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Posted by Nicholas Mersch on May 9th, 2024

Cloud Monsters

In an era marked by historic market concentration and narrative-driven investor reactions, megacap tech companies, defying conventional growth constraints, are leveraging AI and cloud services to propel their expansion.

The clouds are building. Lucky for your summer boating trips, I’m not talking about it being overcast over the lake. When companies get huge, people always ask, “What’s the next growth lever?” The law of large numbers says that you can’t grow at a high percentage year-over-year (YoY) forever. Megacaps are saying, “hold my beer,” as cloud growth reaccelerated this quarter.

Key takeaways:

  • Megacap tech companies continue to defy the law of large numbers, showing significant growth propelled by cloud services and AI.
  • The monetization layer of AI is starting to become more evident as demands for semiconductors and cloud services are increasingly coming into demand for AI model training and inference requirements.
  • Despite uncertain economic indicators and varying earnings results, market narratives around technological advances and future roadmaps, particularly in AI, are still influencing investor reactions and stock prices.

Earnings periods always bring nervous excitement to the market. This quarter was no exception, as we’ve reached historic concentration amongst the megacap names. The top ten companies now represent a record 34% of the S&P 500 market cap, rising by 15% over the last decade.

"Anything But Bonds" bull="long monopolies, short leverage"
Source: BofA Global Investment Strategy, Bloomberg, x.com @kobeissiletter

With this level of concentration, earnings periods are increasingly important as a “boots on the ground” update on the state of the market. While this concentration is important to highlight, it’s critical not to conflate this period for the dot.com bubble. That was a period where we were using crazy metrics like EV/Eyeballs (enterprise value to eyeballs; where companies were valued on how many people typed in and looked at their website). These companies now leading the charge are producing eye-watering profits on a massive scale.

This is why we need to pay close attention to the prints.

We had a majority of the MAG7 enter the earnings confessional, but we still await the golden goose (Nvidia), who has a Jan 31 year-end and reports their quarter on May 22. Earnings reactions were mostly positive as Tesla (+12%), Google (+10.2%), Amazon (+2.3%), Apple (+1.9%), and Microsoft (+1.8%) rose on the day after they printed. The lone loser was Meta (-10.6%).

The one thing that stood out to me was how narrative-driven this quarter was. Tesla’s numbers were terrible by all accounts. Yet, Musk, ever the soothsayer, got on the call to ease investors by accelerating the roadmap on the lower-cost mass adoption model, which was previously thought to be delayed. Meta, on the other hand, posted its best YoY growth quarter since Q3 2021, yet the market held on to the narrative of higher spending being a drag on the stock.

There is a lot of uncertainty in the market. While the 10yr keeps ticking higher, expectations for rate cuts tick lower every month, especially after the May 1 Fed announcement. Rate cut expectations for the year have dropped from six to one. We also had a spike in the amount of times “stagflation” showed up in headlines after the much softer-than-expected US GDP numbers. When there is uncertainty in the market, people need something to hang on to. They like listening to story time.

And the story right now is AI.

We have had several key metrics this quarter that shed some light on the AI opportunity. Currently, the most monetizable layer of AI is semiconductors. We’ve seen this in the quarterly Nvidia numbers that are climbing in a step function. We also can see hints at semiconductor spending every time as hyperscalers (Amazon’s AWS, Google’s GCP, and Microsoft’s Azure) put out CAPEX number increases. Sure, there are other parts of CAPEX that these guys are spending on, but a solid portion of this is chips. Higher CAPEX for Amazon, Google, and Microsoft = higher revenue for Nvidia + others in semis. We also see language from Tesla and Meta as massive spenders in this space.

