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 fourth 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
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here

I’ve split the latest commentary into two parts for the sake of brevity. This is Part 1, and you can Part 2 here. With that, I’ll let the management teams take the stand… 

Airbnb (NASDAQ: ABNB)

Airbnb’s management thinks AI is early and has yet to fundamentally change the travel market for any of the large travel platforms; most travel companies are starting with AI on trip planning but management thinks AI is still too early for trip planning

Here’s what I think about AI. I think it’s still really early. It’s probably similar to like the mid- to late ’90s for the Internet. So I think it’s going to have a profound impact on travel, but I don’t think it’s yet fundamentally changed for any of the large travel platforms…

…So most companies, what they’re actually doing is they’re doing integrations of these other platforms on trip planning. But the trip planning, it’s still early. I don’t think it’s quite bit ready for prime time.

Airbnb’s management is starting with AI in customer service; Airbnb will roll out AI-powered customer support later in 2025; management thinks AI can provide great customer support partly because it can speak all languages 24/7 and read thousands of pages of documents; management will eventually graduate the customer-support AI into a travel and living concierge; management thinks AI can help improve efficiency at Airbnb in customer service

We’re actually starting with customer service. So later this year, we’re going to be rolling out, as part of our Summer Release, AI-powered customer support. As you imagine, we get millions of contacts every year. AI can do an incredible job of customer service. It can speak every language 24/7. It can read a corpus of thousands of pages of documents. And so we’re starting with customer support. And over the coming years, what we’re going to do is we’re going to take the AI-powered customer service agent, and we’re going to bring it into essentially Airbnb search to eventually graduate to be a travel and living concierge…

…[Question] With respect to the AI, I appreciate your answer with respect to outward-looking and how it might change the landscape. What do you think the potential is internally to apply AI for efficiencies inside the company and create an additional layer of potential margin efficiency and/or free cash flow conversion in the years ahead?

[Answer] There’s like a couple like efficiencies that you could imagine at Airbnb. One is obviously customer service. I think that’s like one of the biggest ones. I’ve kind of already covered that, but I think that’s like a massive change for Airbnb.

Airbnb’s management thinks that AI models are getting cheaper and are starting to be commoditised

I think it’s a really exciting time in the space because you’ve seen like with DeepSeek and more competition with models is models are getting cheaper or nearly free. They’re getting faster and they’re getting more intelligent. And they are, for all intent and purpose, starting to get commoditized.

Airbnb’s management thinks that a lot of the value from AI is going to accrue to platforms, and they want Airbnb to be the platform for travel and living that will reap most of the value from AI

What I think that means is a lot of value is going to accrue to the platform. And ultimately, I think the best platform, the best applications are going to be the ones that like most accrue the value from AI. And I think we’re going to be the one to do that with traveling and living.

Airbnb’s management thinks AI can help improve efficiency at Airbnb in engineering productivity; in the short-term, the improvement in engineering productivity has not been material; over the next few years, management thinks AI can drive a 30% increase in engineering productivity at Airbnb; over the long-term, management thinks there can an order of magnitude more productivity; management think younger, more innovative companies, could benefit from AI more than incumbent enterprises

[Question] With respect to the AI, I appreciate your answer with respect to outward-looking and how it might change the landscape. What do you think the potential is internally to apply AI for efficiencies inside the company and create an additional layer of potential margin efficiency and/or free cash flow conversion in the years ahead?

[Answer] The other, I assume, you refer to is essentially engineering productivity. We are seeing some productivity gains. I’ve talked to a lot of other tech CEOs, and here’s what I’ve heard talking to other like tech CEOs. Most of them haven’t seen a material like change in engineering productivity. Most of the engineers are using AI tools. They’re seeing some productivity. I don’t think it’s flowing to like a fundamental step-change in productivity yet. I think a lot of us believe in some kind of medium term of a few years, you could easily see like a 30% increase in technology and engineering productivity. And then, of course, beyond that, I mean, I think it could be like an order of magnitude more productivity because — but that’s going to be like down the road. And I think that’s going to be something that almost all companies benefit from. I think the kind of younger, more innovative, startup-like companies might benefit a little bit more because they’ll have engineers who are more likely to adopt the tools.

Alphabet (NASDAQ: GOOG)

AI Overviews in Search is now available in more than 100 countries; AI Overviews drive higher user satisfaction and search usage; Google’s Gemini model is being used in AI Overviews; with AI Overviews, usage growth of Search is growing over time, especially with younger users; management recently launched ads in AI Overviews and AI Overviews is currently monetising at nearly the same rate as Google Search; Google Search has continued to perform well in this AI age, as overall usage has continued to grow, with stronger growth seen in AI Overviews across all segments

In Search, AI overviews are now available in more than 100 countries. They continue to drive higher satisfaction and search usage…

…That includes Search where Gemini is pairing our AI overviews. People use search more with AI overviews and usage growth increases over-time as people learn that they can ask new types of questions. This behavior is even more pronounced with younger users who really appreciate the speed and efficiency of this new format…

…We’ve already started testing Gemini 2.0 in AI overviews and plan to roll it out more broadly later in the year…

…We recently launched the ads within AI Overviews on mobile in the U.S., which builds on our previous rollout of ads above and below. And as I talked about before, for the AI Overviews, overall, we actually see monetization at approximately the same rate, which I think really gives us a strong base on which we can innovate even more…

…On Search usage, overall, our metrics are healthy. We are continuing to see growth in Search on a year-on-year basis in terms of overall usage. Of course, within that, AI Overviews has seen stronger growth, particularly across all segments of users, including younger users, so it’s being well received. But overall, I think through this AI moment, I think Search is continuing to perform well.

Circle to Search is now available on more than 200 million Android devices; Circle to Search is opening new Search use cases; Circle to Search is popular with younger users; Circle to Search is used to start more than 10% of searches among users who have tried it before

Circle to Search is now available on over 200 million Android devices…

…Circle to Search is driving additional Search use and opening up even more types of questions. This feature is also popular among younger users. Those who have tried Circle to Search before now use it to start more than 10% of their searches…

…In Search, we’re seeing people increasingly ask entirely new questions using their voice, camera or in ways that were not possible before, like with Circle to search.

Alphabet’s management believes Google has a unique infrastructure advantage in AI because the company has developed each component of its technology stack; Alphabet broke ground on 11 new cloud regions and data center campuses in 2024, and announced plans for 7 new subsea cable projects; Google data centers now deliver 4x more computing power per unit of electricity compared to 5 years ago

We have a unique advantage because we develop every component of our technology stack, including hardware, compilers, models and products. This approach allows us to drive efficiencies at every level from training and serving to develop our productivity. In 2024, we broke ground on 11 new cloud regions and data center campuses in places like South Carolina, Indiana, Missouri and around the world.

We also announced plans for seven new subsea cable projects, strengthening global connectivity. Our leading infrastructure is also among the world’s most efficient. Google data centers deliver nearly four times more computing power per unit of electricity compared to just five years ago.

Google Cloud customers consume 8x more compute capacity for training and inference compared to 18 months ago; first-time commitments to Google Cloud more than doubled in 2024; Google Cloud closed a few deals in 2024 worth more than $1 billion each; Google Cloud’s AI hypercomputer utilises both GPUs (graphics processing units) and TPUs (tensor processing units), and has helped Wayfair improve performance and scalability by 25%; Google saw strong uptake of Trillium, its 6th generation TPU, in 2024 Q4; Trillium is 4x better in training and has 3x higher inference throughput than the 5th generation TPU; Google Cloud is offering NVIDIA’s H200 GPUs to customers; Google Cloud is the first cloud provider to provide NVIDIA’s Blackwell GPUs; the capex for Google Cloud is mostly for Google’s own self-designed data centers and TPUs (tensor processing units)

Today, cloud customers consume more than eight times the compute capacity for training and inferencing compared to 18 months ago…

…In 2024, the number of first-time commitments more than double compared to 2023…

…Last year, we closed several strategic deals over $1 billion and the number of deals over $250 million doubled from the prior year…

…We continue to see strong growth across our broad portfolio of AI-powered cloud solutions. It begins with our AI hypercomputer, which delivers leading performance and cost across both GPUs and TPUs. These advantages help Citadel with modeling markets and training and enabled Wayfair to modernize its platform, improving performance and scalability by nearly 25%. 

In Q4, we saw strong uptake of Trillium, our sixth-generation TPU, which delivers four times better training performance and three times greater inference throughput compared to the previous generation. We also continue our strong relationship with NVIDIA. We recently delivered their H200 based platforms to customers. And just last week, we were the first to announce a customer running on the highly-anticipated Blackwell platform…

……Our strategy is mostly to rely our own self-design and build data centers. So, they’re industry-leading in terms of both cost and power efficiency at scale. We have our own customized TPUs. They’re customized for our own workload, so they do deliver outstanding the superior performance and capex efficiency. So, we’re going to be looking at all that when we make decisions as to how we’re going to progress capital investments throughout the coming years.

Google launched an experimental version of its Gemini 2.0 Flash model in December 2024, but the model will be generally available for developers and customers; Google debuted its experimental Gemini 2.0 Flash Thinking model in late-2024 and it has gathered extremely positive reviews; Google is working on even better thinking models; Gemini 2.0’s advances in multimodality and native tool use helps Google build a universal AI assistant; an example of this universal assistant can be seen in Deep Research; Deep Research was launched in Gemini Advanced in December and is being rolled out to Android users globally; the consumer Gemini app debuted on iOS in November 2024 and has seen great product momentum; Project Mariner and Project Astra are AI agent products currently being tested and they will appear in the Gemini app sometime in 2025; Gemini and Google’s video and image generation models consistently excel in industry leaderboards and benchmarks; 4.4 million developers are using Gemini models today, double from just six months ago; Google has 7 products with over 2 billion users each, and all 7 products use Gemini; all Google Workspace business and enterprise customers were recently given access to all of Gemini’s AI capabilities; Gemini 2.0 Flash is one of the most capable models people can access for free; management’s current intention for monetisation of Gemini is through subscriptions and improving the user experience, but they have an eye on advertising revenues

In December, we unveiled Gemini 2.0, our most capable AI model yet, built for the agent era. We launched an experimental version of Gemini 2.0 Flash, our workhorse model with low-latency and enhanced performance. Flash has already rolled-out to the Gemini app, and tomorrow we are making 2.0 Flash generally available for developers and customers, along with other model updates…

…Late last year, we also debuted our experimental Gemini 2.0 Flash Thinking model. The progress to scale thinking has been super-fast and the review so-far have been extremely positive. We are working on even better thinking models and look-forward to sharing those with the developer community soon.

Gemini 2.0’s advances in multimodality and native tool use enable us to build new agents that bring us closer to our vision of a universal assistant. One early example is deep research. It uses agent capabilities to explore complex topics on your behalf and give key findings along with sources. It launched in Gemini Advanced in December and is rolling out to Android users all over the world.

We are seeing great product momentum with our consumer Gemini app, which debuted on iOS last November.

…We have opened up trusted tester access to a handful of research prototypes, including Project Mariner, which can understand and reason across information on a browser screen to complete tasks and Project Astra. We expect to bring features from both to the Gemini app later this year…

…Veo2, our state-of-the-art video generation model and Imagine3, our highest-quality text image model. These generative media models as well as Gemini consistently top industry leaderboards and score top marks across industry benchmarks. That’s why more than 4.4 million developers are using our Gemini models today, double the number from just six months ago…

…We have seven products and platforms with over 2 billion users and all are using Gemini…

…We recently gave all Google Workspace business and enterprise customers access to all of our powerful Gemini AI capabilities to help boost their productivity…

…2.0 Flash. I mean, I think that’s one of the most capable models you can access at the free tier…

…[Question] How should we think about the future monetization opportunity of Gemini? Today, it’s really a premium subscription offering or a free offering. Over time, do you see an ad component?

