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 2, and you can Part 1 here. With that, I’ll let the management teams take the stand… 

Microsoft (NASDAQ: MSFT)

Microsoft’s management is seeing enterprises move to enterprise-wide AI deployments 

Enterprises are beginning to move from proof of concepts to enterprise-wide deployments to unlock the full ROI of AI. 

Microsoft’s AI business has surpassed an annual revenue run rate of $13 billion, up 175% year-on-year; Microsoft’s AI business did better than expected because of Azure, Microsoft Copilot (within Copilot, price per seat was a strength and still retains good signal for value)

Our AI business has now surpassed an annual revenue run rate of $13 billion, up 175% year-over-year…

…[Question] Can you give more color on what drove the far larger-than-expected Microsoft AI revenue? We talked a bit about the Azure AI component of it. But can you give more color on that? And our estimates are that the Copilot was much bigger than we had expected and growing much faster. Any more details on the breakdown of what that Microsoft AI beat would be great.

[Answer] A couple of pieces to that, which you correctly identified, number one is the Azure component we just talked about. And the second piece, you’re right, Microsoft Copilot was better. And what was important about that, we saw strength both in seats, both new seats and expansion seats, as Satya talked about. And usage doesn’t directly impact revenue, but of course, indirectly does as people get more and more value added. And also price per seat was actually quite good. We still have a good signal for value.

Microsoft’s management is seeing AI scaling laws continue to show up in both pre-training and inference-time compute, and both phenomena have been observed internally at Microsoft for years; management has seen gains of 2x in price performance for each new hardware generation, and 10x for each new model generation

AI scaling laws continue to compound across both pretraining and inference time compute. We ourselves have been seeing significant efficiency gains in both training and inference for years now. On inference, we have typically seen more than 2x price performance gain for every hardware generation and more than 10x for every model generation due to software optimizations. 

Microsoft’s management is balancing across training and inference in the buildout of Microsoft’s AI capacity; the buildout going forward will be governed by revenue growth and capability growth; Microsoft’s Azure data center capacity is expanding in line with both near-term and long-term demand signals; Azure has more than doubled its capacity in the last 3 years, and added a record amount of capacity in 2024; Microsoft’s data centres uses both in-house as well as 3rd-party chips

Much as we have done with the commercial cloud, we are focused on continuously scaling our fleet globally and maintaining the right balance across training and inference as well as geo distribution. From now on, it’s a more continuous cycle governed by both revenue growth and capability growth thanks to the compounding effects of software-driven AI scaling laws and Moore’s Law…

…Azure is the infrastructure layer for AI. We continue to expand our data center capacity in line with both near-term and long-term demand signals. We have more than doubled our overall data center capacity in the last 3 years, and we have added more capacity last year than any other year in our history. Our data centers, networks, racks and silicon are all coming together as a complete system to drive new efficiencies to power both the cloud workloads of today and the next-generation AI workloads. We continue to take advantage of Moore’s Law and refresh our fleet as evidenced by our support of the latest from AMD, Intel, NVIDIA, as well as our first-party silicon innovation from Maia, Cobalt, Boost and HSM.

Microsoft’s management is seeing growth in raw storage, database services, and app platform services as AI apps scale, with an example being Azure OpenAI apps that run on Azure databases and Azure App Services

We are seeing new AI-driven data patterns emerge. If you look underneath ChatGPT or Copilot or enterprise AI apps, you see the growth of raw storage, database services and app platform services as these workloads scale. The number of Azure OpenAI apps running on Azure databases and Azure App Services more than doubled year-over-year, driving significant growth in adoption across SQL, Hyperscale and Cosmos DB.

OpenAI has made a new large Azure commitment; OpenAI’s APIs run exclusively on Azure; management is still very happy with the OpenAI partnership; Microsoft has ROFR (right of first refusal) on OpenAI’s Stargate project

As we shared last week, we are thrilled OpenAI has made a new large Azure commitment…

… And with OpenAI’s APIs exclusively running on Azure, customers can count on us to get access to the world’s leading models…

…[Question] I wanted to ask you about the Stargate news and the announced changes in the OpenAI relationship last week. It seems that most of your investors have interpreted this as Microsoft, for sure, remaining very committed to OpenAI’s success, but electing to take more of a backseat in terms of funding OpenAI’s future training CapEx needs. I was hoping you might frame your strategic decision here around Stargate.

[Answer] We remain very happy with the partnership with OpenAI. And as you saw, they have committed in a big way to Azure. And even in the bookings, what we recognized is just the first tranche of it. And so you’ll see, given the ROFR we have, more benefits of that even into the future. 

Microsoft’s management thinks Azure AI Foundry has best-in-class tooling run times for users to build AI agents and access thousands of AI models; Azure AI Foundry already has 200,000 monthly active users after just 2 months; the models available on Azure AI Foundry include DeepSeek’s R1 model, and more than 30 industry-specific models from partners; Microsoft’s Phi family of SLMs (small language model) has over 20 million downloads

Azure AI Foundry features best-in-class tooling run times to build agents, multi-agent apps, AIOps, API access to thousands of models. Two months in, we already have more than 200,000 monthly active users, and we are well positioned with our support of both OpenAI’s leading models and the best selection of open source models and SLMs. DeepSeek’s R1 launched today via the model catalog on Foundry and GitHub with automated red teaming, content safety integration and security scanning. Our Phi family of SLM has now been downloaded over 20 million times. And we also have more than 30 models from partners like Bayer, PAYG AI, Rockwell Automation, Siemens to address industry-specific use cases.

Microsoft’s management thinks Microsoft 365 Copilot is the UI (user interface) for AI; management is seeing accelerated adoption of Microsoft 365 Copilot across all deal sizes; majority of Microsoft 365 Copilot customers purchase more seats over time; daily users of Copilot more than doubled sequentially in 2024 Q4, while usage intensity grew 60% sequentially; more than 160,000 organisations have used Copilot Studio, creating more than 400,000 custom agents in 2024 Q4, uo 2x sequentially; Microsoft’s data cloud drives Copilot as the UI for AI; management is seeing Copilot plus AI agents disrupting business applications; the initial seats for Copilot were for departments that could see immediate productivity benefits, but the use of Copilot then spreads across the enterprise

Microsoft 365 Copilot is the UI for AI. It helps supercharge employee productivity and provides access to a swarm of intelligent agents to streamline employee workflow. We are seeing accelerated customer adoption across all deal sizes as we win new Microsoft 365 Copilot customers and see the majority of existing enterprise customers come back to purchase more seats. When you look at customers who purchased Copilot during the first quarter of availability, they have expanded their seat collectively by more than 10x over the past 18 months. To share just one example, Novartis has added thousands of seats each quarter over the past year and now have 40,000 seats. Barclays, Carrier Group, Pearson and University of Miami all purchased 10,000 or more seats this quarter. And overall, the number of people who use Copilot daily, again, more than doubled quarter-over-quarter. Employees are also engaging with Copilot more than ever. Usage intensity increased more than 60% quarter-over-quarter and we are expanding our TAM with Copilot Chat, which was announced earlier this month. Copilot Chat, along with Copilot Studio, is now available to every employee to start using agents right in the flow of work…

…More than 160,000 organizations have already used for Copilot Studio, and they collectively created more than 400,000 custom agents in the last 3 months alone, up over 2x quarter-over-quarter…

…What is driving Copilot as the UI for AI as well as our momentum with agents is our rich data cloud, which is the world’s largest source of organizational knowledge. Billions of e-mails, documents and chats, hundreds of millions of Teams meetings and millions of SharePoint sites are added each day. This is the enterprise knowledge cloud. It is growing fast, up over 25% year-over-year…

…What we are seeing is Copilot plus agents disrupting business applications, and we are leaning into this. With Dynamics 365, we took share as organizations like Ecolab, Lenovo, RTX, TotalEnergies and Wyzant switched to our AI-powered apps from legacy providers…

…[Question] Great to hear about the strength you’re seeing in Copilot… Would love to get some color on just the common use cases that you’re seeing that give you that confidence that, that will ramp into monetization later.

[Answer] I think the initial sort of set of seats were for places where there’s more belief in immediate productivity, a sales team, in finance or in supply chain where there is a lot of, like, for example, SharePoint grounded data that you want to be able to use in conjunction with web data and have it produce results that are beneficial. But then what’s happening very much like what we have seen in these previous generation productivity things is that people collaborate across functions, across roles, right? For example, even in my own daily habit, it’s I go to chat, I use Work tab and get results, and then I immediately share using Pages with colleagues. I sort of call it think with AI and work with people. And that pattern then requires you to make it more of a standard issue across the enterprise. And so that’s what we’re seeing.

Azure grew revenue by 31% in 2024 Q4 (was 33% in 2024 Q3), with 13 points of growth from AI services (was 12 points in 2024 Q3); Azure AI services was up 157% year-on-year, with demand continuing to be higher than capacity;  Azure’s non-AI business had weaker-than-expected growth because of go-to-market execution challenges

Azure and other cloud services revenue grew 31%. Azure growth included 13 points from AI services, which grew 157% year-over-year, and was ahead of expectations even as demand continued to be higher than our available capacity. Growth in our non-AI services was slightly lower than expected due to go-to-market execution challenges, particularly with our customers that we primarily reach through our scale motions as we balance driving near-term non-AI consumption with AI growth.

For Azure’s expected growth of 31%-32% in 2025 Q1 (FY2025 Q3), management expects  contribution from AI services to grow from increased AI capacity coming online; management expects Azure’s non-AI services to still post healthy growth, but there are still impacts from execution challenges; management expects Azure to no longer be capacity-constrained by the end of FY2025 (2025 Q2); Azure’s capacity constraint has been in power and space

In Azure, we expect Q3 revenue growth to be between 31% and 32% in constant currency driven by strong demand for our portfolio of services. As we shared in October, the contribution from our AI services will grow from increased AI capacity coming online. In non-AI services, healthy growth continues, although we expect ongoing impact through H2 as we work to address the execution challenges noted earlier. And while we expect to be AI capacity constrained in Q3, by the end of FY ’25, we should be roughly in line with near-term demand given our significant capital investments…

…When I talk about being capacity constrained, it takes two things. You have to have space, which I generally call long-lived assets, right? That’s the infrastructure and the land and then you have to have kits. We’re continuing, and you’ve seen that’s why our spend has pivoted this way, to be in the long-lived investment. We have been short power and space. And so as you see those investments land that we’ve made over the past 3 years, we get closer to that balance by the end of this year.

More than half of Microsoft’s cloud and AI-related capex in 2024 Q4 (FY2025 Q2) are for long-lived assets that will support monetisation over the next 15 years and more, while the other half are for CPUs and GPUs; management expects Microsoft’s capex in 2025 Q1 (FY2025 Q3) and 2025 Q2 (FY2025 Q4) to be at similar levels as 2024 Q4 (FY2025 Q2); FY2026’s capex will grow at a lower rate than in FY2025; the mix of spend in FY2026 will shift to short-lived assets in FY2026; Microsoft’s long-lived infrastructure investments are fungible; the long-lived assets are land; the presence of Moore’s Law means that management does not want to invest too much capex in any one year because the hardware and software will become much better in just 1 year; management thinks Microsoft’s AI infrastructure should be continuously upgraded to take advantage of Moore’s Law; Microsoft’s AI capex growth going forward will be tagged to customer contract delivery; the fungibility of Microsoft’s AI infrastructure investments relates to not just inference (which is the primary use case), but also training, post training, and running the commercial cloud business

More than half of our cloud and AI-related spend was on long-lived assets that will support monetization over the next 15 years and beyond. The remaining cloud and AI spend was primarily for servers, both CPUs and GPUs, to serve customers based on demand signals, including our customer contracted backlog…

…Next, capital expenditures. We expect quarterly spend in Q3 and Q4 to remain at similar levels as our Q2 spend. In FY ’26, we expect to continue investing against strong demand signals, including customer contracted backlog we need to deliver against across the entirety of our Microsoft Cloud. However, the growth rate will be lower than FY ’25 and the mix of spend will begin to shift back to short-lived assets, which are more correlated to revenue growth. As a reminder, our long-lived infrastructure investments are fungible, enabling us to remain agile as we meet customer demand globally across our Microsoft Cloud, including AI workloads…

…When I talk about being capacity constrained, it takes two things. You have to have space, which I generally call long-lived assets, right? That’s the infrastructure and the land and then you have to have kits. We’re continuing, and you’ve seen that’s why our spend has pivoted this way, to be in the long-lived investment. We have been short power and space. And so as you see those investments land that we’ve made over the past 3 years, we get closer to that balance by the end of this year…

…You don’t want to buy too much of anything at one time because, in Moore’s Law, every year is going to give you 2x, your optimization is going to give you 10x. You want to continuously upgrade the fleet, modernize the fleet, age the fleet and, at the end of the day, have the right ratio of monetization and demand-driven monetization to what you think of as the training expense…

…I do think the way I want everyone to internalize it is that the CapEx growth is going through that cycle pivot, which is far more correlated to customer contract delivery, no matter who the end customer is…

…  the other thing that’s sometimes missing is when we say fungible, we mean not just the primary use, which we’ve always talked about, which is inference. But there is some training, post training, which is a key component. And then they’re just running the commercial cloud, which at every layer and every modern AI app that’s going to be built will be required. It will be required to be distributed, and it will be required to be global. And all of those things are really important because it then means you’re the most efficient. And so the investment you see us make in CapEx, you’re right, the front end has been this sort of infrastructure build that lets us really catch up not just on the AI infrastructure we needed, but think about that as the building itself, data centers, but also some of the catch-up we need to do on the commercial cloud side. And then you’ll see the pivot to more CPU and GPU. 

