don’t. And if I go back to the example of hyperscalers, we actually have teams at the hyperscalers that use us for application or infrastructure or logs internally, even though they’ve built a lot of that tooling themselves. So I think everything is possible in the long run. But our focus is really on the vast majority of the customer base that’s going to either use those API-based products or tune and run their own models.  

Datadog’s management is seeing a trend of AI-adopters starting with an API-accessible AI model to build applications, before offloading some of the workload to open-sourced AI models

We think there are good/bad weather in terms of what the adoption of AI is going to be from all the other companies, and we definitely see a trend where customers start with an API-driven or API-accessible model, build applications and then offload some of that application to other models that typically come from the open source and they might train, fine-tune themselves to get to a lower cost and lower time to respond.

Management is seeing a lot of interest in Datadog’s new AI-related products; management thinks its AI-related products are a joy to use

We see a lot of interest in the new products. These are new products so we just announced in GA, the Event Management product, which is the main missing building block we had for AIOps platform. And we also just released into GA, Bits for incident management. So there’s a lot of demand for it. The products are actually, I will say it, for Bits for incident management is a joy to use.

Etsy (NASDAQ: ETSY)

Etsy’s management believes the company’s product team is getting more efficient with machine learning

We had double-digit growth in the number of experiments per product engineer that utilize machine learning as well as in our annualized gross GMS from experiments. And the total number of experiments run per engineer increased 20%. Some of this progress can be directly tied to work we told you about last year to democratize ML. These metrics give me confidence that the bold moves to improve customer experience can build over time and play a key role to get Etsy growing again.

Etsy’s management thinks the application of AI is very useful for the company’s Gift Mode initiative

Large language models were really helpful for Gift Mode. So for example, there are 200 different persona in Gift Mode. And then within each persona, there are 3 to 5 different gift ideas and the ability to ask large language models, what are 200 examples of persona, and it wasn’t quite this simple, but it does give you a head start on that. If I’m a foodie who also loves to travel, what are 3 things I might buy on Etsy, 3 different ideas for gifts on Etsy, like, it does help to come up with a lot of ideas more quickly. The productivity gains, large language models are starting to help us with coding productivity as well. 

Etsy’s management finds the use of machine learning (ML) to be particularly useful in removing products that violate Etsy’s policies

We’re doing more than ever to suppress and remove listings that violate our policies. And advances in ML have been particularly powerful as enablers here. In the first quarter, we removed about 115% more listings for violating our handmade policy than in the prior year…

…For example, does this same item exist also on AliExpress. And we assume right now, if that item exists on AliExpress, we assume it’s mass produced and we take it down. You as a seller can appeal that, you can tell us how you made it yourself, and it still ended up on AliExpress. And by the way, that’s true sometimes. You can appeal that, but our default now is we take that down. And that’s just one example. Gen AI is actually going to be, I think, more and more helpful at understanding how much value did this particular seller truly add to the product.

Etsy’s management has used machine learning to improve the estimation of delivery time for products

In terms of shipping timeliness, I’m pleased to report that our initiative to tighten estimated delivery dates, which we believe are an important effort to improve buyer perceptions of our reliability as well as to grow GMS, are already paying off. Our fulfillment team recently launched a new machine learning model, which reduced our estimate of USPS transit times by greater than 1 day, resulting in a nearly tripling of the percentage of eligible orders for which Etsy is now able to show an estimated delivery date of 7 days or less.

Fiverr (NYSE: FVRR)

Fiverr’s management continues to see AI having a net positive impact on the company’s business; AI-related services saw 95% year-on-year growth in GMV on Fiverr’s platform, with chatbot development being especially popular; a hospitality company and an online learning platform are two examples of companies that have used Fiverr for AI chatbot development

AI continued to have a net positive impact on our business, as complex services continue to grow faster and represent a bigger portion of our business. Demand for AI-related services remained strong, as evidenced by 95% year-over-year growth in GMV from AI service categories. Chatbot development was especially popular this quarter as businesses look for ways to lean into GenAI technology to better engage with customers. For example, we have seen a hospitality company building a conversational tool for customers to manage bookings or an online learning platform creating a personalized learning menu and tutoring sessions for children. 

Fiverr has a pool of 10,000 AI experts and it is growing

With an over 10,000 and growing AI expert pool, Fiverr has become the destination for businesses to get help implementing GenAI and take their business to the next level.

Fiverr’s management is seeing very promising signals on Fiverr Neo, the company’s AI assistant for matching buyers with sellers; one-third of buyers who received seller recommendations from Neo sent a project brief to a seller; overall order conversion with Neo is nearly 3x that of the Fiverr marketplace average; management is excited about the potential of AI matching technology 

We have also seen very promising signals on Fiverr Neo, the AI matching assistant that we launched last year. Neo enables our buyers to have a more natural purchasing path by creating a conversational experience that leverages the catalog data and search algo. Answers and steps are provided based on buyers questions and the stage of the search. As a result, we saw that nearly one-third of the buyers who received seller recommendations from Neo ended up sending a project brief to the seller and the overall order conversion is nearly 3 times that of the marketplace average. This really gives us confidence and excitement in the potential we could unlock by investing in AI matching technology.

Fiverr’s product innovation pace had picked up in recent years; the latest set of product innovations will be focused on deepening trust and leveraging AI

Our product innovation pace picked up even more in recent years as the scale of our marketplace significantly expanded. This includes monetization products, such as Promoted Gigs and Seller Plus; AI innovations such as Logo Maker, AI Audition, to the latest ground-breaking Fiverr Neo; Business Solutions offerings, such as Project Partner and Fiverr Certified; and numerous products and features such as Fiverr Discover, Milestones and Subscriptions that empower our community to work better and smarter. We are always leading the curve of innovation that powers growth not only for us, but for the industry.

As our teams work towards our July product release, we are focusing on deepening trust and leveraging AI to reimagine every aspect of the customer journey. This includes improving our catalog and building new experiences to enable high-stakes, high-trust work to happen on Fiverr. We are strengthening our muscle in knowing our customers better in order to provide them with the better matching, better recommendations and better customer care, all of which leads to more trust for Fiverr as a platform. We are already seeing some of the benefits in unlocking wallet share and driving a mix shift towards complex services on Fiverr, and we are going to see more impact down the road

All the work that Fiverr facilitates happens on Fiverr, so management believes that the company has a lot of data (for perspective, in 2023, 38 million files were exchanged on Fiverr, and 2.5 million messages were sent daily between buyers and sellers) to leverage with generative AI to take the matching experience for buyers and sellers to a new level

Second, data and AI matching. Fiverr is unique in the sense that we are not just a platform that connects businesses with freelancers, the entire work actually happens on Fiverr. And that is really the secret sauce that enables us to do matching in such a simple, accurate and seamless way. With Generative AI, there’s incredible potential to take that experience to a whole new level. Just to give you some idea of the scale we operate. In 2023, over 38 million files were exchanged on our platform, and on average, 2.5 million messages were sent between buyers and sellers on a daily basis. We are experimenting with GenAI technology on how to unlock the potential of that massive data on Fiverr in order to enable buyers and sellers to have more information, search and browse in new ways, ask more complex questions, and ultimately, make better, more informed choices on Fiverr.

Fiverr’s management is seeing the presence of AI having a negative impact on the simple, low-value services on the company’s marketplace, but AI is overall a net-positive for Fiverr; management gave an example of how only simple language translation services are being impacted by AI, but the complex translation services are not

We mentioned in the previous earnings the fact that the negative impact that we’re seeing from AI is mostly around the very simple types of services. Those are normally services that would sell for $10, $15, which is — I mean, we are moving. I mean, the majority of contribution is coming from more complex services anyway. And as I said, we continue to see AI as a net positive. So it’s contributing more than the offsetting factors of simple products.It happens across several categories in several verticals, but there’s nothing specific to call out. Even if you look at the areas that you might think that AI would influence significantly like translation. But what you’re seeing is actually the very simple services around translation are being affected, the more complex types of services are not. I mean, if you would publish a book and then want to translate it into a different language that you don’t command, I would doubt that you would let AI translate it and go publish the outcome without actually verifying it.

Fiverr’s management is sure that many experts use AI as part of their workflow, but they do not rely on the AI blindly

I’m sure many experts actually use AI tools in their process of work, but they don’t rely on blindly letting AI run the work for them, but it is more of the modern tech that they use in order to amplify their creative process.

Mastercard (NYSE: MA)

Scam Protect is a new service launched by Mastercard’s management to protect users against cybercrime; Scam Protect combines Mastercard’s identity biometric AI and open banking capabilities

Cybercrime is a growing concern, last year alone, people in the United States lost over $12 billion to Internet scams. Scam Protect builds on the cybersecurity protections we have delivered for years, combines our identity biometric AI and open banking capabilities to identify and prevent scans before they occur. 

Mastercard is partnering with Verizon to design new AI tools to identify and block scammers

By combining Mastercard’s Identity Insights with Verizon’s robust network technologies, new AI power tools will be designed to more accurately identify and block scammers. 

Mastercard’s management has continued to enhance the company’s solutions with generative AI; Decision Intelligence Pro is a real-time transaction fraud solution for banks that is powered by generative AI to improve scoring and fraud detection by 20%; management sees tremendous opportunity with generative AI and has created a central role for AI

We continue to enhance our solutions with generative AI to deliver even more value, a world-leading real-time fraud solution, Decision Intelligence, has been helping banks score and safely approve billions of transactions, ensuring the safety of consumers and the entire payments networks for years. The next-generation technology, Decision Intelligence Pro is supercharged by generative AI to improve the overall score and boost fraud detection rates on average by 20%…

…We see tremendous opportunity on the AI side, particularly on the generative AI side, and we’ve created a central role for that. 

Meta Platforms (NASDAQ: META)

Meta is building a number of different AI services, including Meta AI (an AI assistant), creator AIs, business AIs, internal coding and development AIs, and hardware for AI interactions

We are building a number of different AI services from Meta AI, our AI assistant that you can ask any question across our apps and glasses, to creator AIs that help creators engage their communities and that fans can interact with, to business AIs that we think every business eventually on our platform will use to help customers buy things and get customer support, to internal coding and development AIs, to hardware like glasses for people to interact with AIs and a lot more.

Meta’s management released the company’s new version of Meta AI recently and it is powered by the company’s latest foundational model, Llama 3; management’s goal is for Meta AI to be the world’s leading AI service; tens of millions of people have tried Meta AI and the user feedback has been very positive; Meta AI is currently in English-speaking countries, but will be rolled out in more languages and countries in the coming months; management believes that the Llama3 version of Meta AI is the most intelligent AI assistant; Meta AI can be used within all of Meta’s major apps; besides being able to answer queries, Meta AI can also create animations as well as generate images while users are typing, which is a magical experience; Meta AI can also be used in Search within Meta’s apps, and Feed and Groups on Facebook

Last week, we had the major release of our new version of Meta AI that is now powered by our latest model, Llama 3. And our goal with Meta AI is to build the world’s leading AI service, both in quality and usage. The initial rollout of Meta AI is going well. Tens of millions of people have already tried it. The feedback is very positive. And when I first checked in with our teams, the majority of feedback we were getting was people asking us to release Meta AI for them wherever they are. So we’ve started launching Meta AI in some English speaking countries, and we’ll roll out in more languages and countries over the coming months…

…We believe that Meta AI with Llama 3 is now the most intelligent AI assistant that you can freely use. And now that we have the superior quality product, we’re making it easier for lots of people to use it within WhatsApp, Messenger, Instagram, and Facebook…

…In addition to answering more complex queries, a few other notable and unique features from this

release: Meta AI now creates animations from still images, and now generates high quality images so

fast that it can create and update them as you’re typing, which is pretty awesome. I’ve seen a lot of people commenting about this experience online and how they’ve never seen or experienced anything like it before…

…Along with using Meta AI within our chat surfaces, people will now be able to use Meta AI in Search within our apps, as well as Feed and Groups on Facebook. We expect these integrations will complement our social discovery strategy as our recommendation systems help people to discover and explore their interests while Meta AI enables them to dive deeper on topics they’re interested in. 

Meta’s foundational AI model, Llama3, has three versions with different number of parameters; management thinks the two smaller versions are both best-in-class for their scale; the 400+ billion parameter version of Llama3 is still undergoing training and is on track to be industry-leading; management thinks the Llama3 models will improve from further open source contributions

I’m very pleased with how Llama 3 has come together so far. The 8B and 70B parameter models that we released are best-in-class for their scale. The 400+B parameter model that we’re still training seems on track to be industry-leading on several benchmarks. And I expect that our models are just going to improve further from open source contributions. 

Meta’s management wants the company to invest significantly more in the coming years to build more advanced AI models and the largest scale AI services in the world, but the AI investments will come ahead of any meaningful revenue-generation from these new AI products

This leads me to believe that we should invest significantly more over the coming years to build even more advanced models and the largest scale AI services in the world. As we’re scaling capex and energy expenses for AI, we’ll continue focusing on operating the rest of our company efficiently. But realistically, even with shifting many of our existing resources to focus on AI, we’ll still grow our investment envelope meaningfully before we make much revenue from some of these new products…

… …We anticipate our full-year 2024 capital expenditures will be in the range of $35-40 billion, increased from our prior range of $30-37 billion as we continue to accelerate our infrastructure investments to support our AI roadmap. While we are not providing guidance for years beyond 2024, we expect capex will continue to increase next year as we invest aggressively to support our ambitious AI research and product development efforts.

Meta’s management thinks there are a few ways to build a massive AI business for Meta – these include business messaging, introducing ads and paid content in AI interactions, and selling access to powerful AI models and AI compute – in addition to the benefits to Meta’s current digital advertising business through the use of AI; management thinks business messaging is one of Meta’s nearer-term opportunities; management’s long-term vision for business messaging is to have AI agents that can accomplish goals rather than merely be a chatbot that replies to messages; management thinks that the capabilities of Meta’s business messaging AI technology will see massive improvements in as short as a year’s time

There are several ways to build a massive business here, including scaling business messaging, introducing ads or paid content into AI interactions, and enabling people to pay to use bigger AI models and access more compute. And on top of those, AI is already helping us improve app engagement which naturally leads to seeing more ads, and improving ads directly to deliver more value…

… The cost of engaging with people in messaging is still very high. But AI should bring that down just dramatically for businesses and creators. And I think that, that has the potential. That’s probably the — beyond just increasing engagement and increasing the quality of the ads, I think that, that’s probably one of the nearer-term opportunities, even though that will — it’s not like next quarter or the quarter after that scaling thing, but it’s — but that’s not like a 5-year opportunity either…

…I think that the next phase for a lot of these things are handling more complex tasks and becoming more like agents rather than just chat bots, right? So when I say chatbot, what I mean is if you send a message and it replies to your message, right? So it’s almost like almost a 1:1 correspondence. Whereas what an agent is going to do is you give it an intent or a goal, then it goes off and probably actually performs many queries on its own in the background in order to help accomplish your goal, whether that goal is researching something online or eventually finding the right thing that you’re looking to buy…  I think basically, the larger models and then the more advanced future versions that will be smaller as well are just going to enable much more interesting interactions like that. So I mean if you think about this, I mean, even some of the business use cases that we talked about, you don’t really just want like sales or customer support chatbot that can just respond to what you say. If you’re a business, you have a goal, right? You’re trying to support your customers well and you’re trying to position your products in a certain way and encourage people to buy certain things that map to their interests and would they be interested in? And that’s more of like a multiturn interaction, right?

So the type of business agent that you’re going to be able to enable with just a chatbot is going to be very naive compared to what we’re going to have in a year even, but beyond that, too, is just the reasoning and planning abilities if these things grow to be able to just help guide people through the business process of engaging with whatever your goals are as a creator of a business. So I think that that’s going to be extremely powerful. 

Meta’s AI recommendation system is currently delivering 30% of posts on the Facebook feed (up 2x over the last few years) and more than 50% of the content people see on Instagram (the first time this threshold is reached)

Right now, about 30% of the posts on Facebook feed are delivered by our AI recommendation system. That’s up 2x over the last couple of years. And for the first time ever, more than 50% of the content people see on Instagram is now AI recommended.

Revenue from two of Meta’s end-to-end AI-powered advertising tools, Advantage+ Shopping and Advantage+ App Campaigns, have more than doubled since last year; test results for the single-step automation feature of Advantage+ has resulted in a 28% decrease in cost per click or per objective for advertisers; Meta has significant runway to broaden adoption of the end-to-end automation features of Advantage+ and the company has enabled more conversion types

If you look at our two end-to-end AI-powered tools, Advantage+ Shopping and Advantage+ App Campaigns, revenue flowing through those has more than doubled since last year…

…So on the single-step automation, Advantage Plus audience, for example, has seen significant growth in adoption since we made it the default audience creation experience for most advertisers in Q4, and that enables advertisers to increase campaign performance by just using audience inputs as a suggestion rather than a hard constraint. And based on tests that we ran, campaigns using Advantage Plus audience targeting saw on average, a 28% decrease in cost per click or per objective compared to using our regular targeting.

On the end-to-end automation products like Advantage Plus shopping and Advantage Plus app campaigns, we’re also seeing very strong growth…  We think there’s still significant runway to broaden adoption, so we’re trying to enable more conversion types for Advantage Plus shopping. In Q1, we began expanding the list of conversions that businesses could optimize for. So previously, it only supported purchase events, and now we’ve added 10 additional conversion types. And we’re continuing to see strong adoption now across verticals.

Meta’s management continues to develop Meta’s own AI chips; Meta’s Training and Inference Accelerator chip is less expensive for Meta and has already been running some of Meta’s recommendation workloads

We’ll also keep making progress on building more of our own silicon. Our Meta Training and Inference Accelerator chip has successfully enabled us to run some of our recommendations-related workloads on this less expensive stack, and as this program matures over the coming years we plan to expand this to more of our workloads as well.

Meta’s management sees a market for a fashionable pair of AI glasses without holographic displays; management thinks that glasses are the ideal device for an AI assistant because the glasses can see what you see and hear what you hear; management recently launched Meta AI with Vision on its AI glasses; Meta’s AI glasses continue to do well and are sold out in many styles and colours

I used to think that AR glasses wouldn’t really be a mainstream product until we had full holographic displays — and I still think that will be awesome and is mature state of the product. But now it seems pretty clear that there’s also a meaningful market for fashionable AI glasses without a display. Glasses are the ideal device for an AI assistant because you can let them see what you see and hear what you hear, so they have full context on what’s going on around you as they help you with whatever you’re trying to do. Our launch this week of Meta AI with Vision on the glasses is a good example where you can now ask questions about things you’re looking at…

…The Ray-Ban Meta glasses that we built with Essilor Luxottica continue to do well and are sold out in many styles and colors, so we’re working to make more and release additional styles as quickly as we can.

Meta’s management is improving the monetisation efficiency of the company’s products partly by using larger AI models in its new ads ranking architecture, Meta Lattice (which was rolled out last year) in place of smaller models, as well as using AI to provide more automation – ranging from point-automation to end-to-end automation – for advertisers through its Advantage+ portfolio; Meta Lattice drove improved ad performance over the course of 2023 when it was deployed across Facebook and Instagram

The second part of improving monetization efficiency is enhancing marketing performance. Similar to our work with organic recommendations, AI is playing an increasing role in these efforts. First, we are making ongoing ads modeling improvements that are delivering better performance for advertisers. One example is our new ads ranking architecture, Meta Lattice, which we began rolling out more broadly last year. This new architecture allows us to run significantly larger models that generalize learnings across objectives and surfaces in place of numerous, smaller ads models that have historically been optimized for individual objectives and surfaces. This is not only leading to increased efficiency as we operate fewer models, but also improving ad performance. Another way we’re leveraging AI is to provide increased automation for advertisers. Through our Advantage+ portfolio, advertisers can automate one step of the campaign set up process – such as selecting which ad creative to show – or automate their campaign completely using our end-to-end automation tools, Advantage+ Shopping and Advantage+ App ads. We’re seeing growing use of these solutions, and we expect to drive further adoption over the course of the year while applying what we learn to our broader ads investments…

…We’ve talked a little bit about the new model architecture at Meta Lattice that we deployed last year that consolidates smaller and more specialized models into larger models that can better learn what characteristics improve ad performance across multiple services, like Feed and Reels and multiple types of ads and objectives at the same time. And that’s driven improved ad performance over the course of 2023 as we deployed it across Facebook and Instagram to support multiple objectives.

