Just how much are we spending on AI?

Compared to other massive infrastructure projects, AI is the sixth largest in US history, so far.

World War II dwarfs everything else at 37.8% of GDP. World War I consumed 12.3%. The New Deal peaked at 7.7%. Railroads during the Gilded Age reached 6.0%.

I wrote a post titled Congratulations, Robot. You’ve Been Promoted! in which OpenAI declared that their AI coders were no longer junior engineers but mid-level engineers.

The post triggered the largest unsubscription rate in this blog’s history. It was a 4-sigma event.

A Nassim Taleb black swan, this was something that should happen once every 700 years of a blog author’s career.

Clearly, the post struck a nerve.

In a job market 13% smaller for recent grads than recent years, a subtle fear persists that positive developments in AI accuracy & performance accelerate job losses. Stanford’s research found :

OpenAI has committed to spending $1.15 trillion on hardware & cloud infrastructure between 2025 & 2035.¹

The spending breaks down across seven major vendors: Broadcom ($350B), Oracle ($300B), Microsoft ($250B), Nvidia ($100B), AMD ($90B), Amazon AWS ($38B), & CoreWeave ($22B).²

Using some assumptions, we can generate a basic spending plan through contract completion.³

Across these vendors, estimated annual compute spending grows from $6B in 2025 to $173B in 2029, reaching $295B in 2030. We built a constrained allocation model with the boundary conditions defined in the appendix below, but this is just a guess. The actual growth rates are 124% (2027→2028), 54% (2028→2029), & 70% (2029→2030).

I sleep better knowing my agents work through the night. Less work for me in the morning.

My podcast processor transcribes & analyzes conversations. I started on my laptop, needed a little database to collect podcast data & metadata, & booted up a DuckDB instance.

But then the data started to grow, & I wanted the podcast processor to run by itself. I changed two little letters, & the database moved to the cloud :

Microsoft & Google both announced earnings yesterday, & the scale of AI adoption remains staggering. The infrastructure businesses are growing at accelerating growth rates that are the envy of businesses one-hundredth the size.

Capex

Capital expenditures were $34.9 billion, driven by growing demand for our Cloud and AI offerings. This quarter, roughly half of our spend was on short-lived assets, primarily GPUs and CPUs…. We will increase our total AI capacity by over 80% this year and roughly double our total data center footprint over the next 2 years, reflecting the demand signals we see. Just this quarter, we announced the world’s most powerful AI data center, Fairwater in Wisconsin, which will go online next year and scale to 2 gigawatts alone. - Microsoft

As the world follows every whisper & rumor from AI, what has happened to public SaaS?

The answer is not much! All the fun is in the private markets.

Multiples haven’t moved outside of a narrow band since the post-Covid crash in 2022.

Even here, AI is the story, with AI SaaS companies seeing different valuation correlates : revenue growth. Meanwhile, non-AI SaaS companies are valued more broadly with efficiency metrics included.

There’s this notion that product market fit is this binary condition that once you pass the gate, you’re done. And that’s no longer the case. Product market fit is like United Miles. You continue to fly to maintain 1K. And we saw it between 2021 and 2024, companies that were classic software companies literally overnight lost their product market fit. And that’ll be very much the case.

AI is changing what the market wants & what’s possible so quickly that PMF is no longer static.

AI is changing the way we work. I can’t imagine going back to the way I worked with a computer just a year ago, so powerful is the leverage from AI.

Calvin French-Owen, former co-Founder & CTO of Segment, helped lead the launch of OpenAI’s Codex, one of the most ambitious developer tools of the modern era.

At this Office Hours, we’ll dive into three key topics shaping the next generation of builders :

When I worked at Google, I was lucky to collaborate with some of the brightest machine-learning (ML) engineers. They worked on feature engineering. By picking the factors to guide the ML model, their advances could generate tens to hundreds of millions of additional revenue.

Imagine an Excel spreadsheet with hundreds of columns of data. Add two columns, multiply two, divide by another, and subtract a fourth. Each of these is a feature. ML models used features to predict the best ad to show.

It’s 9:30 AM. Do you know where your agent is?

As we enter the era of agentic AI, this is an increasingly important question. ChatGPT launched the consumer use of AI, compressing all of human knowledge into a single model. Now we are asking AI to act on our behalf at work.

Analyze this mortgage statement. Figure out if this person has medical insurance. Answer this inbound call about a 2026 Honda Odyssey minivan. AI agents are doing all of this today. Plus, AI is coding millions of lines every day.