Microsoft’s management thinks DeepSeek had real innovations, but those are going to be commoditized and become broadly used; management thinks that innovations in AI that reduce the cost of inference will drive more consumption and more apps being developed, and make AI more ubiquitous, which are all positive forces for Microsoft

I think DeepSeek has had some real innovations. And that is some of the things that even OpenAI found in ’01. And so we are going to — obviously, now that all gets commoditized and it’s going to get broadly used. And the big beneficiaries of any software cycle like that is the customers, right? Because at the end of the day, if you think about it, right, what was the big lesson learned from client server to cloud? More people bought servers, except it was called cloud. And so when token prices fall, inference computing prices fall, that means people can consume more, and there will be more apps written. And it’s interesting to see that when I referenced these models that are pretty powerful, it’s unimaginable to think that here we are in sort of beginning of ’25, where on the PC, you can run a model that required pretty massive cloud infrastructure. So that type of optimization means AI will be much more ubiquitous. And so therefore, for a hyperscaler like us, a PC platform provider like us, this is all good news as far as I’m concerned.

Microsoft has been reducing prices of GPT models over the years through inference optimizations

We are working super hard on all the software optimizations, right? I mean, just not the software optimizations that come because of what DeepSeek has done, but all the work we have done to, for example, reduce the prices of GPT models over the years in partnership with OpenAI. In fact, we did a lot of the work on the inference optimizations on it, and that’s been key to driving, right?

Microsoft’s management is aware that launching a frontier AI model that is too expensive to serve is useless

One of the key things to note in AI is you just don’t launch the frontier model, but if it’s too expensive to serve, it’s no good, right? It won’t generate any demand.

Microsoft’s management is seeing many different AI models being used for any one application; management thinks that there will always be a combination of different models used in any one application

What you’re seeing is effectively lots of models that get used in any application, right? When you look underneath even a Copilot or a GitHub Copilot or what have you, you already see lots of many different models. You build models. You fine-tune models. You distill models. Some of them are models that you distill into an open source model. So there’s going to be a combination…

….There’s a temporality to it, right? What you start with as a given COGS profile doesn’t need to be the end because you continuously optimize for latency and COGS and putting in different models.

NVIDIA (NASDAQ: NVDA)

NVIDIA’s Data Center revenue again had incredibly strong growth in 2024 Q4, driven by demand for the Hopper GPU computing platform and the ramping of the Blackwell GPU platform 

In the fourth quarter, Data Center revenue of $35.6 billion was a record, up 16% sequentially and 93% year-on-year, as the Blackwell ramp commenced and Hopper 200 continued sequential growth. 

Blackwell’s sales exceeded management’s expectations and is the fastest product ramp in NVIDIA’s history; it is common for Blackwell clusters to start with 100,000 GPUs or more and NVIDIA has started shipping for multiple such clusters; management architected Blackwell for inference; Blackwell has 25x higher token throughput and 20x lower cost for AI reasoning models compared to the Hopper 100; Blackwell has a NVLink domain that handles the growing complexity of inference at scale; management is seeing great demand for Blackwell for inference, with many of the early GB200 (GB200 is based on the Blackwell family of GPUs) deployments earmarked for inference; management expects NVIDIA’s gross margin to decline slightly initially as the Blackwell family ramps, before rebounding; management expects a significant ramp of Blackwell in 2025 Q1; the Blackwell Ultra, the next generation of GPUs within the Blackwell family, is slated for introduction in 2025 H2; the system architecture between Blackwell and Blackwell Ultra is exactly the same

In Q4, Blackwell sales exceeded our expectations. We delivered $11 billion of Blackwell revenue to meet strong demand. This is the fastest product ramp in our company’s history, unprecedented in its speed and scale…

…With Blackwell, it will be common for these clusters to start with 100,000 GPUs or more. Shipments have already started for multiple infrastructures of this size…

…Blackwell was architected for reasoning AI inference. Blackwell supercharges reasoning AI models with up to 25x higher token throughput and 20x lower cost versus Hopper 100. Its revolutionary transformer engine is built for LLM and mixture of experts inference. And its NVLink domain delivers 14x the throughput of PCIe Gen 5, ensuring the response time, throughput and cost efficiency needed to tackle the growing complexity of inference at scale…

…Blackwell has great demand for inference. Many of the early GB200 deployments are earmarked for inference, a first for a new architecture…

…As Blackwell ramps, we expect gross margins to be in the low 70s. Initially, we are focused on expediting the manufacturing of Blackwell systems to meet strong customer demand as they race to build out Blackwell infrastructure. When fully ramped, we have many opportunities to improve the cost and gross margin will improve and return to the mid-70s, late this fiscal year…

…Continuing with its strong demand, we expect a significant ramp of Blackwell in Q1…

…Blackwell Ultra is second half…

…The next train is on an annual rhythm and Blackwell Ultra with new networking, new memories and of course, new processors, and all of that is coming online…

…This time between Blackwell and Blackwell Ultra, the system architecture is exactly the same. It’s a lot harder going from Hopper to Blackwell because we went from an NVLink 8 system to a NVLink 72-based system. So the chassis, the architecture of the system, the hardware, the power delivery, all of that had to change. This was quite a challenging transition. But the next transition will slot right in. Blackwell Ultra will slot right in.

NVIDIA’s management sees post-training and model customisation has demanding orders of magnitude more compute than pre-training

The scale of post-training and model customization is massive and can collectively demand orders of magnitude, more compute than pretraining.

NVIDIA’s management is seeing accelerating demand for NVIDIA GPUs for inference, driven by test-time scaling and new reasoning models; management thinks reasoning models require 100x more compute per task than one-shot inference models; management is hopeful that future generation of reasoning models will require millions of times more compute; management is seeing that the vast majority of NVIDIA’s compute today is inference

Our inference demand is accelerating, driven by test-time scaling and new reasoning models like OpenAI o3, DeepSeek-R1 and Grok 3. Long thinking reasoning AI can require 100x more compute per task compared to one-shot inferences…

…. The amount of tokens generated, the amount of inference compute needed is already 100x more than the one-shot examples and the one-shot capabilities of large language models in the beginning. And that’s just the beginning. This is just the beginning. The idea that the next generation could have thousands times and even hopefully, extremely thoughtful and simulation-based and search-based models that could be hundreds of thousands, millions of times more compute than today is in our future…

……The vast majority of our compute today is actually inference and Blackwell takes all of that to a new level.

Companies such as ServiceNow, Perplexity, Microsoft, and Meta are using NVIDIA’s software and GPUs to achieve lower costs and/or better performance with their inference workloads

ServiceNow tripled inference throughput and cut costs by 66% using NVIDIA TensorRT for its screenshot feature. Perplexity sees 435 million monthly queries and reduced its inference costs 3x with NVIDIA Triton Inference Server and TensorRT-LLM. Microsoft Bing achieved a 5x speed up at major TCO savings for Visual Search across billions of images with NVIDIA TensorRT and acceleration libraries…

…Meta’s cutting-edge Andromeda advertising engine runs on NVIDIA’s Grace Hopper Superchip serving vast quantities of ads across Instagram, Facebook applications. Andromeda harnesses Grace Hopper’s fast interconnect and large memory to boost inference throughput by 3x, enhanced ad personalization and deliver meaningful jumps in monetization and ROI.

NVIDIA has driven a 200x reduction in inference costs in the last 2 years

We’re driven to a 200x reduction in inference costs in just the last 2 years.

Large cloud service providers (CSPs) were half of NVIDIA’s Data Centre revenue in 2024 Q4, and up nearly 2x year-on-year; large CSPs were the first to stand up Blackwell systems

In Q4, large CSPs represented about half of our data center revenue, and these sales increased nearly 2x year-on-year. Large CSPs were some of the first to stand up Blackwell with Azure, GCP, AWS and OCI bringing GB200 systems to cloud regions around the world to meet surging customer demand for AI. 

Regional clouds increased as a percentage of NVIDIA’s Data Center revenue in 2024 Q4, driven by AI data center build outs globally; management is seeing countries across the world building AI ecosystems

Regional cloud hosting NVIDIA GPUs increased as a percentage of data center revenue, reflecting continued AI factory build-outs globally and rapidly rising demand for AI reasoning models and agents where we’ve launched a 100,000 GB200 cluster-based incidents with NVLink Switch and Quantum-2 InfiniBand…

…Countries across the globe are building their AI ecosystems and demand for compute infrastructure is surging. France’s EUR 200 billion AI investment and the EU’s EUR 200 billion InvestAI initiatives offer a glimpse into the build-out to set redefined global AI infrastructure in the coming years.

NVIDIA’s revenue from consumer internet companies tripled year-on-year in 2024 Q4

Consumer Internet revenue grew 3x year-on-year, driven by an expanding set of generative AI and deep learning use cases. These include recommender systems, vision-language understanding, synthetic data generation, search and agentic AI.

NVIDIA’s revenue from enterprises nearly doubled year-on-year in 2024 Q4, partly with the help of agentic AI demand

Enterprise revenue increased nearly 2x year on accelerating demand for model fine-tuning, RAG and agentic AI workflows and GPU accelerated data processing.

NVIDIA’s management has introduced NIMs (NVIDIA Inference Microservices) focused on AI agents and leading AI agent platform providers are using these tools

We introduced NVIDIA Llama Nemotron model family NIMs to help developers create and deploy AI agents across a range of applications, including customer support, fraud detection and product supply chain and inventory management. Leading AI agent platform providers, including SAP and ServiceNow are among the first to use new models.

Healthcare companies are using NVIDIA’s AI products to power healthcare innovation

Health care leaders, IQVIA, Illumina and Mayo Clinic as well as ARC Institute are using NVIDIA AI to speed drug discovery, enhance genomic research and pioneer advanced health care services with generative and agentic AI.

Hyundai will be using NVIDIA’s technologies for the development of AVs (autonomous vehicles); NVIDIA’s automotive revenue had strong growth year-on-year and sequentially in 2024 Q4, driven by ramp in AVs; automotive companies such as Toyota, Aurora, and Continental are working with NVIDIA to deploy AV technologies; NVIDIA’s AV platform has passed 2 of the automotive industry’s foremost authorities for safety and cybersecurity

 At CES, Hyundai Motor Group announced it is adopting NVIDIA technologies to accelerate AV and robotics development and smart factory initiatives…

…Now moving to Automotive. Revenue was a record $570 million, up 27% sequentially and up 103% year-on-year…

…Strong growth was driven by the continued ramp in autonomous vehicles, including cars and robotaxis. At CES, we announced Toyota, the world’s largest auto maker will build its next-generation vehicles on NVIDIA Orin running the safety certified NVIDIA DriveOS. We announced Aurora and Continental will deploy driverless trucks at scale powered by NVIDIA DRIVE Thor. Finally, our end-to-end autonomous vehicle platform NVIDIA DRIVE Hyperion has passed industry safety assessments like TÜV SÜD and TÜV Rheinland, 2 of the industry’s foremost authorities for automotive-grade safety and cybersecurity. NVIDIA is the first AV platform to receive a comprehensive set of third-party assessments.