Meta’s recommendation products historically each had their own AI models, and a new model architecture to power multiple recommendation products was being developed recently; the new model architecture was tested last year on Facebook Reels and generated 8%-10% increases in watch time; the new model architecture has been extended beyond Reels and management is hopeful that the new architecture will unlock better video recommendations over time

Historically, each of our recommendation products, including Reels, in-feed recommendations, et cetera, has had their own AI model. And recently, we’ve been developing a new model architecture with the aim for it to power multiple recommendations products. We started partially validating this model last year by using it to power Facebook Reels. And we saw meaningful performance gains, 8% to 10% increases in watch time as a result of deploying this. This year, we’re actually planning to extend the singular model architecture to recommend content across not just Facebook Reels, but also Facebook’s video tab as well. So while it’s still too early to share specific results, we’re optimistic that the new model architecture will unlock increasingly relevant video recommendations over time. And if it’s successful, we’ll explore using it to power other recommendations.

Meta’s management is seeing adoption of Meta’s generative AI (GenAI) ad creative features across verticals and different advertiser sizes; some of these features are enjoying outsized adoption; Meta expects improvements to its underlying foundational AI models to improve the output quality of its GenAI ad creative features

The more near-term version is around the GenAI ad creative features that we have put into our ads creation tools. And it’s early, but we’re seeing adoption of these features across verticals and different advertiser sizes. In particular, we’ve seen outsized adoption of image expansion with small businesses, and this will remain a big area of focus for us in 2024, and I expect that improvements to our underlying foundation models will enhance the quality of the outputs that are generated and support new features on the road map. But right now, we have features supporting text variations, image expansion and background generation, and we’re continuing to work to make those more performance for advertisers to create more personalized ads at scale.

In early tests of using business AIs for business messaging, Meta’s management is receiving positive feedback from users

The longer-term piece here is around business AIs. We have been testing the ability for businesses to set up AIs for business messaging that represent them in chats with customers starting by supporting shopping use cases such as responding to people asking for more information on a product or its availability. So this is very, very early. We’ve been testing this with a handful of businesses on Messenger and WhatsApp, and we’re hearing good feedback with businesses saying that the AIs have saved them significant time while customer — consumers noted more timely response times. And we’re also learning a lot from these tests to make these AIs more performant over time as well.

Meta’s management has gotten more optimistic and ambitious on AI compared to just 3 months ago because of the company’s work with Llama3 and Meta AI

[Question] Can you just talk about what’s changed most in your view in the business and the opportunity now versus 3 months ago? 

[Answer]  I think we’ve gotten more optimistic and ambitious on AI. So previously, I think that our work in this — I mean when you were looking at last year, when we released Llama 2, we were very excited about the model and thought that, that was going to be the basis to be able to build a number of things that were valuable that integrated into our social products. But now I think we’re in a pretty different place. So with the latest models, we’re not just building good AI models that are going to be capable of building some new good social and commerce products. I actually think we’re in a place where we’ve shown that we can build leading models and be the leading AI company in the world. And that opens up a lot of additional opportunities beyond just ones that are the most obvious ones for us. So that’s — this is what I was trying to refer to in my opening remarks where I just view the success that we’ve seen with the way that Lama 3 and Meta AI have come together as a real validation technically that we have the talent, the data and the ability to scale infrastructure to do leading work here.

Meta’s AI capex can be categorised into 2 buckets, with one being core AI work that has a very ROI-driven (return on investment driven) approach and which still generates very strong returns, and the other being generative AI and other advanced research work that has tremendous potential but has yet to produce returns; Meta’s AI capex for the 2 buckets are in capacity that is fungible

We’ve broadly categorized our AI investments into 2 buckets. I think of them as sort of core AI work and then strategic bets, which would include Gen AI and the advanced research efforts to support that. And those are just really at different stages as it relates to being able to measure the return and drive revenue for our business.

So with our core AI work, we continue to have a very ROI-driven approach to investment, and we’re still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains.

Now the second area, strategic bets, is where we are much earlier. Mark has talked about the potential that we believe we have to create significant value for our business in a number of areas, including opportunities to build businesses that don’t exist on us today. But we’ll need to invest ahead of that opportunity to develop more advanced models and to grow the usage of our products before they drive meaningful revenue. So while there is tremendous long-term potential, we’re just much earlier on the return curve than with our core AI work.

What I’ll say though is we’re also building our systems in a way that gives us fungibility in how we use our capacity, so we can flex it across different use cases as we identify what are the best opportunities to put that infrastructure toward.

Meta is already shifting a lot of resources from other parts of the company into its AI efforts

I would say broadly, we actually are doing that in a lot of places in terms of shifting resources from other areas, whether it’s compute resources or different things in order to advance the AI efforts. 

Meta has partnered with Google and Bing for Meta AI’s search citations, but management has no intention to build a search ads business

[Question] You partnered with Google and Bing for Meta AI organic search citations. So I guess stepping back, do you think that Meta AI longer term could bring in search advertising dollars at some point?

[Answer] On the Google and Microsoft partnerships, yes, I mean we work with them to have real-time information in Meta AI. It’s useful. I think it’s pretty different from search. We’re not working on search ads or anything like that. I think this will end up being a pretty different business.

Microsoft (NASDAQ: MSFT)

Azure took market share again in 2024 Q1; Microsoft’s management thinks that (1) Azure offers the most diverse selection of AI accelerators, including those from Nvidia, AMD, and Microsoft’s own custom chips, (2) Azure offers the best selection of foundational AI models, including LLMs and SLMs (small language models), and (3) Azure’s Models as a Service offering makes it easy for developers to work with LLMs and SLMs without having to worry about technical infrastructure; >65% of Fortune 500 use Azure OpenAI service; hundreds of paid customers are using Azure’s Models as a Service to access third-party AI models including those from Cohere, Meta, and Mistral; Azure grew revenue by 31% in 2024 Q1 (was 30% in 2023 Q4), with 7 points of growth from AI services (was 6 points in 2023 Q4); Azure’s non-AI consumption business also saw broad greater-than-expected demand 

Azure again took share as customers use our platforms and tools to build their own AI solutions. We offer the most diverse selection of AI accelerators, including the latest from NVIDIA, AMD as well as our own first-party silicon…

…More than 65% of the Fortune 500 now use Azure OpenAI service. We also continue to innovate and partner broadly to bring customers the best selection of frontier models and open source models, LLMs and SLMs…

…Our Models as a Service offering makes it easy for developers to use LLMs and SLMs without having to manage any underlying infrastructure. Hundreds of paid customers from Accenture and EY to Schneider Electric are using it to take advantage of API access to third-party models including, as of this quarter, the latest from Cohere, Meta and Mistral…

… Azure and other cloud services revenue grew 31% ahead of expectations, while our AI services contributed 7 points of growth as expected. In the non-AI portion of our consumption business, we saw greater-than-expected demand broadly across industries and customer segments as well as some benefit from a greater-than-expected mix of contracts with higher in-period recognition. 

Microsoft’s management continues to build on the company’s partnership with OpenAI for AI work

Our AI innovation continues to build on our strategic partnership with OpenAI. 

Microsoft’s management thinks that Phi-3, announced by Microsoft recently, is the most capable and cost-effective SLM and it’s being trialed by a number of companies

With Phi-3, which we announced earlier this week, we offer the most capable and cost-effective SLM available. It’s already being trialed by companies like CallMiner, LTIMindtree, PwC and TCS.

Azure AI customers are growing and spending more with Microsoft; over half of Azure AI customers use Microsoft’s data and analytics tools and they are building applications with deep integration between these tools and Azure AI

All up, the number of Azure AI customers continues to grow and average spend continues to increase…

… Over half of our Azure AI customers also use our data and analytics tools. Customers are building intelligent applications running on Azure, PostgreSQL and Cosmos DB with deep integrations with Azure AI. TomTom is a great example. They’ve used Cosmos DB along with Azure OpenAI service to build their own immersive in-car infotainment system. 

GitHub Copilot now has 1.8 million paid subscribers, up 35% sequentially; even established enterprises are using GitHub Copilot; >90% of Fortune 100 companies are GitHub customers; GitHub’s revenue was up 45% year-on-year

GitHub Copilot is bending the productivity curve for developers. We now have 1.8 million paid subscribers with growth accelerating to over 35% quarter-over-quarter and continues to see increased adoption from businesses in every industry, including Itau, Lufthansa Systems, Nokia, Pinterest and Volvo Cars. Copilot is driving growth across the broader GitHub platform, too. AT&T, Citigroup and Honeywell all increased their overall GitHub usage after seeing productivity and code quality increases with Copilot. All up, more than 90% of the Fortune 100 are now GitHub customers, and revenue accelerated over 45% year-over-year.

Microsoft has new AI-powered features within its low-code and no-code tools for building applications; 30,000 organisations – up 175% sequentially – across all industries have used Copilot Studio to customise or build their own copilot; Cineplex used Copilot Studio to build a copilot for customer service agents to significantly reduce the time needed to handle queries; Copilot Studio can be really useful for enterprises to ground their AIs with enterprise data, and people are really excited about it

Anyone can be a developer with new AI-powered features across our low-code, no-code tools, which makes it easier to build an app, automate workflow or create a Copilot using natural language. 30,000 organizations across every industry have used Copilot Studio to customize Copilot for Microsoft 365 or build their own, up 175% quarter-over-quarter. Cineplex, for example, built a Copilot for customer service agents, reducing query handling time from as much as 15 minutes to 30 seconds…

…Copilot Studio is really off to the races in terms of the product that most people are excited because one of the things in the enterprise is you want to ground your copilot with the enterprise data, which is in all of these SaaS applications, and Copilot Studio is the tool to use there to make that happen.

More than 330,000 organisations, including half of the Fortune 100, have used AI-features within Microsoft’s Power Platform

All up, over 330,000 organizations, including over half of Fortune 100, have used AI-powered capabilities in Power Platform, and Power Apps now has over 25 million monthly active users, up over 40% year-over-year.

In 2024 Q1, Microsoft’s management made Copilot available to all organisations; nearly 60% of Fortune 500 are using Copilot; many large companies have purchased more than 10,000 Copilot seats each; management is seeing higher usage of Copilot from early adopters, including a 50% jump in Copilot-assisted interactions per user in Teams; Microsoft has added more than 150 Copilot capabilities since the start of the year, including Copilot for Service, Copilot for Sales, Copilot for Finance, and Copilot for Security

This quarter, we made Copilot available to organizations of all types and sizes from enterprises to small businesses. Nearly 60% of the Fortune 500 now use Copilot, and we have seen accelerated adoption across industries and geographies with companies like Amgen, BP, Cognizant, Koch Industries, Moody’s, Novo Nordisk, NVIDIA and Tech Mahindra purchasing over 10,000 seats. We’re also seeing increased usage intensity from early adopters, including a nearly 50% increase in the number of Copilot-assisted interactions per user in Teams, bridging group activity with business process workflows and enterprise knowledge…

…We’re accelerating our innovation, adding over 150 Copilot capabilities since the start of the year…

… This quarter, we made our Copilot for Service and Copilot for Sales broadly available, helping customer service agents and sellers at companies like Land O’Lakes, Northern Trust, Rockwell Automation and Toyota Group generate role-specific insights and recommendations from across Dynamics 365 and Microsoft 365 as well as third-party platforms like Salesforce, ServiceNow and Zendesk. And with our Copilot for Finance, we are drawing context from Dynamics as well as ERP systems like SAP to reduce labor-intensive processes like collections and contract and invoice capture for companies like Dentsu and IDC…

…A great example is Copilot for Security, which we made generally available earlier this month, bringing together LLMs with domain-specific skills informed by our threat intelligence and 78 trillion daily security signals to provide security teams with actionable insights.

Microsoft’s management is seeing ISVs (independent software vendors) build their own Copilot integrations, with Adobe being an example

ISVs are also building their own Copilot integrations. For example, new integrations between Adobe Experience Cloud and Copilot will help marketeers access campaign insights in the flow of their work. 

Copilot in Windows is now available on 225 million PCs, up 2x sequentially; Microsoft’s largest PC partners have announced AI PCs in recent months; management recently introduced new Surface devices that comes with NPUs (neural processing units) that can power on-device AI experiences; management thinks that the presence of Copilot can help Microsoft create a new device-category for AI

When it comes to devices, Copilot in Windows is now available on nearly 225 million Windows 10 and Windows 11 PCs, up 2x quarter-over-quarter. With Copilot, we have an opportunity to create an entirely new category of devices purpose built for this new generation of AI. All of our largest OEM partners have announced AI PCs in recent months. And this quarter, we introduced new Surface devices, which includes integrated NPUs to power on device AI experiences like auto framing and live captions. And there’s much more to come. In just a few weeks, we’ll hold a special event to talk about our AI vision across Windows and devices.

More than 200 healthcare organisations are using Microsoft’s DAX Copilot

In health care, DAX Copilot is being used by more than 200 health care organizations, including Providence, Stanford Health care and WellSpan Health. 

Established auto manufacturers are using Microsoft’s AI solutions to improve their factory operations

And in manufacturing, this week at Hannover Messe, customers like BMW, Siemens and Volvo Penta shared how they’re using our cloud and AI solutions to transform factory operations.

LinkedIn AI-assisted messages have a 40% higher acceptance rate and are accepted >10% faster by job seekers; LinkedIn’s AI-powered collaborative articles now have more than 12 million contributions and helped engagement on LinkedIn reach a new record in 2024 Q1; LinkedIn Premium’s revenue was up 29% year-on-year in 2024 Q1, with AI features helping to produce the growth

Features like LinkedIn AI-assisted messages are seeing a 40% higher acceptance rate and accepted over 10% faster by job seekers saving hirers time and making it easier to connect them to candidates. Our AI-powered collaborative articles, which has reached over 12 million contributions are helping increase engagement on the platform, which reached a new record this quarter. New AI features are also helping accelerate LinkedIn Premium growth with revenue up 29% year-over-year. 

Microsoft’s management expects capex to increase materially sequentially in 2024 Q2 (FY2024 Q4) because of cloud and AI infrastructure investments; management sees near-term AI demand as being higher than available capacity; capex in FY2025 is expected to be higher than in FY2024, but this will be driven ultimately by the amount of AI inference demand; operating margin in FY2025 is expected to be down by only 1 point compared to FY2024

We expect capital expenditures to increase materially on a sequential basis driven by cloud and AI infrastructure investments. As a reminder, there can be normal quarterly spend variability in the timing of our cloud infrastructure build-outs and the timing of finance leases. We continue to bring capacity online as we scale our AI investments with growing demand. Currently, near-term AI demand is a bit higher than our available capacity…

…In FY ’25, that focus on execution should again lead to double-digit revenue and operating income growth. To scale to meet the growing demand signal for our cloud and AI products, we expect FY ’25 capital expenditures to be higher than FY ’24. These expenditures over the course of the next year are dependent on demand signals and adoption of our services, so we will manage that signal through the year. We will also continue to prioritize operating leverage. And therefore, we expect FY ’25 operating margins to be down only about 1 point year-over-year, even with our significant cloud and AI investments as well as a full year of impact from the Activision acquisition…

… Then, Amy referenced what we also do on the inference side, which is, one, we first innovate and build products. And of course, we have an infrastructure business that’s also dependent on a lot of ISVs building products that run on our infrastructure. And it’s all going to be demand driven. In other words, we track very closely what’s happening with inference demand, and that’s something that we will manage, as Amy said in her remarks, very, very closely.

Microsoft’s management expects Azure to grow revenue by 30%-31% in constant currency, similar to stronger-than-expected 2024 Q1 results, driven by AI

For Intelligent Cloud, we expect revenue to grow between 19% and 20% in constant currency or USD 28.4 billion to USD 28.7 billion. Revenue will continue to be driven by Azure, which, as a reminder, can have quarterly variability primarily from our per user business and in-period revenue recognition depending on the mix of contracts. In Azure, we expect Q4 revenue growth to be 30% to 31% in constant currency or similar to our stronger-than-expected Q3 results. Growth will be driven by our Azure consumption business and continued contribution from AI with some impact from the AI capacity availability noted earlier.

Management’s AI-related capital expenditure plans for Microsoft has two layers to it, namely, training and inference; for training, management wants Microsoft to have capacity to train large foundation models and stay a leader in that area; for inference, management is watching inference demand

[Question] It looks like Microsoft is on track to ramp CapEx over 50% year-on-year this year to over $50 billion. And there’s media speculation of more spending ahead with some reports talking about like $100 billion data center. So obviously, investments are coming well ahead of the revenue contribution, but what I was hoping for is that you could give us some color on how you as the management team try to quantify the potential opportunities that underlie these investments because they are getting very big. 

[Answer]  At a high level, the way we, as a management team, talk about it is there are 2 sides to this, right? There is training and there’s inference. What — given that we want to be a leader in this big generational shift and paradigm shift in technology, that’s on the training side. We want to be able to allocate the capital required to essentially be training these large foundation models and stay on the leadership position there. And we’ve done that successfully all the way today, and you’ve seen it flow through our P&L, and you can continue to see that going forward. Then, Amy referenced what we also do on the inference side, which is, one, we first innovate and build products. And of course, we have an infrastructure business that’s also dependent on a lot of ISVs building products that run on our infrastructure. And it’s all going to be demand driven. In other words, we track very closely what’s happening with inference demand, and that’s something that we will manage, as Amy said in her remarks, very, very closely.

Microsoft’s management feels good about demand for Azure, because (1) they think Azure is a market-share taker since it has become the go-to choice for anybody who is working on an AI project, (2) they are seeing that AI projects on Azure do not stop with just calling AI models and there are many other cloud computing services in Azure that are required, (3), there’s migration to Azure, and (4) the optimisation cycle from the recent past has given more budget for people to start new workloads

[Question] How would you characterize the demand environment? On one hand, you have bookings in Azure both accelerating year-over-year in the quarter, but we’re seeing a lot of future concern, hesitation from other vendors we all cover. So I think everyone would love to get your sense of budget health for customers this year.

[Answer] On the Azure side, which I think is what you specifically asked, we feel very, very good about the — we’re fundamentally a share taker there because if you look at it from our perspective, at this point, Azure has become a port of call for pretty much anybody who is doing an AI project. And so that’s sort of been a significant help for us in terms of acquiring even new customers…

…The second thing that we’re also seeing is AI just doesn’t sit on its own. So AI projects obviously start with calls to AI models, but they also use a vector database. In fact, Azure Search, which is really used by even ChatGPT, is one of the fastest growing services for us. We have Fabric integration to Azure AI and so — Cosmos DB integration. So the data tier, the dev tools is another place where we are seeing great traction. So we are seeing adjacent services in Azure that get attached to AI…

… lastly, I would say, migration to Azure as well. So this is not just all an AI story. 

We are also looking at customers — I mean, this is something that we have talked about in the past, which is there’s always an optimization cycle. But there’s also — as people optimize, they spend money on new project starts, which will grow and then they’ll optimize. So it’s a continuous side of it. So these are the 3 trends that are playing out on Azure in terms of what at least we see on demand side.

