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- The Cloud Capex Conquest ☁️
Hi Everyone. This is Take Off. We're like a hidden Easter egg in your favorite video game - surprising, delightful, and adding an extra layer of fun to your tech news consumption.
Here's what we're serving up today:
The Cloud Capex Conquest ☁️
Media's AI Handshake 🤝
Asteroids ☄️
The Cloud Capex Conquest ☁️
One of the few places where AI is raking in Big Cash apart from Nvidia has been for the cloud hyperscalers. Here’s a quick look at their latest quarterly revenue:
1/ Amazon Web Services - $25B
2/ Microsoft Intelligent Cloud - $26.7B
3/ Google Cloud Platform - $9.6B
Spend Big To Earn Big
These are unarguably huge numbers. However, one thing to note is that these companies are spending big to earn big. The massive investment in CapEx data centers, power plants and GPUs is stunning.
These are not one-time investments, but part of a broader trend that started to occur after the introduction of GPT 3 in mid-2020. Amazon was the first to invest significantly.
And listening to the companies’ forward-looking statements on their latest earnings calls, that spending is heading even higher:
We expect the combination of AWS's reaccelerating growth and high demand for gen AI to meaningfully increase year over year capital expenditures in 2024, which given the way the AWS business model works, is a positive sign of the future growth. The more demand AWS has, the more we have to procure new data centers power and hardware.
We are committed to making the investments required to keep us at the leading edge in technical infrastructure. You can see that from the increases in our capital expenditures. This will fuel growth in Cloud, help us push the frontiers of AI models, and enable innovation across our services, especially in Search.
We expect capital expenditures to increase materially on a sequential basis driven by cloud and AI infrastructure investments.
Do It Yourself (DIY)
The hyperscalers can afford to invest so heavily because they have the money to do so in the first place, and because these businesses are so big that they’ve started earning gobs of money already from the genAI boom.
However, the hyperscalers still have a huge reliance on Nvidia for the hardware.
Nvidia has capitalized on its position of arguably being the only shovel (GPU) seller in the gold (AI) rush. This reflects in the company’s gross margin growth over just the last few quarters.
Fiscal Year | Profits ($B) | Net Income Margin |
|---|---|---|
2020 | 2.8 | 26% |
2021 | 4.3 | 36% |
2022 | 9.7 | 42% |
2023 | 4.4 | 26% |
2024 | 29.8 | 57% |
LTM | 42.6 | 62% |
Nvidia’s data center revenue totalled $26B this quarter, with about 45% (~$13B) of it coming from the hyperscalers.
These clouds announced that they were spending close to $40B in capex to build out data centers.
This suggests that Nvidia is capturing roughly 33% of the total capex budgets for the hyperscalers.
However, the clouds want to reduce their reliance on Nvidia and improve their own margins meaningfully by developing their custom chips.
Google: Tensor Processing Units (TPUs)
Amazon: Trainium and Inferentia
Microsoft: Maia and Cobalt
Exact revenue contribution numbers are not known for these custom chips but the push by cloud hyperscalers to invest billions of dollars in building custom silicon underlines their strategy to reduce their reliance on NVIDIA and maybe start competing with the GPU Giant as well.
In partnership with
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Media's AI Handshake 🤝
The rise of generative AI has spurred a significant shift in the relationship between AI companies and media organizations.
This shift is characterized by a growing number of licensing deals that allow AI companies to legally use media content to train their models.
The Drive for Data
AI models crave data like we crave our morning coffee. Initially, companies like Google, Meta, and OpenAI relied heavily on scraping publicly available data from the internet.
However, the media magnates haven’t been the biggest supporters of giving their data for free which has led to a whole lot of suing for big dollars.
In response, AI companies have begun to secure licensing agreements with content owners to mitigate legal risks and ensure a steady supply of high-quality data.
Some of the notable licensing deals include:
Reddit-Google: Reddit’s deal with Google, reportedly worth around $60M/yr, allows Google to use Reddit’s vast repository of user-generated content to train its AI models.
OpenAI-NewsCorp: OpenAI sealed what appears to be the largest AI news-licensing deal to date, said to be worth $250M+ over a half decade.
Shutterstock-BigTech: The image-marketplace has licensed its data to Big Tech customers including the likes of Meta, Alphabet, Amazon and Apple. The licensing business with AI companies produced $104M in revenue last year for Shutterstock.
We’ve Read This Before
Before AI news-licensing deals, there were social-media deals. In 2019, Meta launched Facebook’s News tab, signing content partnerships with publishers like the WSJ.
Then it scrapped them in a pandemic switch to creator content, pulverizing its $2B news budget by 95%. Now they are considering the news deals once again - this time for AI training data though.
For media companies, these deals represent both an opportunity and a challenge. On one hand, licensing agreements provide a new revenue stream and a way to monetize content in the age of AI.
On the other hand, there is a risk of becoming overly dependent on AI companies, similar to the reliance on referral traffic from search engines and social media in the past.
Ride the wave of 23% compounded annual growth
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