NVIDIA’s management has introduced the NVIDIA Cosmos World Foundation Model platform for the continued development of autonomous robots; Uber is one of the first major technology companies to adopt the NVIDIA Cosmos World Foundation Model platform

At CES, we announced the NVIDIA Cosmos World Foundation Model Platform. Just as language, foundation models have revolutionized language AI, Cosmos is a physical AI to revolutionize robotics. Leading robotics and automotive companies, including ridesharing giant Uber, are among the first to adopt the platform.

As a percentage of total Data Center revenue, NVIDIA’s Data Center revenue in China is well below levels seen prior to the US government’s export controls; management expects the Chinese market to be very competitive

Now as a percentage of total data center revenue, data center sales in China remained well below levels seen on the onset of export controls. Absent any change in regulations, we believe that China shipments will remain roughly at the current percentage. The market in China for data center solutions remains very competitive.

NVIDIA’s networking revenue declined sequentially in 2024 Q4, but the networking-attach-rate to GPUs remains robust at 75%; NVIDIA is transitioning to NVLink 72 with Spectrum-X (Spectrum-X is NVIDIA’s Ethernet networking solution); management expects networking revenue to resume growing in 2025 Q1; management sees AI requiring a new class of networking, for which the company’s NVLink, Quantum Infiniband, and Spectrum-X networking solutions are able to provide; large AI data centers, including OpenAI’s Stargate project, will be using Spectrum X

Networking revenue declined 3% sequentially. Our networking attached to GPU compute systems is robust at over 75%. We are transitioning from small NVLink 8 with InfiniBand to large NVLink 72 with Spectrum-X. Spectrum-X and NVLink Switch revenue increased and represents a major new growth vector. We expect networking to return to growth in Q1. AI requires a new class of networking. NVIDIA offers NVLink Switch systems for scale-up compute. For scale out, we offer Quantum InfiniBand for HPC supercomputers and Spectrum-X for Ethernet environments. Spectrum-X enhances the Ethernet for AI computing and has been a huge success. Microsoft Azure, OCI, CoreWeave and others are building large AI factories with Spectrum-X. The first Stargate data centers will use Spectrum-X. Yesterday, Cisco announced integrating Spectrum-X into their networking portfolio to help enterprises build AI infrastructure. With its large enterprise footprint and global reach, Cisco will bring NVIDIA Ethernet to every industry.

NVIDIA’s management is seeing 3 scaling laws at play in the development of AI models, namely pre-training scaling, post-training scaling, and test-time compute scaling

There are now multiple scaling laws. There’s the pre-training scaling law, and that’s going to continue to scale because we have multimodality, we have data that came from reasoning that are now used to do pretraining. And then the second is post-training scaling law, using reinforcement learning human feedback, reinforcement learning AI feedback, reinforcement learning, verifiable rewards. The amount of computation you use for post training is actually higher than pretraining. And it’s kind of sensible in the sense that you could, while you’re using reinforcement learning, generate an enormous amount of synthetic data or synthetically generated tokens. AI models are basically generating tokens to train AI models. And that’s post-training. And the third part, this is the part that you mentioned is test-time compute or reasoning, long thinking, inference scaling. They’re all basically the same ideas. And there you have a chain of thought, you’ve search.

NVIDIA’s management thinks the popularity of NVIDIA’s GPUs stems from its fungibility across all kinds of AI model architectures and use cases; NVIDIA’s management thinks that NVIDIA GPUs have an advantage over the ASIC (application-specific integrated circuit) AI chips developed by others because of (1) the general-purpose nature of NVIDIA GPUs, (2) NVIDIA’s rapid product development roadmap, (3) the software stack developed for NVIDIA GPUs that is incredibly hard to replicate

The question is how do you design such an architecture? Some of it — some of the models are auto regressive. Some of the models are diffusion-based. Some of it — some of the times you want your data center to have disaggregated inference. Sometimes it is compacted. And so it’s hard to figure out what is the best configuration of a data center, which is the reason why NVIDIA’s architecture is so popular. We run every model. We are great at training…

…When you have a data center that allows you to configure and use your data center based on are you doing more pretraining now, post training now or scaling out your inference, our architecture is fungible and easy to use in all of those different ways. And so we’re seeing, in fact, much, much more concentration of a unified architecture than ever before…

…[Question] We heard a lot about custom ASICs. Can you kind of speak to the balance between custom ASIC and merchant GPU?

[Answer] We build very different things than ASICs, in some ways, completely different in some areas we intercept. We’re different in several ways. One, NVIDIA’S architecture is general whether you’re — you’ve optimized for auto regressive models or diffusion-based models or vision-based models or multimodal models or text models. We’re great in all of it. We’re great at all of it because our software stack is so — our architecture is flexible, our software stack ecosystem is so rich that we’re the initial target of most exciting innovations and algorithms. And so by definition, we’re much, much more general than narrow…

…The third thing I would say is that our performance and our rhythm is so incredibly fast. Remember that these data centers are always fixed in size. They’re fixed in size or they’re fixed in power. And if our performance per watt is anywhere from 2x to 4x to 8x, which is not unusual, it translates directly to revenues. And so if you have a 100-megawatt data center, if the performance or the throughput in that 100-megawatt or the gigawatt data center is 4x or 8x higher, your revenues for that gigawatt data center is 8x higher. And the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated. And so the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons and capturing the fast ROI…

…The last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We build a new architecture. And the ecosystem that sits on top of our architecture is 10x more complex today than it was 2 years ago. And that’s fairly obvious because the amount of software that the world is building on top of architecture is growing exponentially and AI is advancing very quickly. So bringing that whole ecosystem on top of multiple chips is hard.

NVIDIA’s management thinks that only consumer AI and search currently have well-developed AI use cases, and the next wave will be agentic AI, robotics, and sovereign AI

We’ve really only tapped consumer AI and search and some amount of consumer generative AI, advertising, recommenders, kind of the early days of software. The next wave is coming, agentic AI for enterprise, physical AI for robotics and sovereign AI as different regions build out their AI for their own ecosystems. And so each one of these are barely off the ground, and we can see them.

NVIDIA’s management sees the upcoming Rubin family of GPUs as being a big step-up from the Blackwell family

The next transition will slot right in. Blackwell Ultra will slot right in. We’ve also already revealed and been working very closely with all of our partners on the click after that. And the click after that is called Vera Rubin and all of our partners are getting up to speed on the transition of that and so preparing for that transition. And again, we’re going to provide a big, huge step-up.

NVIDIA’s management sees AI as having the opportunity to address a larger part of the world’s GDP than any other technology has ever had

No technology has ever had the opportunity to address a larger part of the world’s GDP than AI. No software tool ever has. And so this is now a software tool that can address a much larger part of the world’s GDP more than any time in history.

NVIDIA’s management sees customers still actively using older families of NVIDIA GPUs because of the high level of programmability that CUDA has

People are still using Voltas and Pascals and Amperes. And the reason for that is because there are always things that — because CUDA is so programmable you could use it — one of the major use cases right now is data processing and data curation. You find a circumstance that an AI model is not very good at. You present that circumstance to a vision language model, let’s say, it’s a car. You present that circumstance to a vision language model. The vision language model actually looks at the circumstances and said, “This is what happened and I wasn’t very good at it.” You then take that response — the prompt and you go and prompt an AI model to go find in your whole lake of data, other circumstances like that, whatever that circumstance was. And then you use an AI to do domain randomization and generate a whole bunch of other examples. And then from that, you can go train the model. And so you could use the Amperes to go and do data processing and data curation and machine learning-based search. And then you create the training data set, which you then present to your Hopper systems for training. And so each one of these architectures are completely — they’re all CUDA-compatible and so everything runs on everything. But if you have infrastructure in place, then you can put the less intensive workloads onto the installed base of the past. All of our GPUs are very well employed.

Paycom Software (NYSE: PAYC)

Paycom’s management rolled out an AI agent six months ago to its service team, and has seen higher immediate response rates to clients and eliminated service tickets by 25% from a year ago; Paycom’s AI agent is driving internal efficiencies, higher client satisfaction, and higher Net Promoter Scores

Paycom’s AI agent, which was rolled out to our service team 6 months ago, utilizes our own knowledge-based semantic search model to provide faster responses and help our clients more quickly and consistently than ever before. As responses continuously improve over time, our client interactions become more valuable, and we connect them faster to the right solution. As a result, we are seeing improved immediate response rates and have eliminated service tickets by over 25% compared to a year ago…

…With automations like AI agent, we are realizing internal efficiencies, driving increasing client satisfaction and seeing higher Net Promoter Scores.

PayPal (NASDAQ: PYPL)

One of the focus areas for PayPal’s management in 2025 will be on raising efficiency with the help of AI 

Fourth is efficiency and effectiveness. In 2024, we reduced headcount by 10%. We made deliberate investments in AI and automation, which are critical to our future. This year, we are prioritizing the use of AI to improve the customer experience and drive efficiency and effectiveness within PayPal.

PayPal’s management sees AI being a huge opportunity for PayPal given the company’s volume of data; PayPal is using AI on its customer facing side to more efficiently process customer support cases and interactions with customers (PayPal Assistant has been rolled out and it has cut down phone calls and active events for PayPal); PayPal is using AI to personalise the commerce journey for consumers; PayPal is also using AI for back-office productivity and risk decisions

[Question] The ability to use AI for more operating efficiency. And are those initiatives that are requiring some incremental investment near term? Or are you already seeing sort of a positive ROI from that?

[Answer] AI is opening up a huge opportunity for us. First, at our scale, we saw 26 billion transactions on our platform last year. We have a massive data set that we are actively working and investing in to be able to drive our effectiveness and efficiency…

…First, on the customer-facing side, we’re leveraging AI to really become more efficient in our support cases and how we interact with our customers. We see tens of millions of support cases every year, and we’ve rolled out our PayPal Assistant, which is now really cutting down phone calls and active events that we have. 

We also are leveraging AI to personalize the commerce journey, and so working with our merchants to be able to understand and create this really magical experience for consumers. When they show up at a checkout, it’s not just a static button anymore. This really can become a dynamic, personalized button that starts to understand the profile of the consumer, the journey that they’ve been on, perhaps across merchants, and be able to enable a reward or a cash-back offer in the moment or even a Buy Now, Pay Later offer in a dynamic experience…

…In addition, we also are looking at our back office and ensuring that not just on the engineering and employee productivity side, but also in things like our risk decisions. We see billions and billions of risk decisions that often, to be honest, we’re very manual in the past. We’re now leveraging AI to be able to understand globally what are the nature of these risk decisions and how do we automate these across both risk models as well as even just ensuring that customers get the right response at the right time in an automated fashion.

Salesforce (NYSE: CRM)

Salesforce ended 2024 (FY2025) with $900 million in Data Cloud and AI ARR (annual recurring revenue), up 120% from a year ago; management has never seen products grow at this rate before, especially Agentforce

We ended this year with $900 million in Data Cloud and AI ARR. It grew 120% year-over-year. We’ve never seen products grow at these levels, especially Agentforce.

Salesforce’s management thinks building digital labour (AI agents) is a much bigger market than just building software

I’m sure you saw those ARK slides that got released over the weekend where she said that she thought this digital labor revolution, which is really like kind of what we’re in here now, this digital labor revolution, this looks like it’s anywhere from a few trillion to $12 trillion. I mean, I kind of agree with her. I think this is much, much bigger than software. I mean, for the last 25 years, we’ve been doing software to help our customers manage their data. That’s very exciting. I think building software that kind of prints and deploys digital workers is more exciting.