Microsoft’s management thinks that a good place to watch for the level of maturation for AI will be what’s happening in terms of standard issues for software teams; they are seeing Copilots increasingly becoming “standard issue” for software teams; they think companies will need to undergo a cultural shift to fully embrace AI tools and it will take some time, but the rate of adoption of Copilot is also faster than anything they have seen in the past

[Question] We’re seeing companies shifting their IT spending to invest in and learn about AI rather than receiving additional budgets for AI. At some point for AI to be transformative, as everyone expects, it needs to be accretive to spending. Satya, when do you believe AI will hit the maturity level?

[Answer] A good place to start is to watch what’s happening in terms of standard issues for software teams, right? I mean if you think about it, they bought tools in the past. Now you basically buy tools plus Copilot, right? So you could even say that this is characterized as perhaps shift of what is OpEx dollars into effectively tool spend because it gives operating leverage to all of the OpEx dollars you’re spending today, right? That’s really a good example of, I think, what’s going to happen across the board. We see that in customer service. We see that in sales. We see that in marketing, anywhere there’s operations…

…one of the interesting rate limiters is culture change inside of organizations. When I say culture change, that means process change…  That requires not just technology but in fact, companies to go do the hard work of culturally changing how they adopt technology to drive that operating leverage. And this is where we’re going to see firm-level performance differences…

…And so yes, it will take time to — for it to percolate through the economy. But this is faster diffusion, faster rate of adoption than anything we have seen in the past. As evidenced even by Copilot, right, it’s faster than any suite we have sold in the past.

Netflix (NASDAQ: NFLX)

Netflix has been working with machine learning (ML) for almost two decades, with ML being foundational for the company’s recommendation systems; management thinks that generative AI can be used to help creators improve their story-telling, and there will always be a place for creators

[Question]  What is the opportunity for Netflix to leverage generative AI technology in the near and long term? What do you think great storytellers should be focused on as this technology continues to emerge quickly? 

[Answer] Worth noting, I think, that we’ve been leveraging advanced technologies like ML for almost 2 decades. These technologies are the foundation for our recommendation systems that help us find these largest audiences for our titles and deliver the most satisfaction for members. So we’re excited to continue to involve and improve those systems as new technologies emerge and are developed.

And we also think we’re well positioned to be in the vanguard of adoption and application of those new approaches from our just general capabilities that we’ve developed and how we’ve already developed systems that do all these things.

We also think that we have the opportunity to develop and deliver new tools to creators to allow them to tell their stories in even more compelling ways. That’s great for them, it’s great for the stories, and it’s great for our members. 

And what should storytellers be focused on? I think storytellers should be focused on great storytelling. It is incredibly hard and incredibly complex to deliver thrilling stories through film, through series, through games. And storytellers have a unique and critical role in making that happen, and we don’t see that changing.

Nvidia (NASDAQ: NVDA)

Nvidia’s Data Center revenue had incredibly strong growth in 2024 Q1, driven by demand for the Hopper GPU computing platform; compute revenue was up by 5x while networking revenue was up by 3x

Data Center revenue of $22.6 billion was a record, up 23% sequentially and up 427% year-on-year, driven by continued strong demand for the NVIDIA Hopper GPU computing platform. Compute revenue grew more than 5x and networking revenue more than 3x from last year.

Nvidia’s management thinks that cloud providers are getting a 5x return on spending on Nvidia’s AI products over 4 years; management also thinks that cloud providers serving LLMs (large language models) via APIs (application programming interfaces) can earn $7 in revenue for every $1 spent on Nvidia’s H200 servers through running inference 

Training and inferencing AI on NVIDIA CUDA is driving meaningful acceleration in cloud rental revenue growth, delivering an immediate and strong return on cloud providers’ investment. For every $1 spent on NVIDIA AI infrastructure, cloud providers have an opportunity to earn $5 in GPU instant hosting revenue over 4 years…

…H200 nearly doubles the inference performance of H100, delivering significant value for production deployments. For example, using Llama 3 with 700 billion parameters, a single NVIDIA HGX H200 server can deliver 24,000 tokens per second, supporting more than 2,400 users at the same time. That means for every $1 spent on NVIDIA HGX H200 servers at current prices per token, an API provider serving Llama 3 tokens can generate $7 in revenue over 4 years.

Nvidia’s management sees Nvidia GPUs as offering the best time-to-train AI models, the lowest cost to train AI models, and the lowest cost to run inference on AI models

For cloud rental customers, NVIDIA GPUs offer the best time-to-train models, the lowest cost to train models and the lowest cost to inference large language models.

Leading LLM (large language model) providers are building on Nvidia’s AI infrastructure in the cloud

Leading LLM companies such as OpenAI, Adept, Anthropic, Character.ai, Cohere, Databricks, DeepMind, Meta, Mistral, XAi, and many others are building on NVIDIA AI in the cloud.

Tesla is using Nvidia’s GPUs for its FSD (Full Self Driving) version 12 software for AI-powered autonomous driving; Nvidia’s management sees automotive as the largest enterprise vertical within its Data Center business this year

We supported Tesla’s expansion of their training AI cluster to 35,000 H100 GPUs. Their use of NVIDIA AI infrastructure paved the way for the breakthrough performance of FSD version 12, their latest autonomous driving software based on Vision. NVIDIA Transformers, while consuming significantly more computing, are enabling dramatically better autonomous driving capabilities and propelling significant growth for NVIDIA AI infrastructure across the automotive industry. We expect automotive to be our largest enterprise vertical within Data Center this year, driving a multibillion revenue opportunity across on-prem and cloud consumption.

Meta Platform’s Llama3 LLM was trained on a large cluster of Nvidia GPUs

A big highlight this quarter was Meta’s announcement of Llama 3, their latest large language model, which was trained on a cluster of 24,000 H100 GPUs. Llama 3 powers Meta AI, a new AI assistant available on Facebook, Instagram, WhatsApp, and Messenger. Llama 3 is openly available and has kickstarted a wave of AI development across industries.

Nvidia’s management sees inferencing of AI models growing as generative AI makes its way into more consumer internet applications

As generative AI makes its way into more consumer Internet applications, we expect to see continued growth opportunities as inference scales both with model complexity as well as with the number of users and number of queries per user, driving much more demand for AI compute.

Nvidia’s management sees inferencing accounting for 40% of Data Center revenue over the last 4 quarters

In our trailing 4 quarters, we estimate that inference drove about 40% of our Data Center revenue. Both training and inference are growing significantly.

Nvidia’s management is seeing companies build AI factories (large clusters of AI chips); Nvidia worked with more than 100 customers in 2024 Q1 to build AI factories that range in size from hundreds to tens of thousands of GPUs

Large clusters like the ones built by Meta and Tesla are examples of the essential infrastructure for AI production, what we refer to as AI factories. These next-generation data centers host advanced full-stack accelerated computing platforms where the data comes in and intelligence comes out.  In Q1, we worked with over 100 customers building AI factories ranging in size from hundreds to tens of thousands of GPUs, with some reaching 100,000 GPUs.

Nvidia’s management is seeing growing demand from nations for AI infrastructure and they see revenue from sovereign AI reaching high single-digit billions in 2024

From a geographic perspective, Data Center revenue continues to diversify as countries around the world invest in sovereign AI. Sovereign AI refers to a nation’s capabilities to produce artificial intelligence using its own infrastructure, data, workforce, and business networks. Nations are building up domestic computing capacity through various models. Some are procuring and operating sovereign AI clouds in collaboration with state-owned telecommunication providers or utilities. Others are sponsoring local cloud partners to provide a shared AI computing platform for public and private sector use. For example, Japan plans to invest more than $740 million in key digital infrastructure providers, including KDDI, Sakura Internet, and SoftBank to build out the nation’s sovereign AI infrastructure. France-based Scaleway, a subsidiary of the Iliad Group, is building Europe’s most powerful cloud native AI supercomputer. In Italy, Swisscom Group will build the nation’s first and most powerful NVIDIA DGX-powered supercomputer to develop the first LLM natively trained in the Italian language. And in Singapore, the National Supercomputer Centre is getting upgraded with NVIDIA Hopper GPUs, while Singtel is building NVIDIA’s accelerated AI factories across Southeast Asia…

…From nothing the previous year, we believe sovereign AI revenue can approach the high single-digit billions this year.

Nvidia’s revenue in China is down significantly in 2024 Q1 because of export restrictions for leading AI chips; management expects to see strong competitive forces in China going forward

We ramped new products designed specifically for China that don’t require export control license. Our Data Center revenue in China is down significantly from the level prior to the imposition of the new export control restrictions in October. We expect the market in China to remain very competitive going forward.

Because of improvements in CUDA algorithms, Nvidia’s management has been able to drive a 3x improvement in LLM inference speed on the H100 chips, which translates to a 3x cost reduction when serving AI models

Thanks to CUDA algorithm innovations, we’ve been able to accelerate LLM inference on H100 by up to 3x, which can translate to a 3x cost reduction for serving popular models like Llama 3.

Nvidia’s management sees the demand for the company’s latest AI chips to well exceed supply into 2025

We are working to bring up our system and cloud partners for global availability later this year. Demand for H200 and Blackwell is well ahead of supply, and we expect demand may exceed supply well into next year.

Nvidia’s strong networking growth in 2024 Q1 was driven by Infiniband

Strong networking year-on-year growth was driven by InfiniBand. We experienced a modest sequential decline, which was largely due to the timing of supply, with demand well ahead of what we were able to ship. We expect networking to return to sequential growth in Q2.

Nvidia’s management has started shipping its own Ethernet solution for AI networking called Spectrum-X Ethernet; management believes that Spectrum-X is optimised for AI from the ground-up, and delivers 1.6x higher networking performance for AI workloads compared with traditional ethernet; Spectrum-X is already ramping with multiple customers, including in a GPU cluster with 100,000 GPUs; Spectrum-X opens a new AI networking market for Nvidia and management thinks it can be a multi-billion product within a year; management is going all-in on Ethernet for AI networking, but they still see Infiniband as the superior solution; Infiniband started as a computing fabric and became a network, whereas Ethernet was a network that is becoming a computing fabric

In the first quarter, we started shipping our new Spectrum-X Ethernet networking solution optimized for AI from the ground up. It includes our Spectrum-4 switch, BlueField-3 DPU, and new software technologies to overcome the challenges of AI on Ethernet to deliver 1.6x higher networking performance for AI processing compared with traditional Ethernet. Spectrum-X is ramping in volume with multiple customers, including a massive 100,000 GPU cluster. Spectrum-X opens a brand-new market to NVIDIA networking and enables Ethernet-only data centers to accommodate large-scale AI. We expect Spectrum-X to jump to a multibillion-dollar product line within a year…

…But we’re all in on Ethernet, and we have a really exciting road map coming for Ethernet. We have a rich ecosystem of partners. Dell announced that they’re taking Spectrum-X to market. We have a rich ecosystem of customers and partners who are going to announce taking our entire AI factory architecture to market.

And so for companies that want the ultimate performance, we have InfiniBand computing fabric. InfiniBand is a computing fabric, Ethernet to network. And InfiniBand, over the years, started out as a computing fabric, became a better and better network. Ethernet is a network and with Spectrum-X, we’re going to make it a much better computing fabric. And we’re committed, fully committed, to all 3 links, NVLink computing fabric for single computing domain, to InfiniBand computing fabric, to Ethernet networking computing fabric. And so we’re going to take all 3 of them forward at a very fast clip. 

Nvidia’s latest AI chip-platform, Blackwell, delivers 4x faster training speeds, 30x faster inference speeds, and 25x lower total cost of ownership, compared to the H100 chip and enables real-time generative AI on trillion-parameter LLMs; the Blackwell platform includes Nvidia’s Inifiniband and Ethernet switches; management has built Blackwell to be compatible with all kinds of data centers; the earliest deployers of Blackwell include Amazonn, Google, Meta, and Microsoft; Nvidia’s management is on a 1-year development rhythm with the Blackwell platform-family, so there will be a new version of Blackwell in the next 12 months

At GTC in March, we launched our next-generation AI factory platform, Blackwell. The Blackwell GPU architecture delivers up to 4x faster training and 30x faster inference than the H100 and enables real-time generative AI on trillion-parameter large language models. Blackwell is a giant leap with up to 25x lower TCO and energy consumption than Hopper. The Blackwell platform includes the fifth-generation NVLink with a multi-GPU spine and new InfiniBand and Ethernet switches, the X800 series designed for a trillion-parameter scale AI. Blackwell is designed to support data centers universally, from hyperscale to enterprise, training to inference, x86 to Grace CPUs, Ethernet to InfiniBand networking, and air cooling to liquid cooling. Blackwell will be available in over 100 OEM and ODM systems at launch, more than double the number of Hoppers launched and representing every major computer maker in the world… 

…Blackwell time-to-market customers include Amazon, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and XAi…

…I can announce that after Blackwell, there’s another chip. And we are on a 1-year rhythm.

Nvidia’s management has introduced AI software called Nvidia Inference Microservices that allow developers to quickly build and deploy generative AI applications across a broad range of use cases including text, speech, imaging, vision, robotics, genomics, and digital biology

We announced a new software product with the introduction of NVIDIA Inference Microservices, or NIM. NIM provides secure and performance-optimized containers powered by NVIDIA CUDA acceleration in network computing and inference software, including Triton and PrintServer and TensorRT-LLM with industry-standard APIs for a broad range of use cases, including large language models for text, speech, imaging, vision, robotics, genomics, and digital biology. They enable developers to quickly build and deploy generative AI applications using leading models from NVIDIA, AI21, Adept, Cohere, Getty Images, and Shutterstock, and open models from Google, Hugging Face, Meta, Microsoft, Mistral AI, Snowflake and Stability AI. NIMs will be offered as part of our NVIDIA AI enterprise software platform for production deployment in the cloud or on-prem.

Nvidia’s GPUs that are meant for gaming on personal computers (PCs) can also be used for running generative AI applications on PCs; Nvidia and Microsoft has a partnership that help Windows to run LLMs up to 3x faster on PCs equipped with Nvidia’s GeForce RTX GPU

From the very start of our AI journey, we equipped GeForce RTX GPUs with CUDA Tensor cores. Now with over 100 million of an installed base, GeForce RTX GPUs are perfect for gamers, creators, AI enthusiasts, and offer unmatched performance for running generative AI applications on PCs. NVIDIA has full technology stack for deploying and running fast and efficient generative AI inference on GeForce RTX PCs…

…Yesterday, NVIDIA and Microsoft announced AI performance optimizations for Windows to help run LLMs up to 3x faster on NVIDIA GeForce RTX AI PCs.

Nvidia’s management is seeing game developers using the company’s AI services to create non-playable life-like characters in games

Top game developers, including NetEase Games, Tencent and Ubisoft are embracing NVIDIA Avatar Character Engine (sic) [ Avatar Cloud Engine ] to create lifelike avatars to transform interactions between gamers and non-playable characters.

Nvidia’s management thinks that the combination of generative AI and the Omniverse can drive the next wave of professional visualisation growth; the Ominverse has helped Wistron to reduce production cycle times by 50% and defect rates by 40%

We believe generative AI and Omniverse industrial digitalization will drive the next wave of professional visualization growth…

…Companies are using Omniverse to digitalize their workflows. Omniverse power digital twins enable Wistron, one of our manufacturing partners, to reduce end-to-end production cycle times by 50% and defect rates by 40%. 

Nvidia’s management sees generative AI driving a platform shift in the full computing stack

With generative AI, inference, which is now about fast token generation at massive scale, has become incredibly complex. Generative AI is driving a from-foundation-up full stack computing platform shift that will transform every computer interaction. From today’s information retrieval model, we are shifting to an answers and skills generation model of computing. AI will understand context and our intentions, be knowledgeable, reason, plan and perform tasks. We are fundamentally changing how computing works and what computers can do, from general purpose CPU to GPU accelerated computing, from instruction-driven software to intention-understanding models, from retrieving information to performing skills and, at the industrial level, from producing software to generating tokens, manufacturing digital intelligence.

Nvidia’s management sees token generation from LLMs driving multi-year build out of AI factories

Token generation will drive a multiyear build-out of AI factories…

… Large clusters like the ones built by Meta and Tesla are examples of the essential infrastructure for AI production, what we refer to as AI factories. These next-generation data centers host advanced full-stack accelerated computing platforms where the data comes in and intelligence comes out.

Nvidia’s management does not think that the demand they are seeing for the company’s AI chips is a pull-ahead of demand, because the the chips are being consumed

[Question] How are you ensuring that there is enough utilization of your products and that there isn’t a pull-ahead or a holding behavior because of tight supply, competition or other factors? 

[Answer] The demand for GPUs in all the data centers is incredible. We’re racing every single day. And the reason for that is because applications like ChatGPT and GPT-4o, and now it’s going to be multi-modality, Gemini and its ramp and Anthropic, and all of the work that’s being done at all the CSPs are consuming every GPU that’s out there. There’s also a long line of generative AI startups, some 15,000, 20,000 startups that are in all different fields, from multimedia to digital characters, of course, all kinds of design tool application, productivity applications, digital biology, the moving of the AV industry to video so that they can train end-to-end models to expand the operating domain of self-driving cars, the list is just quite extraordinary. We’re racing actually. Customers are putting a lot of pressure on us to deliver the systems and stand those up as quickly as possible. And of course, I haven’t even mentioned all of the sovereign AIs who would like to train all of their regional natural resource of their country, which is their data, to train their regional models. And there’s a lot of pressure to stand those systems up. So anyhow, the demand, I think, is really, really high and it outstrips our supply.

Nvidia’s management thinks that AI is not merely a chips problem – it is a system problem

The third reason has to do with the fact that we build AI factories. And this is becoming more apparent to people that AI is not a chip problem only. It starts, of course, with very good chips and we build a whole bunch of chips for our AI factories, but it’s a systems problem. In fact, even AI is now a systems problem. It’s not just one large language model. It’s a complex system of a whole bunch of large language models that are working together. And so the fact that NVIDIA builds this system causes us to optimize all of our chips to work together as a system, to be able to have software that operates as a system, and to be able to optimize across the system.

Nvidia’s management sees the highest performing AI chip as having the lowest total cost of ownership (TCO)

Today, performance matters in everything. This is at a time when the highest performance is also the lowest cost because the infrastructure cost of carrying all of these chips cost a lot of money. And it takes a lot of money to fund the data center, to operate the data center, the people that goes along with it, the power that goes along with it, the real estate that goes along with it, and all of it adds up. And so the highest performance is also the lowest TCO.

From the point of view of Nvidia’s management, customers do not mind buying Nvidia’s AI chips today even though better ones are going to come out tomorrow because they are still very early in their build-out of their AI infrastructure, and they want to ship AI advancements fast

[Question]  I’ve never seen the velocity that you guys are introducing new platforms at the same combination of the performance jumps that you’re getting…  it’s an amazing thing to watch but it also creates an interesting juxtaposition where the current generation of product that your customers are spending billions of dollars on is going to be not as competitive with your new stuff very, very much more quickly than the depreciation cycle of that product. So I’d like you to, if you wouldn’t mind, speak a little bit about how you’re seeing that situation evolve itself with customers. 

[Answer]  If you’re 5% into the build-out versus if you’re 95% into the build-out, you’re going to feel very differently. And because you’re only 5% into the build-out anyhow, you build as fast as you can… there’s going to be a whole bunch of chips coming at them, and they just got to keep on building and just, if you will, performance-average your way into it. So that’s the smart thing to do. They need to make money today. They want to save money today. And time is really, really valuable to them. Let me give you an example of time being really valuable, why this idea of standing up a data center instantaneously is so valuable and getting this thing called time-to-train is so valuable. The reason for that is because the next company who reaches the next major plateau gets to announce a groundbreaking AI. And the second one after that gets to announce something that’s 0.3% better. And so the question is, do you want to be repeatedly the company delivering groundbreaking AI or the company delivering 0.3% better?

