How did we end up with trillions on compute?

Nov 28, 2025

The prevailing wisdom in Silicon Valley, so far, has been that the world needs a lot of AI compute. Data center spending is expected to hit $425B in 2025 (S&P Global report 1). OpenAI alone has $1T in compute deals lined up (S&P Global report 2). By 2030, total AI data center spending could amount to $5T (McKinsey report).

How much of this spending is driven by AI demand outstripping supply in the near-term? Probably some non-trivial amount. How much of this spending is driven by anticipation of future AI demand in a fixed timeframe? A lot. Sounds a bit irrational, eh? In a BBC interview this month, Alphabet CEO Sundar Pichai called out ‘elements of irrationality’ in the current AI boom (BBC report).

Let us use two frameworks to dissect some of the irrationality.

Framework #1: Keynesian beauty contest

In his 1936 book — The General Theory of Employment, Interest and Money — John Maynard Keynes wrote the following about behavioral dynamics among investors:

…professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees.

If we were to generalize Keynes’ analogy, we would say that some market participants engage in first degree thinking — they pick the options they like the most. There are participants that act based on second degree thinking — they pick the options that they think participants with first degree thinking might choose. Then there are participants with third degree thinking — they pick the options that they think participants with second degree thinking might choose. And so on.

In a sense, you can say that participants with first degree thinking are the most naive, or perhaps the most irrational. It follows that second degree participants are acting based on their understanding of the judgment of the most irrational participants. Third degree participants are acting based on second degree participants. You could say, at some level, that irrational participants are driving the game.

Framework #2: Prisoner’s dilemma

This framework came about in the form of a puzzle in 1950. Here is a version of the puzzle (UMich’s website).

Two bank robbers happen to meet. They decide to pull a job together.

The cops nab them, but without enough evidence to convict. They need a confession. And they know both robbers are unlikely to talk, since if neither implicates the other, the cops can keep them in jail for only 30 days.

So they put the two in separate cells. They go to the first prisoner and say:

“If you rat on your partner and he stays mum, we’ll let you go and he’ll do ten years.”

“If you both rat on each other, you’ll both do eight years.”

Then they go to the second prisoner and say the same thing.

If the two robbers could cooperate, they would likely agree to stay silent. This gets both of them out of jail in 30 days. However, because they cannot cooperate, each robber is likely to pursue their self-interest and rat on the other. This gets both of them eight years in jail. Here, pursuing self-interest gives you a worse outcome than acting as if you were cooperating i.e., acting based on second degree thinking.

Getting to trillions on compute

Our framework #1, Keynesian beauty contest, tells us that irrational market participants could breed more irrationality in the market. Because there are very few companies vying to hyperscale compute, irrational investments from just one player could spur investments from others. In the piece about everyone wanting their own AI chips, we talked how nobody wants to be left behind in the AI race. When you are in a race to secure massive amounts of compute, data center spending can become your interim scorecard.

Our framework #2, Prisoner’s dilemma, tells us that the collective outcome of a group suffers in the absence of cooperation. Obviously, corporate cooperation, aka collusion, is a no-no. Although nobody is stopping you from acting based on second or higher degree thinking. However, there is little trust among market participants. They are working with different information and nobody wants to share the spoils. As a result, each participant ends up maximizing their self-interest. This is how we end up spending trillions of dollars on compute.

Some caveats

1. Nobody knows how much compute would be needed and in what timeframe.

2. Irrationality could be in the eye of the beholder.

• A high-risk, high-reward strategy that appears irrational to most of the market could be a rational decision for you depending on your situation.

• Some market participants could be working with superior information and/or structural advantages. Their seemingly irrational decisions could be more rational than we think.