/AI3h ago

AI Scaling Moats Hinge On Algorithmic Depth And Returns To Automation

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bayes#1291
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bayes@bayeslord#1291inAI

How far ahead the labs can get depends in part on the returns to automation and scale, which includes the returns to greater algorithmic depth. If deep learning practice (and theory) is forever shallow then the moat will mostly not be algorithmic on the longer term because secrets will be relatively cheap to discover. Eventually distillation + data + time can catch up to compute scale, potentially slowly. So far this seems partly where we’re at, but even if true there are no guarantees it will continue this way.

If things become less shallow as we scale then every increment of automation and scale buy you algorithmic secrets that are increasingly out of reach for anyone else. This too seems partly where we're at. The end point in either case is when marginal utility returns to scale and research saturate. We don’t know where that is. Could be 2 OOMs or 20 away from where we are today. No one knows.

bayes@bayeslord

Compute is going to keep improving. Today’s best matmul machines are nowhere near the physical limits of AI accelerators. There’s a lot of room to get better at digital silicon. There are also many candidates for new substrates, and the algorithmic debt they owe will be automated to its limits, but we don’t yet know what the optimal one is for AI in space/energy/time/manufacturability/cost. Photonics and stochastic silicon are both interesting candidates, but I also expect the singularity to be surprising.

11:39 AM · Jun 4, 2026 · 477 Views
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bayes@bayeslord

It’s possible that despite these forces apparently in its favor academic and open source will languish because of the cost and opportunity cost of compute. E.g. is some set of B200s more valuable serving Kimi K2.6 or is it more valuable serving GPT 5.6 Pro? Is it more valuable doing non-frontier research in some academic lab or building Mythos 2 inside of Anthropic? The market will solve for where demand is greatest, which right now does seem to be the labs. This means that open source labs could become even more compute starved *even if they have capital*, if they don’t already have compute capacity locked in. And even then they will be calculating opportunity cost of their research vs renting. See Colossus x Anthropic.

bayes@bayeslord

Automated AI experimentation will enable widespread discovery of algorithmic secrets as these are naturally more distributable than full-scale training runs. It’s unclear how far this can go but I expect pretty far. As mentioned above the fundamental depth of deep learning is still unknown and the upper bounds on this point depends on that.

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bayes@bayeslord

The nature of the intelligence supply chain is changing. Right now it’s very centralized around labs. But labs are automating the main thing that makes them good: researchers and the discovery of algorithmic advantages. Once this starts happening, assuming open source trails not too far behind, and especially if the labs don't lock down AI researcher models, the labs’ advantages will come from easier capital, having more compute, having special data, business relationships, and good products. This does depend on how the algorithmic depth point above resolves, among other things.

bayes@bayeslord

The intelligence supply chain

Compute will be a highly contested resource for at least a few years. But in that time it will start commoditizing and we will laugh at the impoverished 2020s. Scale is increasing and working, capital is following to turn the wheel again and again. More matmul machines, more fabs and more energy are coming. Bottlenecks of intelligence production are temporary. Potential economic speed bumps notwithstanding.

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bayes@bayeslord

There are progress laws everywhere. In deep learning they are called scaling laws. It's hard to tell when the S-curve saturation will happen on any given line, it's hard to tell when there are new S-curves just over the horizon. The thing to understand here is that the engine of civilizational progress itself has a progress law. Most likely our progress will be of the saturating type like most natural processes we observe, but we actually don’t know where that happens. Technological and civilizational maturity could be close or far. We are (a) in the part of history where we’ve barely put any resources to progress but that is rapidly changing, and (b) we are automating the machine that directly outputs more progress. Ours are interesting times.

bayes@bayeslord

Progress

Science is automating and virtualizing. This means much of the progress we need in the world is going to come from automated labs and simulations. We don’t know the full computational limits of virtualization, but such robotically-driven labs for biology, materials science, and more are going to remove a large number of the bottlenecks, and along the way they will push the limits of validated virtualization to increase sample efficiency and the net returns to reification. Basically in every area we will have some combination of neural models, explicit simulations, and real world experiments all contributing to improving the returns per dollar and per time in areas like biology, materials science, and the like.

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bayes@bayeslord

I'm aware of this point. I think there are a lot of things that are nth-order labor bottlenecked (e.g. energy production itself, or novel, more efficient designs of mining equipment, operations, etc. (b) my expectation for all areas of human endeavor is that there are more gains lying in wait than we think, in part for reasons we don't currently expect. Ultimate physical limits are a true bottleneck though. Work is work!

Herbie Bradley@herbiebradley

IMO this is the least predictive tweet in the thread and I expect these industries to change slowly. A common misunderstanding is thinking that production bottlenecks in physical world industries like mining or shipping are downstream of labor cost. Usually, they are downstream of energy cost. eg there are already autonomous trucks in mining, but the vast majority of the cost of moving rocks with them is from fuel (energy)

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