All of Nvidia’s AI-related hardware products runs on its CUDA software; management thinks that AI performance for Nvidia AI-hardware users can improve over time simply from improvements that the company will be making to CUDA in the future

And all of it — the beautiful thing is all of it runs CUDA. And all of it runs our entire software stack. So if you invest today on our software stack, without doing anything at all, it’s just going to get faster and faster and faster. And if you invest in our architecture today, without doing anything, it will go to more and more clouds and more and more data centers and everything just runs. 

Shopify (NASDAQ: SHOP)

Shopify Magic is Shopify’s suite of AI products and management’s focus is on providing AI tools for merchants to simplify business operations and enhance productivity

Touching briefly on AI. Our unique position enables us to tap into the immense potential of AI for entrepreneurship and our merchants. Currently, the most practical applications of AI are found in tools that simplify business operations and enhance productivity, all of which we’ve been developing deeper capabilities with our AI product suite, Shopify Magic. 

Shopify’s management is using AI tools for precision marketing, and drove a 130% increase in merchant ads within its primary marketing channel from 2023 Q4 to 2024 Q1 while still being within payback guardrails

Our goal is to always get the most out of every existing channel up to our guardrail limits and continuingly find and experiment with new channels. That is what we build our tools and our AI models to do, and we’re using them to create some incredibly compelling opportunities. Let me give you a very recent example. At the end of last year and early into January, we drove significant efficiency improvements in one of our primary channels in performance marketing, where teams have created and leveraged advanced models using AI and machine learning, which now allows us to target our audiences with unprecedented precision. Using these models and strategies, we drove nearly 130% increase in merchant ads within our primary marketing channel from Q4 to Q1, while still remaining squarely within our payback guardrails.

Shopify has produced good revenue growth despite its headcount remaining flat for 3 quarters; management thinks Shopify can keep headcount growth low while the business continues to grow; the use of AI internally is an important element of how Shopify can continue to drive growth while keeping headcount growth low; an example of an internal use-case of AI is merchant support, where Shopify has (1) seen more than half of support interactions being assisted, and often fully-resolved, by AI, (2) been able to provide 24/7 live support in 8 additional languages that previously were offered only for certain hours, (3) decreased the duration of support interactions, (4) reduce the reluctance of merchants to ask questions, and (4) reduced the amount of toil on support staff

We know our team is one of our most valuable assets. And given that it makes up over half of our cost base, we believe we’ve architected ourselves to be faster and more agile, which has enabled us to consistently deliver 25% revenue growth, excluding logistics, all while keeping our headcount flat for 3 straight quarters. More importantly, because of the structure and the automation we have worked to put in place, we think we can continue to operate against very limited headcount growth while achieving a continued combination of consistent top line growth and profitability…

…We continue to remain disciplined on headcount with total headcount remaining essentially flat for the past 3 quarters, all while maintaining and, in fact, accelerating our product innovation capabilities and continuing the top line momentum of our business. How we leverage AI internally is an important element of how we are able to do that…

…During Q1, over half of our merchant support interactions were assisted with AI and often fully resolved with the help of AI. AI has enabled 24/7 live support in 8 additional languages that previously were offered only certain hours of the day. We have significantly enhanced the merchant experience. The average duration of support interactions has decreased. And the introduction of AI has helped reduce the reluctance that some merchants previously had towards asking questions that they might perceive as trivial or naive. Additionally, our support staff has experienced a significant reduction in the amount of toil that is part of their jobs. We are improving the merchant support process and achieving much greater efficiency than ever before.

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s management confirmed that there are no major damages to the company’s fabs and major operations from the recent earthquake in Taiwan – the largest in the region in 25 years – so there are no major disruptions to the supply of AI chips

On April 3, an earthquake of 7.2 magnitude struck Taiwan, and the maximum magnitude of our fabs was 5. Safety systems and protocols at our fabs were initiated immediately and all TSMC personnel are safe. Based on TSMC’s deep experience and capabilities in earthquake response and damage prevention as well as regular disasters trials, the overall tool recovery in our fabs reached more than 70% within the first 10 hours and were fully recovered by the end of the third day. There were no power outages, no structural damage to our fabs, and there’s no damage to our critical tools, including all our EUV lithography tools. That being said, a certain number of wafers in process were impacted and had to be scrapped, but we expect most of the lost production to be recovered in the second quarter and thus, minimum impact to our second quarter revenue. We expect the total impact from the earthquake to reduce our second quarter gross margin by about 50 basis points, mainly due to the losses associated with wafer scraps and material loss…

…Although it was largest earthquake in Taiwan in the last 25 years, we worked together tirelessly and were able to resume for operation at all our fab within 3 days with minimal disruptions, demonstrating the resilience of our operation in Taiwan.

TSMC’s management is seeing a strong surge in AI-related demand, and thinks that this supports their view of a structural acceleration in demand for energy-efficient computing

The continued surge in AI-related demand supports our already strong conviction that structural demand for energy-efficient computing is accelerating in an intelligent and connected world. 

TSMC’s management sees the company as a key enabler of AI; the increase in complexity of AI models, regardless of the approaches taken, requires increasingly powerful semiconductors, and this is where TSMC’s value increases, because the company excels at manufacturing the most advanced semiconductors

TSMC is a key enabler of AI applications. AI technology is evolving to use our increasingly complex AI models, which needs to be supported by more powerful semiconductor hardware. No matter what approach is taken, it requires use of the most advanced semiconductor process technologies. Thus, the value of our technology position is increasing as customers rely on TSMC to provide the most advanced process and packaging technology at scale, with a dependable and predictable cadence of technology offering. In summary, our technology leadership enable TSMC to win business and enables our customer to win business in the AI market.

TSMC’s management is seeing nearly every AI innovator working with the company

Almost all the AI innovators are working with TSMC to address the insatiable AI-related demand for energy-efficient computing power. 

TSMC’s management is forecasting the company’s revenue from AI processors to more than double in 2024 and account for low-teens percentage of total revenue; management expects AI processor revenue to grow at 50% annually over the next 5 years and account for more than 20% of TSMC’s total revenue by 2028; management has a narrow definition of AI processors and expect them to be the strongest growth driver for TSMC’s overall HPC (high performance computing) platform and overall revenue over the next few years

We forecast the revenue contribution from several AI processors to more than double this year and account for low teens percent of our total revenue in 2024. For the next 5 years, we forecast to grow at 50% CAGR and increase to higher than 20% of our revenue by 2028. Several AI processes are narrowly defined as GPUs, AI accelerators, and CPUs performing training and inference functions, and do not improve the networking, edge or on-device AI. We expect several AI processor to be the strongest driver of our HPC platform for growth and the largest contributor in terms of our overall incremental revenue growth in the next several years.

TSMC’s management thinks that strong HPC and AI demand means that it is strategically important for the company to expand its global manufacturing footprint

Given the strong HPC and AI-related demand, it is strategically important for TSMC to expand our global manufacturing footprint to continue to support our U.S. customers, increased customer trust, and expand our future growth potential.

TSMC has received strong support from the US government for its Arizona fabs and one of them has been upgraded to be a fab for 2nm process technology to support AI-demand, and it is scheduled for volume production in 2028; management is confident that the Arizona fabs will have the same quality as TSMC’s Taiwan fabs

In Arizona, we have received a strong commitment and support from our U.S. customers and plan to build 3 fabs, which help to create greater economies of scale..

…Our second fab has been upgraded to utilize 2-nanometer technologies to support a strong AI-related demand in addition to the previously announced 3-nanometer. We recently completed the taping of in which the last construction beam was raised into place and volume production is scheduled to begin in 2028…

…We are confident that once we begin volume production, we will be able to deliver the same level of manufacturing quality and reliability in each of our fab in Arizona as from our fab in Taiwan.

TSMC’s management believes the company’s 2nm technology is industry-leading and nearly every AI innovator is working with the company on its 2nm technology; management thinks 2nm will enable TSMC to capture AI-related growth opportunities in the years ahead

Finally, I will talk about our N2 status. Our N2 technology leads industry in addressing the industry’s insatiable need for energy-efficient computing, and almost all AI innovators are working with TSMC…

… With our strategy of continuous enhancement, N2 and its derivative will further extend our technology leadership position and enable TSMC to capture the AI-related growth opportunities well into future.

TSMC’s management is seeing very, very strong AI-related data center demand, while traditional server demand is slow; there is a shift in wallet-share from hyperscalers from traditional servers to AI servers and that is favourable for TSMC because TSMC has a lower presence in the traditional CPU-centric server space; TSMC is doubling its production capacity for AI-related data centre chips, but it’s still not enough to meet its customers’ demand

However, AI-related data center demand is very, very strong. And traditional server demand is slow, lukewarm…

…The budget for hyperscale player, their wallet-share shift from traditional server to AI server is favorable for TSMC. And we are able to capture most of the semiconductor content in an AI servers area as we defined GPU, ACA networking processor, et cetera. Well, we have a lower presence in those CPU-only, CPU-centric traditional server. So we expect our growth will be very healthy…

…Let me say it again, the demand is very, very strong, and we have done our best we put all the effort to increase the capacity. It probably more than double this year as compared with last year. However, not enough to meet the customers’ demand, and we leverage our OSAT partners that to complement of TSMC’s capacity to fulfill our customers need. Still not enough, of course. 

TSMC’s management is working on selling TSMC’s value in the manufacture of AI chips

[Question] I think it’s clear that AI is producing a large profit pool at your owners. And the HBM is also driving super normal returns for memory players. So my question is, does TSMC believe they’re getting their fair share of the returns in the AI value chain today? And is there a scope for TSMC to raise pricing for AI chips in the future?

[Answer] We always say that we want to sell our value, but it is a continuous process for TSMC. And let me tell you that we are working on it. We are happy that our customers are doing well. And if customers do well, TSMC does well.

TSMC’s management still expects the company’s capex intensity (capex as a percentage of revenue) to level off somewhere around the mid-30s range in the next several years even with the AI-boom, but they are ready to increase capex if necessary

[Question] My second question is just relating to the upward expectations you gave for the AI accelerators. Curious how that time, how you’re looking at the CapEx, if you say that we’re entering either higher growth or investment cycle, where capital intensity could need to rise up above that mid-30s range that you set 

[Answer] We work with our customers closely and our CapEx and capacity planning are always based on the long-term structural market demand profile that is underpinned by the multiyear megatrends….  The capital intensity, in the past few years, it was high as we invested heavily to meet the strong customer demand. Now the increase — the rate of increase for the capex is leveling off, so this year and the next several years, we are expecting that the capital intensity is somewhere at the mid-30s level. But as I just said, if there are opportunities in the future years, then we will invest accordingly.

TSMC’s management wants to support all of TSMC’s AI customers’ needs, and not just the needs of its major AI customer (presumably Nvidia)

 We want to make sure that all our customers get supported, probably not enough this year. But for next year, we try. We try very hard. And you mentioned about giving up some market share, that’s not my consideration. My consideration is to help our customers to be successful in their market…

…[Question] So since your major customers said there’s no room for other type of AI computing chips, but it seems like TSMC is happy to assist some similar customers, right? So is that right interpretation about your comments.

[Answer] Yes.

Most of TSMC’s AI customers are using the 5nm or 4nm technologies, but they are working with TSMC on even more advanced nodes – such as 3nm and 2nm – because the advanced nodes are more energy-efficient, and energy efficiency in AI data centres is really important; in the past, TSMC’s then-leading edge chips only see smartphone demand, but with 2nm, TSMC will see demand from smartphones and HPC, so the early-revenue from 2nm is expected to be even larger than 3nm’s early-revenue 

[Question] I think currently, most of the AI accelerator, mostly in 5-nanometers, which is N minus 1 comparing to a smartphone for now. So when do we expect them to catch up or surpass in terms of technology node? Do we see them to be the technology driver in 2 nanometers or above?

[Answer] Today, all the AI accelerators, most of them are in the 5- or 4-nanometer technology. My customers are working with TSMC for the next node, even for the next, next node, they have to move fast because, as I said, the power consumption has to be considered in the AI data center. So the energy-efficient is very important. So our 3-nanometer is much better than the 5-nanometer. And again, it will be improved in the 2-nanometer. So all I can say is all my customers are working on this kind of trend from 4-nanometer to 3 to 2…

…[Question] Do we see a bigger revenue in the first 2 years of the 2 nanometers because in the past, it’s only smartphone, but in 2-nanometer, it would be both smartphone and HPC customers.

[Answer] With the demand that we’re seeing, we do expect N2 revenue contribution to be even larger than N3, just like 3 is a larger contribution or larger node than 5, et cetera, et cetera.

TSMC’s management is seeing die sizes increase with edge-AI or on-device AI; management thinks that the replacement cycle for smartphones and PCs will be a little accelerated in the future and the edge-AI trend will be very positive for TSMC

Let me mention the edge-AI or the on-device AI, the first order of magnitude is the die size. We saw with AI for neuro processor inside, the die size will be increased, okay? That’s the first we observed. And it’s happening. And then for the future, I would think that replacement cycle for smartphone and kind of a PC will be accelerated a little bit in the future, at least. It’s not happening yet, but we do expect that will happen soon. And all in all, I would say that on-device AI will be very positive for TSMC because we kept the larger share of the market. 

Tencent (NASDAQ: TCEHY)

Engagement of Weixin users is increasingly supplemented by consumption of content in chat at moments and recommended content on video accounts and mini programs; this was driven by AI recommendations 

For Weixin, users are increasingly supplementing their stable consumption of social graph supply content in chat at moments with consumption of algorithmically recommended content in official accounts and video accounts and engagement with Mini Programs diverse range of services.  This trend benefits from our heavy investment in AI, which makes the recommendation better and better over time.

Official accounts achieved healthy year-on-year pageview growth, driven AI-powered recommendation algorithms

For official accounts, which enable creators to share text and images and chosen topics with interested followers, it achieved healthy year-on-year pageview growth. As AI-powered recommendation algorithms allow us to provide targeted high-quality content more effectively.

Tencent’s online advertising revenue was up 26% in 2024 Q1 because of increased engagements from AI-powered ad targeting; ad spend from all major categories increased in 2024 Q1 except for automotives; during the quarter, Tencent upgraded its ad tech platform and made generative AI-powered ad creation tools available to boost ad creation efficiency and better targeting

For online advertising, our revenue was RMB 26.5 billion in the quarter up 26% year-on-year, benefiting from increased engagements in AI-powered ad targeting. Ad spend from all major categories except automotive increased year-on-year, particularly from games, internet services and consumer goods sectors. During the quarter, we upgraded our ad tech platform to help advertisers manage ad campaigns more effectively, and we made generative AI-powered ad creation tools available to all advertisers. These initiatives enable advertisers to create ads more efficiently and to deliver better targeting.

Hunyuan (Tencent’s foundational LLM) was scaled up using the mixture of experts approach; management is deploying Hunyuan in more Tencent services; management is open-sourcing a version of Hunyuan that provides text-image generative AI

And for Hunyuan, the main model achieved significant progress as we’ve scaled up using the mixture of experts approach, and we’re deploying Hunyuan in more of our services. Today, we announced that we’re making a version of Hunyuan providing text image generative AI available on an open source basis.

Tencent’s operating capex was RMB6.6b in 2024 Q1, up massively from a low base in 2023 Q1 but down slightly sequentially, because of spending on GPUs and servers to support Hunyuan and the AI ad recommendation algo

Operating CapEx was RMB 6.6 billion, up 557% year-on-year from a low base quarter last year, mainly driven by investment in GPUs and servers to support our Hunyuan and AI ad recommendation algorithms.

Tencent’s management expects advertising revenue growth to decelerate from 2024 Q1’s level, but still expects to outpace the broader industry because (1) Tencent’s ad load is still small relative to the advertising real estate available, and (2) AI will help the advertising business and can easily double or even triple Tencent’s currently low click-through rates; management thinks Tencent’s advertising business will benefit from AI disproportionately vis-a-vis competitors who also use AI because Tencent has been under-monetising and has lower click-through rates, so any AI-driven improvements will have a bigger impact; Hunyuan is part of the AI technologies that management has deployed for the advertising business

Around advertising, I’d say that, as you would expect, given the economies mix, advertiser sentiment is also quite mixed and it’s certainly a challenging environment in which to set advertising. The first quarter for us is a slightly unusual quarter because it’s a small quarter for advertising due to the Chinese New Year effect. And so sometimes the accelerations or the decelerations get magnified as a result. So we would expect our advertising growth to be less rapid in subsequent quarters of the year than it was in the first quarter and more similar to consensus expectations for our advertising revenue growth for the rest of the year. But that said, we think that we are in a good position to continue taking share of the market at a rapid rate, given we’re very early in increasing our ad load on video accounts, which is currently around 1/4 of the ad loads of our major competitors with short video products. 

And also given we’re early in capturing the benefits of deploying AI to our ad tech stack. And we think that we will — we are benefiting and will continue to benefit disproportionately from applying AI to our ad tech because historically, as a social media platform, our click-through rates were low. And so starting from that lower base, we can — we have seen we can double or triple click-through rates in a way that’s not possible for ad services that are starting from much higher click through rates…

… [Question] In the future, do you think like under the AI developments like our competitors such as like ByteDance or Alibaba, they also applies AI to their ad business so how do you think that AI will drive to add market share to change in the longer term?

[Answer] Your question around a number of competitors are obviously applying AI as well. And we believe that all of them will benefit from AI, too. But we think that the biggest beneficiaries will be those companies, of which we are one that have very substantial under monetized time spent and now able to monetize that time spend more effectively by deploying AI because the deployment of AI enables an upward structural shift in click-through rates, and that shift is most pronounced for those inventories where the click-through rates were lower to begin with, such as the social media inventory. Those tools also allow advertisers who previously were able to create advertisements for search, which are text in nature, but not to create advertisements for social media, which are image and video in nature, to now use generative AI to create advertisements to social media. So in general, we think there’ll be a reallocation of advertising spend toward those services, which have high time spent, high engagement and are now able to deliver increasing click through rates, increasing transaction volume more commensurate with the time spent and engagement superiority…

…  So on ad tech, we’re innovating around the process of targeting the ads using artificial intelligence. We’re innovating around helping advertisers manage their advertising campaigns. And then most recently, we’ve been — we are now deploying Hunyuan to facilitate advertisers, creating the advertising content.

Tencent’s management thinks that WeChat will be a great distribution channel for AI products, but they are still figuring out the best use case for AI (including Tencent’s own Hunyuan LLM); management is actively testing, and they will roll out the products they think are the best over time

I think we do believe that with the right product than our WeChat platform and our other products, which have a lot of user engagement would be great — will be great distribution channels for these AI products. But I think at this point in time, everybody is actually trying out different products that may work. No one has really come up with a killer application yet with the exception of probably OpenAI, that question and answer from it so I think you should be confident that we have been developing the technology, and we are having a best-in-class technology in Hunyuan and at the same time, we are actively creating and testing out different products to see what would make sense for our existing products and as the time comes, these products will be rolled out on our platform.

Tencent’s management thinks that Hunyuan is currently best being deployed in Tencent’s gaming business for customer service purposes; management has been deploying AI in Tencent’s games, but not necessarily generative AI; Hunyuan will be useful for developing games when it gains multi-modal capabilities, especially in creating high-quality videos, but it will be some time before Hunyuan reaches that level

I think for Hunyuan — it can be assisting game business in multiple ways. Right now, the best the best contributor is actually on the customer service front. When Hunyuan is actually deployed to answer questions and the customer service bought for a lot of our games is actually achieving very high customer satisfaction level. And AI, in general, has already been deployed in our games, but not necessarily the generative AI technology yet. In terms of Hunyuan and, I think, over time, when we actually sort of can move Hunyuan into a multi-modal and especially if we can start creating really high-quality, high fidelity videos, then that would actually be helpful. Before that happens, Hunyuan can actually sort of be using MPCs and create a certain sort of interactive experiences but it’s not going to be able to take over the very heavy growth of content creation in gaming yet. I think you’ll probably be a couple more generations before it can be for game production.

