By the same token, continuing to give trillions to AI companies because they experienced early success with theory-free inference at scale tells us nothing about how to solve the *vast* range of problems that theory-free inference at scale sucks at. Doubling down on AI to overcome its increasingly obvious limitations is like doubling down on building post-war suburbs to fix today's housing market.

34/

As rent-burdened millennials who abandoned avocado toast and fancy coffee and *still* can't afford a downpayment will tell you, the fact that being born in 1945 made it easy to trip and land on a couple million dollars' worth of real estate wealthy by the time you reached retirement age tells us nothing about how to solve the housing crisis of 2026.

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The AI sector was born of world-historical forces that favored massively parallel computing, forces that had also conjured up an internet with trillions of documents that could be fed into those massively parallel computers to conduct theory-free inference. Like every success, AI was born on third base.

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From a research perspective, it is interesting to learn about the potential and limitations of a model trained on the entire internet. From a societal and industrial perspective, it is often grossly wasteful, inefficient and unreliable to swap scale for understanding.

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But, having ridden some world-historic forces and adjacent possibles to great fortunes and stature, they cannot be dissuaded from their conviction that theory-free inference and scale can do *everything*. They can't be convinced that in many cases, the things that scale and theory-free inference *can* do are much better accomplished through causal understandings and conventional computing techniques.

30/

The first conventional chess-playing programs ran on electromechanical proto-computers, and they played a better game of chess than an LLM that uses *billions* of times more computing power and energy:

https://www.organizedmoney.fm/p/an-ai-expert-explains-the-hype

The AI companies have proved that there are many domains and applications where we can swap scale for understanding.

29/

As Gary Marcus describes in a recent *Organized Money* interview, an LLM can recite the rules of chess, but it can't *play* chess because - lacking a theory of how chess works - it will just emit statistically likely chess moves, even if those moves cause pieces to illegally move through other pieces.

28/

The problem is that the AI sector has raised literally *trillions* of dollars by assuring investors that the era of hand-made, causal world models that let computers act on the world is hopelessly inefficient and outdated. But there are many, many tasks that are vastly more efficient and reliable when done through conventional computer programs, rather than through "AI."

27/

The difference is that when your spouse says something entirely unexpected and unprecedented to you (say, "I want a divorce"), the fact that you have a theory about *why* your spouse said all the things they said up to that moment can help you understand why they've said this new thing. But a machine learning model that relies on theory-free statistical modeling to predict your spouse's next words will be entirely at sea. Theory-free inference works well, but it fails badly.

26/

And they *are* limited. Theory-free inference is good at predicting what your spouse will type into their phone based on all the things they've ever typed into their phone. You are *also* good at guessing what your spouse will say based on the things they've said before.

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Which is all to say: it's reasonable to be excited about a breakthrough in theory-free inference. But just like a boomer who thinks that buying a house to live in makes them a shrewd real-estate speculator, someone who achieves great things through theory-free inference runs the risk of missing the limitations to those techniques.

24/

Theory-free inference is a very pragmatic way to approach the world: "I don't need it good, I need it *Thursday*." Scientists burn to know why a molecule stopped you from dying, but you are likely satisfied to not be dead. What's more, our ability to observe correlations will always race ahead of our understanding of causality, so the power of theory-free inferences pushes out the frontier of things we can act on, beyond the realm of the understood.

23/

Lots of stuff in the world works this way. Our understanding of the causal relationships that make up reality has massive holes in it that we fill with mere correlation. Correlations are easier to discover than causes, and while correlation is (famously) not causation, causes and effects *are* correlated, and if you can evince the effect you're seeking without understanding precisely what happened to make that effect appear, well, at least you got the effect you were seeking.

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