[Answer] On the monetization side, obviously, for now, we are focused on a free tier and subscriptions. But obviously, as you’ve seen in Google over time, we always want to lead with user experience. And we do have very good ideas for native ad concepts, but you’ll see us lead with the user experience. And — but I do think we’re always committed to making the products work and reach billions of users at scale. And advertising has been a great aspect of that strategy. And so, just like you’ve seen with YouTube, we’ll give people options over time. But for this year, I think you’ll see us be focused on the subscription direction.

Google Cloud’s AI developer platform, Vertex AI, had a 5x year-on-year increase in customers in 2024 Q4; Vertex AI offers more than 200 foundation models form Google; Vertex AI’s usage grow 20x in 2024

Our AI developer platform, Vertex AI, saw a 5x increase in customers year-over-year with brands like, International and WPP building new applications and benefiting from our 200 plus foundation models. Vertex usage increased 20x during 2024 with particularly strong developer adoption of Gemini Flash, Gemini 2.0, and most recently VEO.

Alphabet’s management will be introducing Veo2, Google’s video generation model, for creators in Youtube in 2025; advertisers around the world can now promote Youtube creator videos and ad campaigns across all AI-powered campaign types and Google ads

Expanding on our state-of-the-art video generation model, we announced Veo2, which creates incredibly high-quality video in a wide range of subjects and styles. It’s been inspiring to see how people are experimenting with it. We’ll make it available to creators on YouTube in the coming months…

…. All advertisers globally can now promote YouTube creator videos and ad campaigns across all AI-powered campaign types and Google Ads, and creators can tag partners in their brand videos…

Alphabet’s management announced the first beta of Android 16 in January 2025; there will be deeper Gemini integration for the new Samsung Galaxy S25 smartphone series; Alphabet has announced Android XR, the first Android platform for the Gemini era

Last month, we announced the first beta of Android 16 plus new Android updates, including a deeper Gemini integration coming to the new Samsung Galaxy S25 series. We also recently-announced Android XR, the first Android platform built for the Gemini era. Created with Samsung and Qualcomm, Android XR is designed to power an ecosystem of next-generation extended reality devices like headsets and glasses.

Waymo is now serving more than 150,000 trips per week (was 150,000 in 2024 Q3); Waymo is expanding in new markets in the USA this year and in 2026; Waymo will soon be launched in Tokyo; Waymo is developing its 6th-gen driver, which will significantly reduce hardware costs

It’s now averaging over 150,000 trips each week and growing. Looking ahead, Waymo will be expanding its network and operations partnerships to open up new markets, including Austin and Atlanta this year and Miami next year. And in the coming weeks, Waymo One vehicles will arrive in Tokyo for their first international road trip. We are also developing the sixth-generation Waymo driver, which will significantly lower hardware costs.

Alphabet’s management introduced a new Google shopping experience, infused with AI, in 2024 Q4, and there was 13% more daily active users in Google shopping in December 2024 compared to a year ago; the new Google Shopping experience helps users speed up their shopping research

Google is already present in over half of journeys where a new brand, product or retailer are discovered by offering new ways for people to search, we’re expanding commercial opportunities for our advertisers…

…Retail was particularly strong this holiday season, especially on Black Friday and Cyber Monday, which each generated over $1 billion in ad revenue. Interestingly, despite the U.S. holiday shopping season being the shortest since 2019, retail sales began much earlier in October, causing the season to extend longer than anticipated.

People shop more than 1 billion times a day across Google. Last quarter, we introduced a reinvented Google shopping experience, rebuilt from the ground up with AI. This December saw roughly 13% more daily active users in Google shopping in the U.S., compared to the same period in 2023…

…The new Google Shopping experiences specifically to your question, users to really intelligently show the most relevant products, helping to speed up and simplify your research. You get an AI-generated brief with top things to consider for your search plus maybe products that meet your needs. So, shoppers very often want low prices. So, the new page not only includes like deal-finding tools like price comparison, price insights, price tracking throughout. But it’s also a new and dedicated personalized deals page, which can browse deals for you, and all this is really built on the backbone of AI.

Shoppers can now take a photo and use Lens to quickly find information about the product; Lens is now used in over 20 billion visual searches per month (was over 20 billion in 2024 Q3); majority of Lens searches are incremental

Shoppers can now take a photo of a product and using Lens quickly find information about the product, reviews, similar products and where they can get it for a great price. Lens is used for over 20 billion visual search queries every month and the majority of these searches are incremental.

Alphabet’s management continues to infuse AI capabilities into Google’s advertising business; Petco used Demand Gen campaigns to achieve a 275% increase in return on ad spend and a 74% increase in click-through rates compared to social benchmarks; Youtube Select Creator Takeovers is now generally available in the US and will be rolled out across the world; PMax was recently strengthened with new controls and easier reporting functions; Event Ticket Center used PMax and saw a 5x increase in production of creative assets, driving a 300% increase in conversions compared to using manual assets; Meridian, Google’s marketing mix model, was recently made generally available and it delivers 17% higher return on advertising spend on Youtube compared to manual campaigns

We continue investing in AI capabilities across media buying, creative and measurement. As I’ve said before, we believe that AI will revolutionize every part of the marketing value chain.

And over the past quarter, we’ve seen how our customers are increasingly focusing on optimizing the use of AI. As an example, [ Petco ], used Demand Gen campaigns across targeting, creative generation and bidding to find new pet parent audiences across YouTube. They achieved a 275% higher return on ad spend and a 74% higher click-through rate than their social benchmarks.

On media buying, we made YouTube Select Creator Takeovers generally available in the U.S. and will be expanding to more markets this year. Creators know their audience the best and creator takeovers help businesses connect with consumers through authentic and relevant content.

Looking at Creative, we introduced new controls and made reporting easier in PMax, helping customers better understand and reinvest into their best-performing assets. Using asset generation in PMax, Event Ticket Center achieved a 5x increase in production of creative assets saving time and effort. They also increased conversions by 300% compared to the previous period when they used manual assets…

…Last week, we made Meridian, our marketing mix model, generally available for customers, helping more business reinvest into creative and media buying strategies that they know work. Based on the Nielsen meta analysis of marketing mix models, on average, Google AI-powered video campaigns on YouTube delivered 17% higher return on advertising spend than manual campaigns.

Sephora used Demand Gen Shorts-only channel for advertising that drove an 82% increase in searches for Sephora Holiday

Sephora used demand gen Shorts-only channel to boost traffic and brand searches for the holiday gift guide campaign and leverage greater collaborations to find the best gift. This drove an 82% relative uplift in searches for Sephora holiday.

Citi is using Google Cloud for its generative AI initiatives across customer service, document summarisation, and search

Another expanding partnership is with Citi, who is modernizing its technology infrastructure with Google Cloud to transform employee and customer experiences. Using Google Cloud, it will improve its digital products, streamline employee workflows and use advanced high-performance computing to enable millions of daily computations. This partnership also fuels Citi’s generate AI initiatives across customer service, document summarization and search to reduce manual processing.

Google Cloud had 30% revenue growth in 2024 Q4 (was 35% in 2024 Q3) driven by growth in core GCP products, AI infrastructure, and generative AI solutions; operating margin was 17.5% (was 17% in 2024 Q3 and was 9.4% in 2023 Q4); GCP grew at a much higher rate than Google Cloud overall; Google Cloud had more AI demand than capacity in 2024 Q4; management is thinking about Google’s capital intensity, but they want to invest because they are seeing strong AI demand both internally and externally; the capex Google is making can be repurposed across its different businesses

Turning to the Google Cloud segment, which continued to deliver very strong results this quarter. Revenue increased by 30% to $12 billion in the fourth quarter, reflecting growth in GCP, across core GCP products, AI infrastructure, and generative AI solutions. Once again, GCP grew at a rate that was much higher than cloud overall. Healthy Google Workspace growth was primarily driven by increase in average revenue per seat. Google Cloud operating income increased to $2.1 billion and operating margin increased from 9.4% to 17.5%…

…We do see and have been seeing very strong demand for our AI products in the fourth quarter in 2024. And we exited the year with more demand than we had available capacity. So, we are in a tight supply demand situation, working very hard to bring more capacity online…

…[Question] How do you think about long-term capital intensity for this business?

[Answer] On the first one, certainly, we’re looking ahead, but we’re managing very responsibly. It was a very rigorous, even internal governance process, looking at how do we allocate the capacity and what would we need to support the customer demand externally, but also across the Google — the Alphabet business. And as you’ve seen in the comment I’ve just made on Cloud, we do have demand that exceeds our available capacity. So, we’ll be working hard to address that and make sure we bring more capacity online. We do have the benefit of having a very broad business, and we can repurpose capacity, whether it’s through Google Services or Google Cloud to support, as I said, whether it’s search or GDM, or Google Cloud customers, we can do that in a more efficient manner.

Alphabet’s management thinks Google’s AI models are in the lead when compared to DeepSeek’s, and this is because of Google’s full-stack development

If you look at one of the areas in which the Gemini model shines is the Pareto frontier of cost, performance, and latency. And if you look at all three attributes, I think we are — we lead this period of frontier. And I would say both our 2.0 Flash models, our 2.0 Flash thinking models, they are some of the most efficient models out there, including comparing to DeepSeek’s V3 and R1. And I think a lot of it is our strength of the full stack development, end-to-end optimization, our obsession with cost per query.

Alphabet’s management has seen the proportion of AI spend on inference growing over the last 3 years when compared to training; management thinks reasoning AI models will accelerate this trend

A couple of things I would say are if you look at the trajectory over the past three years, the proportion of the spend toward inference compared to training has been increasing, which is good because, obviously, inferences to support businesses with good ROIC…

…I think the reasoning models, if anything, accelerates that trend because it’s obviously scaling upon inference dimension as well.

Alphabet’s management thinks that AI agents and Google Search are not competing in a zero-sum game

[Question] With your own project Mariner efforts and a competitor’s recent launch, it seems there’s suddenly really strong momentum on AI consumer agents and kind of catching up to that old Google Duplex Vision. I think when you look a few years ahead, where do you see consumer agents going? And really, what does it mean to Google Search outside of Lens? Is there room for both to flourish?

[Answer] Gemini 2.0 was definitely built with the view of enabling more agentic use cases. And so, I actually — we are definitely seeing progress inside. And I think we’ll be able to do more agentic experiences for our users. Look, I actually think all of this expands the opportunity space. I think it — historically, we’ve had information use cases, but now you can have — you can act on your information needs in a much deeper way. It’s always been our vision when we have talked about Google Assistant, etc. So, I think the opportunity space expands. I think there’s plenty of it, feels very far from a zero-sum game. There’s plenty of room, I think, for many new types of use cases to flourish. And I think for us, we have a clear sense of additional use cases we can start to tackle for our users in Google Search.

Alphabet’s management has been passing on cost differentiations arising from Google Cloud’s end-to-end stack approach to customers

Part of the reason we have taken the end-to-end stack approach is so that we can definitely drive a strong differentiation in end-to-end optimizing and not only on a cost but on a latency basis, on a performance basis. Be it the Pareto frontier we mentioned, and I think our full stack approach and our TPU efforts all play give a meaningful advantage. And we plan — you already see that. I know you asked about the cost, but it’s effectively captured when we price outside, we pass on the differentiation. 