Tesla (NASDAQ: TSLA)

Tesla’s FSD v12 is a pure AI-based self driving technology; FSD v12 is now turned on for all North American Tesla vehicles – around 1.8 million vehicles – that are running on Hardware 3 or later and it is used on around half of the vehicles, with the percentage of users increasing each week; more than 300 billion miles have been driven with FSD v12; management thinks that it’s only a matter of time before Tesla’s autonomous driving capabilities exceeds human-reliability

Regarding FSD V12, which is the pure AI-based self-driving, if you haven’t experienced this, I strongly urge you to try it out. It’s profound and the rate of improvement is rapid. And we’ve now turned that on for all cars, with the cameras and inference computer, everything from Hardware 3 on, in North America. So it’s been pushed out to, I think, around 1.8 million vehicles, and we’re seeing about half of people use it so far and that percentage is increasing with each passing week. So we now have over 300 billion miles that have been driven with FSD V12…

…I think it should be obvious to anyone who’s driving V12 in a Tesla that it is only a matter of time before we exceed the reliability of humans and we’ve not much time with that. 

Tesla’s management believes that the company’s vision-based approach with end-to-end neural networks for full self driving is better than other approaches, because it mimics the way humans drive, and the global road networks are designed for biological neural nets and eyes

Since the launch of Full Self-Driving — Supervised Full Self-Driving, it’s become very clear that the vision-based approach with end-to-end neural networks is the right solution for scalable autonomy. And it’s really how humans drive. Our entire road network is designed for biological neural nets and eyes. So naturally, cameras and digital neural nets are the solution to our current road system…

… I think we just need to — it just needs to be obvious that our approach is the right approach. And I think it is. I think now with 12.3, if you just have the car drive you around, it is obvious that our solution with a relatively low-cost inference computer and standard cameras can achieve self-driving. No LiDARs, no radars, no ultrasonic, nothing.

Tesla has reduced the subscription price of FSD to US$99 a month; management is talking to one major auto manufacturer on licensing Tesla’s FSD software; it will take time for third-party automakers to use Tesla’s autonomous driving technology as a massive design change is needed for the vehicles even though all that is needed is for cameras and an inference computer to be installed

To make it more accessible, we’ve reduced the subscription price to $99 a month, so it’s easy to try out…

…We’re in conversations with one major automaker regarding licensing FSD…

…I think we just need to — it just needs to be obvious that our approach is the right approach. And I think it is. I think now with 12.3, if you just have the car drive you around, it is obvious that our solution with a relatively low-cost inference computer and standard cameras can achieve self-driving. No LiDARs, no radars, no ultrasonic, nothing… No heavy integration work for vehicle manufacturers…

… So I wouldn’t be surprised if we do sign a deal. I think we have a good chance we do sign a deal this year, maybe more than one. But yes, it would be probably 3 years before it’s integrated with a car, even though all you need is cameras and our inference computer. So just talking about a massive design change.

Tesla’s management has been expanding the company’s core AI infrastructure and the company is no longer training-constrained; Tesla has 35,000 H100 GPUs that are currently working, and management expects to have 85,000 H100 GPUs by end-2024 for AI training

Over the past few months, we’ve been actively working on expanding Tesla’s core AI infrastructure. For a while there, we were training-constrained in our progress. We are, at this point, no longer training-constrained, and so we’re making rapid progress. We’ve installed and commissioned, meaning they’re actually working, 35,000 H100 computers or GPUs. GPU is a wrong word, they need a new word. I always feel like a [ wentz ] when I say GPU because it’s not. GPU stands — G stands for graphics. Roughly 35,000 H100S are active, and we expect that to be probably 85,000 or thereabouts by the end of this year in training, just for training. 

Tesla’s AI robot, Optimus, is able to do simple factory tasks and management thinks it can do useful tasks by the end of this year; management thinks Tesla can sell Optimus by the end of next year; management still thinks that Optimus will be an incredibly valuable product if it comes to fruition; management thinks that Tesla is the best-positioned manufacturer of humanoid robots with efficient AI inference to be able to reach production at scale

[Question] What is the current status of Optimus? Are they currently performing any factory tasks? When do you expect to start mass production?

[Answer] We are able to do simple factory tasks or at least, I should say, factory tasks in the lab. In terms of actually — we do think we will have Optimus in limited production in the factory — in natural factory itself, doing useful tasks before the end of this year. And then I think we may be able to sell it externally by the end of next year. These are just guesses. As I’ve said before, I think Optimus will be more valuable than everything else combined. Because if you’ve got a sentient humanoid robots that is able to navigate reality and do tasks at request, there is no meaningful limit to the size of the economy. So that’s what’s going to happen. And I think Tesla is best positioned of any humanoid robot maker to be able to reach volume production with efficient inference on the robot itself.

The vision of Tesla’s management for autonomous vehicles is for the company to own and operate some autonomous vehicles within a Tesla fleet, and for the company to be an Airbnb- or Uber-like platform for other third-party owners to put their vehicles into the fleet; management thinks Tesla’s fleet can be tens of millions of cars worldwide – even more than 100 million – and as the fleet grows, it will act as a positive flywheel for Tesla in terms of producing data for training

And something I should clarify is that Tesla will be operating the fleet. So you can think of like how Tesla — you think of Tesla like some combination of Airbnb and Uber, meaning that there will be some number of cars that Tesla owns itself and operates in the fleet. There will be some number of cars — and then there’ll be a bunch of cars where they’re owned by the end user. That end user can add or subtract their car to the fleet whenever they want, and they can decide if they want to only let the car be used by friends and family or only by 5-star users or by anyone. At any time, they could have the car come back to them and be exclusively theirs, like an Airbnb. You could rent out your guestroom or not any time you want. 

So as our fleet grows, we have 7 million cars — 9 million cars, going to eventually tens of millions of cars worldwide. With a constant feedback loop, every time something goes wrong, that gets added to the training data and you get this training flywheel happening in the same way that Google Search has the sort of flywheel. It’s very difficult to compete with Google because people are constantly doing searches and clicking and Google is getting that feedback loop. So same with Tesla, but at a scale that is maybe difficult to comprehend. But ultimately, it will be tens of millions…

… And then I mean if you get like to the 100 million vehicle level, which I think we will, at some point, get to, then — and you’ve got a kilowatt of useable compute and maybe your own Hardware 6 or 7 by that time, then you really — I think you could have on the order of 100 gigawatts of useful compute, which might be more than anyone more than any company, probably more than any company.

Tesla’s management thinks that the company can sell AI inference compute capacity that’s sitting in Tesla vehicles when they are not in use; Tesla cars are running Hardware 3 and Hardware 4 now, while Hardware 5 is coming; unlike smartphones or computers, the computing capacity of Tesla vehicles is entirely within Tesla’s control, and the company has skills on deploying compute workloads to each individual vehicle

I think there’s also some potential here for an AWS element down the road where if we’ve got very powerful inference because we’ve got a Hardware 3 in the cars, but now all cars are being made with Hardware 4. Hardware 5 is pretty much designed and should be in cars hopefully towards the end of next year. And there’s a potential to run — when the car is not moving, to actually run distributed inference. So kind of like AWS, but distributed inference. Like it takes a lot of computers to train an AI model, but many orders of magnitude less compute to run it. So if you can imagine a future [ path ] where there’s a fleet of 100 million Teslas, and on average, they’ve got like maybe a kilowatt of inference compute, that’s 100 gigawatts of inference compute distributed all around the world. It’s pretty hard to put together 100 gigawatts of AI compute. And even in an autonomous future where the car is perhaps used instead of being used 10 hours a week, it is used 50 hours a week. That still leaves over 100 hours a week where the car inference computer could be doing something else. And it seems like it will be a waste not to use it…

…And then I mean if you get like to the 100 million vehicle level, which I think we will, at some point, get to, then — and you’ve got a kilowatt of useable compute and maybe your own Hardware 6 or 7 by that time, then you really — I think you could have on the order of 100 gigawatts of useful compute, which might be more than anyone more than any company, probably more than any company…

…Yes, probably because it takes a lot of intelligence to drive the car anyway. And when it’s not driving the car, you just put this intelligence to other uses, solving scientific problems or answer in terms of [ this horse ] or something else… We’ve already learned about deploying workloads to these nodes… And unlike laptops and our cell phones, it is totally under Tesla’s control. So it’s easier to see the road products plus different nodes as opposed to asking users for permission on their own cell phones would be very tedious… 

… So like technically, yes, I suppose like Apple would have the most amount of distributed compute, but you can’t use it because you can’t get the — you can’t just run the phone at full power and drain the battery. So whereas for the car, even if you’re a kilowatt-level inference computer, which is crazy power compared to a phone, if you’ve got 50 or 60 kilowatt hour pack, it’s still not a big deal. Whether you plug it or not, you could run for 10 hours and use 10 kilowatt hours of your kilowatt of compute power.

Safety is very important for Tesla; management has been conducting safety-training for Tesla’s AI-powered self driving technology through the use of millions of clips of critical safety events collected from Tesla vehicles; the company runs simulations for safety purposes before pushing out a new software version to early users and before it gets pushed to external users; once the new software is with external users, it’s constantly monitored by Tesla; FSD v12’s feedback loop of issues, fixes, and evaluations happens automatically because the AI model learns on its own based on data it is getting

Yes, we have multiple years of validating the safety. In any given week, we train hundreds of neural networks that can produce different trajectories for how to drive the car, replay them through the millions of clips that we have already collected from our users and our own QA. Those are like critical events, like someone jumping out in front or like other critical events that we have gathered database over many, many years, and we replay through all of them to make sure that we are net improving safety. 

And then we have simulation systems. We also try to recreate this and test this in close to fashion. And some of this is validated, we give it to our QA networks. We have hundreds of them in different cities, in San Francisco, Los Angeles, Austin, New York, a lot of different locations. They are also driving this and collecting real-world miles, and we have an estimate of what are the critical events, are they net improvement compared to the previous week builds. And once we have confidence that the build is a net improvement, then we start shipping to early users, like 2,000 employees initially that they would like it to build. They will give feedback on like if it’s an improvement or they’re noting some new issues that we did not capture in our own QA process. And only after all of this is validated, then we go to external customers.

And even when we go external, we have like live dashboards of monitoring every critical event that’s happening in the fleet sorted by the criticality of it. So we are having a constant pulse on the build quality and the safety improvement along the way. And then any failures like Elon alluded to, we’ll get the data back, add it to the training and that improves the model in the next cycle. So we have this like constant feedback loop of issues, fixes, evaluations and then rinse and repeat.

And especially with the new V12 architecture, all of this is automatically improving without requiring much engineering interventions in the sense that engineers don’t have to be creative and like how they code the algorithms. It’s mostly learning on its own based on data. So you see that, okay, every failure or like this is how a person chooses, this is how you drive this intersection or something like that, they get the data back. We add it to the neural network, and it learns from that trained data automatically instead of some engineers saying that, oh, here, you must rotate the steering wheel by this much or something like that. There’s no hard inference conditions. If everything is neural network, it’s pretty soft, it’s probabilistic and circular. That’s probabilistic distribution based on the new data that it’s getting.

Tesla’s management has good insight on the level of improvement Tesla’s AI-powered self-driving technology can be over a 3-4 month time frame, based on a combination of model size scaling, data scaling, training compute scaling, and architecture scaling

And we do have some insight into how good the things will be in like, let’s say, 3 or 4 months because we have advanced models that our far more capable than what is in the car, but have some issues with them that we need to fix. So they are there’ll be a step change improvement in the capabilities of the car, but it will have some quirks that are — that need to be addressed in order to release it. As Ashok was saying, we have to be very careful in what we release the fleet or to customers in general. So like — if we look at say 12.4 and 12.5, which are really could arguably even be V13, V14 because it’s pretty close to a total retrain of the neural nets and in each case, are substantially different. So we have good insight into where the model is, how well the car will perform, in, say, 3 or 4 months…

… In terms of scaling, people in here coming and they generally talk about models scaling, where they increase the model size a lot and then their corresponding gains in performance, but we have also figured out scaling loss and other access in addition to the model side scaling, making also data scaling. You can increase the amount of data you use to train the neural network and that also gives similar gains and you can also scale up by training compute. You can train it for much longer and one more GPUs or more Dojo nodes, and that also gives better performance. And you can also have architecture scaling where you count with better architectures for the same amount of compute produce better results. So a combination of model size scaling, data scaling, training compute scaling and the architecture scaling, we can basically extrapolate, okay, with the continue scaling based at this ratio, we can predict future performance. 

The Trade Desk (NASDAQ: TSLA)

Trade Desk’s management will soon roll out a game-changing AI-fueled forecasting tool on the company’s Kokai platform

We are quickly approaching some of the biggest UX and product rollouts of Kokai that nearly all of our customers will begin to use and see benefits from over the next few quarters, including a game-changing AI-fueled forecasting tool.

Trade Desk’s management has been using AI since 2016; management has always thought about AI as a copilot for humans even before Trade Desk was founded

We’ve been deploying AI in our platform since we launched Koa in 2016…

… To that end, we’ve known since before our company existed that the complexity of assessing millions of ad opportunities every second, along with hundreds of variables for each impression, is beyond the scope of any individual human. We have always thought about AI as a copilot for our hands-on keyboard traders.

Through Kokai, Trade Desk is bringing AI to many decision-points in the digital advertising process; Trade Desk is also incorporating AI into new relevance indices in Kokai for advertisers to better understand the relevance of different ad impressions in reaching their target audience; US Cellular used Trade Desk’s TV Quality Index to improve its conversion rate by 71%, reach 66% more households, and decrease cost per acquisition by 24%

And with Kokai, we are bringing the power of AI to a broader range of key decision points than ever, whether it’s in relevant scoring forecasting, budget optimization, frequency management or upgraded measurement. AI is also incorporated into a series of new indices that score relevance, which advertisers can use to better understand the relevance of different ad impressions in reaching their target audience. For example, U.S. Cellular worked with their agency, Harmelin Media, to leverage our TV Quality Index to better reach new customers. Their conversion rates improved 71%. They reached 66% more households by optimizing frequency management, and their cost per acquisition decreased 24%. I think it’s important to understand how we’re putting AI to work in Kokai because this kind of tech dislocation will bring new innovators. 

Visa (NYSE: V)

Visa’s management is using AI to improve the company’s risk offerings; the company’s Visa Protect for account-to-account payments feature is powered by AI-based fraud detection models; another of the features, Visa Deep Authorization, is powered by a deep-learning recurrent neural network model for risk scoring of e-commerce payments specifically in the USA

Across our risk offerings, we continue to bolster them through our technology, innovation, and AI expertise and are expanding their utility beyond the Visa network. Recently, we announced 3 such capabilities in our Visa Protect offering. The first is the expansion of our signature solutions, Visa Advanced Authorization and Visa Risk Manager for non-Visa card payments, making them network-agnostic. This allows issuers to simplify their fraud operations into a single fraud detection solution. The second is the release of Visa Protect for account-to-account payments, our first fraud prevention solution built specifically for real-time payments, including P2P digital wallets, account-to-account transactions and Central Bank’s instant payment systems. Powered by AI-based fraud detection models, this new service provides a real-time risk score that can be used to identify fraud on account-to-account payments. We’ve been piloting both of these in a number of countries, and our strong results thus far have informed our decision to roll these out globally. The third solution is Visa Deep Authorization. It is a new transaction risk scoring solution tailored specifically to the U.S. market to better manage e-commerce payments powered by a world-class deep-learning recurrent neural network model and petabytes of contextual data…

…What we found in the U.S. e-commerce market is that, on the one hand, it’s the most developed e-commerce market on the planet. On the other hand, it’s become the place of the most sophisticated fraud and attack vectors that we see anywhere in the world. And so what we are bringing to market with Visa Deep Authorization is an e-commerce transaction risk scoring platform and capability that is specifically tailored and built for the unique sets of attack vectors that we’re seeing in the U.S. So as I was mentioning in my prepared remarks, it’s built on deep learning technology that’s specifically tuned to some of the sequential and contextual view of accounts that we’ve had in the U.S. market. 

Wix (NASDAQ: WIX)

Wix’s management released its AI website builder in 2024 Q1, which is the company’s cornerstone product; the AI website builder utilises a conversational AI chat experience where users describe their intent and goals, and it is based on Wix’s decade-plus of knowledge in website creation and user behaviour; the AI-generated sites include all relevant pages, business solutions (such as scheduling and e-commerce), and functions; management thinks the AI website builder is a unique product in the market; management is seeing strong utilisation of the AI website builder, with hundreds of thousands of sites already been created in a few months since launch by both Self Creators and Partners

Notably, this quarter, we released the highly anticipated AI website builder. This is our cornerstone AI product. It leverages our 10-plus years of web creation expertise and unparalleled knowledge based on users’ behavior through a conversational AI chat experience. Users describe their intent and goals. Our AI technology then creates a professional, unique, and fully built-out website that meets the users’ needs. Importantly, the AI-generated site includes all relevant pages with personalized layout themes, text, images and business solutions such as scheduling, e-commerce and more. Best of all, this website are fully optimized with Wix-reliable infrastructure, including security and performance as well as built in marketing, SEO, CRM and analytics tools. There is truly nothing like this on the market. Excitingly, feedback on the AI website building has been incredible. In just a few short months since its launch, hundreds of thousands of sites have been already been created using this tool by both Self Creators and Partner. This strong response and utilization is a testament to the depth of our AI expertise and strength of our product. 

Wix released AI-powered image enhancement tools within Wix Product Studio in April which allow users to edit images in a high-quality manner through prompts

In April, we released a suite of AI-powered image enhancement tools that provide users with the capability to create professional images on their own. High-quality images are an essential part of a professional website but often hard to achieve without the help of professional photographer. New users will be able to easily erase objects, generate images, edit them to add or replace objects with a simple prompt, all without ever leaving the Wix Product Studio. 

Wix will be releasing more AI products in 2024; the upcoming products include AI business assistants; the AI business assistants are in beta testing and management is seeing great feedback

This new capabilities are just the start of a robust pipeline of AI-enabled products still to come this year, including a variety of vertical AI business assistants that will be released for the year. A couple of these assistants are currently in beta testing and seeing great results and feedback. 

Wix is seeing that its AI products are resulting in better conversion of users into premium subscribers; management believes that Wix’s AI products will be a significant driver of Self Creators growth in the years ahead

We are seeing a tangible benefit from our entire AI offering particularly a better conversions among users into premium subscription. I strongly believe that our AI capability will be significant — a significant driver of Self Creators growth in 2024 and beyond.

 Wix’s AI tools will be exposed very frequently to both existing and new users of the Wix platform

[Question] I wanted to kind of follow on to that and just kind of understand with respect to the AI tools. Do you see this primarily impacting the new customers? 

[Answer] When users are building their websites, all the website creation tools are visible to them and are helping them. Most of our users will stay a few years or more than that with the same website and sometimes — and they’ll update it, but they’re not going to recreate it. So, in that term, of course, the exposure is limited. But the integration of the vertical assistance is something that means that every time you go to the website, you’re going to have a recommendation, and the ideas and things you can do with AI. So, the exposure will be pretty much every time you go into the website. And that is significantly higher. And if you think about the fact that we have a lot of people that run their business in top of Wix, it means that all of those guys will be daily or almost daily exposed to new products with AI…

…You’re going to find AI tools, but they are not going to replace what you already know how to do. Sometimes, if you want to change an image, for example, it’s easier to click on change image instead of writing to the prompt, hey, please change the third image from the top, right? So, it’s always about the combination of how you do things in a balanced way, while allowing users to feel comfortable with the changes, not move beyond that. 