Salesforce’s unified platform, under one piece of code, combining customer data and an agentic platform, is what gives Agentforce its accuracy; Agentforce already has 3,000 paying customers just 90 days after going live; management thinks Agentforce is unique in the agentic capabilities it is delivering; Salesforce is Customer Zero for Agentforce; Agentforce has already resolved 380,000 service requests for Salesforce, with an 84% resolution rate, and just 2% of requests require human escalation; Agentforce has accelerated Salesforce’s sales-quoting cycles by 75% and increased AE (account executive) capacity by 7%; Agentforce is helping Salesforce engage more than 50 leads per day, freeing up the sales team for higher-value conversations; management wants every Salesforce customer to be using Agentforce; Data Cloud is at the heart of Agentforce; management is seeing customers across every industry deploying Agentforce; management thinks Salesforce’s agentic technology works better than many other providers, and that other providers are just whitewashing their technology with the “agent” label; Agentforce is driving growth across Salesforce’s portfolio; Salesfroce has prebuilt 170 specialised Agentforce industry skills; Agentforce’s 3,000 customers come from a diverse set of industries

Our formula now really for our customers is this idea that we have these incredible Customer 360 apps. We have this incredible Data Cloud, and this incredible agentic platform. These are the 3 layers. But that it is a deeply unified platform, it’s a deeply unified platform, it’s just one piece of code, that’s what makes it so unique in this market…

…It’s this idea that it’s a deeply unified platform with one piece of code all wrapped in a beautiful layer of trust. And that’s what gives Agentforce this incredible accuracy that we’re seeing…

…Just 90 days after it went live, we’ve already have 3,000 paying Agentforce customers who are experiencing unprecedented levels of productivity, efficiency and cost and cost savings. No one else is delivering at this level of capability…

…We’re seeing some amazing results on Salesforce as Customer Zero for Agentforce. Our digital labor force is resolving tens of thousands of customer service inquiries, freeing our human employees to focus on the most nuanced issues and customer relationships. We’re seeing tremendous momentum and success stories emerge as we execute our vision to make every company, every single company, every customer of ours, an Agentforce company, that is, we want every customer to be an Agentforce customer…

…We also continued phenomenal growth with Data Cloud this year, which is the heart of Agentforce. Data Cloud is the fuel that powers Agentforce and our customers are investing in it…

…We’re seeing customers deploy Agentforce across every industry…

…You got to be aware of the false agent because the false agent is out there where people can use the word agent or they kind of — they’re trying to whitewash all the agent, the thing, everywhere. But the reality is there is the real agents and there are the false agents, and we’re very fortunate to have the real stuff going on here. So we’ve got a lot more groundbreaking AI innovation coming…

…Today, we’re live on Agentforce across service and sales, our business technology organization, customer support and more. And the results are phenomenal. Since launching on our Salesforce help portal in October, Agentforce has autonomously handled 380,000 service requests, achieving an incredible 84% resolution rate and only 2% of the requests require human escalation. And we’re using Agentforce for quoting, accelerating our quoting cycles by more than 75%. In Q4, we increased our AE [account executive] capacity while still driving productivity up 7% year-over-year. Agentforce is transforming how we do outbound prospecting, already engaging more than 50 leads per day with personalized outreach and timely follow-ups, freeing up our teams to focus on high-value conversation. Our reps are participating in thousands of sales coaching training sessions each month…

…Agentforce is revolutionizing how our customers work by bringing AI-powered insights and actions directly into the workflows across the Customer 360 applications. This is driving strong growth across our portfolio. Sales Cloud and Service Cloud both achieved double-digit growth again in Q4. We’re seeing fantastic momentum with Slack, with customers like ZoomInfo, Remarkable and MIMIT Health using Agentforce and Slack to boost productivity…

…We’ve prebuilt over 170 specialized Agentforce industry skills and a team of 400 specialists, supporting transformations across sectors and geographies…

…We closed more than 3,000 paid Agentforce deals in the quarter. As customers continue to harness the value of AI deeply embedded across our unified platform, it is no surprise that these customers average nearly 4 clouds. And these customers came from a diverse set of industries with more than half in technology, manufacturing, financial services and HLS.

Lennar, the USA’s largest homebuilder, has been a Salesforce customer for 8 years and it is deploying Agentforce to fulfill their management’s vision of selling all kinds of new products; jewelry company, Pandora, an existing Salesforce customer, is deploying Agentforce with the aim of handling 30%-60% of its service cases with Agentforce; pharmaceutical giant Pfizer is using Agentforce to augment its sales teams; Singapore-based airline, Singapore Airlines, is now a customer of Agentforce and wants to deliver service through it; Goodyear is using Agentforce to automate and increase the effectiveness of its sales efforts; Accenture is using Agentforce to coach its sales team and expects to achieve higher win rates; Deloitte is using Agentforce and expects to achieve significant productivity gains

We’ve been working with Lennar, the nation’s largest homebuilder. And most of you know Lennar is really an incredible company, and they’ve been a customer of ours for about 8 years…

…You probably know Stuart Miller, Jon Jaffe, amazing CEOs. And those co-CEOs called me and said, “Listen, these guys have done a hackathon around Agentforce. We’ve got 5 use cases. We see incredible opportunities on our margin, incredible opportunities in our revenue. And do you have our back if we’re going to deploy this?” And we said, “Absolutely. We’ve deployed it ourselves,” which is the best evidence that this is real. And they are just incredible, their vision as a homebuilder providing 24/7 support, sales leads through all their digital channels. They’re able to sell all kinds of new products. I think they’re going to sell mortgages and insurance and all kinds of things to their customers. And the cool thing is they’re using our sales product, our service product, marketing, MuleSoft, Slack, Tableau, they use everything. But they are able to leverage it all together by realizing that just by turning it on, they get this incredible Agentforce capability…

…I don’t know how many of you know about Pandora. If you’ve been to a shopping center, you will see the Pandora store. You walk in, they have this gorgeous jewelry. They have these cool charm bracelets. They have amazing products. And if you know their CEO, Alex, he’s absolutely phenomenal…

…They’re in 100 countries. They employ 37,000 people worldwide. And Alex has this great vision to augment their employees with digital labor. And this idea that whether you’re on their website or in their store, or whatever it is, that they’re going to be able to do so much more with Agentforce. They already use — first of all, they already use Commerce Cloud. So if you’ve been to pandora.com and bought their products — and if you have it, by the way, it’s completely worthwhile. It’s great. And you can experience our Commerce Cloud, but it’s deeply integrated with our Service Cloud, with Data Cloud. It’s the one unified platform approach. And now they’re just flipping the switch, turning agents on, and they’re planning to deliver 30% to 60% of their service cases with Agentforce. That is awesome. And I really love Alex’s vision of what’s possible….

…The last customer I really want to hit on, which I’m so excited about, is Pfizer. And Albert is an incredible CEO. They are doing unbelievable things. They’ve been a tremendous customer. But now they’re really going all in on our Life Sciences Cloud…

…And with Agentforce, sales agents, for example, with Pfizer, that’s — they’ve got 20,000 customer-facing employees and customer-facing folks. That is just a radical extension for them with agents…

…I’m sure a lot of you — like, I have flown in Singapore Air. You know what? It’s a great airline. The CEO, Goh, is amazing. And he has a huge vision that also came out of Dreamforce, where — they’ve already delivered probably the best service of any airline in the world — they want to deliver it through agents. So whether you’re doing it with service or sales or marketing or commerce or all the different things that Singapore Air is doing with us, you’re going to be able to do this right on Singapore Air…

…Goodyear is partnering with us on their transformation, using Agentforce to automate and increase the effectiveness of their sales efforts. With Agentforce for Field Service, Goodyear will be able to reduce repair time by assisting technicians with answers to vehicle-related questions and autonomously scheduling field tech appointments…

…Accenture is using Agentforce Sales Coach, which provides personalized coaching and recommendations for sales teams, which is expected to lead to higher win rates. And Deloitte is projecting significant productivity gains and saved workforce hours as they roll out Agentforce over the next few years.

Salesforce’s management expects modest revenue contribution from Agentforce in 2025 (FY2026); contribution from Agentforce is expected to be more meaningful in 2026 (FY2027)

Starting with full fiscal year ’26. We expect revenue of $40.5 billion to $40.9 billion, growth of approximately 7% to 8% year-over-year in nominal and constant currency. And for subscription and support revenue, we expect growth of approximately 9% year-over-year in constant currency…

…On Agentforce, we are incredibly excited about the customer momentum we are seeing. However, the adoption cycle is still early as we focus on deployment with our customers. As a result, we are assuming a modest contribution to revenue in fiscal ’26. We expect the momentum to build throughout the year, driving a more meaningful contribution in fiscal ’27.

Salesforce has long had a mix of per-seat and consumption pricing models; for now, Agentforce is a consumption product, but management sees Agentforce evolving to a mix of per-seat and consumption pricing models; there was a customer that bought Agentforce in 2024 Q4 (FY2025 Q4) along with other Salesforce products and the customer signed a $7 million Agentforce contract and a $13 million contract for the other products; based on early days of engagement with Agentforce customers, management sees significant future upside to Salesforce’s pricing structure; Agentforce’s pricing will also take into account whether Agentforce will bring other human-based clouds into the customer; Agentforce is currently creating some halo around Salesforce’s other products

We’ve kind of started the company out with the per user pricing model, and that’s about humans. We price per human, so you’re kind of pricing per human. And then we have products, though, that are also in the consumption world as well. And of course, those started in the early days, things like our sandboxes, even things like our Commerce Cloud, even our e-mail marketing product, our Marketing Cloud. These are consumption-based products we’ve had for years…

…Now we have these kind of products that are for agents also, and agents are also a consumption model. So when we look at our Data Cloud, for example, that’s a consumption product. Agentforce is a consumption product. But it’s going to be a mix. It’s going to be a mix between what’s going on with our customers with how many humans do they have and then how many agents are they deploying…

…In the quarter, we did a large transaction with a large telecommunications company… we’re rebuilding this telecommunications company. So it’s Sales Cloud, it’s Service Cloud, it’s Marketing Cloud. It’s all of our core clouds, but then also it’s Agentforce. And the Agentforce component, I think, was maybe $7 million in the transaction. So she was buying $7 million of Agentforce. She bought $13 million in our products for humans, and I think that was about $20 million in total…

…We will probably move into the near future from conversations as we price most of our initial deals to universal credit. It will allow our customers far more flexibility in the way they transact with us. But we see this as a significant upside to our pricing structures going forward. And that’s what we’ve seen in the early days with our engagement with customers…

…Here’s a transaction that you’re doing, let’s say, a customer comes in, they’re very interested in building an agentic layer on their company, is that bringing other human-based clouds along with it?…

…[Question] Is Agentforce having a bit of a halo effect around some of your other products, meaning, as we are on the journey to get more monetization from Agentforce, are you seeing pickups or at least higher activity levels in some of your other products?

[Answer] That’s exactly right. And we’re seeing it in the way that our customers are using our technology, new ideas, new workflows, new engagements. We talked about Lennar as an example, their ability to handle leads after hours that they weren’t able to get back to or respond to in a quick time frame are now able to touch and engage with those leads. And that, of course, flows into their Salesforce automation system. And so we are seeing this halo effect with our core technology. It is making every single one of our core apps better as they deliver intelligence, underpinning these applications.

Salesforce’s management sees the combination of apps, data, and agents as the winning combination in an AI-world; management disputes Microsoft’s narrative that software apps will become a dumb database layer in an AI-dominated world, because it is the combination of apps, data, and agents that is important

I don’t know any company that’s 100% agents. I don’t know of any company that doesn’t need automation for its humans. I don’t know of any company that doesn’t need a data cloud where it needs a consistent common data repository for all of its agents to gain their intelligence. And I don’t know of any company that’s not going to need an agentic layer. And that idea of having apps, data and agents, I think, is going to be the winning combination…

…[Question] As part of that shift to agentic technology, there’s been a lot of debate about the SaaS technology and the business model. The SaaS tech stack that you built and pioneered, how does that fit into the agentic world? Is there a risk that SaaS just becomes a CRUD database?

[Answer] I’ve heard that Microsoft narrative, too. So I watched the podcast you watched, and that’s a very interesting idea. Here’s how I look at it, which is, I believe there is kind of a holy trinity here of AI CRM, which is the apps, the data and the agents. And these three things have to kind of work together. And I kind of put my money where our mouth is where we kind of built it and we delivered it. And you can see the 380,000 conversations that we had as point of evidence here in the last 90 days on our service and with a very high resolution rate of 84%. You can go to help.salesforce.com, and you can see that today.