Amazon (NASDAQ: AMZN)

AWS grew 19% year-on-year in 2024 Q4, and is now at a US$115 billion annualised revenue run rate; management expects lumpiness in AWS’s growth in the next few years, but is incredibly optimistic about AWS’s growth; management thinks the future will be one where (a) every app is infused with generative AI that has inference as a core building block, and (b) companies will have AI agents accomplishing tasks and interacting with each other; management believes this future will be built on the cloud, and mostly on AWS; the shift by enterprises from on-premises to the cloud, which is a non-AI activity, continues for AWS; AWS continues to innovate in non-AI areas; AWS’s growth in 2024 Q4 was driven by both generative AI and non-generative AI offerings; AWS had a massive 48% year-on-year jump in operating income in 2024 Q4, helped partly by an increase in estimated useful life of servers that started in 2024; management sees AWS being capable of faster growth today if not for supply constraints; the constraints relate to (1) chips from 3rd-party partners (most likely referring to NVIDIA), (2) AWS’s own Trainium chips, (3) power for data centers, and (4) other supply chain components; management sees the AWS constraints starting to relax in 2025 H2; AWS’s AI services come with lower margins right now, but management thinks the AI-related margin will over time be on par with the non-AI margin

In Q4, AWS grew 19% year-over-year and now has a $115 billion annualized revenue run rate. AWS is a reasonably large business by most folks’ standards. And though we expect growth will be lumpy over the next few years as enterprise adoption cycles, capacity considerations and technology advancements impact timing, it’s hard to overstate how optimistic we are about what lies ahead for AWS’ customers and business…

…While it may be hard for some to fathom a world where virtually every app has generative AI infused in it, with inference being a core building block just like compute, storage and database, and most companies having their own agents that accomplish various tasks and interact with one another, this is the world we’re thinking about all the time. And we continue to believe that this world will mostly be built on top of the cloud with the largest portion of it on AWS…

…While AI continues to be a compelling new driver in the business, we haven’t lost our focus on core modernization of companies’ technology infrastructure from on-premises to the cloud. We signed new AWS agreements with companies, including Intuit, PayPal, Norwegian Cruise Line Holdings, Northrop Grumman, The Guardian Life Insurance Company of America, Reddit, Japan Airlines, Baker Hughes, The Hertz Corporation, Redfin, Chime Financial, Asana, and many others. Consistent customer feedback from our recent AWS re:Invent gathering was appreciation that we’re still inventing rapidly in non-AI key infrastructure areas like storage, compute, database and analytics…

…During the fourth quarter, we continued to see growth in both generative AI and non-generative AI offerings as companies turn their attention to newer initiatives, bring more workloads to the cloud, restart or accelerate existing migrations from on-premise to the cloud, and tap into the power of generative AI…

…AWS reported operating income of $10.6 billion, an increase of $3.5 billion year-over-year. This is a result of strong growth, innovation in our software and infrastructure to drive efficiencies, and continued focus on cost control across the business. As we’ve said in the past, we expect AWS operating margins to fluctuate over time driven in part by the level of investments we’re making. Additionally, we increased the estimated useful life of our servers starting in 2024, which contributed approximately 200 basis points to the AWS margin increase year-over-year in Q4…

……It is true that we could be growing faster, if not for some of the constraints on capacity. And they come in the form of, I would say, chips from our third-party partners, come a little bit slower than before with a lot of midstream changes that take a little bit of time to get the hardware actually yielding the percentage-healthy and high-quality servers we expect. It comes with our own big new launch of our own hardware and our own chips and Trainium2, which we just went to general availability at re:Invent, but the majority of the volume is coming in really over the next couple of quarters, the next few months. It comes in the form of power constraints where I think the world is still constrained on power from where I think we all believe we could serve customers if we were unconstrained. There are some components in the supply chain, like motherboards, too, that are a little bit short in supply for various types of servers…

…I predict those constraints really start to relax in the second half of ’25…

…At the stage we’re in right now, AI is still early stage. It does come originally with lower margins and a heavy investment load as we’ve talked about. And in the short term, over time, that should be a headwind on margins. But over the long term, we feel the margins will be comparable in non-AI business as well.

Amazon’s management sees NVIDIA being an important partner of AWS for a long time; management does not see many large-scale generative AI apps existing right now; when generative AI apps reach scale, their costs to operate can rise very quickly, and management believes this will drive customers to demand better price performance from chips, which is why AWS built its custom AI chips; Trainium 2, AWS’s custom AI chip, was launched in December 2024; EC2 instances powered by Trainium 2 is 30%-40% more price performant than instances powered by other GPUs; important technology companies such as Adobe, Databricks, and Qualcomm have seen impressive results after testing Trainium 2; Anthropic is building its future frontier models on Trainium 2; AWS is collaborating with Anthropic on Project Rainier, which is a cluster of a few hundred thousand Trainium 2 chips that have 5x the exaflops Anthropic used to train its current set of models; management is already 

Most AI compute has been driven by NVIDIA chips, and we obviously have a deep partnership with NVIDIA and will for as long as we can see into the future. However, there aren’t that many generative AI applications of large scale yet. And when you get there, as we have with apps like Alexa and Rufus, cost can get steep quickly. Customers want better price performance and it’s why we built our own custom AI silicon. Trainium2 just launched at our AWS re:Invent Conference in December. And EC2 instances with these chips are typically 30% to 40% more price performant than other current GPU-powered instances available. That’s very compelling at scale. Several technically-capable companies like Adobe, Databricks, Poolside and Qualcomm have seen impressive results in early testing of Trainium2. It’s also why you’re seeing Anthropic build their future frontier models on Trainium2. We’re collaborating with Anthropic to build Project Rainier, a cluster of Trainium2 UltraServers containing hundreds of thousands of Trainium2 chips. This cluster is going to be 5x the number of exaflops as the cluster that Anthropic used to train their current leading set of cloud models. We’re already hard at work on Trainium3, which we expect to preview late in ’25 and defining Trainium4 thereafter.

Building outstanding performant chips that deliver leading price performance has become a core strength of AWS’, starting with our Nitro and Graviton chips in our core business and now extending to Trainium and AI and something unique to AWS relative to other competing cloud providers.

Amazon’s management has seen Amazon SageMaker AI, AWS’s fully-managed AI service, become the go-to service for AI model builders; SageMaker’s HyperPod automatically splits training workloads across many AI accelerators and prevents interruptions, saving training time up tp 40%; management recently released new features for SageMaker, such as the ability to prioritise which workloads to receive capacity when budgets are reached; the latest version of SageMaker is able to integrate all of AWS’s data analytics and AI services into one surface

I won’t spend a lot of time in these comments on Amazon SageMaker AI, which has become the go-to service for AI model builders to manage their AI data, build models, experiment and deploy these models, except to say that SageMaker’s HyperPod capability, which automatically splits training workloads across many AI accelerators, prevents interruptions by periodically saving checkpoints, and automatically repairing faulty instances from their last saved checkpoint and saving training time by up to 40%. It continues to be a differentiator, received several new compelling capabilities at re:Invent, including the ability to manage costs at a cluster level and prioritize which workloads should receive capacity when budgets are reached, and is increasingly being adopted by model builders…

…There were several key launches customers were abuzz about, including Amazon Aurora DSQL, our new serverless distributed SQL database that enables applications with the highest availability, strong consistency, PostgreS compatibility and 4x faster reads and writes compared to other popular distributed SQL databases; Amazon S3 tables, which make S3 the first cloud object store with fully managed support for Apache Iceberg for faster analytics; Amazon S3 Metadata, which automatically generates queryable metadata, simplifying data discovery, business analytics, and real-time inference to help customers unlock the value of their data in S3; and the next generation of Amazon SageMaker, which brings together all of the data analytics services and AI services into one interface to do analytics and AI more easily at scale.

Amazon Bedrock is AWS’s fully-managed service for developers to build generative AI applications by leverage on frontier models; management recently introduced more than 100 popular emerging models on Bedrock, including DeepSeek’s R1 models; management recently introduced new features to Bedrock to help customers lower cost and latency in inference workloads; management is seeing Bedrock resonate strongly with customers; management recently released Amazon’s own Nova family of frontier models on Bedrock; customers are starting to experiment with DeepSeek’s models

Amazon Bedrock is our fully managed service that offers the broadest choice of high-performing foundation models with the most compelling set of features that make it easy to build a high-quality generative AI application. We continue to iterate quickly on Bedrock announcing Luma AI poolside and over 100 other popular emerging models to Bedrock at re:Invent. In short order, we also just added DeepSeek’s R1 models to Bedrock and SageMaker…

…We delivered several compelling new Bedrock features at re:Invent, including prompt caching, intelligent prompt routing and model distillation, all of which help customers achieve lower cost and latency in their inference. Like SageMaker AI, Bedrock is growing quickly and resonating strongly with customers…

…We also just launched Amazon’s own family of frontier models in Bedrock called Nova…

…We moved so quickly to make sure that DeepSeek was available both in Bedrock and in SageMaker faster than you saw from others. And we already have customers starting to experiment with that.

The Nova family has comparable intelligence with other leading AI models, but also offers lower latency and price, and integration with important Bedrock features; many large enterprises, including Palantir, Deloitte, and SAP, are already using Nova

We also just launched Amazon’s own family of frontier models in Bedrock called Nova. These models compare favorably in intelligence against the leading models in the world but offer lower latency; lower price, about 75% lower than other models in Bedrock; and are integrated with key Bedrock features like fine-tuning, model distillation, knowledge bases of RAG and agentic capabilities. Thousands of AWS customers are already taking advantage of the capabilities and price performance of Amazon Nova models, including Palantir, Deloitte, SAP, Dentsu, Fortinet, Trellix, and Robinhood, and we’ve just gotten started.

Amazon’s management still thinks Amazon Q is the most capable AI-powered software development assistant; early testing shows that Amazon Q can now shorten a multi-year mainframe migration by 50%

Amazon Q is the most capable generative AI-powered assistant for software development and to leverage your own data…

…We obliged with our recent deliveries of Q Transformations that enable moves from Windows.NET applications to Linux, VMware to EC2, and accelerates mainframe migrations. Early customer testing indicates that Q can turn what was going to be a multiyear effort to do a mainframe migration into a multi-quarter effort, cutting by more than 50% the time to migrate mainframes. This is a big deal and these transformations are good examples of practical AI.

Amazon’s management expects capital expenditures of around US$105 billion for the whole of 2025 (was around $75 billion in 2024); the capex in 2025 will be for AWS as well as the retail business, but will primarily be for AWS’s AI infrastructure; reminder that the faster AWS grows, the faster Amazon needs to invest capital for hardware; management will only spend on capex if they see significant signals of demand; management thinks AI is a once-in-a-lifetime business opportunity, and that it’s a good sign on the long-term growth opportunities AWS has when capex is expanding

Capital investments were $26.3 billion in the fourth quarter, and we think that run rate will be reasonably representative of our 2025 capital investment rate. Similar to 2024, the majority of the spend will be to support the growing need for technology infrastructure. This primarily relates to AWS, including to support demand for our AI services, as well as tech infrastructure to support our North America and International segments. Additionally, we’re continuing to invest in capacity for our fulfillment and transportation network to support future growth. We’re also investing in same-day delivery facilities and our inbound network as well as robotics and automation to improve delivery speeds and to lower our cost to serve. These capital investments will support growth for many years to come…

…The vast majority of that CapEx spend is on AI for AWS. The way that AWS business works and the way the cash cycle works is that the faster we grow, the more CapEx we end up spending because we have to procure data center and hardware and chips and networking gear ahead of when we’re able to monetize it. We don’t procure it unless we see significant signals of demand. And so when AWS is expanding its CapEx, particularly in what we think is one of these once-in-a-lifetime type of business opportunities like AI represents, I think it’s actually quite a good sign, medium to long term, for the AWS business…

…We also have CapEx that we’re spending this year in our Stores business, really with an aim towards trying to continue to improve the delivery speed and our cost to serve. And so you’ll see us expanding the number of same-day facilities from where we are right now. You’ll also see us expand the number of delivery stations that we have in rural areas so we can get items to people who live in rural areas much more quickly, and then a pretty significant investment as well on robotics and automation so we can take our cost to serve down and continue to improve our productivity.