Wix’s management believes that AI will be a boom for new technologies and innovation and will lead to more growth for Wix

I believe that there’s so much potential for new things coming with AI, so much potential with new things coming with market trends and new technologies introduced into the market that I believe that we’re going to continue to see significant innovation, growing innovation coming from small businesses and bigger businesses in the world, which will probably result in the formation of additional growth for us. 

Zoom Video Communications (NASDAQ: ZM)

Zoom is now far beyond just video conferencing, and AI is infused across its platform

Our rapid innovation over the years has taken us far beyond video conferencing. Every step of the way has been guided by our mission to solve customer problems and enable greater productivity. In the process, we have very deliberately created a communication and collaboration powerhouse with AI infused natively across the platform.

Zoom’s management announced Zoom Workplace, an AI-powered collaboration platform in March; Zoom Workplace already has AI-powered features but will soon have Ask AI Companion; Zoom Workplace also improves other Zoom products through AI Companion capabilities; the AI features in Zoom Workplace are provided at no additional cost

In March we announced Zoom Workplace, our AI-powered collaboration platform designed to help our customers streamline communications, improve productivity, increase employee engagement, and optimize in-person time. Within the launch of Zoom Workplace are new enhancements and capabilities like multi-speaker view, document collaboration, AI-powered portrait lighting, along with upcoming features and products like Ask AI Companion, which will work across the platform to help employees make the most of their time. The Workplace launch also boosts Zoom Phone, Team Chat, Events and Whiteboard with many more AI Companion capabilities to help make customers more productive…

…When you look at our Workplace customers, guess what, AI is not only a part of that but also at no additional cost, right? So that is our vision.

Expedia has signed a quadruple-digit seat deal for Zoom Revenue Accelerator, which includes AI products that can help Expedia to drive revenue

Let me thank Expedia, who needs no introduction, for becoming a Lighthouse Zoom Revenue Accelerator customer in the quarter, leaning heavily into our AI products to drive revenue. A power user of Zoom Phone for years, they wanted to better automate workflows, coach sellers and drive efficiencies. We partnered with them on an initial quadruple-digit seat Zoom Revenue Accelerator deal, which includes working directly with their team to improve and tailor the product based on their business model and industry-specific use case.

Centerstone, a nonprofit organisation, expanded Zoom Phone and Zoom Contact Center in 2024 Q1 to leverage AI to provide better care for its beneficiaries

Let me also thank Centerstone, a nonprofit health system specializing in mental health and substance use disorder treatments for individuals, families, and veterans, for doubling down on Zoom. Seeing strong value from their existing Zoom Meetings, Phone and Rooms deployment, in Q1, they expanded Zoom Phone and added Zoom Contact Center in order to leverage AI to provide better care, and Zoom Team Chat in order to streamline communications all from a single platform.

Zoom AI Companion is now enabled in >700,000 customer accounts just 8 months after launch; AI Companion improves the value proposition of all of Zoom’s products and it’s provided to customers without charging customers more; AI Companion also helps Zoom improve monetisation because its presence in Zoom’s Business Services enables Zoom to charge a premium price because the AI features are a key differentiator; management will leverage AI Companion to build a lot of new things

Zoom AI Companion has grown significantly in just eight months with over 700,000 customer accounts enabled as of today. These customers range all the way from solopreneurs up to enterprises with over 100,000 users…

… I think AI Companion not only help our Meetings, Phone, or Team Chat, it’s across the entire Zoom Workplace platform plus all the Business Services, right? Our approach, if you look at our Workplace, the deployment, right, for the entire collaboration platform not only makes all those services better but also customers appreciate it, right, without charging the customers more, right? We do add more value to customers at no additional cost, right? That’s kind of the power part of the Zoom company. At the same time, in terms of monetization, as I mentioned earlier, if you look at our Business Services, AI is a key differentiation, right, AI and we charge a premium price as well, and that’s the value. At the same time, we also are going to leverage AI Companion to build a lot of new things, new services like Ask AI that will be introduced later this year and also some other new services that we’re working on as well.

One of Zoom’s management’s key priorities is to embed AI across all of Zoom Workplace and Business Services

Embedding AI across all aspects of Zoom Workplace and Business Services is a key priority as we continue to drive productivity and engagement for our customers.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Adobe, Alphabet, Amazon, Apple, Coupang, Datadog, Etsy, Fiverr, Mastercard, Meta Platforms, Microsoft, Netflix, Shopify, TSMC, Tesla, The Trade Desk, Visa, Wix, and Zoom. Holdings are subject to change at any time.

I’ve been studying stock-based compensation across a wide range of companies for a few years now.

One thing I’ve learnt is that stock-based compensation can come in a variety of different forms. Each will have a different impact on the future cash flows from a company that accrues to shareholders.

With this in mind, here is a short primer on four of the most common types of stock-based compensation and how they impact the shareholder.

Restricted stock units

Restricted stock units, or RSUs, is the most common form of stock-based compensation I’ve encountered. It is essentially the issuance of new shares to employees.

Well-known companies such as Meta Platforms and Zoom Video Communications issue RSUs to employees. Typically, employees are given RSUs when they join the company. However, these RSUs only turn into shares – or “vest” – over a period of time. Only when they vest can an employee sell them or receive any dividends from holding them.

You can find the number of RSUs outstanding for a company on the notes section of their financial reports . By looking at the RSUs outstanding, you can gauge what is the future diluted share count once all these RSUs have vested.

Options

Another common form of stock-based compensation is options. Options give employees the right to buy new shares of a company at a predetermined price by a certain date.

The good thing about options for shareholders is that unlike RSUs, the company receives cash when these options are exercised. This increases the company’s cash balance and if the exercise price is above the book value, it also increases the book value per share.

If the stock price is below the exercise price upon expiry, employees will not exercise the options and will result in the options expiring worthless. When this happens, no new shares are issued and no cash exchange hands.

A well-known company that predominantly uses options as its form of stock-based compensation is Netflix.

Performance stock units

As its name suggests, performance stock units, or PSUs, are converted to shares for an employee based on a company achieving certain performance goals. PSUs are typically only given to senior executives of a company.

Companies that use PSUs tend to combine it with other forms of stock-based compensation.

Employee share purchase plan

Employee share purchase plans, or ESPPs for short, are programs that allow a company’s employees to purchase new shares of the company with a portion of their salary.

So instead of getting all their salary in cash, they receive some in cash and some in shares. Employees are often incentivized to purchase shares using the ESPP as they can buy new shares of a company at a discount to market prices.

However, employees are only allowed to purchase up to a certain amount of shares per year. Companies such as Medtronic offer employees an ESPP if they wish to make use of it.

Like options, this form of dilution is not as bad for shareholders as a company receives cash in return, unlike both RSUs and PSUs.

Round up

The type of SBC that investors need to be most concerned about is usually RSUs and PSUs. These are dilutive to shareholders and companies do not receive cash back in return.

ESPPs are the least concerning as they usually only result in minimal dilution because of the cap that is imposed on how many shares an employee can purchase in a year, and discount that employees get tends to be small.

Options are also not as concerning as long as the exercise price is reasonably high. Most companies issue options that have an exercise price similar to the market price at the point of grant. However, some companies such as Wise issue options that have exercise prices much lower than market prices. In this case, these options are practically like RSUs and shareholders need to monitor the impact closely.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have a vested interest in Meta Platforms, Netflix, Wise, and Zoom Video Communications. Holdings are subject to change at any time.

Nearly a month ago, I published The Latest Thoughts From American Technology Companies On AI (2023 Q4). In it, I shared commentary in earnings conference calls for the fourth quarter of 2023, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2023’s fourth quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here

Here they are, in no particular order:

Adobe (NASDAQ: ADBE)

Adobe’s management thinks the company is a leader in delivering generative AI and has a highly differentiated approach through the use of proprietary data and by being friendly with intellectual property

We’re a leader in delivering generative AI across all our clouds. We’re taking a highly differentiated approach across data, models, and interfaces. Our proprietary data is built on decades of deep domain expertise across creative, documents and customer experience management. We leverage large language models as well as have invested in building and delivering our proprietary models in the creative, document, and marketing domains. Our IP-friendly approach is a differentiator for creators and enterprises.

Adobe’s management sees an immense market opportunity for the company in AI and that the company is uniquely positioned to capture the opportunity; Adobe’s end-to-end generative AI solution, GenStudio, is already seeing success with entreprises; GenStudio is a generative AI application that helps marketers plan create, store, and deliver marketing content; GenStudio is integrated across Creative Cloud and Experience Cloud

Every student, communicator, creative professional, and marketer is now focused on leveraging generative AI to imagine, ideate, create and deliver content and applications across a plethora of channels. Adobe is uniquely positioned through the combination of Express, Firefly, Creative Cloud, Acrobat, and Experience Cloud to deliver on this immense market opportunity. The success we are already seeing with our GenStudio offering in the enterprise is validation of our leadership, and we expect that success to translate into other segments as we roll out these solutions throughout the year…

…Adobe GenStudio is a generative AI-first application that allows marketers to quickly plan, create, store, deliver, and measure marketing content in a single intuitive offering. With state-of-the-art generative AI powered by Firefly services, marketers can create on-brand content with unprecedented scale and agility to deliver personalized experiences. Adobe GenStudio natively integrates with multiple Adobe applications across Creative Cloud and Experience Cloud, including Express, Firefly, Workfront, Experience Manager, Customer Journey Analytics and Journey Optimizer. It can be used by brands and their agency partners to unlock new levels of creativity and efficiency in marketing campaigns.

Adobe’s management is seeing strong usage, value and demand for its AI solutions across all customer segments

We’re driving strong usage, value and demand for our AI solutions across all customer segments.

Acrobat AI Assistant uses generative AI to summarise long PDFs, answer questions through a chat interface, help with generating emails, reports, and presentations; AI Assistant has strong data security; Adobe’s management is pleased with the English language beta of AI Assistant and Adobe will be releasing other languages later in the year; management will monetise AI Assistant through a monthly add-on for Reader and Acrobat users; management thinks there’s a lot of monetisation opportunity with AI Assistant, including consumption-based monetisation

The world’s information, whether it’s an enterprise legal contract, a small business invoice, or a personal school form, lives in trillions of PDFs. We were thrilled to announce Acrobat AI Assistant, a massive leap forward on our journey to bring intelligence to PDFs. With AI Assistant, we’re combining the power of generative AI with our unique understanding of the PDF file format to transform the way people interact with and instantly extract additional value from their most important documents. Enabled by a proprietary attribution engine, AI Assistant is deeply integrated into Reader and Acrobat workflows. It instantly generates summaries and insights from long documents, answers questions through a conversational interface, and provides an on-ramp for generating e-mails, reports and presentations. AI Assistant is governed by secure data protocols so that customers can use the capabilities with confidence. We’re pleased with the initial response to the English language beta and look forward to usage ramping across our customer base as we release other languages later in the year. We will monetize this functionality through a monthly add-on offering to the hundreds of millions of Reader users as well as the Acrobat installed base across individuals, teams, and enterprises…

…Everyone is looking at AI Assistant in Acrobat. I certainly hope all of you are using it. It should make your lives more effective. Not just for insight, we think that there’s a lot of opportunity for monetization of insight for AI Assistant on our core base of Acrobat users but also, for the first time, doing consumption-based value. So the hundreds of millions of monthly active users of Reader will also be able to get access to AI Assistant and purchase an add-on pack there, too. So it’s a really broad base to look at how we monetize that.

Adobe’s generative AI model for creative work, Adobe Firefly, has been released for around a year and has been integrated into Creative Cloud and within Adobe Express; users of Creative Cloud and Adobe Express have generated >6.5 billion creative assets to-date (was 4.5 billion in 2023 Q3) across images, vectors, designs, and text effects; Firefly has a web-based interface which has seen tremendous adoption; enterprises can now embed Firefly into their own workflow through Firefly Services, which is commercially safe for enterprises to use

Adobe Express is inspiring millions of users of all skill levels to design more quickly and easily than ever before. In the year since we announced and released Adobe Firefly, our creative generative AI model, we have aggressively integrated this functionality into both our Creative Cloud flagship applications and more recently, Adobe Express, delighting millions of users who have generated over 6.5 billion assets to date.

In addition to creating proprietary foundation models, Firefly includes a web-based interface for ideation and rapid prototyping, which has seen tremendous adoption. We also recently introduced Firefly Services, an AI platform which enables every enterprise to embed and extend our technology into their creative production and marketing workflows. Firefly Services is currently powered by our commercially safe models and includes the ability for enterprises to create their own custom models by providing their proprietary data sets as well as to embed this functionality through APIs into their e-mail, media placement, social, and web creation process…

…… The 6.5 billion assets generated to date include images, vectors, designs, and text effects. 

IBM is an early adopter of Firefly and has used it to generate marketing assets much faster than before and that have produced much higher engagement

Early adopters like IBM are putting Firefly at the center of their content creation processes. IBM used Adobe Firefly to generate 200 campaign assets and over 1,000 marketing variations in moments rather than months. The campaign drove 26x higher engagement than its benchmark and reached more key audiences.

Firefly is now available in mobile workflows through the Adobe Express mobile app beta and has a first-of-its-kind integration with TikTok’s creative assistant; the introduction of Firefly for enterprises has helped Adobe win enterprise clients in the quarter

The launch of the new Adobe Express mobile app beta brings the magic of Adobe Firefly AI models directly into mobile workflows. The first-of-its-kind integration with TikTok’s creative assistant makes the creation and optimization of social media content quicker, easier and more effective than ever before…

…  The introduction of Firefly services for enterprises drove notable wins in the quarter, including Accenture, IPG, and Starbucks. Other key enterprise wins include AECOM, Capital Group, Dentsu, IBM, Nintendo, and RR Donnelley.

During 2023 Q4 (FY2024 Q1), Adobe’s management saw the highest adoption of Firefly-powered tools in Photoshop since the release of Generative Fill in May 2023

Generative Fill in Photoshop continues to empower creators to create in new ways and accelerate image editing workflows. Q1 saw the highest adoption of Firefly-powered tools in Photoshop since the release of Generative Fill in May 2023, with customers adopting these features across desktop, web and most recently, iPad, which added Generative Fill and Generative Expand in December.

Adobe’s management expects Adobe’s AI-powered product features to drive an acceleration in the company’s annual recurring revenue (ARR) in the second half of the year; management thinks the growth drivers are very clear

You can expect to see the product advances in Express with Firefly on mobile, Firefly services and AI Assistant in Acrobat drive ARR acceleration in the second half of the year…

…As we look specifically at Creative Cloud, I just want to sort of make sure everyone takes a step back and looks at our strategy to accelerate the business because I think the growth drivers here are very clear. We are focused on expanding access to users with things like Express on mobile. We want to introduce new offers across the business with things like AI Assistant and also existing capabilities for Firefly coming into our core Firefly, our core Photoshop and Illustrator and flagship applications. We want to access new budget pools with the introduction of Firefly services and GenStudio as we talked about…

…And as we enter the back half of the year, we have capabilities for Creative Cloud pricing with Firefly that have already started rolling out late last year as we talked about, and we’ll be incrementally rolling out throughout the year. We’re ramping Firefly services and Express in enterprise. As we talked about, we saw a very good beginning of that rollout at the — toward the end of Q1. We also expect to see the second half ramping with Express Mobile and AI Assistant coming through. So we have a lot of the back-end capabilities set up so that we can start monetizing these new features, which are still largely in beta starting in Q3 and beyond…

…We are very excited about all the innovation that’s coming out, that’s just starting to ramp in terms of monetization and/or still in beta on the Creative Cloud side. We expect that to come out in Q3 and we’ll start our monetization there. So we continue to feel very confident about the second half acceleration of Creative Cloud…

…Usage of Firefly capabilities in Photoshop was at an all-time high in Q1, Express exports more than doubling with the introduction of Express mobile in beta now, going to GA in the coming months, AI Assistant, Acrobat, same pack pattern. You can see that momentum as we look into the back half of the year. And from an enterprise standpoint, the performance in the business was really, really superb in Q1, strongest Q1 ever in the enterprise. So there’s a lot of fundamental components that we’re seeing around performance of the business that give us confidence as we look into the back half of the year.

Adobe’s management believes that the roll out of personalisation at scale has been limited by the ability of companies to create content variations and this is where generative AI can help

Today, rollout of personalization at scale has been limited by the number of content variations you can create and the number of journeys you can deploy. We believe harnessing generative AI will be the next accelerant with Creative Cloud, Firefly services and GenStudio providing a comprehensive solution for the current supply chain and generative experience model automating the creation of personalized journeys.

Adobe’s management believes that AI augments human ingenuity and expands the company’s market opportunity

We believe that AI augments human ingenuity and expands our addressable market opportunity.

Adobe’s management is monetising Adobe’s AI product features in two main ways – via generative packs and via whole products – and they are progressing in line with expectations; management thinks that it’s still early days for Adobe in terms of monetising its AI product features

I think where there’s tremendous interest and where, if you look at it from an AI monetization, the 2 places that we’re monetizing extremely in line with our expectations, the first is as it relates to the Creative Cloud pricing that we’ve rolled out. And as you know, the generative packs are included for the most part in how people now buy Creative Cloud, that’s rolling out as expected. And the second place where we are monetizing it is in the entire enterprise as it relates to Content and GenStudio. And I’m really happy about how that’s monetizing it. And that’s a combination, Brent, of when we go into an enterprise for creation, whether we provide Creative Cloud or a combination of Express, what we are doing with asset management and AEM, Workflow as well as Firefly services to enable people to do custom models as well as APIs. We’re seeing way more monetization earlier, but again, very much in line with expected…

…As it relates to the monetization of AI, I think we’re in early stages as it relates to experimentation. So we’re looking at both what the value utilization is as well as experimentation. The value utilization is actually really positive for us. I think as it relates to the monetization and the experimentation, we have the generative packs, as you know, in Creative Cloud. I think you will see us more and more have that as part of the normal pricing and look at pricing, because that’s the way in which we want to continue to see people use it. I think in Acrobat, as you’ve seen, we are not actually using the generative packs. We’re going to be using more of an AI Assistant model, which is a monthly model. As it relates to the enterprise, we have both the ability to do custom models, which depends on how much content that they are creating as well as an API and metering. We’ve rolled that out and we started to sell that as part of our GenStudio solution.

Adobe’s management pushed out the enforcement of generative AI credit limits beyond April 2024 because Adobe is still in user-acquisition mode for its AI product features

[Question] You pushed out the enforcement of generative credit limits for some products beyond April that were originally expected sooner. What’s the thinking behind this decision? And what are you seeing thus far in terms of credit consumption and purchasing patterns of those credit packs? 

[Answer] In terms of the timing of the — when we start enforcing credits, don’t read anything into that other than right now we are still very much in acquisition mode. We want to bring a lot of users in. We want to get them using the products as much as possible. We want them coming back and using it…

…So right now, look, the primary point is about proliferation and usage. 

Adobe recently released generative AI capabilities in music composition, voice-dubbing, and lip-syncing; these capabilities will require a lot of generative AI credits from users

 In the last few weeks, we’ve done a couple of sneaks that could also be instructive. Last month, we snuck music composition where you can take any music track, you can give it a music type like hip-hop or orchestra or whatever, and it will transform that initial track into this new type of music. Just this morning, we snuck our ability to do auto-dubbing and lip-syncing where you give it a video of someone talking in, say, English and then you can translate it automatically to French or Spanish or Portuguese or whatever. As you can imagine, those actions will not take 1 credit. Those actions will be much more significant in terms of what they cost.