Now Microsoft has had Copilot available for, I think, about 2 years or more than 2 years. And I know that they’re the reseller of OpenAI and they’ve invested, they kind of repackaged this ChatGPT, whatever. But where on their side are they delivering agents? Where in their company have they done this? Are they a best practice? Because I think that while they can say such a thing, do they have humans and agents working together to create customer success? Are they rebalancing their workforce with humans and agents? I think that it’s a very interesting point that, yes, the agentic layer is very important, but it doesn’t operate by itself. It operates with data, with a Data Cloud that has to be federated through your company, to all your data sources. And humans, we’re still here

Salesforce’s management is seeing Agentforce deliver tremendous efficiency in Salesforce’s customer support function that they may rebalance some customer-support roles into other roles; management is currently seeing AI coding tools improve the productivity of Salesforce’s engineering team by 30% and thinks even more productivity can be found; management will not be expanding Salesforce’s engineering team this year, but will grow the sales team

We really are seeing tremendous efficiency with help.salesforce.com. So we may see the opportunity to rebalance some of those folks into sales and marketing and other functions…

…We definitely have seen a lot of efficiency with engineering and with some of the new tools that I’ve seen, especially some of these high-performance coding tools. One of the key members of my staff who’s here in the room with us has just showed me one of his new examples of what we’re able to do with these coding tools, pretty awesome. And we’re not going to hire any new engineers this year. We’re seeing 30% productivity increase on engineering. And we’re going to really continue to ride that up…

…We’re going to grow sales pretty dramatically this year. Brian has got a big vision for how to grow the sales organization. probably another 10% to 20%, I hope, this year because we’re seeing incredible levels of demand.

Salesforce’s management thinks that AI agents is one of the catalysts to drive GDP growth

So if you want productivity to go up and you want GDP to grow up and you want growth, I think that digital labor is going to be one of the catalysts to make that happen.

Shopify (NASDAQ: SHOP)

Shopify launched its first AI-powered search integration with Perplexity in 2024

Last year, we… launched our first AI-powered search integration with Perplexity, enabling new ways for buyers to find merchants.

One of Shopify’s management’s focus areas in 2025 is to continue embracing AI by investing more in Sidekick and other AI capabilities that help merchants launch and grow faster; management wants to shift Shopify towards producing goal-oriented software; management believes Shopify is well-positioned as a leader for commerce in an AI-driven world

We will continue to embrace the transformative potential of AI. This technology is not just a part of the future, it is redefining it. We’ve anticipated this. So we’re already transforming Shopify into a platform where users and machines work seamlessly together. We plan to deepen our investment in Sidekick and other AI capabilities to help not just brand-new merchants to launch, but also to help larger merchants scale faster and drive greater productivity. Our efforts to shift towards more goal-oriented software will further help to streamline operations and improve decision-making. This focus on embracing new ways of thinking and working positions us not only as the platform of choice today, but also as a leader for commerce in the AI-driven era with a relentless focus on cutting-edge technology.

Shopify’s management believes Shopify will be one of the major net beneficiaries in the AI era as the company is leveraging AI really well, such as its partnerships with Perplexity and OpenAI

I actually think Shopify will very much be one of the major net beneficiaries in this new AI era. I think we are widely recognized as one of the best companies that foster long-term partnership. And so when it comes to partnership in AI, whether it’s Perplexity, where we’re now powering their search results with incredible product across the Shopify product catalog or OpenAI where we’re using — we have a direct set of their APIs to help us internally, we are really leveraging it as best as we can.

In terms of utilising AI, Shopify’s management sees 2 angles; the 1st angle is Shopify using AI to help merchants with mundane tasks and allow merchants to focus only on the things they excel at; the 2nd angle is Shopify using AI internally to make developers and customer-support teams more effective (with customer-support teams, Shopify is using AI to handle low-quality conversations with customers)

[Question] A question in regards to AI and the use of AI internally. Over the last year or so, you’ve made significant investments. Where are you seeing it operationally having the most impact? And then what has been the magnitude of productivity gains that you’ve seen?

[Answer] We think about it in sort of 2 ways. The first is from a merchant perspective, how can we make our merchants way more successful, get them to do things faster, more effectively. So things like Sidekick or media editor or Shopify Inbox, Semantic Search, Sidekick, these are things that now — that every merchant should want when they’re not just getting started, but also scaling their business. And those are things that are only available from Shopify. So we’re trying to make some of the more mundane tasks far more easy to do and get them to focus on things that only they can — only the merchants can do. And I think that’s an important aspect of what Shopify will bring…

…Internally, however, this is where it gets really interesting, because not only can we use it to make our developers more effective, but also, if you think about our support organization, now we can ensure that our support team is actually having very high-quality conversations with merchants, whereas a lot of low-quality conversations, things like configuring a domain or a C name or a user name and password issue, that can be handled really elegantly by AI.

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s AI accelerators revenue more than tripled in 2024 and was mid-teens percent of overall revenue in 2024, but management expects AI accelerators revenue to double in 2025; management sees really strong AI-related demand in 2025

Revenue from AI accelerators, which we now define as AI GPU, AI ASICs and HBM controller for AI training and inference in the data center, accounted for close to mid-teens percent of our total revenue in 2024. Even after more than tripling in 2024, we forecast our revenue from AI accelerator to double in 2025 as the strong surge in AI-related demand continues…

…[Question] Try to get a bit more clarity on the cloud growth for 2025. I think, longer term, without a doubt, the technology definitely has lots of potential for demand opportunities, but I think — if we look at 2025 and 2026, I think there could be increasing uncertainties coming from maybe [indiscernible] spending, macro or even some of the supply chain challenges. And so I understand the management just provided a pretty good guidance for this year for sales to double. And so if you look at that number, do you think there is still more upside than downside as we go through 2025?

[Answer] I certainly hope there is upside, but I hope I get — my team can supply enough capacity to support it. Does that give you enough hint? 

TSMC’s management saw a mixed year of recovery for the global semiconductor industry in 2024 with strong AI-related demand but mild recovery in other areas

2024 was a mixed year of recovery for the global semiconductor industry. AI-related demand was strong, while other applications saw only a very mild recovery as macroeconomics condition weighed on consumer sentiment and the end market demand. 

TSMC’s management expects mid-40% revenue CAGR from AI accelerators in the 5-years starting from 2024 (previous forecast was for 50% CAGR for the 5-years starting from 2024, but off a lower base); management expects AI accelerators to be the strongest growth driver for TSMC’s overall HPC  platform and overall revenue over the next few years

Underpinned by our technology leadership and broader customer base, we now forecast the revenue growth from AI accelerators to approach a mid-40% CAGR for the 5-year period starting off the already higher base of 2024. We expect AI accelerators to be the strongest driver of our HPC platform growth and the largest contributor in terms of our overall incremental revenue growth in the next several years.

TSMC’s management expects 20% revenue CAGR in USD terms in the 5-years starting from 2024, driven by growth across all its platforms; management thinks that in the next few years, TSMC’s smartphone and PC end-markets will have higher silicon content and faster replacement cycle, driven by AI-related demand, which will in turn drive robust demand for TSMC’s chip manufacturing service; the AI-related demand in the smartphone and PC end-markets are related to edge-AI

Looking ahead, as the world’s most reliable and effective capacity provider, TSMC is playing a critical and integral role in the global semiconductor industry. With our technology leadership, manufacturing excellence and customer trust, we are well positioned to address the growth from the industry megatrend of 5G, AI and HPC with our differentiated technologies. For the 5-year period starting from 2024, we expect our long-term revenue growth to approach a 20% CAGR in U.S. dollar term, fueled by all 4 of our growth platform, which are smartphone, HPC, IoT and automotive…

…[Question] I believe that 20% starting from a very — already very high base in 2024 is a really good long-term objective but just wondering that, aside from the strong AI demand, what’s your view on the traditionals, applications like PC and the smartphone, growth and particularly for this year.

[Answer] This year is still mild growth for PC and smartphone, but everything is AI related, all right, so you can start to see why we have confidence to give you a close to 20% CAGR in the next 5 years. AI: You look at a smartphone. They will put AI functionality inside, and not only that. So the silicon content will be increased. In addition to that, actually the replacement cycle will be shortened. And also they need to go into the very advanced technology because of, if you want to put a lot of functionality inside a small chip, you need a much more advanced technology to put those [indiscernible]. Put all together, that even smartphone, the unit growth is almost low single digit, but then the silicon and the replacement cycle and the technology migration, that give us more growth than just unit growth; similar reason for PC…

…On the edge AI, in our observation, we found out that our customers start to put up more neural processing inside. And so we estimated the 5% to 10% more silicon being used. [ Can it be ] every year 5% to 10%? Definitely it is no, right? So they will move to next node, the technology migration. That’s also to TSMC’s advantage. Not only that, I also say that, the replacement cycle, I think it will be shortened because of, when you have a new toy that — with AI functionality inside it, everybody want replacing, replace their smartphone, replace their PCs. And [ I count that one ] much more than the — a mere 5% increase.

TSMC’s upcoming A16 process technology is best suited for specific HPC (high-performance computing) products, which means it is best suited for AI-related workloads

We will also introduce A16 featuring Super Power Rail or SPR as separate offering. TSMC’s SPR is a innovative, best-in-class backside power delivery solution that is first in the industry to incorporate a novel backside metal scheme that preserves gate density and device width flexibility to maximize product benefit. Compared with N2P, A16 provide a further 8% to 10% speed improvement at the same power or 15% to 20% power improvement at the same speed, and additional 7% to 10% chip density gain. A16 is the best suitable for specific HPC product with a complex signal route and dense power delivery network. Volume production is scheduled for second half 2026.

TSMC’s management thinks that the US government’s latest AI restrictions will only have a minimal impact on the company’s business

[Question] Overnight, the U.S. seems to put a new framework on restricting China’s AI business, right? So I’m wondering whether that will create some business impact to your China business.

[Answer] We don’t have all analysis yet, but the first look is not significantly. It’s manageable. So that meaning that, my customers who are being restricted [ or something ], we are applying for the special permit for them. And we believe that we have confidence that they will get some permission, so long as they are not in the AI area, okay, especially automotive industry. Or even you talk about crypto mining, yes.

TSMC’s management does not want to reveal the level of demand for AI-related ASICs (application-specific integrated circuits) from the cloud hyperscalers, but they are confident that the demand is real, and that the cloud hyperscalers will be working with TSMC as they all need leading-edge technology for their AI-related ASICs

[Question] Broadcom’s CEO recently laid out a large SAM for AI hyperscalers building out custom silicon. I think he was talking about million clusters from each of the customers he has in the next 2 or 3 years. What’s TSMC’s perspective on all this? 

[Answer] I’m not going to answer the question of the specific number, but let me assure you that, whether it’s ASIC or it’s graphic, they all need a very leading-edge technology. And they’re all working with TSMC, okay, so — and the second one is, is the demand real. Was — is — as a number that my customers said. I will say that the demand is very strong.

AI makes up all of the current demand for CoWoS (chip on wafer on substrate) capacity that TSMC’s management is seeing, but they think non-AI-related demand for CoWoS will come in the near future from CPUs and servers; there are rumours of a cut in orders for CoWoS, but management is not seeing any cuts; it appears that HBM (high bandwidth memory) is the key constraint on AI demand, instead of CoWoS; CoWoS was over 8% of TSMC’s revenue in 2024 and will be over 10% in 2025; CoWoS gross margin is better than before, but still lower than the corporate average

[Question] When can we see non-AI application such as server, smartphone or anything else can be — can start to adopt CoWoS capacity in case there is any fluctuation in the AI demand?

[Answer] Today is all AI focused. And we have a very tight capacity and cannot even meet customers’ need, but whether other products will adopt this kind of CoWoS approach, they will. It’s coming and we know that it’s coming.

[Question] When?

[Answer] It’s coming… On the CPU and on the server chip. Let me give you a hint…

…[Question] About your CoWoS and SoIC capacity ramp. Can you give us more color this year? Because recently there seemed to be a lot of market noises. Some add orders. Some cut orders, so I would like to see your view on the CoWoS ramp.

[Answer] That’s a rumor. I assure you. We are working very hard to meet the requirement of my customers’ demand, so “cut the order,” that won’t happen. We actually continue to increase, so we are — again I will say that. We are working very hard to increase the capacity…

…[Question] A question on AI demand. Is there a scenario where HBM is more of a constraint on the demand, rather than CoWoS which seems to be the biggest constraint at the moment? 

[Answer] I don’t comment on other supplier, but I know that we have a very tight capacity to support the AI demand. I don’t want to say I’m the bottleneck. TSMC, always working very hard with customer to meet their requirement…

…[Question] So we have observed an increasing margin of advanced packaging. Could you remind us the CoWoS contribution of last year? And do you expect the margin to kind of approach the corporate average or even exceed it after the so-called — the value reflection this year?

[Answer] Overall speaking, advanced packaging accounted for over 8% of revenue last year. And it will account for over 10% this year. In terms of gross margins, it is better. It is better than before but still below the corporate average. 