Examples of this were seen in earnings reports:

  • Google’s CFO on the call: “Our reported CAPEX in the first quarter was $12B, once again driven overwhelmingly by investment in our technical infrastructure with the largest component for servers followed by data centers.”
  • Microsoft’s capital expenditures rose 79% to a record $14B in the March quarter. Amy Hood, Microsoft’s chief financial officer, told analysts that CAPEX will keep growing in the 2025 fiscal year, which starts in July.
  • Amazon said CAPEX would “meaningfully” grow in 2024 and called the $14B Q1 level a low point. Some estimates have increased from $56B to $60-65B, indicating an increase of 25-35% Y/Y.
  • Don’t forget about Meta; they built their own open-source model and raised 2024 CAPEX to $35B-$40B, up from $30B-$37B.

The second-most monetizable layer is the Cloud

Ever wonder how ChatGPT can write that anniversary poem you forgot to write for your spouse? They and other AI frontier LLMs (Large Language Models) must train these models using extensive compute from cloud players. They also have to pay whenever an end-user queries the chatbot, AKA inference. Training and inference are exploding at AI companies, and cloud players stand to reap the benefits. These businesses are experiencing very impressive growth rates even from massive bases. In the chart below, you can see the growth rebound due to optimization headwinds abating and the AI growth kicker.

Hyperscaler YoY Growth Rates
Source: Company Filings, Purpose Investments

Now… there are some nuances when it comes to cloud players and their investment in AI companies that you need to be aware of. Amazon recently announced the completion of its $4B investment in AI company Anthropic. As part of the deal, Anthropic will use AWS as its primary cloud provider, and Trainium and Inferentia chips will be used to build and train its models.

This means that these AI companies are required to train and inference their models on the hyperscalers’ cloud solutions (AWS, GCP, Azure) as a prerequisite to receiving investment. This is guaranteed recognized revenue that is paid from the AI companies to the cloud providers. Microsoft did a similar thing with OpenAI, where OpenAI is contractually obligated to use Azure.

So, when you see “Megacap Tech company ABC invest $XXB in AI company XYZ,” this is not all cash they’re getting. There is a portion of cash that is used to hire talent, but a significant portion of this investment is in the form of compute credits, which are ultimately revenue paid back to the hyperscalers.

This brings us to cloud earnings. One of the best examples of this opportunity being stripped out is with Microsoft. Satya commented on the call that more than 65% of Fortune 500 companies use Azure OpenAI services. Additionally, 7% of the 31% YoY cloud growth at Azure was directly attributed to AI. So what does this mean? Jamin Ball at Altimeter did a great job stripping this out:

Azure YoY Growth (cc), Estimated Azure AI Quarterly Run Rate ($M)
Source: @Jaminball x.com

The graph on the left shows that the AI component of Azure growth is not only accelerating but also mitigated a slowdown in the ex-AI business during the ’23 period of cloud optimization. Turning to the graph on the right, you first go: “Cool! $4B revenue run rate after only being in the market for a year!” Then you ask why the quarter-over-quarter (QoQ) jump isn’t bigger. On the call, Microsoft indicated they are capacity-constrained, saying that near-term AI demand is slightly higher than available capacity. Capacity constrained?! This means more GPUs, which is even better for Nvidia.

While we are still trying to figure out AI’s monetization layer, it looks like we’re finally starting to get early revenue metrics. I’ve talked about semiconductors and now cloud players. In another issue, we’ll start to talk about the app layer.

When it comes to AI, to “skate to where the puck is going,” you’re going to need more speed than when Connor McDavid sees a gap between two D-men. You also need to adjust and adapt accordingly.

Strong Convictions. Loosely Held.

— Nick Mersch, CFA, Portfolio Manager


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Nicholas Mersch, CFA

Nicholas Mersch has worked in the capital markets industry in several capacities over the past 10 years. Areas include private equity, infrastructure finance, venture capital and technology focused equity research. In his current capacity, he is an Associate Portfolio Manager at Purpose Investments focused on long/short equities.

Mr. Mersch graduated with a bachelors of management and organizational studies from Western University and is a CFA charterholder.