Amazon’s management completed a useful life study for its servers and network equipment in 2024 Q4 and has decreased the useful life estimate; management early retired some servers and network equipment in 2024 Q4; the decrease in useful life estimate and the early retirement will lower Amazon’s operating income, primarily in the AWS segment

In Q4, we completed a useful life study for our servers and network equipment, and observed an increased pace of technology development, particularly in the area of artificial intelligence and machine learning. As a result, we’re decreasing the useful life for a subset of our servers and network equipment from 6 years to 5 years, beginning in January 2025. We anticipate this will decrease full year 2025 operating income by approximately $700 million. In addition, we also early retired a subset of our servers and network equipment. We recorded a Q4 2024 expense of approximately $920 million from accelerated depreciation and related charges and expect this will also decrease full year 2025 operating income by approximately $600 million. Both of these server and network equipment useful life changes primarily impact our AWS segment.

Amazon’s management sees AI as the biggest opportunity since cloud and the internet

From our perspective, we think virtually every application that we know of today is going to be reinvented with AI inside of it and with inference being a core building block, just like compute and storage and database. If you believe that, plus altogether new experiences that we’ve only dreamed about are going to actually be available to us with AI, AI represents, for sure, the biggest opportunity since cloud and probably the biggest technology shift and opportunity in business since the Internet.

Amazon’s management has been impressed with DeepSeek’s innovations

I think like many others, we were impressed with what DeepSeek has done, I think in part impressed with some of the training techniques, primarily in flipping the sequencing of reinforcement learning being earlier and without the human-in-the-loop. We thought that was interesting ahead of the supervised fine-tuning. We also thought some of the inference optimizations they did were also quite interesting

Amazon’s management’s core belief remains that generative AI apps will use multiple models and different customers will use different AI models for different workloads 

You have a core belief like we do that virtually all the big generative AI apps are going to use multiple model types, and different customers are going to use different models for different types of workloads.

Amazon’s management thinks that the cheaper AI inference becomes, the more inference spending there will be; management believes that the cost of AI inference will fall substantially over time

Sometimes people make the assumptions that if you’re able to decrease the cost of any type of technology component, in this case, we’re really talking about inference, that somehow it’s going to lead to less total spend in technology. And we have never seen that to be the case. We did the same thing in the cloud where we launched AWS in 2006, where we offered S3 object storage for $0.15 a gigabyte and compute for $0.10 an hour, which, of course, is much lower now many years later, people thought that people would spend a lot less money on infrastructure technology. And what happens is companies will spend a lot less per unit of infrastructure, and that is very, very useful for their businesses, but then they get excited about what else they could build that they always thought was cost prohibitive before, and they usually end up spending a lot more in total on technology once you make the per unit cost less. And I think that is very much what’s going to happen here in AI, which is the cost of inference will substantially come down. What you heard in the last couple of weeks, DeepSeek is a piece of it, but everybody is working on this. I believe the cost of inference will meaningfully come down. I think it will make it much easier for companies to be able to infuse all their applications with inference and with generative AI.

Amazon’s management currently sees 2 main ways that companies are getting value out of AI; the 1st way is through productivity and cost savings, and it is the lowest-hanging fruit; the 2nd way is by building new experiences

There’s kind of two macro buckets of how we see people, both ourselves inside Amazon as well as other companies using AWS, how we see them getting value out of AI today. The first macro bucket, I would say, is really around productivity and cost savings. And in many ways, this is the lowest-hanging fruit in AI…

….I’d say the other big macro bucket are really altogether new experiences.

Amazon has built a chatbot with generative AI and it has lifted customer satisfaction by 500 basis points; Amazon has built a generative AI application for 3rd-party sellers to easily fill up their product detail pages; Amazon has built generative AI applications for inventory management that improve inventory forecasting by 10% and regional predictions by 20%; the brains of Amazon’s robotics are infused with generative AI

If you look at customer service and you look at the chatbot that we’ve built, we completely rearchitected it with generative AI. It’s delivering. It already had pretty high satisfaction. It’s delivering 500 basis points better satisfaction from customers with the new generative AI-infused chatbot.

If you look at our millions of third-party selling partners, one of their biggest pain points is, because we put a high premium on really organizing our marketplace so that it’s easy to find things, there’s a bunch of different fields you have to fill out when you’re creating a new product detail page, but we’ve built a generative AI application for them where they can either fill in just a couple of lines of text or take a picture of an image or point to a URL, and the generative AI app will fill in most of the rest of the information they have to fill out, which speeds up getting selection on the website and easier for sellers.

If you look at how we do inventory management and trying to understand what inventory we need, at what facility, at what time, the generative AI applications we’ve built there have led to 10% better forecasting on our part and 20% better regional predictions.

In our robotics, we were just talking about the brains in a lot of those robotics are generative AI-infused that do things like tell the robotic claw what’s in a bin, what it should pick up, how it should move it, where it should place it in the other bin that it’s sitting next to. So it’s really in the brains of most of our robotics.

Amazon’s Rufus is an AI-infused shopping assistant that is growing significantly; users can take a picture of a product with Amazon Lens and have the service surface the exact item through the use of AI; Amazon is using AI to know the relative sizing of clothes and shoes from different brands so that it can recommend the right sizes to shoppers; Amazon is using AI to improve the viewing experience of sporting events; Rufus provides a significant improvement to the shopping experience for shoppers and management expects the usage of Rufus to increase throughout 2025

You see lots of those in our retail business, ranging from Rufus, which is our AI-infused shopping assistant, which continues to grow very significantly; to things like Amazon Lens, where you can take a picture of a product that’s in front of you, you check it out in the app, you can find it in the little box at the top, you take a picture of an item in front of you, and it uses computer vision and generative AI to pull up the exact item in search result; to things like sizing, where we basically have taken the catalogs of all these different clothing manufacturers and then compare them against one another so we know which brands tend to run big or small relative to each other. So when you come to buy a pair of shoes, for instance, it can recommend what size you need; to even what we’re doing in Thursday Night Football, where we’re using generative AI for really inventive features like it sends alerts where we predict which players are going to put quarterback or defensive vulnerabilities, where we were able to show viewers what area of the field is vulnerable…

…I do think that Rufus, if you look at how it impacts the customer experience and if you actually use it month-to-month, it continues to get better and better. If you’re buying something and you’re on our product detail page, our product detail pages provide so much information that sometimes it’s hard, if you’re trying to find something quickly, to scroll through and find that little piece of information. And so we have so many customers now who just use Rufus to help them find a quick fact about a product. They also use Rufus to figure out how to summarize customer reviews so they don’t have to read 100 customer reviews to get a sense of what people think about that product. If you look at the personalization, really, most prominently today, your ability to go into Rufus and ask what’s happened to an order or what did I just order or can you pull up for me this item that I ordered 2 months ago, the personalization keeps getting much better. And so we expect throughout 2025, that the number of occasions where you’re not sure what you want to buy and you want help from Rufus are going to continue to increase and be more and more helpful to customers.

Amazon has around 1,000 generative AI applications that it has built or is building

We’ve got about 1,000 different generative AI applications we’ve either built or in the process of building right now.

Apple (NASDAQ: AAPL)

Apple Intelligence was first released in the USA in October 2024, with more features and countries introduced in December 2024; Apple Intelligence will be rolled out to even more countries in April 2025; management sees Apple Intelligence as a breakthrough for privacy in AI; SAP is using Apple Intelligence in the USA to improve the employee as well as customer experience; the Apple Intelligence features that people are using include Writing Tools, Image Playground, Genmoji, Visual Intelligence, Clean Up, and more; management has found Apple Intelligence’s email summarisation feature to be very useful; management thinks that different users will find their own “killer feature” within Apple Intelligence

In October, we released the first set of Apple Intelligence features in U.S. English for iPhone, iPad and Mac, and we rolled out more features and expanded to more countries in December.

Now users can discover the benefits of these new features in the things they do every day. They can use Writing Tools to help find just the right words, create fun and unique images with Image Playground and Genmoji, handle daily tasks and seek out information with a more natural and conversational Siri, create movies of their memories with a simple prompt and touch up their photos with Clean Up. We introduced visual intelligence with Camera Control to help users instantly learn about their surroundings. Users can also seamlessly access ChatGPT across iOS, iPadOS and macOS.

And we were excited to recently begin our international expansion with Apple Intelligence now available in Australia, Canada, New Zealand, South Africa and the U.K. We’re working hard to take Apple Intelligence even further. In April, we’re bringing Apple Intelligence to more languages, including French, German, Italian, Portuguese, Spanish, Japanese, Korean and simplified Chinese as well as localized English to Singapore and India. And we’ll continue to roll out more features in the future, including an even more capable Siri.

Apple Intelligence builds on years of innovations we’ve made across hardware and software to transform how users experience our products. Apple Intelligence also empowers users by delivering personal context that’s relevant to them. And importantly, Apple Intelligence is a breakthrough for privacy in AI with innovations like Private Cloud Compute, which extends the industry-leading security and privacy of Apple devices into the cloud…

…We’re excited to see leading enterprises such as SAP leverage Apple Intelligence in the U.S. with features like Writing Tools, summarize and priority notifications to enhance both their employee and customer experiences…

…In terms of the features that people are using, they’re using all of the ones that I had referenced in my opening comments, from Writing Tools to Image Playground and Genmoji, to visual intelligence and more. And so we see all of those being used. Clean Up is another one that is popular, and people love seeing that one demoed in the stores as well…

…I know from my own personal experience, once you start using the features, you can’t imagine not using them anymore. I know I get hundreds of e-mails a day, and the summarization function is so important…

…[Question] Do you guys see the upgraded Siri expected in April as something that will, let’s say, be the killer application among the suite of features that you have announced in Apple Intelligence?

[Answer] I think the killer feature is different for different people. But I think for most, they’re going to find that they’re going to use many of the features every day. And certainly, one of those is the — is Siri, and that will be coming over the next several months.

Many customers are excited about the iPhone 16 because of Apple Intelligence; the iPhone 16’s year-on-year performance was stronger in countries where Apple Intelligence was available compared to countries where Apple Intelligence was not available

Our iPhone 16 lineup takes the smartphone experience to the next level in so many ways, and Apple Intelligence is one of many reasons why customers are excited…

…We did see that the markets where we had rolled out Apple Intelligence, that the year-over-year performance on the iPhone 16 family was stronger than those where Apple Intelligence was not available…

Apple’s management thinks the developments in the AI industry brought on by DeepSeek’s emergence is a positive for Apple

[Question] There’s a perception that you’re a big beneficiary of lower cost of compute. And I was wondering if you could give your worldly perspective here on the DeepSeek situation.

[Answer] In general, I think innovation that drives efficiency is a good thing. And that’s what you see in that model. Our tight integration of silicon and software, I think, will continue to serve us very well.

Arista Networks (NYSE: ANET)

Cloud and AI titans were a significant contributor to Arista Networks’ revenue in 2024; management considers Oracle an AI titan too

Now shifting to annual sector revenue for 2024. Our cloud and AI titans contributed significantly at approximately 48%, keeping in mind that Oracle is a new member of this category.

Arista Networks’ core cloud AI and data center products are built off its extensible OS (operating system) and goes up to 800 gigabit Ethernet speeds

Our core cloud AI and data center products are built off a highly differentiated, extensible OS stack and is successfully deployed across 10, 25, 100, 200, 400 and 800 gigabit Ethernet speeds. It delivers power efficiency, high availability, automation and agility as the data centers demand, insatiable bandwidth capacity and network speeds for both front-end and back-end storage, compute and AI zones.

Arista Networks’ management expects the company’s 800 gigabit Ethernet switch to emerge as an AI back-end cluster in 2025

We expect 800 gigabit Ethernet to emerge as an AI back-end cluster in 2025.