AI makes up all of the current demand for SoIC (system on integrated chips) that TSMC’s management is seeing, but they think non-AI-related demand for SoIC will come in the future

Today, SoIC’s demand is still focused on AI applications, okay? For PC or for other area, it’s coming but not right now.

Tesla (NASDAQ: TSLA)

Tesla’s management thinks Tesla’s FSD (Full Self Driving) technology has grown up a lot in the past few years; management thinks that car-use can grow from 10 hours per week to 55 hours per week with autonomous vehicles; autonomous vehicles can be used for both cargo and people delivery; FSD currently works very well in the USA , and will soon work well everywhere else; the constraint Tesla is currently experiencing with autonomous vehicles is in battery packs; FSD makes traffic commuting safer; FSD is currently on Version 13, and management believes Version 14 will have a significant step-improvement; Tesla has launched the Cortex training cluster at Gigafactory Austin, and it has played a big role in advancing FSD; Tesla will launch unsupervised FSD in June 2025 in Austin; Tesla already has thousands of its cars driving autonomously daily in its factories in Fremont and Texas, and Tesla will soon do that in Austin and elsewhere in the world; Tesla’s solution for autonomous vehicles is a generalised AI solution which does not need high-precision maps; Tesla’s unsupervised FSD work outside of Austin even when it’s launched only in June 2025 in Austin, but management just wants to be cautious; management thinks Tesla will release unsupervised FSD in many parts of the USA by end-2025; management’s safety-standard for FSD is for it to be far, far, far superior to humans; management thinks Tesla will have unsupervised FSD in almost every market this year

For a lot of people, like their experience of Tesla autonomy is like if it’s even a year old, if it’s even 2 years old, it’s like meeting someone when they’re like a toddler and thinking that they’re going to be a toddler forever. But obviously not going to be a toddler forever. They grow up. But if their last experience was like, “Oh, FSD was a toddler.” It’s like, well, it’s grown up now. Have you seen it? It’s like walks and talks…

…My #1 recommendation for anyone who doubts is simply try it. Have you tried it? When is the last time you tried it? And the only people who are skeptical, the only people who are skeptical are those who have not tried it.

So a car goes — a passenger car typically has only about 10 hours of utility per week out of 168, a very small percentage. Once that car is autonomous, my rough estimate is that it is in use for at least 1/3 of the hours per week, so call it, 50, maybe 55 hours of the week. . And it can be used for both cargo delivery and people delivery…

…That same asset, the thing that — these things that already exist with no incremental cost change, just a software update, now have 5x or more the utility than they currently have. I think this will be the largest asset value increase in human history…

…So look, the reality of autonomy is upon us. And I repeat my advice, try driving the car or let it drive you. So now it works very well in the U.S., but of course, it will, over time, work just as well everywhere else…

…Our current constraint is battery packs this year but we’re working on addressing that constraint. And I think we will make progress in addressing that constraint…

…So a bit more on full self-driving. Our Q4 vehicle safety report shows continued year-over-year improvement in safety for vehicles. So the safety numbers, if somebody has supervised full self-driving turn on or not, the safety differences are gigantic…

…People have seen the immense improvement with version 13, and with incremental versions in version 13 and then version 14 is going to be yet another step beyond that, that is very significant. We launched the Cortex training cluster at Gigafactory Austin, which was a significant contributor to FSD advancement…

…We’re going to be launching unsupervised full self-driving as a paid service in Austin in June. So I talked to the team. We feel confident in being able to do an initial launch of unsupervised, no one in the car, full self-driving in Austin in June…

…We already have Teslas operating autonomously unsupervised full self-driving at our factory in Fremont, and we’ll soon be doing that at our factory in Texas. So thousands of cars every day are driving with no one in them at our Fremont factory in California, and we’ll soon be doing that in Austin and then elsewhere in the world with the rest of our factories, which is pretty cool. And the cars aren’t just driving to exactly the same spot because, obviously, it all — [ implied ] at the same spot. The cars are actually programmed with where — with what lane they need to park into to be picked up for delivery. So they drive from the factory end of line to their destination parking spot and to be picked up for delivery to customers and then doing this reliably every day, thousands of times a day. It’s pretty cool…

…Our solution is a generalized AI solution. It does not require high-precision maps of locality. So we just want to be cautious. It’s not that it doesn’t work beyond Austin. In fact, it does. We just want to be — put our toe in the water, make sure everything is okay, then put a few more toes in the water, then put a foot in the water with safety of the general public as and those in the car as our top priority…

…I think we will most likely release unsupervised FSD in many regions of the country of the U.S. by the end of this year…

…We’re looking for a safety level that is significantly above the average human driver. So it’s not anywhere like much safer, not like a little bit safer than human, way safer than human. So the standard has to be very high because the moment there’s any kind of accident with an autonomous car, that immediately gets worldwide headlines, even though about 40,000 people die every year in car accidents in the U.S., and most of them don’t even get mentioned anywhere. But if somebody [ scrapes a shed ] within autonomous car, it’s headline news…

…But I think we’ll have unsupervised FSD in almost every market this year, limited simply by regulatory issues, not technical capability. 

Tesla’s management thinks the compute needed for Optimus will be 10x that of autonomous vehicles, even though a humanoid robot has 1,000x more uses than an autonomous vehicle; management has seen the cost of training Optimus (or AI, in general) dropping dramatically over time; management thinks Optimus can produce $10 trillion in revenue, and that will still make the training needs of $500 billion in compute a good investment; management realises their revenue projections for Optimus sound insane, but they believe in it (sounds like a startup founder trying to get funding from VCs); it’s impossible for management to predict the exact timing for Optimus because everything about the robot has to be designed and built from the ground up by Tesla (nothing could be bought off-the-shelf by Tesla), but management thinks Tesla will build a few thousand Optimus robots by end-2025, and that these robots will be doing useful work in Tesla’s factories in the same timeframe; management’s goal is to ramp up Optimus production at a far faster rate than anything has ever been ramped; Optimus can even do delicate things such as play the piano; Optimus is still not design-locked for production; Tesla might be able to deliver Optimus to external clients by 2026 H2; management is confident that at scale, Optimus will be cheaper to produce than a car

The training needs for Optimus, our Optimus humanoid robot are probably at least ultimately 10x what is needed for the car, at least to get to the full range of useful role. You can say like how many different roles are there for a humanoid robot versus a car? A humanoid robot has probably 1,000x more uses and more complex things than in a car. That doesn’t mean the training scales by 1,000 but it’s probably 10x…

…It doesn’t mean like — or Tesla’s going to spend like $500 billion in training compute because we will obviously train Optimus to do enough tasks to match the output of Optimus robots. And obviously, the cost of training is dropping dramatically with time. But it is — it’s one of those things where I think long-term, Optimus will be — Optimus has the potential to be north of $10 trillion in revenue, like it’s really bananas. So that you can obviously afford a lot of training compute in that situation. In fact, even $500 billion training compute in that situation will be quite a good deal…

…With regard to Optimus, obviously, I’m making these revenue predictions that sound absolutely insane, I realize that. But they are — I think they will prove to be accurate…

…There’s a lot of uncertainty on the exact timing because it’s not like a train arriving at the station for Optimus. We are designing the train and the station and in real time while also building the tracks. And sort of like, why didn’t the train arrive exactly at 12:05? And like we’re literally designing the train and the tracks and the station in real-time while like how can we predict this thing with absolute precision? It’s impossible. The normal internal plan calls for roughly 10,000 Optimus robots to be built this year. Will we succeed in building 10,000 exactly by the end of December this year? Probably not. But will we succeed in making several thousand? Yes, I think we will. Will those several thousand Optimus robots be doing useful things by the end of the year? Yes, I’m confident they will do useful things…

…Our goal is to run Optimus production faster than maybe anything has ever been ramped, meaning like aspirationally in order of magnitude, ramp per year. Now if we aspire to an order of magnitude ramp per year, perhaps, we only end up with a half order of magnitude per year. But that’s the kind of growth that we’re talking about. It doesn’t take very many years before we’re making 100 million of these things a year, if you go up by let’s say, a factor by 5x per year…

…This is an entirely new supply chain, it’s entirely new technology. There’s nothing off the shelf to use. We tried desperately with Optimus to use any existing motors, any actuators, sensors. Nothing worked for a humanoid robot at any price. We had to design everything from physics-first principles to work for humanoid robot and with the most sophisticated hand that has ever been made before by far. Optimus will be also able to play the piano and be able to thread a needle. I mean this is the level of precision no one has been able to achieve…

…Optimus is not design-locked. So when I say like we’re designing the train as it’s going — we’re redesigning the train as it’s going down the tracks while redesigning the tracks and the train stations…

…I think probably with version 2, it is a very rough guess because there’s so much uncertainty here, very rough guess that we start delivering Optimus robots to companies that are outside of Tesla in maybe the second half of next year, something like that…

…I’m confident at 1 million units a year, that the production cost of Optimus will be less than $20,000. If you compare the complexity of Optimus to the complexity of a car, so just the total mass and complexity of Optimus is much less than a car.

The buildout of Cortex accelerated the rollout of FSD Version 13; Tesla has invested $5 billion so far in total AI-related capex

The build-out of Cortex was accelerated because of the role — actually accelerate the rollout of FSD Version 13. Our cumulative AI-related CapEx, including infrastructure, so far has been approximately $5 billion. 

Tesla’s management is seeing significant interest from some car manufacturers in licensing Tesla’s FSD technology; management thinks that car manufacturers without FSD technology will go bust; management will only entertain situations where the volume would be very high

What we’re seeing is at this point, significant interest from a number of major car companies about licensing Tesla full self-driving technology…

…We’re only going to entertain situations where the volume would be very high. Otherwise, it’s just not worth the complexity. And we will not burden our engineering team with laborious discussions with other engineering teams until we obviously have unsupervised full self-driving working throughout the United States. I think the interest level from other manufacturers to license FSD will be extremely high once it is obvious that unless you have FSD, you’re dead.

Compared to Version 13, Version 14 of FSD will have a larger model size, longer context length, more memory, more driving-context, and more data on tricky corner cases

[Question] What technical breakthroughs will define V14 of FSD, given that V13 already covered photon to control? 

[Answer] So continuing to scale the model size a lot. We scale a bunch in V13 but then there’s still room to grow. So we’re going to continue to scale the model size. We’re going to increase the context length even more. The memory sort of like limited right now. We’re going to increase the amount of memory [indiscernible] minutes of context for driving. They’re going to add audio and emergency vehicles better. Add like data of the tricky corner cases that we get from the entire fleet, any interventions or any kind of like user intervention. We just add to the data set. So scaling in basically every access, training compute, [ asset ] size, model size, model context and also all the reinforcement learning objectives.

Tesla has difficulties training AI models for autonomous vehicles in China because the country previously did not allow Tesla to transfer training videos outside of China while the US government did not allow Tesla to do training in China; a workaround Tesla did was to train on publicly available videos of streets in China; Tesla also had to build a simulator for its AI models to train on bus lanes in China because they are complicated 

In China, which is a gigantic market, we do have some challenges because they weren’t — they currently allow us to transfer training video outside of China. And then the U.S. government won’t let us do training in China. So we’re in a bit of a buying there. It’s like a bit of a quandary. So what we’re really solving then is by literally looking at videos of streets in China that are available on the Internet to understand and then feeding that into our video training so that publicly available video of street signs and traffic rules in China can be used for training and then also putting it in a very accurate simulator. And so it will train using SIM for bus lanes in China. Like bus lanes in China, by the way, one of our biggest challenges in making FSD work in China is the bus lanes are very complicated. And there’s like literally like hours of the day that you’re allowed to be there and not be there. And then if you accidently go in at a bus lane at the wrong time, you get an automatic ticket instantly. And so it was kind of a big deal, bus lanes in China. So we put that into our simulator train on that, the car has to know what time of the day it is, read the sign. We’ll get this solved.

Elon Musk knows LiDAR technology really well because he built a LiDAR system for SpaceX that is in-use at the moment, but he thinks LiDAR is simply the wrong technology to use for autonomous vehicles because it has issues, and because humans are driving vehicles simply with our eyes and our biological neural nets

[Question] You’ve said in the past about LiDAR, for EVs, that LiDAR is a crutch, a fool’s errand. I think you even told me once, even if it was free, you’d say you wouldn’t use it. Do you still feel that way?