Adobe’s management thinks that developments in generative AI models for video, such as the recent release of Sora by OpenAI, are a net positive for Adobe; Adobe is also developing its own generative AI video models and will be releasing them later this year

[Question] Clearly, a lot of news around video creation using generative AI during the quarter, of course, with the announcement of Sora. Maybe the question for you folks is can we just talk a little bit about how you think about the market impact that generative AI can have in the video editing market and how maybe Firefly can participate in that trend?

[Answer] So really great advances, but net-net, video is going to be even more of a need for editing applications in order to truly take advantage of generative AI…

…We see the proliferation of video models to be a very good thing for Adobe. We’re going to work with OpenAI around Sora. You’re going to see us obviously developing our own model. You’re going to see others develop their model. All of that creates a tailwind because the more people generate video clips, the more they need to edit that content, right? So whether it’s Premier or After Effects or Express, they have to assemble those clips. They have to color correct those clips. They have to tone-match. They have to enable transitions. So we’re excited about what we’re building, but we’re just as excited about the partnerships that we see with OpenAI and others coming down this path. And if you take a step back, you should expect to see more from us in the weeks ahead with imaging and vector, design, text effects, in the months ahead with audio and video and 3D. We’re very excited about what all of this means, not just for the models, but for our APIs and our tools.

Adobe’s management thinks that Adobe is in a great position to attract talent for technical AI work because they believe that the company has one of the best AI research labs and can provide access to AI computing hardware; management also thinks that Adobe is in a great position to attract talent for AI sales

And so that’s not just an Adobe perspective, but it’s playing out, obviously, in the enterprises as they look at what are the models that they can consider using for production workflows. We’re the only one with the full suite of capabilities that they can do. It’s a really unique position to be in. But it’s also being noticed by the research community, right? And as the community starts looking at places, if I’m a PhD that wants to go work in a particular environment, I start to ask myself the question of which environment do I want to pick. And a lot of people want to do AI in a responsible way. And that has been a very, very good opportunity for us to bring in amazing talent. So we are investing. We do believe that we have the best — one of the best, if not the best, research labs around imaging, around video, around audio, around 3D, and we’re going to continue to attract that talent very quickly. We’ve already talked about we have the broadest set of creative models for imaging, for vector, for design, for audio, for 3D, for video, for fonts and text effects. And so this gives us a broad surface area to bring people in. And that momentum that starts with people coming in has been great.

The second part of this, too, is managing access to GPUs while maintaining our margins. We’ve been able to sort of manage our cost structure in a way that brings in the talent and gives them the necessary GPUs to do their best work…

…Regarding the sales positions in enterprises. In enterprise, we’re in a strong position because what we — this area of customer experience management, it remains a clear imperative for enterprise customers. Everybody is investing in this personalization, at scale and current supply chain. These help drive both growth and profitability. So when you look at these areas, these, from an enterprise perspective, these are a must-have. This is not a need-to-have. And that’s helping us really attract the right kind of talent. We just onboarded, this week, a VP of Sales who had prior experience, a lot of experience in Cisco and Salesforce, et cetera. 

Adobe’s management believes that Adobe’s tools will be in demand even in an AI dominated world and will not be automated away

[Question] I just wanted to give you an opportunity to debunk this hypothesis that is going around that AI, it is generating videos and pictures, but the next step is, it’s going to do the actual editing and put out Premier Pro use or whatnot. So that is probably the existential threat that people are debating.

[Answer] So as it relates to generative content, I’m going to sort of break it up into 2 parts. One is around the tooling and how you create the content and the second is around automation associated with the content…

…So I think the core part of this is that as more of this content creates, you need more toolability, the best models are going to be the models that are safe to use and have control built-in from the ground up. And I think we have the best controls of anyone in the industry. And they need to be able to be done in an automated fashion that can embed into your workflow. 

Adobe’s management believes that as generative AI models proliferate in society, the demand for Adobe’s products will increase partly because there will be a rise in the number of interfaces that people use for creative content

I think the first question that I hear across many folks is, hey, with the advent of AI and the increase in the number of models that people are seeing, whether they be image models or video models, does that mean that the number of seats, both for Adobe and in the world, do they increase? Or do they decrease? To me, there’s no question in my mind that when you talk about the models and interfaces that people will use to do creative content, that the number of interfaces will increase. So Adobe has to go leverage that massive opportunity. But big picture, models will only cause more opportunity for interfaces. And I think we’re uniquely qualified to engage in that, so that’s the first one.

Adobe’s management wants Adobe to work with many different AI models, even those from third-parties

Do we only leverage the Adobe model? Or is there a way in which we can leverage every other model that exists out there? Much like we did with plug-ins, with all of our Creative applications, any other model that’s out there, we will certainly provide ways to integrate that into our applications, so anybody who’s using our application benefits not just from our model creation but from any other model creation that’s out there…

…But long term certainly, as I’ve said with our partnerships, we will have the ability for Adobe, in our interfaces, to leverage any other model that’s out there, which again further expands our opportunity.

MongoDB (NASDAQ: MDB)

MongoDB’s management thinks that AI will be a long-term growth driver for the company, but it’s still early days; management sees three layers to the AI stack – the first being compute and LLMs (large language models), the second being fine-tuning the models, and the third being the building of AI applications – and most of the AI spend today is at the first layer where MongoDB does not compete; MongoDB’s customers are still at the experimentation and prototyping stages of building their initial AI applications and management expects the customers to take time to move up to the second and third layers; management believes that MongoDB will benefit when customers start building AI application

While I strongly believe that AI will be a significant driver of long-term growth for MongoDB we are in the early days of AI, akin to the dial-up phase of the Internet era. To put things in context, it’s important to understand that there are 3 layers to the ad stack. The first layer is the underlying compute and LLMs the second layers of fine-tuning of models and building of AI applications. And the third layer is deploy and running applications that end users interact with. MongoDB’s strategy is to operate at the second and third layers to enable customers to build AI applications by using their own proprietary data together with any LLM, closed or open source on any computing infrastructure.

Today, the vast majority of AI spend is happening in the first layer that is investments in compute to train and run LLM, neither are areas in which we compete. Our enterprise customers today are still largely in the experimentation and prototyping stages of building their initial AI applications, first focused on driving efficiencies by automating existing workflows. We expect that will take time for enterprises to deploy production workloads at scale. However, as organizations look to realize the full benefit of these AI investments, they will turn to companies like MongoDB, offering differentiated capabilities in the upper layers of the stack. Similar to what happened in the Internet area, era when value accrued over time to companies offering services and applications, leveraging the built-out Internet infrastructure, platforms like MongoDB will benefit as customers build AI applications to drive meaningful operating efficiencies and create compelling customer experiences and pursue new growth opportunities…

…While it’s early days, we expect that AI will not only support the overall growth of the market, but also compel customers to revisit both their legacy workloads and build more ambitious applications. This will allow us to win more new and existing workloads and to ultimately establish MongoDB as a standard enterprise accounts. 

MongoDB’s management is already seeing the company’s platform resonate with AI startups that are building applications across wide use cases, and this gives management confidence that MongoDB is a good fit for sophisticated AI workloads

We already see our platform resonating with innovative AI startups building exciting applications for use cases such as real-time patient diagnostics for personalized medicine, cyber threat data analysis for risk mitigation, predictive maintenance for maritime fleets and auto generated animations for personalized marketing campaigns…

…we do see some really interesting start-ups who are building on top of MongoDB. So it gives us confidence about our platform fit for these sophisticated workloads. 

There are three elements that are important when migrating from a relational database to a non-relational database and MongoDB’s current relational migrator offering helps automate the first two elements; the third element – rewriting the application code – is manually intensive and management believes that generative AI can help to tremendously improve the experience there

There are 3 elements to migrating and application to transform the schema, moving the data and rewriting the application code. Our current relational migrator offering is designed to automate large parts of the first 2 elements, but rewriting application code is the most manually intensive element. Gen AI holds tremendous promise to meaningfully reduce the cost and time of rewriting application code. We will continue building AI capabilities into relational migrator, but our view is that the solution will be a mix of products and services.

Samsung Electronics’ Digital Appliances division migrated from its previous MySQL database to MongoDB Atlas; the Samsung Smart Home Service can leverage MongoDB’s document database model to collect real-time data for training AI services; the migration improved response times by >50% and the latency was reduced from 3 seconds to 18 milliseconds

Samsung Electronics Digital Appliances division transitioned from their previous MySQL database to MongoDB Atlas to manage clients’ data more effectively. By leveraging MongoDB’s document model, Samsung’s Smart Home Service can collect real-time data from the team’s AI-powered home appliances and use it for a variety of data-driven initiatives such as training AI services. Their migration to MongoDB Atlas improved response times by more than 50% and this re-latency was reduced from 3 seconds to 18 millisecond, significantly improving availability and developer productivity.

MongoDB’s management believes that the performance and cost of AI applications are still not up to mark, using ChatGPT as an example

And also these technologies maturing, but from both the performance and from a cost point of view, if you played with chat GPT or any of the other chatbots out there or large language models, you’ll know that the performance of these applications to get a response time in the 1 to 2 to 3 seconds, depending on the type of question you’re asking. And so naturally, a chatbot is a very simple but easy to understand use case, but to embed that technology into a sophisticated application, making real-time decisions based on on real-time data, the performance and to some degree, the cost of these architectures are still not there…

…The performance of some of these systems is — I would classify as okay, not great. The cost of inference is quite expensive. So people have to be quite careful about the types of applications they deploy.

MongoDB’s management thinks that this year is when MongoDB’s customers start rolling out a few AI applications and learn; it will be at least another year when the positive impacts of AI to MongoDB’s business really starts showing up

Cstomers are still in the learning phase. They’re — they’re experimenting, they’re prototyping. But I would say you’re not seeing a lot of customers really deploy AI applications at scale. So I think it’s going to take them — I would say, this year is a year where they’re going to do probably roll out a few applications, learn…

… I think it’s going to show up in a business when people are deploying AI apps at scale, right? So I think that’s going to be at least another year.

MongoDB’s management believes that the company is very well-positioned to capture AI application workloads because of the technologies underneath its platform and because it is capable of working with a wide range of AI models

We feel very good about our positioning because from an architecture point of view, the document model, the flexible schema, the ability to handle real-time data, performance at scale, the unified platform, the ability to handle data, metadata and vector data with the same query language, same semantics, et cetera, is something that makes us very, very attractive…

… We feel like we’re well positioned we feel that people really resonate with the unified platform, one way to handle data, metadata and vector data that we are open and composable that we integrate to not only all the different LLMs, we are integrated to different embedding models, and we also essentially also integrate with some of the emerging application frameworks that developers want to use. So we think we’re well positioned

MongoDB’s management is seeing that AI-related decisions are being made at the senior levels of a company, and so MongoDB is engaging with customers at that senior level

The other thing that we’re finding is unlike a typical sale where someone is deciding to either build a new workload or modernize a workload. The AI decision is more of a central decision — centralized decision more than ever. So it allows us to actually go higher in the organization. So we’re actually engaging with customers at much more senior levels because, obviously, this is coming down as a top-down initiative.

MongoDB’s management is seeing the first few wave of AI use cases among companies being focused on reducing costs, co-generation, and increasing developer productivity

In regards to use cases, we’re seeing most customers focus on driving efficiencies in their business because their existing baseline of costs are well known. So it’s much easier for them to determine how much value they can derive by using some of these new AI technologies. So I see the first wave of applications being around reducing costs. You’ve seen some announcements by some customers are saying, focusing on things like customer support and customer service, they really have been — they have found ways to dramatically reduce their cost. That’s not surprising to me. I think co-generation and this increasing developer productivity is another area. I think those are going to be kind of 2 areas where there’s low-hanging fruit. 

MongoDB’s management is seeing high interest in AI across almost every industry

In terms of across industries, I think it’s — obviously, there’s some constraints on some customers based on the regulated nature of their industry. But in general, we see basically high interest across almost every industry that we operate in.

Customers migrate off relational databases to MongoDB for three key reasons – (1) their data model has become brittle with relational architecture, (2) their legacy systems are not scaling properly, and (3) the costs have become high – and they are accompanied by a compelling event; customers also conduct the migration to make their data more AI-enabled

Even for IPO, we had a meaningful number of customers migrating off relational to MongoDB. So they to come in 3 categories of reasons why. First is that the data model has become so brittle with relational architecture that is very hard to build new features and be responsive to their customers. And so they just feel like their ability to innovate has slowed down. The second reason is that the system is just not scaling or performing given the increased number of users or the large amount of data that they have to process that they realize that they have to get off a legacy platform. And the third reason is just the cost of the underlying platform and relative to the ROI that application. So it typically falls in one of those three buckets. Sometimes customers may have all 3 or maybe 2 of the 3 that are driving that demand. And then there’s typically some compelling event. Maybe there’s some milestones they want to hit. Maybe there’s a renewal coming up with the incumbent vendor that’s driving them to potentially move off that vendor as quickly as possible…

… On top of the 3 reasons I gave you in terms of why people moved this now in the both reason which is enabling their data and their applications to be more AI-enabled. And so it’s not just moving to a more modern platform, but making them more AI enabled. And so that’s also something that’s getting customers’ interest.

Okta (NASDAQ: OKTA)

Okta’s management has built a strong pipeline of products that are powered by Okta AI

We’re expanding on the world’s most robust and modern identity platform, and we have a strong pipeline of products and functionality powered by Okta AI.

Okta’s management believes that Spera (the company’s new acquisition) is going to help with threat protection; threat protection with Okta AI and the Spera product will be packaged and monetised as add ons

And so you’re seeing customers really starting as they lean in and do more with modern identity, they’re also at the same time saying, what is this class of tools and technologies and capabilities are going to protect that? And that’s where offerings like Underneath Threat Protection with Okta AI or the Spera product are really going to help. And so I think in terms of how we’re going to price and package and monetize these things, think of — they’re both additional, they’re both additional capabilities with additional licensing fee. 

Salesforce (NYSE: CRM)

Salesforce’s management believes that the company is the world’s No.1 AI CRM

Salesforce is the world’s #1 AI CRM, #1 in sales, #1 in service, #1 in marketing, #1 data cloud, incredible.

In Salesforce’s management’s conversations with CEOs, they are hearing three things that the CEOs want – productivity, higher value customer relationships, and higher margins – and these things can happen through AI; Salesforce’s management thinks that company-leaders know they need to make major investments in AI right now

As I talk to CEOs around the world, they tell me, they want 3 things. You may have heard me say this already, but I’ll say it again. One, they want more productivity, and they’re going to get that productivity through the fundamental augmentation of their employees through artificial intelligence. It’s happening. It’s empirical. Number two is they want higher value customer relationships, which is also going to happen through this AI. And they want higher margins, which we are seeing empirically as well through the — when they use this artificial intelligence in these next-generation products. As we look at productivity, as we look at higher value customer relationships, as we look at higher margins, how do our customers get these things? How are they achieving these goals? It is AI. It is why every CEO and company knows they need to make major investments in AI right now.

Salesforce’s management thinks that the current AI moment will give companies an unprecedented level of intelligence and Salesforce’s Einstein 1 platform can help companies achieve this

And I believe this is the single most important moment in the history of the technology industry. It’s giving companies an unprecedented level of intelligence that will allow them to connect with their customers in a whole new way.  And with our Einstein 1 Platform, we’re helping out our customers transform for the AI future.

Salesforce’s management thinks that popular AI models are not trusted solutions for enterprises because they are trained on amalgamated public data and could hallucinate, providing customers with services that do not exist; this was the exact problem faced by an airline recently, and the airline was a Salesforce customer who did not want to work with Salesforce’s AI technologies

The truth is that these AI models are all trained on amalgamated public data. You all understand that. You’ve all seen the New York Times lawsuit of OpenAI or others who are really going to take, hey, this is all — this amalgamated stolen public data, much of it used without permission, unlicensed, but amalgamated into these single consolidated data stores…

… These AI models, well, they could be considered very confident liars, producing misinformation, hallucinations. Hallucinations are not a feature, okay?…

…And there’s a danger though for companies, for enterprises, for our customers that these are not trusted solutions. And let me point out why that is, especially for companies who are in regulated markets. Why this is a big, big deal. These models don’t know anything about the company’s customer relationships and, in some cases, are just making it up. Enterprises need to have the same capabilities that are captivating consumers, those amazing things, but they need to have it with trust and they need to have it with security, and it’s not easy. Look, we all read the story. Now it just happened last week. An airline chatbot prompts by a passenger to book a flight with a 90-day refund window. It turns out the chatbot, running on one of these big models, we won’t have to use any brand names here. We all know who it was, hallucinate the option. It did not exist… 

…The airline said, “Oh, listen, that was just the chatbot. It gets that way some time. We’re so sorry — you know what, that’s just a separate technical entity, a separate legal entity and the airline, “We can’t — we’re not going to hold liability for that.” Well, guess what? That defense did not work in a court of law. The court of law said that, that AI chatbot that made up that incredible new policy for that company, well, that company was going to be held responsible, liable for that policy, that they were going to be held liable for the work of that chatbot. Just as they would for a human employee, they were being held liable for a digital employee…

…And that story I told you on the script. When I saw that last week, I’m like, I’m putting this in the script, that this company, which is a great company and a customer of ours, but did not use our technology, went out there and used some kind of rogue AI that they picked off the Internet. Some engineer just hobbled it, hooked it up, and then it started just skewing these hallucinations and false around their loyalty program, and the courts are holding them liable. Good. Let every CEO wake up and realize, we are on the verge of one of the greatest transformations in the history of technology, but trust must be our highest value.

Salesforce’s management believes that there are three essential components for enterprises to deliver trusted AI experiences, (1) a compelling user interface, (2) a high-quality AI model, and (3) data and metadata; management thinks that Salesforce excels in all three components; management has found that Salesforce customers who are experts on AI have realised that it is the integration of AI models with data and metadata that is the important thing in powering AI experiences, and this is why customers are turning to Salesforce

The reality for every enterprise is that to deliver trusted AI experiences, you need these 3 essential components now.

You need that compelling user interface. There’s no question, a natural and effortless experience. And at Salesforce, we have some of the most intuitive user interfaces that deliver insights and intelligence across sales and service and marketing and commerce and industries. Many of you are on Slack right now. Many of you are on Tableau. Many of you are on MuleSoft are, one of our other products.

Okay. Now what else do you need? Number two, you need a world-class AI model. And now we know there’s many, many models available. Just go to hugging face, which is a company that we’re investor in or look at all the other models. And by the way, not only the thousands of models right now, but there are tens of thousands, hundreds of thousands of models coming. And all the models that are available today will be obsolete 12 months from now. So we have to have an open, extensible and trusted framework inside Salesforce to be receptacles for these models. That’s why Einstein 1 is so important. Then you have to be able to use these AI models. The ones that Salesforce is developing or these public models on Hugging Face or other things, or even bring your own model. Customers are even making their own models, fantastic. Of course, we have great partnerships with OpenAI, with Mythropic, with Cohere with many other AI models. This is the second key component. One is the UI, the second is the model, all right?…

…Now we’re in the enterprise. In the enterprise, you need deep integration of data and metadata for the AI to understand and deliver the critical insights and intelligence that customers need across their business, across sales, service, marketing, commerce, whatever it is. That deep integration of the data and metadata that’s not so easy. That’s not just some amalgamate stolen public data set. In the enterprise, that deep integration of data and metadata. Oh, that’s what Salesforce does. We are a deep integration of data and metadata. That is why it’s very, very exciting…

…And they try to stitch together a variety of AI tools and copilots and this and that and whatever I’ve had so many funny conversations with so many customers that come to me that they’re experts in AI and their. And then I just say to them, but how are you going to deliver this experience? And then finally, they realize, “Oh, I need the deep integration with the data and the metadata. The reason why the metadata is so important is because it describes the data. That’s why so many companies are turning to Salesforce for their AI transformation. Only Salesforce offers these critical layers of AI for our customers, the UI, the model and the deep integration of the data and the metadata make the AI smart and intelligent and insightful. And without the hallucinations and without all of these other — all the other problems. For more than 2 decades, we’ve been trusted with our customers’ data and metadata. And we have a lot of it. 