Arista Networks’ management is still optimistic that AI revenues will reach $1.5 billion in 2025, including $750 million in AI back-end clusters; the $750 million in revenue from AI back-end clusters will have a major helping hand from 3 of the 5 major AI trials Arista Networks is working on that are rolling out a cumulative 100,000 GPUs in 2025 (see more below)

We remain optimistic about achieving our AI revenue goal of $1.5 billion in AI centers, which includes the $750 million in AI back-end clusters in 2025…

…[Question] You are reiterating $750 million AI back-end sales this year despite the stalled or the fifth customer. Can you talk about where is the upside coming from this year? Is it broad-based or 1 or 2 customers?

[Answer] We’re well on our way and 3 customers deploying a cumulative of 100,000 GPUs is going to help us with that number this year. And as we increased our guidance to $8.2 billion, I think we’re going to see momentum both in AI, cloud and enterprises. I’m not ready to break it down and tell you which where. I think we’ll see — we’ll know that much better in the second half. But Chantelle and I feel confident that we can definitely do the $8.2 billion that we historically don’t call out so early in the year. So having visibility if that helps.

Arista Networks is building some of the world’s greatest Arista AI centers at production scale and it’s involved with both the back-end clusters and front-end networks; Arista Networks’ management sees the data traffic flow of AI workloads as having significant differences from traditional cloud workloads and Arista AI centers can seamlessly connect to the front end compute storage with its backend Ethernet portfolio; Arista’s AI networking portfolio consists of 3 families and over 20 Etherlink switches

Networking for AI is also gaining traction as we move into 2025, building some of the world’s greatest Arista AI centers at production scale. These are constructed with both back-end clusters and front-end networks…

…The fidelity of the AI traffic differs greatly from cloud workloads in terms of diversity, duration and size of flow. Just one slow flow can flow the entire job completion time for a training workload. Therefore, Arista AI centers seamlessly connect to the front end of compute storage WAN and classic cloud networks with our back-end Arista Etherlink portfolio. This AI accelerated networking portfolio consists of 3 families and over 20 Etherlink switches, not just 1 point switch.

Arista Networks’ management’s AI for Networking strategy is doing well and it includes software that have superior AI ops

Our AI for networking strategy is also doing well, and it’s about curating the data for higher-level network functions. We instrument our customer’s networks with our published subscribed state Foundation with our software called Network Data Lake to deliver proactive, predictive and prescriptive platforms that have superior AI ops with A care support and product functions.

Arista Networks’ management is still committed to 4 of the 5 major AI trials that they have been discussing in recent earnings calls; the remaining AI trial is still stalled and the customer is not a Cloud Titan and is waiting for funding; 3 of the 4 trials that are active are expected to roll out a cumulative 100,000 GPUs in 2025 and they are all waiting for the next-generation NVIDIA GPU; Arista Networks’ management expects to do very well on the back-end with those 3 trials; the remaining trial of the 4 active trials is migrating from Infiniband to Ethernet to test the viability of Ethernet, and Arista Networks’ management expects to enter production in 2026

I want to say Arista is still committed to 4 out of our 5 AI clusters that I mentioned in prior calls, but just one is a little bit stalled. It is not a Cloud Titan. They are awaiting GPUs and some funding too, I think. So I hope they’ll come back next year, but for this year, we won’t talk about them. But the remaining 4, let me spend some — jgive you some color, 3 out of the 4 customers are expected to this year rolled out a cumulative of 100,000 GPUs. So we’re going to do very well with 3 of them on the back end. And you can imagine, they’re all pretty much one major NVIDIA class of GPU — it’s — they will be waiting for the next generation of GPUs. But independent of that, we’ll be rolling out fairly large numbers. On the fourth one, we are migrating right now from InfiniBand to proving that Ethernet is a viable solution, so we’re still — they’ve historically been InfiniBand. And so we’re still in pilot and we expect to go into production next year. We’re doing very well in 4 out of 4, the Fifth one installed and 3 out of the 4 expected to be 100,000 GPUs this year.

Arista Networks thinks the market for AI networking is large enough that there will be enough room for both the company and other whitebox networking manufacturers; management also thinks Arista Networks’ products have significant differentiation from whitebox products, especially in the AI spine in a typical leaf-spine network, because Arista Networks’ products can automatically provide an alternate connection when a GPU in the network is in trouble

[Question] Can you maybe share your perspective that when it comes to AI network especially the back-end networks, how do you see the mix evolving white box versus OEM solution?

[Answer] This TAM is so huge and so large. We will always coexist with white boxes and operating systems that are non-EOS, much like Apple coexists on the iPhone with other phones of different types. When you look at the back end of an AI cluster, there are typically 2 components, the AI lead and the AI spine. The AI lead connects to the GPUs and therefore, is the first, if you will, point of connection. And the AI spine aggregates all of these AI leads. Almost in all the back-end examples we’ve seen, the AI spine is generally 100% Arista-branded EOS. You’ve got to do an awful lot of routing, scale, features, capabilities that are very rich that would be difficult to do in any other environment. The AI leads can vary. So for example, the — let’s take the example of the 5 customers I mentioned a lot, 3 out of the 5 are all EOS in the [indiscernible] spine. 2 out of the 5 are kind of hybrids. Some of them have some form of SONic or FBOSS. And as you know, we co-develop with them and coexist in a number of use cases where it’s a real hybrid combination of EOS and an open OS. So for most part, I’d just like to say that white box and Arista will coexist and will provide different strokes for different folks…

…A lot of our deployments right now is 400 and 800 gig, and you see a tremendous amount of differentiation, not only like I explained to you in scale and routing features, but cost and load balancing, AI visibility and analytics at real time, personal queuing, congestion control, visibility and most importantly, smart system upgrade because you sort of want your GPUs to come down because you don’t have the right software to accelerate so that the network provides the ideal foundation that if the GPU is in trouble, we can automatically give a different connection and an alternate connection. So tremendous amount of differentiation there and even more valid in a GPU which costs typically 5x as much as a CPU…

…When you’re buying these expensive GPUs that cost $25,000, they’re like diamonds, right? You’re not going to string a diamond on a piece of thread. So first thing I want to say is you need a mission-critical network, whether you want to call it white box, blue box, EOS or some other software, you’ve got to have mission-critical functions, analytics, visibility, high availability, et cetera. As I mentioned, and I want to reiterate, they’re also typically a leaf spine network. And I have yet to see an AI spine deployment that is not EOS-based. I’m not saying it can’t happen or won’t happen. But in all 5 major installations, the benefit of our EOS features for high availability for routing, for VXLAN, for telemetry, our customers really see that. And the 7800 is the flagship AI spine product that we have been deploying last year, this year and in the future. Coming soon, of course, is also the product we jointly engineered with Meta, which is the distributed [Ecolink] switch. And that is also an example of a product that provides that kind of leaf spine combination, both with FBOSS and EOS options in it. So in my view, it’s difficult to imagine a highly resilient system without Arista EOS in AI or non-AI use cases.

On the leaf, you can cut corners. You can go with smaller buffers, you may have a smaller installation. So I can imagine that some people will want to experiment and do experiment in smaller configurations with non-EOS. But again, to do that, you have to have a fairly large staff to build the operations for it. So that’s also a critical element. So unless you’re a large Cloud Titan customer, you’re less likely to take that chance because you don’t have the staff.

Arista Networks’ management is seeing strong demand from its Cloud Titan customers

Speaking specifically to Meta, we are obviously in a number of use cases in Meta. Keep in mind that our 2024 Meta numbers is influenced by more of their 2023 CapEx, and that was Meta’s year of efficiency where their CapEx was down 15% to 20%. So you’re probably seeing some correlation between their CapEx being down and our revenue numbers being slightly lower in ’24. In general, I would just say all our cloud titans are performing well in demand, and we shouldn’t confuse that with timing of our shipments. And I fully expect Microsoft and Meta to be greater than 10% customers in a strong manner in 2025 as well. Specific to the others we added in, they’re not 10% customers, but they’re doing very well, and we’re happy with their cloud and AI use cases.

Arista Networks’ management thinks the emergence of DeepSeek will lead to AI development evolving from back-end training that’s concentrated in a handful of users, to being distributed more widely across CPUs and GPUs; management also thinks DeepSeek’s emergence is a positive for Arista Networks because DeepSeek’s innovations can drive the AI industry towards a new class of CPUs, GPUs, AI accelerators and Arista Networks is able to scale up network for all kinds of XPUs

DeepSeek certainly deep fixed many stocks, but I actually see this as a positive because I think you’re now going to see a new class of CPUs, GPUs, AI accelerators and where you can have substantial efficiency gains that go beyond training. So that could be some sort of inference or mixture of experts or reasoning and which lowers the token count and therefore, the cost. So what I like about all these different options is Arista can scale up network for all kinds of XPUs and accelerators. And I think the eye-opening thing here for all of our experts who are building all these engineering models is there are many different types and training isn’t the only one. So I think this is a nice evolution of how AI will not just be a back-end training only limited to 5 customers type phenomenon, but will become more and more distributed across a range of CPUs and GPUs.

Arista Networks’ management thinks hyper-scale GPU clusters, such as Project Stargate, will drive the development of vertical rack integration in the next few years and Andy Bechtolsheim, an Arista Networks co-founder, is personally involved in these projects

If you look at how we have classically approached GPUs and connected libraries, we’ve largely looked at it as 2 separate building blocks. There’s the vendor who provides the GPUs and then there’s us who provides the scale-out networking. But when you look at Stargate and projects like this, I think you’ll start to see more of a vertical rack integration where the processor, the scale up, the scale out and all of the software to provide a single point of control and visibility starts to come more and more together. This is not a 2025 phenomenon, but definitely in ’26 and ’27, you’re going to see a new class of AI accelerators for — and a new class of training and inference, which is extremely different than the current more pluggable label type of version. So we’re very optimistic about it.

Andy Bechtolsheim is personally involved in the design of a number of these next-generation projects and the need for this type of shall we say, pushing Moore’s Law of improvements in density of performance that we saw in the 2000s is coming back, and you can boost more and more performance per XPU, which means you have to boost the network scale from 800 gig to 1.16.

Arista Networks’ management sees a $70 billion total addressable market in 2028, of which roughly a third is related to AI

[Question] If you can talk to the $70 billion TAM number for 2028, how much is AI?

[Answer] On the $70 billion TAM in 2028, I would roughly say 1/3 is AI, 1/3 is data center and cloud and 1/3 is campus and enterprise. And obviously, absorbed into that is routing and security and observability. I’m not calling them out separately for the purpose of this discussion.

Arista Networks’ management sees co-packaged optics (CPO) as having weak adoption compared to co-packaged copper (CPC) because CPO has been experiencing field failures

Co-packaged optics is not a new idea. It’s been around 10 to 20 years. So the fundamental reason, let’s go through why co-packaged optics has had a relatively weak adoption so far is because of field failures and most of it is still in proof of concept today. So going back to networking, the most important attribute of a network switch is reliability and troubleshooting. And once you solder a co-packaged optics on a PCB, you lose some of that flexibility and you don’t get the serviceability and manufacturing. That’s been the problem. Now a number of alternatives are emerging, and we’re a big fan of co-packaged copper as well as pluggable optics that can complement this like linear drive or LTO as we call it.

Now we also see that if co-packaged optics improves some of the metrics it has right now. For example, it has a higher channel count than the industry standard of 8-channel pluggable optics, but we can do higher channel pluggable optics as well. So some of these things improve, we can see that both CPC and CPO will be important technologies at 224 gig or even 448 gig. But so far, our customers have preferred a LEGO approach that they can mix and match pluggable switches and pluggable optics and haven’t committed to soldering them on the PCB. And we feel that will change only if CPO gets better and more reliable. And I think CPC can be a nice alternative to that.