[Answer] Obviously humans drive without shooting lasers out of their eyes. I mean unless you’re superman. But like humans drive just with passive visual — humans drive with eyes and a neural net — and a brain neural net, sort of biological — so the digital equivalent of eyes and a brain are cameras and digital neural nets or AI. So that’s — the entire road system was designed for passive optical neural nets. That’s how the whole real system was designed and what everyone is expecting, that’s how we expect other cars to behave. So therefore, that is very obviously a solution for full self-driving as a generalized — but the generalized solution for full self-driving as opposed to the very specific neighborhood-by-neighborhood solution, which is very difficult to maintain, which is what our competitors are doing…

…LiDAR has a lot of issues. Like SpaceX Dragon docks with the space station using LiDAR, that’s a program that I personally spearheaded. I don’t have some fundamental bizarre dislike of LiDAR. It’s simply the wrong solution for driving cars on roads…

…I literally designed and built our own red LiDAR. I oversaw the project, the engineering thing. It was my decision to use LiDAR on Dragon. And I oversaw that engineering project directly. So I’m like we literally designed and made a LiDAR to dock with the space station. If I thought it was the right solution for cars, I would do that, but it isn’t.

The Trade Desk (NASDAQ: TTD)

Trade Desk’s management continues to invest in AI and thinks that AI is game-changing for forecasting and insights on identity and measurement; Trade Desk’s AI efforts started in 2017 with Koa, but management sees much bigger opportunities today; management is asking every development team in Trade Desk to look for opportunities to introduce AI into Trade Desk’s platform; there are already hundreds of AI enhancements to Trade Desk’s platform that have been shipped or are going to be shipped in 2025

AI is providing next-level performance in targeting and optimization, but it is also particularly game-changing in forecasting and identity and measurement. We continue to look at our technology stack and ask, where can we inject AI and enhance our product and client outcomes? Over and over again, we are finding new opportunities to make AI investments…

…We started our ML and AI efforts in 2017 with the launch of Koa, but today, the opportunities are much bigger. We’re asking every scrum inside of our company to look for opportunities to inject AI into our platform. Hundreds of enhancements recently shipped and coming in 2025 would not be possible without AI. We must keep the pedal to the metal, not to chest them on stages, which everyone else seems to be doing, but instead to produce results and win share.

Wix (NASDAQ: WIX)

Wix’s AI Website Builder was launched in 2024 and has driven stronger conversion and purchase behaviour from users; more than 1 million sites have been created and published with AI Website Builder; most new Wix users today are creating their websites through Wix’s AI tools and AI Website Builder and these users have higher rates of converting to paid subscribers

2024 was also the year of AI innovation. In addition to the significant number of AI tools introduced, we notably launched our AI Website Builder, the new generation of our previous AI site builder introduced in 2016. The new AI Website Builder continues to drive demonstrably stronger conversion and purchase behavior…

…Over 1 million sites have been created and published with the Website Builder…

…Most new users today are creating their websites through our AI-powered onboarding process and Website Builder which is leading to a meaningful increase in conversion of free users to paid subscriptions, particularly among Self Creators.

Wix’s management launched Wix’s first directly monetised AI product – AI Site Chat – in December 2024; AI Site Chat will help businesses converse with customers round the clock; users of AI Site Chat have free limited access, with an option to pay for additional usage; AI Site Chat’s preliminary results look very promising

In December, we also rolled out our first directly monetized AI product – the AI Site Chat…

…The AI Site-Chat was launched mid-December to Wix users in English, providing businesses with the ability to connect with visitors 24/7, answer their questions, and provide relevant information in real time, even when business owners are unavailable. By enhancing availability and engagement on their websites, the feature empowers businesses to meet the needs of their customers around the clock, ultimately improving the customer experience and driving potential sales. Users have free limited access with the option to upgrade to premium plans for additional usage…

…So if you’re a Wix customer, you can now install a chat, AI-powered chat on your website, and this will handle customer requests, product inquiries and support request. And from — and again, it’s very early in days and the preliminary results, but it looks very promising. 

AI agents and assistants are an important part of management’s product roadmap for Wix in 2025; Wix is testing (1) an AI assistant for its Wix Business Manager dashboard, and (2) Marketing Agent, a directly monetizable AI agent that helps users accomplish marketing tasks; Marketing Agent is the first of a number of specialised AI agents management will roll out in 2025; management intends to test monetisation opportunities with the new AI agents

AI remains a major part of our 2025 product roadmap with particular focus on AI-powered agents and assistants…

… Currently, we are testing our AI Assistant within the Wix Business Manager as well as our AI Marketing Agent.

The AI Assistant in the Wix Business Manager is a seamlessly integrated chat interface within the dashboard. Acting as a trusted aide, this assistant guides users through their management journey by providing answers to questions and valuable insights about their site. With its comprehensive knowledge, the AI Assistant empowers users to better understand and leverage available resources, assisting with site operations and business tasks. For instance, it can suggest content options, address support inquiries, and analyze analytics—all from a single entry point.

The AI Marketing Agent helps businesses to market themselves online by proactively generating tailored marketing plans that align with users’ goals and target audiences. By analyzing data from their website, the AI delivers personalized strategies to enhance SEO, create engaging content, manage social media, run email campaigns and optimize paid advertising—all with minimal effort from the user. This solution not only simplifies marketing but also drives Wix’s monetization strategy, seamlessly guiding users toward high-impact paid advertising and premium marketing solutions. As businesses invest in growing their online presence, Wix benefits through a share of ad spend and premium feature adoption—fueling both user success and revenue growth.

We will continue to release and optimize specialized AI agents that assist our users in building the online presence they envision. We are exploring various monetization strategies as we fully roll out these agents and adoption increases.

Wix’s management is seeing Wix’s gross margin improve because of AI integration in customer care

Creative Subscriptions non-GAAP gross margin improved to 85% in Q4’24 and to 84% for the full year 2024, up from 82% in 2023. Business Solutions non-GAAP gross margin increased to 32% in Q4’24 and to slightly above 30% for the full year 2024. Continued gross margin expansion is the product of multiple years of cost structure optimization and efficiencies from AI integration across our Customer Care operations.

Wix’s management believes the opportunity for Wix in the AI era is bigger than what came before

There’s a lot of discussions about a lot of theories about it. But I really believe that the opportunity there is bigger than anything else because what we have today are going to continue to dramatically evolve into something that is probably more powerful and more enabling for small businesses to be successful. Overall, the Internet has a tendency to do it every 10 years or so, right, in the ’90s, the Internet started and became HTML, then it became images and then later on videos and then it became mobile, right? And I think they became interactive, everything become an application, kind of an application. And I think how website will look at the AI universe is the next step, and I think there’s a lot of exciting things we can offer our users there.

Visa (NYSE: V)

Visa is an early adopter of AI and management continues to drive adoption; Visa has seen material gains in engineering productivity; Visa has deployed AI in many functions, such as analytics, sales, finance, and marketing

We were very early adopters of artificial intelligence, and we continue to drive hard at the adoption of generative AI as we have for the last couple of years. So we’ve been working to embed AI and AI tooling into our company’s operations, I guess, broadly. We’ve seen material gains in productivity, particularly in our engineering teams. We’ve deployed AI tooling in client services, sales, finance, marketing, really everywhere across the company. And we were a very early adopter of applied AI in the analytics and modeling space, very early by like decades, we’ve been using AI in that space. So our data science and risk management teams have, at this point, decades of applied experience with AI, and they’re aggressively adopting the current generations of AI technology to enhance both our internal and our market-facing predictive and detective modeling capabilities. Our product teams are also aggressively adopting gen AI to build and ship new products.

Zoom Communications (NASDAQ: ZM)

Zoom AI Companion’s monthly active users (MAUs) grew 68% quarter-on-quarter; management has added new agentic AI capabilities to Zoom AI Companion; management will launch the Custom AI Companion add-on in April 2025; management will launch AI Companion for clinicians in March 2025; Zoom AI Companion is added into a low-end Zoom subscription plan at no added cost, and customers do not want to leave their subscriptions because of the added benefit of Zoom AI Companion; Zoom will be monetising Zoom AI Companion from April 2025 onwards through the Custom AI Companion add-on; the Custom AI Companion add-on would be $12 a seat when it’s launched in April 2025 and management thinks this price would provide a really compelling TCO (total cost of ownership) for customers; management thinks Custom AI Companion would have a bigger impact on Zoom’s revenue in 2026 (FY2027) than in 2025 (FY2026); see Point 28 for use cases for Custom AI Companion

Growth in monthly active users of Zoom AI Companion has accelerated to 68% quarter-over-quarter, demonstrating the real value AI is providing customers…

…As part of AI Companion 2.0, we added advanced agentic capabilities, including memory, reasoning, orchestration and a seamless integration with Microsoft and Google services. In April, we’re launching Custom AI Companion add-on to automate workplace tasks through custom agents. This will personalize AI to fit customer needs, connect with their existing data, and work seamlessly with their third-party tools. We’re also enhancing Zoom Workplace for Clinicians with an upgraded AI Companion that will enable clinical note-taking capabilities and specialized medical features for healthcare providers starting in March…

…If you look at our low SMB customer online buyers, AI Companion is part of that at no additional cost, made our service very sticky and also the customers give a very basic example, like meeting summary, right? It works so well, more and more customers follow the value…

…For high end, for sure, and we understand that today’s AI Companion and additional cost we cannot monetize. However, in April, we are going to announce the customized Companion for interested customers. We can monetize…

…[Question] So in April, when the AI customization, the AI Companion becomes available, I think it’s $11 or $12 a seat. Can you maybe help us understand how you’re thinking about like what’s the real use case?

[Answer] In regards to your question about what are sort of the assumptions or what’s the targeting in our [ head ] with the $12 Custom AI Companion SKU. I would say, starting with enterprise customers, obviously, the easiest place to sort of pounce on them is our own customer base and talk about that, but certainly not just limited to that. But we’ll be probably giving a lot more, I would say, at Enterprise Connect, which you can see on the thing there. But I would say we’ve assumed some degree of monetization in FY ’26, I think you’ll see more of it in ’27. And we think that the $12 price point is going to be a really compelling TCO story for our customers, it’s differentiated from what others in the market are pricing now. 

The Zoom Virtual Agent feature will soon be able to handle complex tasks

Zoom Virtual Agent will soon expand reasoning abilities to handle complex tasks while maintaining conversational context for more natural and helpful outcomes.

Zoom’s management believes Zoom is uniquely positioned to win in agentic AI for a few reasons, including Zoom having exception context of users’ ongoing conversations, and Zoom’s federated AI approach where the company can use the best models for each task

We’re uniquely positioned to succeed in agentic AI for several reasons:

● Zoom is a system of engagement for our users with recent information in ongoing conversations. This exceptional context along with user engagement allows us to drive greater value for customers.

● Our federated AI approach lets us combine the best models for each task. We can use specialized small language models where appropriate, while leveraging larger models for more complex reasoning – driving both quality and cost efficiency

Zoom’s management is seeing large businesses want to use Zoom because of the AI features of its products

You take a Contact Center, for example, why we are winning? Because a lot of AI features like AI Expert Assist. AI, a lot of features built into our quality management and so on and so forth. 

Zoom’s management sees Zoom’s AI business services as a great way to monetise AI

You take a Contact Center, for example, why we are winning? Because a lot of AI features like AI Expert Assist. AI, a lot of features built into our quality management and so on and so forth. But all those business services, that’s another great way for us to monetize AI.

Zoom’s management thinks Zoom’s cost of ownership with AI is lower than what competitors are offering

And I look at our AI Companion, all those AI Companion core features today at no additional cost, right? And customer really like it because of the quality, they’re getting better and better every quarter and very useful, right? Not like some other competitors, right? They talk about their AI strategy and when customers realize that, wow, it’s very expensive. And the total cost of ownership is not getting better because cost of the value is not [ great ], but also it’s not [ free ] and they always try to increase price.

A good example of a use case for Custom AI Companion

[Question] So in April, when the AI customization, the AI Companion becomes available, I think it’s $11 or $12 a seat. Can you maybe help us understand how you’re thinking about like what’s the real use case?

