Big Tech Battles On AI, Here’s The Winner

The major theme over the past month in Big Tech and AI semiconductors has been the durability of demand: essentially, what is Big Tech’s return on more than $150 billion in capex over the last twelve months (primarily for AI infrastructure), and if the companies can generate a substantial enough return from AI products to continue catalyzing GPU demand and revenue for Nvidia.

Though Big Tech’s June quarter earnings were met mostly with rather gloomy reactions, management teams reiterated positivity on the long-term potential of generative AI products and services, and the need for continual investment in AI infrastructure.

Microsoft stands out as the clear leader with multiple different monetization pathways from generative AI, whether through Azure or GitHub Copilot, while AWS has seen growth reaccelerate as AI’s revenue contribution reaches a multi-billion-dollar run rate.

Alphabet has touted billions in AI related revenue, while Meta is seeing an in-house effect with AI playing an increasingly large role in engagement and ad delivery across Meta’s family of apps.

The Quest for ROI

Sequoia Capital recently raised an alarm on Big Tech’s massive AI investments, and whether companies will be able to realize large enough returns to justify these expenditures, calling it “AI’s $600B Question ,” in a follow up to their September 2023 analysis and the $200B question. Sequoia’s analysis suggests that based on Nvidia’s annualized $150B data center run rate by Q4, the revenue required for payback on capex would be $600B, based on a 50% software margin for CSPs and 50% operating cost from GPUs.

This sparked fears as Big Tech is not yet able to convince investors or analysts that these investments will pay off. This has led to analysts pressing management over monetization and potential overinvestment in capacity in earnings calls.

Lead Tech Analyst Beth Kindig recently discussed this with Bloomberg Asia following Alphabet’s earnings, saying that investors are looking for an ROI from Big Tech in terms of quarter-over-quarter revenue accelerations from AI in the cloud, and if these accelerations are enough to justify the amount of capex spent.

Sign up for I/O Fund's free newsletter with gains of up to 2600% because of Nvidia's epic run - Click here .

Capex Growth Continues

We recapped Big Tech’s capex and commentary following Q1’s earnings in mid-May for our newsletter readers, saying at the time that Big Tech “will likely commit upwards of $200 billion, maybe even $210 billion, combined in capex this year, predominantly for AI infrastructure – from data center construction and expansion, to GPU procurement and custom silicon efforts and more.

It’s no wonder the four are boosting capex by more than 35% YoY, given positive outlooks on AI’s potential to drive revenue growth in the billions and how demand continues to outstrip GPU supply.”

Following the recent Q2 reports, Big Tech did, indeed, commit $210 billion to capex.

In the first half of 2024, Alphabet, Amazon, Meta and Microsoft spent nearly $104 billion in capex, up 47% YoY, with more than half of that total coming in Q2. Microsoft and Alphabet saw the largest YoY increases among the four, driving capex 78% and 91% higher for the first half of the year, respectively.

Big Tech spent nearly $104 billion in capex in the first half of 2024, up 47% YoY, with more than half of that total coming in Q2.

Source: I/O Fund

Microsoft: Capex this quarter was $19 billion, an increase of nearly 78% YoY from $7.8 billion in the year ago quarter, and a QoQ increase of almost 36% from $14 billion last quarter. Microsoft’s fiscal 2024 (ending June) capex was $55.7 billion, up nearly 75% YoY, and management is guiding for a YoY increase in capex in FY’25.

Meta: Capex was almost $8.5 billion in Q2, up more than 33% YoY and 26% QoQ. Meta’s first half capex totaled $15.2 billion, with management raising the lower-bound of their 2024 capex guidance range by $2 billion, from a prior view for $35 to $40 billion to $37 to $40 billion. This would imply ~37% YoY growth at midpoint for the full year, and indicate a significant acceleration in the back half, with more than $23 billion in capex projected at midpoint. Meta also expects “significant” capex growth in 2025 to support AI initiatives.

Alphabet: Capex totaled $13.2 billion in Q2, up 91% YoY and approximately 10% QoQ. Management said the surge in Q2 was “driven overwhelmingly by investment in our technical infrastructure with the largest component for servers followed by data centers.” For the year, management expects quarterly capex to be flat or above Q1’s $12 billion figure, implying capex of $50 billion or more.

Amazon: Capex was $16.5 billion in Q2, with Amazon the second-largest spender in the quarter after Microsoft. Amazon projected capex in the back half to be higher, suggesting 2024’s capex will come in well above $60 billion, with management saying the majority will go to support AWS infrastructure to meet high demand for both generative AI and non-generative AI services.

Big Tech’s capex is a barometer for the AI semiconductor industry, one that we closely track as we have a heavy allocation of stocks in this booming industry. Learn more about the I/O Fund’s holdings and consistent deep dive research on AI stocks, crypto and more here .

Analysts Pressing Big Tech Over ROI

Given this significant spending through 2023 and 2024, analysts are questioning whether monetization is going to match the level of investment, and grilled management teams over ROI timelines and AI capacity.

