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.

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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.