Arista Networks’ management is seeing customers start moving towards actual use-cases for AI, but the customers are saying that these AI projects take time to implement

For the AI perspective, speaking with the customers, it’s great to move from kind of a theory to more specific conversation, and you’re seeing that in the banks and some of the higher tier Global 2000, Fortune 500 companies. And so they’re moving from theory to actual use cases they’re speaking to. And the way they describe it is it takes a bit of time. They’re working mostly with cloud service providers at the beginning, kind of doing some training and then they’re deciding whether they bring that on-prem and inference. So they’re making those decisions.

Arista Networks’ management is seeing a new class of Tier 2 specialty AI cloud providers emerge

We are seeing a new class of Tier 2 specialty cloud providers emerge that want to provide AI as a service and want to be differentiated there. And there’s a whole lot of funding, grant money, real money going in there. So service providers, too early to call. But Neo clouds and specialty providers, yes, we’re seeing lots of examples of that.

The advent of AI has accelerated the speed-transitions in networking data switches, but there’s still going to be a long runway for Arista Networks’ 400 gig and 800 gig products, with 1.6 tera products being deployed in a measured way

The speed transitions because of AI are certainly getting faster. It used to take when we went from 200 gig, for example, at Meta or 100 gig in some of our Cloud Titans to 400, that speed transition typically took 3 to 4, maybe even 5 years, right? In AI, we see that cycle being almost every 2 years…

…2024 was the year of real 400 gig. ’25 and ’26, I would say, is more 800 gig. And I really see 1.6T coming into the picture because we don’t have chips yet, maybe in what do you say, John, late ’26 and real production maybe in ’27. So there’s a lot of talk and hype on it, just like I remember talk and hype on 400 gig 5 years ago. But I think realistically, you’re going to see a long runway for 400 and 800 gig. Now as we get into 1.6T, part of the reason I think it’s going to be measured and thoughtful is many of our customers are still awaiting their own AI accelerators or NVIDIA GPUs, which with liquid cooling that would actually push that kind of bandwidth. So new GPUs will require new bandwidth, and that’s going to push it out a year or 2.

Arista Networks’ management sees a future where the market share between NVIDIA GPUs and custom AI accelerators (ASICs) is roughly evenly-split, but Arista Networks’ products will be GPU-agnostic

[Question] There’s been a lot of discussion over the last few months between the general purpose GPU clusters from NVIDIA and then the custom ASIC solutions from some of your popular customers. I guess just in your view, over the longer term, does Arista’s opportunity differ across these 2 chip types?

[Answer] I think I’ve always said this, you guys often spoke about NVIDIA as a competitor. And I don’t see it that way. I see that — thank you, NVIDIA. Thank you, Jensen, for the GPUs because that gives us an opportunity to connect to them, and that’s been a predominant market for us. As we move forward, we see not only that we connect to them, but we can connect to AMD GPUs and built in in-house AI accelerators. So a lot of them are in active development or in early stages. NVIDIA is the dominant market share holder with probably 80%, 90%. But if you ask me to guess what it would look like 2, 3 years from now, I think it could be 50-50. So Arista could be the scale-out network for all types of accelerators. We’ll be GPU agnostic. And I think there’ll be less opportunity to bundle by specific vendors and more opportunity for customers to choose best-of-breed. 

ASML (NASDAQ: ASML)

AI will be the biggest driver of ASML’s growth and management sees customers benefiting very strongly from it; management thinks ASML will hit the upper end of the revenue guidance range for 2025 if its customers can bring on additional AI-related capacity during the year, but there are also risks that could result in only the lower end of the guidance coming true

We see total revenue for 2025 between €30 billion and €35 billion and the gross margin between 51% and 53%. AI is the clear driver. I think we started to see that last year. In fact, at this point, we really believe that AI is creating a shift in the market and we have seen customers benefiting from it very strongly…

…If AI demand continues to be strong and customers are successful in bringing on additional capacity online to support that demand, there is potential opportunity towards the upper end of our range. On the other hand, there are also risks related to customers and geopolitics that could drive results towards the lower end of the range.

ASML’s management is still very positive on the long-term outlook for ASML, with AI being a driver for growth; management expects AI to create a shift in ASML’s end-market products towards more HPC (high performance computing) and HBM (high bandwidth memory), which requires more advanced logic and DRAM, which in turn needs more critical lithography exposures

I think our view on the long term is still, I would say, very positive…

…Looking longer term, overall the semiconductor market remains strong with artificial intelligence creating growth but also a shift in market dynamics as I highlighted earlier. These dynamics will lead to a shift in the mix of end market products towards more HPC and HBM which requires more advanced logic and DRAM. For ASML, we anticipate that an increased number of critical lithography exposures for these advanced Logic and Memory processes will drive increasing demand for ASML products and services. As a result, we see a 2030 revenue opportunity between 44 billion euros and 60 billion euros with gross margins expected between 56 percent and 60 percent, as we presented in Investor Day 2024.

ASML’s management is seeing aggressive capacity addition among some DRAM memory customers because of demand for high bandwidth memory (HBM), but apart from HBM, other DRAM memory customers have a slower recovery

 I think that high-bandwidth memory is driving today, I would say, also an aggressive capacity addition, at least for some of the customer. I think on the normal DRAM, I would say, my comment is similar to the one on mobile [ photology ] before. I think there are also nothing spectacular, but there is some recovery, which also called for more capacity. So that’s why we still see DRAM pretty strong in 2025.

Datadog (NASDAQ: DDOG)

Datadog launched LLM Observability in 2024; management continues to see increased interest in next-gen AI; 3,500 Datadog customers at the end of 2024 Q4 used 1 or more Datadog AI integrations; when it comes to AI inference, management is seeing most customers using a 3rd-party AI model through an API or a 3rd-party inference platform, and these customers want to observe whether the model is doing the right thing, and this need is what LLM Observability is serving; management is seeing very few customers running the full AI inference stack currently, but they think this could happen soon and it would be an exciting development

We launched LLM observability, in general availability to help customers evaluate, safely deploy and manage their models in production, and we continue to see increased interest in next-gen AI. At the end of Q4, about 3,500 customers use 1 or more Datadog AI integrations to send this data about their machine learning, AI, and LLM usage…

…On the inference side, the — mostly still what customers do is they use a third-party model either through an API or through a third-party inference platform. And what they’re interested in is measuring whether that model is doing the right thing. And that’s what we serve right now with LLM observability, for example, as well, we see quite a bit of adoption that does not come largely from the AI-native companies. So that’s what we see today.

In terms of operating the inference stack fully and how we see relatively few customers with that yet, we think that’s something that’s going to come next. And by the way, we’re very excited by the developments we see in the space. So it looks like there is many, many different options that are going to be viable for running your AI inference. There’s a very healthy set of commercial API-gated services. There’s models that you can install in the open source. There are models in the open source today that are rivalling in quality with the best closed API models. So we think the ecosystem is developing into a rich diversification that will allow customers to have a diversity of modalities for using AI, which is exciting. 

AI-native customers accounted for 6% of Datadog’s ARR in 2024 Q4 (was 6% 2024 Q3); AI-native customers contributed 5 percentage points to Datadog’s year-on-year growth in 2024 Q4, compared to 3 percentage points in 2023 Q4; among customers in the AI-native cohort, management has seen optimisation of usage and volume discounts related to contracts in 2024 Q4, and management thinks these customers will continue to optimise cloud and observability usage in the future; the dynamics with the AI-native cohort that happened in 2024 Q4 was inline with management’s expectations

We continue to see robust contribution from AI native customers who represented about 6% of Q4 ARR roughly the same as the quarter — as last quarter and up from about 3% of ARR in the year-ago quarter. AI native customers contributed about 5 percentage points of year-over-year revenue growth in Q4 versus 4 points in the last quarter and about 3 points in the year-ago quarter. So we saw strong growth from AI native customers in Q4. We believe that adoption of AI will continue to benefit Datadog in the long term. Meanwhile, we did see some optimization and volume discounts related to contract renewals in Q4. We remain mindful that we may see volatility in our revenue growth on the backdrop of long-term volume growth from this cohort as customers renew with us on different terms, and as they may choose to optimize cloud and observability usage. ..

… [Question] I’m trying to understand if the AI usage and commits are kind of on the same trajectory that they were on or whether you feel that there are some oscillations there.

[Answer] What happened during the quarter is pretty much what we thought would happen when we discussed it in the last earnings call. When you look at the AI cohort, we definitely saw some renewals with higher commit, better terms and optimization usage all at the same time, which is fairly typical, which typically happens with larger end customers in particular is at the time of renewal, customers are going to trying and optimize what they can. They’re going to get better prices from us, up their commitments and we might see a flat or down a month or quarter after that, with a still sharp growth from the year before and growth to come in the year to come. So that’s what we typically see. When you look at the cohort as a whole, even with that significant renewal optimization and better unit economics this quarter is wholly stable, this quarter as a whole is stable quarter-to-quarter in its revenue and it’s growing a lot from the quarter before, even with all that.

Datadog’s management sees some emerging opportunities in Infrastructure Monitoring that are related to the usage of GPUs (Graphics Processing Units), but the opportunities will only make sense if there is broad usage of GPUs by a large number of customers, which is not happening today

There’s a number of new use cases that are emerging that are related to infrastructure that we might want to cover. Again, we — when I say they’re emerging, they’re actually emerging, like we still have to see what the actual need is from a large number of customers. I’m talking in particular about infrastructure concerns around GPU management, GPU optimization, like there’s quite a lot going on there that we can potentially do. But we — for that, we need to see broad usage of the raw GPUs by a large number of customers as opposed to usage by a smaller number of native customers, which is mostly what we still see today.

Datadog’s management thinks it’s hard to tell where AI agents can be the most beneficial for Datadog’s observability platform because it’s still a very nascent field and management has observed that things change really quickly; when management built LLM Observability, the initial use cases were for AI chatbots and RAG (retrieval augmented generation), but now the use cases are starting to shift towards AI agents

[Question] Just curious, when we think about agents, which parts of the core observability platform that you think are most relevant or going to be most beneficial to your business as you start to monitor those?

[Answer] It’s a bit hard to tell because it’s a very nascent field. So my guess is in a year if we probably look different from what it looks like today. Just like this year, it looks very different from what it looks like last year. What we do see, though, is that — so when we built — we started building our LLM Observability product, most of the use cases we saw there from customers were chatbot in nature or RAG in nature, trying to access information and return the information. Now we see more and more customers building agents on top of that and sending the data from their agents. So we definitely see a growing trend there of adoption and the LLM Observability product is a good level of abstraction, at least for the current iteration of these agents to get them. So that’s what we can see today.

Datadog’s management sees AI touching many different areas of Datadog, such as how software is being written and deployed, how customer-support is improved, and more

What’s fascinating about the current evolution of AI, in particular, is that it touches a lot of the different areas of the business. The first area for company like ours the first area to be transformed is really the way software is being built. What engineers use, how they write software, how they debug software, how do they also operate systems. And part of that is outside tooling we’re using for writing software. Part of that is dogfooding, or new products for incident resolution and that sort of thing. So that’s the first area. There’s a number of other areas that are going to see large improvements in productivity. Typically, everything that has to do with supporting customers, helping with onboarding and helping troubleshoot issues like all of that is in acceleration. In the end, we expect to see improvements everywhere, from front office to back office.