The management teams offered similar responses – which is that the predominant risk in AI is for those arrive late. Note, that it’s quite rare to have management teams from this many different companies agree (on anything really); and they are not only saying it in words, rather are showing us the seriousness of what is being stated in their budgets. Due to the sheer amount of capex, plus the unanimous agreement we are seeing across companies with $1T+ market cap on the importance of this capex, we are quoting the management teams directly.

Amazon:

Eric Sheridan (Analyst): “There's been a theme during the last couple of weeks of earnings of the potential to over-invest as opposed to under-invest in AI as a broad theme. I'm curious, Andy, if you have a perspective on that in terms of thinking about elements of capitalizing on the theme longer term against the potential for pace or cadence of investment on AWS as a segment.”

Amazon CEO Andy Jassy: “We also are getting a lot of signals from customers on what they need. I think that it's — the reality right now is that while we're investing a significant amount in the AI space and in infrastructure, we would like to have more capacity than we already have today. I mean we have a lot of demand right now. And I think it's going to be a very, very large business for us.”

Jassy also discussed the challenges in managing a business of AWS’ scale, and delivering too little or too much capacity, saying AWS understands the balance and how to manage capacity “reasonably well” to ensure AWS deploys the “right amount of capacity."

Alphabet:

Ross Sandler (Analyst): “Just two questions on the AI CapEx. So it looks like from the outside at least, the hyperscaler industry is going from kind of an under bill situation this time last year to better meeting the demand with capacity right now to potentially being overbuilt next year if these CapEx growth rates keep up. So do you think that's a fair characterization? And how are we thinking about the return on invested capital with this AI CapEx cycle.”

Alphabet CEO Sundar Pichai: “I think the one way I think about it is when we go through a curve like this, the risk of under-investing is dramatically greater than the risk of over-investing for us here, even in scenarios where if it turns out that we are over investing. … But I think not investing to be at the frontier, I think definitely has much more significant downside.”

Meta:

Brian Nowak (Analyst): “You have a lot of CapEx priorities from building new infrastructure for next-generation models, compute capacity. Just walk us through again on the CapEx philosophy and any guardrails you have around ensuring you generate a healthy return on invested capital for investors from all the CapEx.”

Meta CFO Susan Li: “On the ROI part of your question, I’d broadly characterize our AI investments into two buckets; core AI and Gen AI. And the two are really at different stages, as it relates to driving revenue for our businesses and our ability to measure returns. On our core AI work, we continue to take a very ROI based approach to our investment here. We are still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains and it makes sense for us to continue investing here.

Gen AI is where we are much earlier. ... We don't expect our Gen AI products to be a meaningful driver of revenue in 2024. But we do expect that they are going to open up new revenue opportunities over time that will enable us to generate a solid return off of our investment while we are also open sourcing subsequent generations of Llama. And we've talked about the four primary areas that we are focused here on the Gen AI opportunities to enhance the core ads business, to help us grow in business messaging, the opportunities around Meta AI, and the opportunities to grow core engagement over time.

… So while we do expect that we are going to grow CapEx significantly in 2025, we feel like we have a good framework in place in terms of thinking about where the opportunities are and making sure that we have the flexibility to deploy it, as makes the most sense.”

CEO Mark Zuckerberg said he would “rather risk building capacity before it is needed rather than too late,” as the “people who bet on those early indicators tend to do pretty well,” in a reference to Meta AI’s early success and it being “on track to achieve our goal of being the most used AI assistant by the end of this year”.

Microsoft:

Keith Weiss(Analyst): “Right now, there's an industry debate raging around the CapEx requirements around Generative AI and whether the monetization is actually going to match with that. Is CapEx still an appropriate leading indicator for cloud growth? Or does the shift in gross margin profile change that equation? Or said another way, maybe can you give us a little bit more help in understanding the timing between the CapEx investments and the yield on those investments?”

Microsoft CEO Satya Nadella: “So I would say – and obviously, the Azure AI growth, that's the first place we look at. That then drives bulk of the CapEx spend, basically, that's the demand signal ,,, we will only be scaling training as we see the demand accrue in any given period in time. So I would say it's more important to manage to capture the opportunity with the right product portfolio that's driving value.”

“The asset, as Amy said, is a long-term asset, which is land and the data center, which, by the way, we don't even construct things fully, we can even have things which are semi-constructed, we call [cold] (PH ) shelves and so on. So we know how to manage our CapEx spend to build out a long-term asset and a lot of the hydration of the kit happens when we have the demand signal.”

CFO Amy Hood:

“[…] when we did this last transition, the first transition to the Cloud, which seems a long time ago sometimes, it rolled out quite differently. We rolled out more geo by geo and this one because we have demand on a global basis, we are doing it on a global basis, which is important. We have large customers in every geo. And so hopefully, with that sort of shape of our capital expense, it helps people see how much of that is sort of near-term monetization driver as well as a much longer duration.”

Microsoft reiterated that capacity was and will continue to be the primary constraint for AI and Azure’s growth. They noted the need to invest ahead of demand with respect to land and data centers on a global basis, which necessitates an elevated level of capex to maintain growth over the long-term. However, they noted that they are waiting to fully outfit data centers to align with demand and thus the first clue to when capex might slow down likely will be seen in a QoQ stagnation in AI-driven Azure revenues from Microsoft.