Salesforce’s management believes that most AI models that are available today will be obsoleted in 12 months’ time, and that a flood of new AI models will be coming soon – because of this, it’s important that Salesforce needs to have an open, extensible framework to work with all kinds of models, and this is where Einstein 1 has an important role to play

And by the way, not only the thousands of models right now, but there are tens of thousands, hundreds of thousands of models coming. And all the models that are available today will be obsolete 12 months from now. So we have to have an open, extensible and trusted framework inside Salesforce to be receptacles for these models. That’s why Einstein 1 is so important.

Salesforce’s management believes that data is even more important for AI than chips, and this is why management is so excited about Salesforce: Because the company has one of the largest data repositories in the world for its customers

 I love NVIDIA, by the way, and what Jensen has done is amazing, and they are delivering very much. In the era of the gold rush, the Levi’s jeans to the gold miners. But we all know where the gold is: the data. The gold is the data. And that’s why we’re so excited about Salesforce because we are one of the very largest repositories of enterprise data and metadata in the world for our customers. And customers are going to start to realize this right now…

…For more than 2 decades, we’ve been trusted with our customers’ data and metadata. And we have a lot of it.

There is a lot of trapped data in Salesforce’s customers which is hindering their AI work; Salesforce’s Data Cloud helps to integrate all the disparate data sources, and it is why the service is Salesforce’s fastest-growing product ever; Data Cloud is now integrated across the entire Salesforce platform, and management is totally focused on Data Cloud in FY2025; using Data Cloud adds huge value for customers who are using other Salesforce services; Data Cloud and Einstein 1 are built on the same metadata framework – which allows customer apps to securely access and understand the data that is on Salesforce’s platform – and this prevents hallucinations and it is something only Salesforce can do

Many of our customers also have islands and thousands of systems of trapped data…

… Trap data is all over the enterprise. Now what trap data could be is you might be using a great company like Snowflake and I less Snowflake or Databricks or Microsoft or you might be using Amazon system or even something like Google, what do you say, BigQuery, all these various databases…

…if you’re using Salesforce, Sales Cloud, Service Cloud, Tableau, Slack, we need to be able to, through our zero copy, automatically integrate into our data cloud, all of those systems and then seamlessly provide that data back into these amazing tools. And that is what we are doing because so many of our customers have islands of trapped data in all of these systems, but the AI is not going to work because it needs to have the seamless amalgamated data experience of data and metadata, and that’s why our Data Cloud is like a rocket ship.

The entire AI revolution is built on this foundation of data, and it’s why we’re so excited about this incredible data cloud. It’s now deeply integrated into all of our apps into our entire platform. Its self-service for all of our customers to turn on. It is our fastest-growing product ever. It’s our total focus for fiscal year ’25.

With Salesforce Data Cloud, Salesforce can unlock this trap data and bring together all of their business and customer data into one place for AI, all while keeping their data safe and secure, and it’s all running inside our Einstein Trust layer, and we’ve deployed it to all of our customers, we unleash now the copilot as well to all of our customers deeply built on our pilot on our data and metadata. And while other copilots just sit and spin because they can’t figure out what the data means and if you haven’t seen the demonstrations, you can see these co-pilots spin, but when they use Salesforce and all of a sudden becomes intelligent, and that is the core of the NSN platform. And all of our apps, all of our AI capabilities, all of the customer data and 1 deeply integrated trusted metadata platform, and that’s why we’re seeing incredible demand for data cloud. Data Cloud brings it all together…

…We’ve never seen traction like this of a new product because you can just easily turn on the Data Cloud and it adds huge value to Sales Cloud. It adds huge value to Service Cloud, the Marketing Cloud and the CDP…

… Because Data Cloud and all of Einstein 1 is built on our metadata framework, as I just described, every customer app can securely access and understand the data and use any bottle, use an EUI workflow, integrate with the platform. That means less complexity, more flexibility, faster innovation, but also we want to say goodbye to these hallucinations. We want to say goodbye to all of these crazy experiences or having with these bots that don’t know what they’re doing because they have no data or metadata, okay? Or the data that they have metadata is like productivity data like the highest level data that’s not deeply integrated data. So only Salesforce can do this.

Payroll company ADP has been a long-time customer of Salesforce but wanted to evaluate other AI solutions; ADP realised that the data and metadata component was lacking in other AI solutions and it is something only Salesforce can provide

We all know the HR and payroll leader, ADP and their incredible new CEO, [indiscernible], amazing. ADP has been a great sales cloud customer for 2 decades. They’ve used Einstein for years. They are one of the first customers we ever have…

…And the company wanted to transform now customer service with AI to give their agents real-time insights, next best actions, auto generating case summaries. But what I have to say to you, it was a little bit embarrassing Salesforce is not #1 on their list. And I said to them, “How can that be? We’re the #1 service cloud. We’re #1 in the Q. We’re #1 in this. Number went work.” “No, we’re going to go evaluate this. We’re going to look at all the different solutions — we’re going to look at all the new AI models. We think we’re just going to hook this model up to this, and we’re going to do that.” And it sounds like a big Rube Goldberg invention what was going to happen there. And so we had to go in and we just wanted to partner with them and say, “All right, show us what you want to do. We’re going to work with you, we’re going to be trusted partners. Let’s go.” 

But like a lot of our customers move into AI, ADP realized it didn’t have a comprehensive deeply integrated platform of data and metadata that could bring together all of this into a single source of truth — and then you get the incredible customer service. Then you get the results that you’re looking for. And it’s deeply integrated with their sales systems with marketing and custom applications. And ADP discovered the only sales force can do this. We were able to show ADP how we could unlock trap data with data cloud, 0 copy, drive intelligence, productivity, efficiency for their sales team with Einstein to levels unimagined just a year ago

Salesforce has a new copilot, Einstein Copilot, which management believes is the first conversational AI assistant the is truly trusted; Einstein Copilot can read across all the data and metadata in Salesforce’s platform to surface valuable sales-actions to take, and that is something human users cannot do; management believes that other copilots cannot do what Einstein Copilot can currently can without deep data integration; management thinks that Einstein Copilot is a cut above other copilots

We’re now incredibly excited to work with all of our customers to take their AI to the next level with Einstein copilot, which is going live tomorrow. Einstein CoPilot, which if you haven’t seen it, and if you haven’t, please come to TrailheadDx next week. This is the first conversational AI assistant for the enterprise that’s truly trusted. It’s amazing. It can answer questions. It can summarize. It can create new content, dynamically automate task on behalf of the user. From the single consistent user experience embedded directly within our platform. 

But let me tell you the 1 thing that can do that’s more important than all of that. It is able to read across all the data and metadata in our platform to get that insight instantly. And you’re going to see that — so the sales rep might ask the Einstein CoPilot, what lead I should focus on or what is the most important thing I need to do with this opportunity. And it may say, you need to resolve this customer’s customer case because this escalation has been around for a week or you better go and answer that lead that came in on the Marketing Cloud before if you want to move this opportunity for it because it’s reading across the entire data set. That is something that individual users cannot do that the copilot can do. With access to customer data and the metadata and sales force, including all this real-time data and website engagement and the ability to read through the data set, that’s why Einstein copilot has all the context to understand the question and surface belied that has the highest value and likelihood to convert. And it can also instantly generate the action plan with the best steps to close the deal, such as suggesting optimal meeting times on the lead contacts, known preferences even draping e-mail. If you haven’t seen the video that I put on my Twitter feed last night, there’s a 5-minute video that goes through — all of these incredible things that it’s able to do, there’s never been an enterprise AI capability quite like it. It’s amazing…

… I assure you, without the deep integration of the day of the metadata across the entire platform within copilots deep integration of that data, they cannot do it. I assure you they cannot because they cannot. — because they don’t have the data on the meta data, which is so critical to making an AI assistant so successful…

And I encourage you to try the demos yourself to put our copilot up against any other copilot. Because I’ll tell you that I’ve seen enterprise copilots from these other companies and actions and they just spend and spin and spin…

…I’ve used those copilots from the competitors, have not seen them work yet….

…Einstein is the only copilot with the ability to truly understand what’s going on with your customer relationships. It’s one conversational AI assistant, deeply connected to trusted customer data and metadata.

Einstein 1 is driving sales price uplift in existing Salesforce customers, while also attracting new customers to Salesforce; Salesforce closed 1,300 Einstein deals in FY2024; Einstein 1 has strong early signs after being launched for just 4-plus months

In fact, we continue to see significant average sales price uplift from existing customers who upgrade to Einstein 1 edition. It’s also attracting new customers to Salesforce, 15% of the companies that purchased our Einstein 1 addition in FY ’24 were net new logos…

… In FY ’24, we closed 1,300 Einstein deals, as more customers are leveraging our generative and predictive AI capabilities…

…. I think the way to think about the price uplift moving to Einstein 1 addition used to be a limited edition plus, is really about the value that we’re providing to our customers because at the end of the day, our ability to get increased price is about the value that we’re going to provide. And so as customers start to ramp up their abilities on AI, ramp up their learnings and understand what it means for them economically, our ability to get price will be dictated by that. Early signs of that are pretty strong. We feel good about the progress we’ve seen. It’s only been in market for 4-plus months now in FY ’24, but we’re encouraged by what we’re seeing.

Slack now comes with AI-search features; Salesforce’s management thinks Slack can become a conversational interface for any application

We just launched SlackAI with features like AI search channel recaps and thread summaries to meet the enormous demand for embedded AI in the flow of work from customers like Australian Post and OpenAI. It’s amazing to see what Slack has accomplished in a decade. And frankly, it’s just the beginning, we have a great vision for the future of Slack as a conversational interface for any application. 

Bajaj Finance in India is using Einstein for AI experiences and in 2023 Q4, Bajaj become Salesforce’s second largest Data Cloud customer globally

India continues to be a bright spot for us, growing new business at 35% year-over-year, and we continue to invest in the region to meet the needs of customers, including Bajaj Finance. I had the great opportunity to meet with our CEO, Rajeev Jain in January, and a top priority for him was using Einstein to deliver predictive and generative AI across their entire lending business, which they run on Salesforce. In Q4, Bajaj became the second largest data cloud customer globally, building their AI foundation on the Einstein One platform

Salesforce’s management would be very surprised if other companies can match Salesforce’s level when it comes to AI

Because if you see anyone else being able to deliver on the promise of enterprise AI at the level of quality and scale and capability of Salesforce, I’ll be very surprised. 

Salesforce is deploying its own AI technologies internally and management is seeing the benefits

We are a big believer on sales on Salesforce. We are deploying our own AI technology internally. Our sales teams are using it. Absolutely, we are seeing benefits right now. But the biggest benefit we’ve seen actually has been in our support operation, with case summaries our ability to get — to tap in a knowledge base is faster to get knowledge surfaced within the flow of work. And so it absolutely is part of our margin expansion strategy going forward, which is how do we leverage our own AI to drive more efficiencies in our business to augment the work that’s being done in sales and in service and in marketing and even into our commerce efforts as well…

…We have to be customer #1 and use it, and I’m excited that we are.

Tencent (NASDAQ: TCEHY)

Tencent’s management thinks that its foundational AI model, Tencent Hunyuan, is now among the best large language models in China and worldwide; Hunyuan excels in multiturn conversations, logical inference and numerical reasoning; Hunyuan has 1 trillion parameters; Hunyuan is increasingly used by Tencent for co-pilot services in the company’s SaaS products; management’s focus for Hunyuan is on its text-related capabilities, especially text-to-video

Our Tencent Hunyuan foundation model is now among the top tier of large language model in China with a notable strength in advanced logical reasoning…

… After deploying leading-edge technologies such as the mixture of experts (MoE) architecture, our foundation model, Tencent Hunyuan, is now achieving top-tier Chinese language performance among large language models in China and worldwide. The enhanced Hunyuan excels particularly in multiturn conversations, logical inference and numerical reasoning, areas which has been challenging for large language models. We have scaled the model up to the 1 trillion perimeter mark, leveraging the MoE architecture to enhance performance and reduce inference costs, and we are rapidly improving the model text to picture and text to video capabilities. We’re increasingly integrating Hunyuan to provide co-pilot services from enterprise SaaS products, including Tencent Meeting and Tencent Docs…

…Among our enterprise Software-as-a-Service products, we deployed AI for real-time content comprehension in Tencent Meeting, deployed AI for prompt based document generation Tencent Docs and rolled out a paid customer acquisition tool for eCom…

… At this point in time, we are actually very focused on the text technology because this is actually the fundamentals of the model. And from text, we have built out text to picture from text, we build out text to video capabilities. And the next important evolution is actually what we have seen with [indiscernible], right? [indiscernible] has done an incredible job with text to a [ long ] video, and we — this is something which we would be developing in [ the next turn ]. When we continue to improve the text fundamental capability of Hunyuan, at the same time, we’ll be developing the text to video capability because we actually think that this is actually very relevant to our core business, which is a content-driven business in the area of short video, long video and games. And that’s the area in which we’ll be developing and moving our Hunyuan into. 

Tencent’s management is developing new generative AI tools for internal content production; management thinks that the main benefits of using AI for internal content production is not to reduce costs, but to enable more rapid monetisation and thus, higher revenue generation

 And we are also developing new gen AI tools for effective content production internally…

…We are increasingly going to be deploying AI, including generative AI in areas such as accelerating the creation of animated content, which is a big business for Tencent Video and a profitable business for Tencent Video in terms of game content, as we discussed earlier, potentially in terms of creating [ code ] in general. But the benefit will show up, not in the substantial cost reductions. It will show up in more rapid content creation, and therefore, more rapid monetization and revenue generation.

Tencent’s management is starting to see significant benefits to Tencent’s business results from deploying AI technology in the company’s businesses; the benefits are particularly clear in Tencent’s advertising business, especially in the short-term; Tencent has seen a 100% increase in click-through rates in the past 18 months in its advertising business through the use of AI

More generally, deploying AI technology in our existing businesses have begun to deliver significant revenue benefits. This is most obvious in our advertising business, where our AI-powered ad tech platform is contributing to more accurate ad targeting, higher ad click-through rates and thus, faster advertising revenue growth rates. We’re also seeing early stage business opportunities in providing AI services to Tencent Cloud customers…

…In terms of the AI short-term benefits, I think financial benefits should be much more indexed towards the advertising side because if you think about the size of our advertising business as call it RMB 100 billion [ a year ]. And if you can just have a 10% increase, right, that’s RMB 10 billion and mostly on profit, right? So that’s the scale of the benefits on the advertising side and especially as we see continued growth of our advertising business and when we add in the Video Accounts e-commerce ecosystem, that just has a very long track of growth potential and also the low ad load right now within Video Accounts.

But on the other hand, if you look at the cloud and business services customers, then you are really facing a relatively nascent market. You still have to sell to these customers. And we spend a lot of time working with all the customers in different industries and trying to figure out what’s the best way of leveraging AI for their business. And then you have to go through a long sales cycle. And then at the same time, it’s competitive because your competitors will actually come in and say, “Oh, they can also provide a similar service.” And despite we believe we have a superior technology and product, it’s actually [ very careful ] and your competitor may actually sort of come in and say they’re going to cut prices, even though there’s an inferior product.

So all these things, all the low-margin, highly competitive and long sales cycle of the 2B business would actually come in to play in that side of the business. So when you compare the two sides of the equation, you can actually clearly see that ramping up advertising is actually going to be much more profitable from the short term. Of course, we’ll continue to do both, right?…

… Martin gave the example of if we can improve click-through rates by 10%, then that’s CNY 10 billion in incremental revenue, probably CNY 8 billion in incremental gross operating profit. In reality, you should view 10% as being in the nature of a floor, not a ceiling. Facebook has seen a substantially bigger improvements in click-through rates for some of our most important inventories, we’ve actually seen our click-through rates increase by 100% in the past 18 months. So when we’re thinking about where the financial benefits of AI, then it’s advertising, click-through rates and therefore, advertisement revenue first and foremost, and that’s a very high flow-through business for us.

Tencent’s management believes that AI technology can be applied in its games business in terms of creating innovative gameplay as well as generating content in existing games, but these will take some time to manifest

In terms of the application of AI to games, then like many things, the boundary between [indiscernible] reality is a function of how far forward [indiscernible] willing to look and [ we’re willing to look very far ] forward. And all of the areas you mentioned, such as AI-powered [ MPCs ], such as AI accelerated graphical content generation, graphical asset generation are areas that [ over for years ] to come, not over the months to come will benefit meaningfully from the deployment of AI. And I think it’s also fair to say that the game industry has always been a mixture of, on the one hand, innovation around gameplay techniques. And on the other hand, deployment of enhanced content — renewed content into existing gameplay. And it’s reasonable to believe that AI will be most beneficial for the second of those activities. But one will continue to require very talented individuals and teams due to focus on the first of those opportunities, which is the creation of innovative game play.

Veeva Systems (NYSE: VEEV)

Veeva’s management has seen very specialised AI models being used for some time – prior to the introduction of large language models to the consumer public – to help with drug discovery, especially in areas such as understanding protein folding

[Question] what are you seeing out of life sciences companies in terms of how AI is changing things. Whether that’s accelerating drug development, whether that’s more targeted marketing, maybe if you could walk us through kind of what those conversations would look like? And what sort of role you think you can play in those changes?

[Answer] I would say the most direct impact and it’s been happening a while before large language models as well with AI and drug discovery. Very, very targeted AI models that can do things like protein folding and analyzing retina images, things like that. So this is — this is very powerful, but very therapeutic area specific, very close to the science in the R&D, and I — there’s not just one AI model there is multiple specialized AI models.

Veeva’s management has seen some experimentation going on with the use of large language models in improving general productivity in the life sciences industry

Then in terms of other areas, really, there’s a lot of experimentation with large language models. And what people look at it for are: a, can I just have general productivity for my people, can they write an e-mail faster? Can they check their e-mail faster? Can they research some information faster. So that’s one thing that’s going on. Also, specific use cases like authoring, can I — can I author a protocol faster? Can I author a regulatory document faster. Now faster is one thing, but also have to be very accurate. So I would say there’s experimentation on that. There’s not yet broad production use on that. And certainly, some of these critical things has to be lot of quality control on it. So those are probably the two biggest use cases — really three: research, general productivity and authoring.

Veeva’s management has developed a product to make Veeva’s data platform extract data in a much faster way so that it works well with AI applications, but otherwise, the company has not invested in LLMs (large language models) because they are not as relevant in the company’s field

And then as far as our role, we’ve been doing some really heavy work over the last 2 years on something in our Vault platform that’s called the Direct Data API. And that’s a pretty revolutionary way of making the data come out of Vault in a consistent — transactionally consistent manner much, much faster, roughly 100x faster than it happens now. That’s going to be critical for all kinds of AI applications on the top, which we may develop, which our customers may develop, and we’re also utilizing that for some really fast system to system transfer between our different Vault family. So that’s been the biggest thing that we’ve done. We haven’t really invested heavily in large language models. So far, we just don’t see quite the application in our application areas, not to say that, that wouldn’t change in the future.

Veeva’s management thinks that the important thing for AI is data – AI models will be a commodity – and Veeva has the advantage in this

I would say we’re in a pretty good position because AI really — the durable thing about AI is the data sources, the data sources. The AI models will come on top, and that will be largely a tech commodity, but the control and the access to the data sources, that’s pretty important, and that’s kind of where Veeva plays.

---

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Adobe, Meta Platforms, MongoDB, Okta, Salesforce, Starbucks, Tencent, and Veeva Systems. Holdings are subject to change at any time.