Fiverr (NYSE: FVRR)

Fiverr’s management launched Fiverr Go in February 2025, an open platform for personalised AI tools designed to give creators full control over their creative processes and rights; Fiverr Go enables freelancers to build personalised AI models (there was a presentation on this recently) without having to know AI engineering; Fiverr Go is designed to be personalised for the creator, so the creator becomes more important compared to the AI technology; Fiverr Go is generative AI technology with human accountability (will be interesting to see if Fiverr Go is popular; people can create designs/images with other AI models, so customers who use Fiverr Go are those who need the special features that Fiverr Go offers); Fiverr Go generates content that is good enough for mission critical business tasks, unlike what’s commonly happening with other AI-generated content; Fiverr Go is no different from a direct order from the freelancer themself, except it is faster and easier for buyers; Fiverr Go has personalised AI assistants for freelancers; Fiverr Go has an open developer platform for 3rd-party developers to build generative AI apps

Fiverr Go is an open platform for personalized AI tools that include the personalized AI assistant and the AI creation model. Different from other AI platforms that often exploit human creativity without proper attribution or compensation, Fiverr Go is uniquely designed to reshape this power dynamic by giving creators full control over their creative process and rights. It also enables freelancers to build personalized AI models without the need to collect training data sets or understand AI engineering, thanks to Fiverr’s unparalleled foundation of over 6.5 billion interactions and nearly 150 million transactions on the marketplace and most importantly, it allows freelancers to become a one-person production house, making more money while focusing on the things that matter. By giving freelancers control over configuration, pricing and creative rights and leveling the playing field of implementing AI technology, Fiverr Go ensures that creators remain at the center of the creative economy. It decisively turned the power dynamic between humans and AI towards the human side…

…For customers, Fiverr Go is also fundamentally different from other AI platforms. It is GenAI with human accountability. AI results often feel unreliable, generic and very hard to edit. What is good enough for a simple question and answer on ChatGPT does not cut it for business mission-critical tasks. In fact, many customers come to Fiverr today with AI-generated content because they miss the confidence that comes from another human eye and talent, helping them perfect the results for their needs. Fiverr Go eliminates all of this friction and frustration. Every delivery on Fiverr Go is backed by the full faith of the creator behind it with an included revision as the freelancer defines. This means that the quality and service you get from Fiverr Go is no different from a direct order from the freelancers themselves, except for a faster, easier and more delightful experience. The personalized AI assistant on Fiverr Go can communicate with potential clients when the freelancer is away or busy, handle routine tasks and provide actionable business insights, effectively becoming the creator’s business partner. It often feels smarter than an average human assistant because it’s equipped with all the history of how the freelancer works as well as knowledge of trends and best practices on the Fiverr marketplace…

…We’ve also announced an open developer platform on Fiverr Go to allow AI specialists and model developers to build generative AI applications across any discipline. We provide them with an ecosystem to collaborate with domain experts on Fiverr and the ability to leverage Fiverr’s massive data assets so that these applications can solve real-world problems and most important of all, Fiverr provides them an avenue to generate revenue from those applications through our marketplace…

…So from our experience with AI, what we come to learn is that a lot of the creation process using AI is very random and take you through figuring out what are the best tools because there’s thousands of different options around AI. And each one operates slightly differently. And you need to master each one of them. And you need to become a prompt engineer. And then editing is extremely, extremely hard. Plus you don’t get the feedback that comes from working with a human being that can actually look at the creation from a human eye and give you a sense if this is actually capturing what you’re trying to do. It allows us or allows freelancers to design their own model in a way that rewards them but remains extremely accurate to their style, allowing customers to get the results they expect to get because they see the portfolio of their freelancer, like the style of writing or design or singing or narration, and they can get exactly this. So we think that, that combination and that confidence that comes from the fact that the creator itself is always there.

The AI personal assistant in Fiverr Go can help to respond to customer questions based on individual freelancers; the first 3 minutes after a buyer writes to a freelancer is the most crucial time for conversion and this is where the AI assistant can help; there are already thousands of AI assistants running on Fiverr Go, converting customers

Fiverr Go is actually a tool for conversion. That’s the entire idea because we know that customers these days expect instant responses and instant results. And as a result of that, we designed those 2 tools, the AI personal assistant, which is able to answer customer questions immediately even if the freelancer is away or busy. We know that the first 3 minutes after a customer writes to a freelancer are the most crucial time for conversion and this is why we designed this tool. And this tool is essentially encapsulating the entire knowledge of the freelancer and basing itself on it, being able to address any possible question and bring it to conversion…

…It’s fresh from yesterday, but we have many thousands of assistants running on the system, converting customers already, which is an amazing sign.

Fiverr Go is a creator tool that can create works based off freelancers’ style and allows customers to get highly-accurate samples of a freelancers’ work to lower friction in selecting freelancers

When we think about the creation model, the creation model allows customers to get the confidence that this is the freelancer, this is the style that they’re looking for, because now instead of asking a freelancer for samples, waiting for it, causing the freelancer to essentially work for free, they can get those samples right away. Now the quality of these samples is just mind-blowing. The level of accuracy that these samples produce are exact match with the style of the freelancer, which gives the customer the confidence that if they played and liked it, this is the type of freelancer that they should engage with.

The Fiverr Go open developer platform is essentially an app store for AI apps; the open developer platform allows developers to train AI models on Fiverr’s transactional data set, which is probably the largest dataset of its kind in existence

Now what we’re doing with this is actually we’re opening up the Go platform to outside developers. Think about it as an app store in essence. So what we’re doing is we’re allowing them to develop models, APIs, workflows, but then train those models on probably the biggest transactional data set in existence today that we hold so that they can actually help us build models that freelancers can join — can enjoy from. And we believe that by doing so and giving those developers incentives to do so because every time their app is going to be used for a transaction, they’re going to make money out of it.

Fiverr Go’s take rate will be the same for now and management will learn as they go

[Question] Would your take rate be different in Fiverr Go?

[Answer] For now, the take rate remains the same for Go. And as we roll it out and as we see usage, we will figure out what to do or what’s the right thing to do. For now, we treat it as a normal transaction with the same take rate.

Mastercard (NYSE: MA)

Mastercard closed the Recorded Future acquisition in 2024 Q4 (Recorded Future provides AI-powered solutions for real-time visibility into potential threats related to fraud); Recorded Future has been deploying AI for over a decade, just like Mastercard has; Recorded Future uses AI to analyse threat data across the entire Internet; the acquisition of Recorded Future improves Mastercard’s cybersecurity solutions

Our diverse capabilities in payments and services and solutions, including the acquisition of Recorded Future this quarter set us apart…

…Recorded Future is the world’s largest threat intelligence company with more than 1,900 customers across 75 countries. Customers include over 50% of the Fortune 100 and government agencies in 45 countries, including more than half of the G20. We’ve been deploying AI at scale for well over a decade, so has Recorded Future. They leverage AI-powered insights to analyze threat data from every corner of the Internet and customers gain real-time visibility and actionable insights to proactively reduce risks. We now have an even more robust set of powerful intelligence, identity, dispute, fraud and scan prevention solutions. Together, these uniquely differentiated technologies will enable us to create smarter models, distribute these capabilities more broadly and help our customers anticipate threats before cyber-attacks can take place. That means better protection for governments, businesses, banks, consumers the entire ecosystem and well beyond the payment transactions. We’re also leveraging our distribution at scale to deepen market penetration of our services and solutions.

Meta Platforms (NASDAQ: META)

Meta’s management expects Meta AI to be the leading AI assistant in 2025, reaching more than 1 billion people; Meta AI is already the most-used AI assistant in the world with more than 700 million monthly actives; management believes Meta AI is at a scale that allows it to develop a durable long-term advantage; management has an exciting road map for Meta AI in 2025 that focuses on personalisation; management does not believe that there’s going to be only one big AI that is the same for everyone; there are some fun surprises for Meta AI in 2025 that management has up their sleeves; Meta AI can now remember certain details of people’s prior queries; management sees a few possible paths for Meta AI’s monetisation, but their focus right now is just on building a great user experience; WhatsApp has the strongest Meta AI usage, followed by Facebook; people are using Meta AI on WhatsApp for informational, educational, and emotional purposes 

 In AI, I expect that this is going to be the year when a highly intelligent and personalized AI assistant reaches more than 1 billion people, and I expect Meta AI to be that leading AI assistant. Meta AI is already used by more people than any other assistant. And once a service reaches that kind of scale, it usually develops a durable long-term advantage.

We have a really exciting road map for this year with a unique vision focused on personalization. We believe that people don’t all want to use the same AI. People want their AI to be personalized to their context, their interests, their personality, their culture, and how they think about the world. I don’t think that there’s just going to be one big AI that everyone uses that does the same thing. People are going to get to choose how their AI works and what it looks like for them. I continue to think that this is going to be one of the most transformative products that we’ve made, and we have some fun surprises that I think people are going to like this year…

… Meta AI usage continues to scale with more than 700 million monthly actives. We’re now introducing updates that will enable Meta AI to deliver more personalized and relevant responses by remembering certain details from people’s prior queries and considering what they engage with on Facebook and Instagram to develop better intuition for their interest and preferences…

…Our initial focus for Meta AI is really about building a great consumer experience, and that’s frankly, where all of our energies are kind of directed to right now. There will, I think, be pretty clear monetization opportunities over time, including paid recommendations and including a premium offering, but really not where we are focused in terms of the development of Meta AI today…

…WhatsApp continues to see the strongest Meta AI usage across our family of apps. People there are using it most frequently for information seeking and educational queries along with emotional support use cases. Most of the WhatsApp engagement is in one-on-one threads, though we see some usage in group messaging. And on Facebook, which is the second largest driver of Meta AI engagement, we’re seeing strong engagement from our feed deep dives integration that lets people ask Meta AI questions about the content that is recommended to that. So across, I would say, all query types, we continue to see signs that Meta AI is helping people leverage our apps for new use cases. We talked about information gathering, social interaction and communication Lots of people use it for humor and casual conversation. They use it for writing and editing research recommendations. 

Meta’s management thinks Llama will become the most advanced and widely-used AI model in 2025; Llama 4 is making great progress; Meta has a reasoning Llama model in the works; management’s goal for Llama 4 is for it be the leading AI model; Llama 4 is built to be multi-modal and agentic; management expects Llama 4 to unlock a lot of new use cases

I think this will very well be the year when Llama and open-source become the most advanced and widely used AI models as well. Llama 4 is making great progress in training, Llama 4 Mini is doing with pretraining and our reasoning models and larger model are looking good too. 

Our goal with Llama 3 was to make open source competitive with closed models. And our goal for Llama 4 is to lead. Llama 4 will be natively multimodal. It’s an omni model, and it will have agenetic capabilities. So it’s going to be novel, and it’s going to unlock a lot of new use cases.

Meta’s management thinks it will be possible in 2025 to build an AI engineering agent that is as capable as a human mid-level software engineer; management believes that the company that builds this AI engineering agent first will have a meaningful advantage in advancing AI research; Meta already has internal AI coding assistants, powered by Llama; management has no plan to release the AI engineer as an external product anytime soon, but sees the potential for it in the longer-term; management does not expect the AI engineer to be extremely widely deployed in 2025, with the dramatic changes happening in 2026 and beyond

I also expect that 2025 will be the year when it becomes possible to build an AI engineering agent that has coding and problem-solving abilities of around a good mid-level engineer. And this is going to be a profound milestone and potentially one of the most important innovations in history, like as well as over time, potentially a very large market. Whichever company builds this first, I think it’s going to have a meaningful advantage in deploying it to advance their AI research and shape the field…

…As part of our efficiency focus over the past 2 years, we’ve made significant improvements in our internal processes and developer tools and introduce new tools like our AI-powered coding assistant, which is helping our engineers write code more quickly. Looking forward, we expect that the continuous advancements in Llama’s coding capabilities will provide even greater leverage to our engineers, and we are focused on expanding its capabilities to not only assist our engineers in writing and reviewing our code but to also begin generating code changes to automate tool updates and improve the quality of our code base…

…And then the AI engineer piece, I’m really excited about it. I mean, I don’t know that that’s going to be an external product anytime soon. But I think for what we’re working on, our goal is to advance AI research and advance our own development internally. And I think it’s just going to be a very profound thing. So I mean that’s something that I think will show up through making our products better over time. But — and then as that works, there will potentially be a market opportunity down the road. But I mean, for now and this year, we’re really — I think this is — I don’t think you’re going to see this year like an AI engineer that is extremely widely deployed, changing all of development. I think this is going to be the year where that really starts to become possible and lays the groundwork for a much more dramatic change in ’26 and beyond.