[Answer] So regarding the Custom AI Combined on use cases, high levels, we give a customer ability to customize their needs. I’ll give a few examples. One feature like we have a Zoom Service Call video clip, and we are going to support the standard template, right? How to support every customer? They have a customized template for each of the users, and this is a part of combining AI Studio, right? And also all kinds of third-party integration, right? And they like they prefer, right, some of those kind of sort of third-party application integration. With their data, with the knowledge, whether the [ big scenery ], a lot of things, right? Each company is different, they would not customized, so we can leverage our combining studio to work together with the customer to support their needs and also at same time commodities.

Zoom’s management expects the cost from AI usage to increase and so that will impact Zoom’s margins in the future, but management is also building efficiencies to offset the higher cost of AI

[Question] As we think about a shift more towards AI contribution, aren’t we shifting more towards a consumption model rather than a seat model over time, why wouldn’t we see margin compression longer term?

[Answer] Around how to think about margins and business models and why we don’t see compression. And what I would say is that — what we expect to see is similar to what you saw in FY ’25, which is we’re seeing obvious increase in cost from AI.  And that we have an ongoing methodical kind of efficiency list to offset, and we certainly expect that broadly to continue into FY ’26. So I think we feel good about our ability to kind of moderate that. There’s other things we do more holistically where we can offset stuff that’s maybe not in AI in our margins, things like [ colos ], et cetera, that we’ve talked about previously. 

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

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

With the latest earnings season for the US stock market – for the 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.

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

I first came across a niche corner of the US stock market known as thrift conversions in January 2024. Upon further research over the subsequent months, I realised it could be an interesting hunting ground for potential bargains. 

For the purpose of this article, thrifts, which have roots in the USA tracing back to the early 19th century, are small community banks in the country that are mutually owned by their depositors. The mutual ownership structure means that these thrifts have no shareholders. As a result, a thrift’s depositors – despite being owners – have no legal way to access its economics. In the 1970s, regulations were introduced to allow thrifts to convert their ownership structure (hence the term “thrift conversions”) and become public-listed companies with shareholders. Today, there are two main ways for thrifts to convert:

• The first is a standard conversion, where a thrift converts fully into a public-listed entity at one go.
• The second is a two-step conversion. In the first-step, a thrift converts only a minority interest in itself into a public-listed entity and thus still has a partial mutual ownership structure. In the second-step, a thrift that has undergone the first-step conversion process goes on to convert fully into a public-listed entity. As far as we know, there’s no time limit for a thrift that has undergone the first-step conversion to partake in the second-step of the process.

Subsequently in this article, I will be using the word “conversion”, or other forms of the same word, to refer only to the standard conversion, unless otherwise stated.

A thrift conversion can be thought of as a thrift undergoing an initial public offering (IPO). During a conversion, the incentives of a thrift’s management and those of its would-be shareholders are highly aligned. In the process, a thrift offers shares to management and depositors first; if there’s insufficient demand, the thrift will then offer shares to outsiders. Importantly, management would be buying the thrift’s shares during the conversion at the same price as other would-be shareholders (the other would-be shareholders are the depositors and outsiders; as a reminder, prior to a conversion, a thrift has no shareholders¹). This means it’s very likely that management wants a thrift’s shares to have as cheap a valuation as possible during the conversion. Moreover, new capital that’s raised from management and would-be shareholders in the conversion goes directly to the thrift’s coffers. This new capital adds to the thrift’s equity (calculated by deducting the thrift’s liabilities from its assets) that it has built from the profits it has accumulated over time from providing banking services. These features mean that a thrift often becomes a full public-listed entity at a low valuation while having a high equity-to-assets ratio. It’s worth noting that a thrift can conduct share buybacks and sell itself to other financial institutions after the one-year and three-year marks, respectively, from its conversion.²

Investor Jim Royal’s comprehensive book on thrift conversions (referring to both standard and two-step conversions), aptly titled The Zen of Thrift Conversions, referenced a 2016 study by investment bank Piper Jaffray. The study showed that since 1982, thrifts that became full public-listed entities did so at an average price-to-tangible book (P/TB) ratio of just 0.75. After becoming public-listed entities, thrifts tend to continue trading at low P/TB ratios. This is because they also tend to have very low returns on equity – a consequence of them having a high equity-to-assets ratio after their conversion – and a bank with a low return on equity deserves to trade at a low P/TB ratio. But the chronically low P/TB ratio is why thrift conversions could be a fertile space for bargains.

Assuming that converted thrifts have low P/TB ratios of less than 1, those that conduct share buybacks increase their tangible book value per share over time even when they have low returns on equity. Moreover, as mentioned earlier, converted thrifts tend to have high equity-to-asset ratios, which means they have overcapitalised balance sheets and thus have plenty of excess capital to buy back shares without harming their financial health. To top it off, the 2016 study from Piper Jaffray also showed that since 1982, 70% of thrifts were acquired after the third anniversary of them becoming full public-listed entities and these thrifts were acquired at an average P/TB ratio of 1.4³ (the median time between them becoming fully public and them being acquired was five years).

The growth in a converted thrift’s tangible book value per share from buybacks, and the potential increase in its P/TB ratio when acquired, could result in a strong annualised return for an investor. For example, consider a thrift conversion with the following traits:

1. It has a return on equity of 3% in each year;
2. It has a P/TB ratio that consistently hovers at 0.7;
3. It buys back 5% of its outstanding shares annually for four years after the first anniversary of its conversion, and;
4. It gets acquired at a P/TB ratio of 1.4 five years after its conversion

Such a thrift will generate a handsome annualised return of 20% over five years. Investing in the thrift on the third-anniversary of its conversion – when the thrift can legally sell itself to other financial institutions – will result in an even more impressive annualised return of 52% when the thrift’s acquired⁴. There are also past examples of converted thrifts that go on to produce impressive gains even without being acquired. In his book Beating The Street, Peter Lynch, the famed ex-manager of the Fidelity Magellan Fund, shared many examples. Here’s a sample (emphasis is mine):

“In 1991, 16 mutual thrifts and savings banks came public. Two were taken over at more than four times the offering price, and of the remaining 14, the worst is up 87 percent in value. All the rest have doubled or better, and there are four triples, one 7-bagger, and one 10-bagger. Imagine making 10 times your money in 32 months by investing in Magna Bancorp, Inc., of Hattiesburg, Mississippi.”

But not every thrift conversion leads to a happy ending. Table 1 below shows some pertinent figures of Mid-Southern Bancorp, a thrift which produced a pedestrian return from its second-step conversion in July 2018 to its acquisition by Beacon Credit Union in January 2024.

There are a few important things I look out for in thrift conversions⁵:

• The equity-to-assets ratio: The higher the better, as it signifies an over-capitalised and strong balance sheet, and would make a thrift look attractive to a would-be acquirer
• The P/TB ratio: The lower the better, as a P/TB ratio that is materially below 1 will (a) make share buybacks a value-enhancing activity for a thrift’s shareholders, and (b) enhance the potential return for us as investors
• Share buybacks: The more buybacks that happen at a P/TB ratio below 1, the better, as it is not only value-enhancing, but also indicates that management has a good understanding of capital allocation
• Non-performing assets as a percentage of total assets: The lower the better, as it signifies a thrift that is conducting its banking business conservatively
• Net income: If the play is for a potential acquisition of a thrift, we want to avoid a chronically loss-making thrift as consistent losses indicate risky lending practices, but the amount of net income earned by the thrift is not important because an acquirer would be improving the thrift’s operations; if the play is for a thrift to generate strong returns for investors from its underlying business growth, then we would want to see a history of growth in net income and at least a decent return on equity (say, 8% or higher)
• Change in control provisions: This relates to payouts that a thrift’s management can receive upon being acquired and such information can typically be found in a thrift’s DEF 14-A filing; if management can receive a nice payout when a thrift is acquired, management is incentivised to sanction a sale
• Management’s compensation: The annual compensation of a thrift’s management should not be high relative to the monetary value of management’s ownership stakes in the thrift

Expanding on the last point of what I look out for, I’ve seen cases of fully-public thrifts with poor long-term business results have management teams with high compensation and relatively low dollar-amounts in ownership stakes. In such cases, I think there’s a low possibility of these thrifts being acquired in a reasonable amount of time to maximise shareholder value because it’s lucrative for the management teams to entrench their positions.

If any of you reading this letter is interested to have deeper conversations about investing in thrifts, please reach out, I would love to engage.

^1. Thrifts that undertake the two-step conversion process would have no shareholders prior to the first-step conversion. After the first-step conversion is completed and before the second-step conversion commences, these thrifts would have shareholders who own only a minority economic interest in them.  

^2. Thrifts that decide to participate in the second-step of the two-step conversion process after completing the first step can begin share buybacks after the first anniversary of the second-step; they can also be acquired on the third anniversary. 

^3. Why would a converted thrift (referring to both standard conversions and two-step conversions) be an attractive acquisition target and be acquired at a premium to its tangible book value? This is because the acquirer of a converted thrift can easily cut significant costs and make more efficient use of the thrift’s overcapitalised balance sheet; this means an acquirer can pay a premium to book value (i.e. a P/TB ratio of more than 1) for a converted thrift and still end up with a good deal. 

^4. The potential return of a thrift that has completed the second-step of the two-step conversion process is identical to a thrift that has completed the standard conversion, ceteris paribus. This is because the former has the same important features as the latter, such as the low valuation, the over-capitalised balance sheet, and the possibility of being acquired by other financial institutions at a premium to tangible book value. 

^5. What I look out for in a thrift that has completed the standard conversion is the same as what I look out for in a thrift that has completed the second-step of the two-step conversion.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have no vested interest in any company mentioned. Holdings are subject to change at any time.

I’ve long been sector-agnostic when it comes to the companies I’m interested in because I believe that great companies – and thus, great stocks – can come from anywhere. 

My belief was formed because of something I learnt more than a dozen years ago about the US-based airline, Southwest Airlines. Ned Davis Research was tasked by CNN’s MONEY Magazine in 2002 to find the five US-listed stocks with the highest returns over the past 30 years. The winner was Southwest Airlines with its annualised return of 26% from 1972 to 2002; a $1,000 investment at the start of the period would have become $1 million by the end. What is noteworthy here is airlines were widely regarded back then as businesses with horrendous economics. In Berkshire Hathaway’s 2007 annual shareholders’ letter, Warren Buffett wrote (emphasis is mine): 

“The worst sort of business is one that grows rapidly, requires significant capital to engender the growth, and then earns little or no money. Think airlines. Here a durable competitive advantage has proven elusive ever since the days of the Wright Brothers. Indeed, if a farsighted capitalist had been present at Kitty Hawk, he would have done his successors a huge favor by shooting Orville down.

The airline industry’s demand for capital ever since that first flight has been insatiable. Investors have poured money into a bottomless pit, attracted by growth when they should have been repelled by it. And I, to my shame, participated in this foolishness when I had Berkshire buy U.S. Air preferred stock in 1989. As the ink was drying on our check, the company went into a tailspin, and before long our preferred dividend was no longer being paid. But we then got very lucky. In one of the recurrent, but always misguided, bursts of optimism for airlines, we were actually able to sell our shares in 1998 for a hefty gain. In the decade following our sale, the company went bankrupt. Twice.” 

And yet, it was an airline that topped the charts in 2002 for the best-performing US stock in the past 30 years. The timeframe of 30 years is also sufficiently long, such that Southwest Airlines’ gains had to be the result of its business’s excellent long-term performance, and not some fortunate short-term hiccup in the fortunes of its business or its stock price.

A recent study from the highly-regarded investment researcher Michael Maubossin, titled Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation, bolsters my belief. He found that differences in the return on invested capital (ROIC) between industries is lower than the differences in ROICs of companies within industries. In Mauboussin’s data-set, the industry with the highest median ROIC from 1963 to 2023 is Personal Care Products at around 18%. But within Personal Care Products, the companies have ROICs ranging from a low of around 5% to a high of around 40%. Meanwhile, the Wireless Telecom Services industry has one of the lowest median ROICs at around 1%. Yet, the companies within have ROICs ranging from just below 40% to deeply negative figures. Said another way, the best company in a poor industry (Wireless Telecom Services) still has an excellent business that performs significantly better than the median company in a great industry (Personal Care Products). 

I continue to believe that excellent investing opportunities can be found everywhere, so I will, for the foreseeable future, remain sector-agnostic. Sometimes, a gleaming diamond can be found amongst a lump of coal for those with the ability to spot a true bargain.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have no vested interest in any company mentioned. Holdings are subject to change at any time.