Every Thursday at 4:30 pm Eastern, the I/O Fund team holds a webinar for premium members to discuss how to navigate the broad market, as well as various stock entries and exits. We offer trade alerts plus an automated hedging signal. The I/O Fund team is one of the only audited portfolios available to individual investors. Learn more here .

Microsoft Leads in AI Monetization, Amazon Close Behind

When it comes to Big Tech’s ability to monetize AI features and services, Microsoft leads the pack, with multiple different AI revenue streams and multiple billions in revenue. Amazon follows closely behind with AWS, while Meta and Google are both improving revenue generation and profitability via AI integrations in core products.

For Microsoft’s Azure, AI services contributed 8 percentage points of growth in the quarter, up from 7% in the prior quarter. Azure AI Services revenue run rate is estimated to be ~$5 billion, up 900% YoY, with 60% YoY growth in customers to more than 60,000. Though management guided for slightly softer Azure growth next quarter (fiscal Q1’25), demand continues to outstrip capacity, and management expects an acceleration in the second half of fiscal 2025 as AI capacity increases.

Microsoft is also seeing strong AI growth via Copilot offerings in GitHub and Office. GitHub’s ARR has reached $2B, with GitHub Copilot accounting for over 40% of GitHub’s revenue growth this year. GitHub Copilot has been adopted by over 77,000 companies, up 180% YoY. Copilot for Microsoft 365 continues to gain traction in just its second quarter of availability, with the number of people using Copilot daily at work nearly doubling QoQ. Copilot customers increased 60% QoQ and the number of customers with over 10,000 seats more than doubled QoQ. Copilot Studio, a low-code tool for creating and maintaining copilots, saw a 70% QoQ increase in organizations using it to 50,000.

Amazon has not provided a clear-cut breakdown of what percentage of AWS’ growth is being driven by AI, but management pointed out that Amazon has “a multibillion-dollar revenue run rate already in AI, and it's so early.” Management also noted that AWS “has launched more than twice as many machine learning and generative AI features into general availability than all the other major cloud providers combined.”

Amazon continues to roll out AI services and features across its businesses, recently unveiling its AI-powered shopping assistant Rufus, to assist customers with e-commerce purchases. Amazon believes Amazon Q is the “most capable generative AI powered assistant for software development,” while it is also deploying AI and computer vision in fulfillment centers to optimize deliveries and uncover product defects.

Alphabet similarly has two core businesses where it can integrate and monetize AI at a large scale, in cloud and advertising, with management seeing AI generating “billions in revenue.” Alphabet said it sees “tremendous ongoing momentum in Search and great progress in Cloud with our AI initiatives driving new growth,” with Cloud driving billions in AI revenue year-to-date.

In addition, Alphabet’s developer tools and Gemini are witnessing strong adoption, with more than 2 million developers using its AI tools, and more than 1.5 million developers utilizing Gemini. Alphabet added that a “majority of [its] top 100 customers” are adopting its generative AI solutions. For Search, AI features are improving profit optimization for advertisers – when “paired with Search or PMax,” Alphabet’s new AI-powered DemandGen ad campaigns deliver “an average of 14% more conversions,” more efficient cost-per-click rates, and profit uplifts.

Unlike Microsoft and Amazon, Meta’s AI monetization is not as visible, with AI aiding in engagement and advertising. CEO Mark Zuckerberg noted that AI is already enabling increased engagement and better targeting across the business, as its unified AI systems have “already increased engagement on Facebook Reels more than our initial move from CPUs to GPUs did.” For advertising, Meta says that it has “seen promising early results since introducing our first Generative AI ad features, image expansion, background generation, and text generation with more than 1 million advertisers using at least one of these solutions in the past month.”

However, Meta said that it does not “expect [its] Gen AI products to be a meaningful driver of revenue in 2024” with Mark Zuckerberg referencing his philosophy of maximizing engagement first before focusing on monetization “I think you all know this from following our business for a while, but we have a relatively long business cycle of starting a new product, scaling it to something that reaches 1 billion people or more and only then really focusing on monetizing at scale…before we are really talking about monetization of any of those things [Meta AI or AI Studio] by themselves, I mean I don't think that anyone should be surprised that I would expect that — that will be years”, implying that the timeline for fully recognizing real revenue tailwinds will take more than just a few quarters.

Conclusion

We’ve seen concerns rise recently that Big Tech may be overspending on AI capacity, with not enough revenue to justify this level of expenditure. However, comments from the largest four management teams highlighted one crucial similarity – demand remains above capacity, and they would rather risk overbuilding than underbuilding when it comes to AI capacity.

The weight of four Big Tech CEOs speaking in unison on this topic is either a staggering coincidence —- or they have important insights that are leading to the same conclusion, which is that AI’s primary risk is for those companies that are not early enough to capture it. It’s interesting Big Tech CEOs feel that way, as the I/O Fund’s stance is similar for investors, which is that the primary risk to a portfolio over the next 3-5 years is not being early enough to capture the powerful trend of AI.