The Ray-Ban Meta AI glasses are a big hit so far but management thinks 2025 will be the pivotal year to determine if the AI glasses can be on a path towards being the next computing platform and selling hundreds of millions, or more, units; management continues to think that glasses are the perfect form factor for AI; management is optimistic about AI glasses, but there’s still uncertainty about the long-term trajectory

Our Ray-Ban Meta AI glasses are a real hit. And this will be the year when we understand the trajectory for AI glasses as a category. Many breakout products in the history of consumer electronics have sold 5 million to 10 million units and they’re third generation. This will be a defining year that determines if we’re on a path towards many hundreds of millions and eventually billions of AI glasses and glasses being the next computing platform like we’ve been talking about for some time or if this is just going to be a longer grind. But it’s great overall to see people recognizing that these glasses are the perfect form factor for AI as well as just great stylish glasses…

…There are a lot of people in the world who have glasses. It’s kind of hard for me to imagine that a decade or more from now, all the glasses aren’t going to basically be AI glasses as well as a lot of people who don’t wear glasses today, finding that to be a useful thing. So I’m incredibly optimistic about this…

…But look, the Ray-Ban Metas were hit. We still don’t know what the long-term trajectory for this is going to be. And I think we’re going to learn a lot this year. 

Meta will bring ~1 gigawatt of AI data center capacity online in 2025 and is building an AI data center that is at least 2 gigawatts in capacity; management intends to fund the investments through revenue growth that is driven by its AI advances; most of Meta’s new headcount growth will go towards its AI infrastructure and AI advances; management expects compute will be very important for the opportunities they want Meta to pursue; management is simultaneously growing Meta’s capacity and increasing the efficiency of its workloads; Meta is increasing the useful lives of its non-AI and AI servers to 5.5 years (from 4-5 years previously), which will lead to lower depreciation expenses per year; Meta started deploying its own MTIA (Meta Training and Inference Accelerator) AI chips in 2024 for inference workloads; management expects to ramp up MTIA usage for inference in 2025 and for training workloads in 2026; management will continue to buy third-party AI chips (likely referring to NVIDIA), but wants to use in-house chips for unique workloads; management thinks MTIA helps Meta achieve greater compute efficiency and performance per cost and power; management has been thinking about the balance of compute used in pre-training versus inference, but this does not mean that Meta will need less compute; management thinks that inference-time compute (or test-time compute) scaling will help Meta deliver a higher quality of service and that Meta has a strong business model to support the delivery of inference-time compute scaling; management believes that investing heavily in AI infrastructure is still going to be a strategic advantage over time, but it’s possible the reverse may be true in the future; management thinks it’s too early to tell what the long-run capacity intensity will look like

I announced last week that we expect to bring online almost a gigawatt of capacity this year. And we’re building a 2 gigawatt and potentially bigger AI data center that is so big that it will cover a significant part of Manhattan if we were placed there. We’re planning to fund all of this by, at the same time, investing aggressively in initiatives that use these AI advances to increase revenue growth…

…That’s what a lot of our new headcount growth is going towards and how well we execute on this will also determine our financial trajectory over the next few years…

…We expect compute will be central to many of the opportunities we’re pursuing as we advance the capabilities of Llama, drive increased usage of generative AI products and features across our platform and fuel core ads and organic engagement initiatives. We’re working to meet the growing capacity needs for these services by both scaling our infrastructure footprint and increasing the efficiency of our workloads…

…Our expectation going forward is that we’ll be able to use both our non-AI and AI [indiscernible] servers for a longer period of time before replacing them, which we estimate will be approximately 5.5 years. This will deliver savings in annual CapEx and resulting depreciation expense, which is already included in our guidance.

Finally, we’re pursuing cost efficiencies by deploying our custom MTIA silicon in areas where we can achieve a lower cost of compute by optimizing the chip to our unique workloads. In 2024, we started deploying MTIA to our ranking and recommendation inference workloads for ads and organic content. We expect to further ramp adoption of MTIA for these use cases throughout 2025, before extending our custom silicon efforts to training workloads for ranking and recommendations next year…

…We expect that we are continuing to purchase third-party silicon from leading providers in the industry. And we are certainly committed to those long-standing partnerships, but we’re also very invested in developing our own custom silicon for unique workloads, where off-the-shelf silicon isn’t necessarily optimal and specifically because we’re able to optimize the full stack to achieve greater compute efficiency and performance per cost and power because our workloads might require a different mix of memory versus network, bandwidth versus compute and so we can optimize that really to the specific needs of our different types of workloads.

Right now, the in-house MTIA program is focused on supporting our core ranking and recommendation inference workloads. We started adopting MTIA in the first half of 2024 for core ranking and recommendations in [indiscernible]. We’ll continue ramping adoption for those workloads over the course of 2025 as we use it for both incremental capacity and to replace some GPU-based servers when they reach the end of their useful lives. Next year, we’re hoping to expand MTIA to support some of our core AI training workloads and over time, some of our Gen AI use cases…

…There’s already sort of a debate around how much of the compute infrastructure that we’re using is going to go towards pretraining versus as you get more of these reasoning time models or reasoning models where you get more of the intelligence by putting more of the compute into inference, whether just will mix shift how we use our compute infrastructure towards that. That was already something that I think a lot of the — the other labs and ourselves were starting to think more about and already seemed pretty likely even before this, that — like of all the compute that we’re using, that the largest pieces aren’t necessarily going to go towards pre-training. But that doesn’t mean that you need less compute because one of the new properties that’s emerged is the ability to apply more compute at inference time in order to generate a higher level of intelligence and a higher quality of service, which means that as a company that has a strong business model to support this, I think that’s generally an advantage that we’re now going to be able to provide a higher quality of service than others who don’t necessarily have the business model to support it on a sustainable basis…

…I continue to think that investing very heavily in CapEx and infra is going to be a strategic advantage over time. It’s possible that we’ll learn otherwise at some point, but I just think it’s way too early to call that…

…I think it is really too early to determine what long-run capital intensity is going to look like. There are so many different factors. The pace of advancement in underlying models, how efficient can they be? What is the adoption and use case of our Gen AI products, what performance gains come from next-generation hardware innovations, both our own and third party and then ultimately, what monetization or other efficiency gains our AI investments unlock. 

In 2024 H2, Meta introduced a new machine learning system for ads ranking, in partnership with Nvidia, named Andromeda; Andromeda has enabled a 10,000x increase in the complexity of AI models Meta uses for ads retrieval, driving an 8% increase in quality of ads that people see; Andromeda can process large volumes of ads and positions Meta well for a future where advertisers use the company’s generative AI tools to create and test more ads

In the second half of 2024, we introduced an innovative new machine learning system in partnership with NVIDIA called Andromeda. This more efficient system enabled a 10,000x increase in the complexity of models we use for ads retrieval, which is the part of the ranking process where we narrow down a pool of tens of millions of ads to the few thousand we consider showing someone. The increase in model complexity is enabling us to run far more sophisticated prediction models to better personalize which ads we show someone. This has driven an 8% increase in the quality of ads that people see on objectives we’ve tested. Andromeda’s ability to efficiently process larger volumes of ads also positions us well for the future as advertisers use our generative AI tools to create and test more ads.

Advantage+ has surpassed a $20 billion annual revenue run rate and grew 70% year-on-year in 2024 Q4; Advantage+ will now be turned on by default for all campaigns that optimise for sales, app, or lead objectives; more than 4 million advertisers are now using at least one of Advantage+’s generative AI ad creative tools, up from 1 million six months ago; Meta’s first video generation tool, released in October, already has hundreds of thousands of advertisers using it monthly

 Adoption of Advantage+ shopping campaigns continues to scale with revenues surpassing a $20 billion annual run rate and growing 70% year-over-year in Q4. Given the strong performance and interest we’re seeing in Advantage+ shopping and our other end-to-end solutions, we’re testing a new streamlined campaign creation flow. So advertisers no longer need to choose between running a manual or Advantage+ sales or app campaign. In this new setup, all campaigns optimizing for sales, app or lead objectives will have Advantage+ turned on from the beginning. This will allow more advertisers to take advantage of the performance Advantage+ offers while still having the ability to further customize aspects of their campaigns when they need to. We plan to expand to more advertisers in the coming months before fully rolling it out later in the year.

Advantage+ Creative is another area where we’re seeing momentum. More than 4 million advertisers are now using at least one of our generative AI ad creative tools, up from 1 million six months ago. There has been significant early adoption of our first video generation tool that we rolled out in October, Image Animation, with hundreds of thousands of advertisers already using it monthly.

Meta’s management thinks the emergence of DeepSeek makes it even more likely for a global open source standard for AI models to develop; the presence of DeepSeek also makes management think it’s important that the open source standard be made in America and that it ’s even more important for Meta to focus on building open source AI models; Meta is learning from DeepSeek’s innovations in building AI models; management currently does not have a strong opinion on how Meta’s capex plans for AI infrastructure will change because of the recent news with DeepSeek 

I also just think in light of some of the recent news, the new competitor DeepSeek from China, I think it also just puts — it’s one of the things that we’re talking about is there’s going to be an open source standard globally. And I think for our kind of national advantage, it’s important that it’s an American standard. So we take that seriously, and we want to build the AI system that people around the world are using and I think that if anything, some of the recent news has only strengthened our conviction that this is the right thing for us to be focused on…

…I can start on the DeepSeek question. I think there’s a number of novel things that they did that I think we’re still digesting. And there are a number of things that they have advances that we will hope to implement in our systems. And that’s part of the nature of how this works, whether it’s a Chinese competitor or not…

…It’s probably too early to really have a strong opinion on what this means for the trajectory around infrastructure and CapEx and things like that. There are a bunch of trends that are happening here all at once.

Meta’s capex in 2025 is going to grow across servers, data centers, and networking; within each of servers, data centers, and networking, management expects growth in both AI and non-AI capex; management expects most of the AI-related capex in 2025 to be directed specifically towards Meta’s core AI infrastructure, but the infrastructure Meta is building can support both AI and non-AI workloads, and the GPU servers purchased can be used for both generative AI and core AI purposes

[Question] As we think about the $60 billion to $65 billion CapEx this year, does the composition change much from last year when you talked about servers as the largest part followed by data centers and networking equipment. And how should we think about that mix between like training and inference

[Answer] We certainly expect that 2025 CapEx is going to grow across all 3 of those components you described.

Servers will be the biggest growth driver that remains the largest portion of our overall CapEx budget. We expect both growth in AI capacity as we support our gen AI efforts and continue to invest meaningfully in core AI, but we are also expecting growth in non-AI capacity as we invest in the core business, including to support a higher base of engagement and to refresh our existing servers.

On the data center side, we’re anticipating higher data center spend in 2025 to be driven by build-outs of our large training clusters and our higher power density data centers that are entering the core construction phase. We’re expecting to use that capacity primarily for core AI and non-AI use cases.

On the networking side, we expect networking spend to grow in ’25 as we build higher-capacity networks to accommodate the growth in non-AI and core AI-related traffic along with our large Gen AI training clusters. We’re also investing in fiber to handle future cross-region training traffic.

And then in terms of the breakdown for core versus Gen AI use cases, we’re expecting total infrastructure spend within each of Gen AI, non-AI and core AI to increase in ’25 with the majority of our CapEx directed to our core business with some caveat that, that is — that’s not easy to measure perfectly as the data centers we’re building can support AI or non-AI workloads and the GPU-based servers, we procure for gen AI can be repurposed for core AI use cases and so on and so forth.

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