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PaperclipPerfector  12d ago (text post)   32598 thread views

Culture War Roundup for the week of March 24, 2025

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But a lot of people are like you, so these models will start to get used everywhere, destroying quality like never before.

I can however imagine a future workflow where these models do basic tasks (answer emails, business operations, programming tickets) overseen by someone that can intervene if it messes up. But this won't end capitalism.

This conveys to me the strong implication that in the near term, models will make minimal improvements.

At the very beginning, he said that benchmarks are Goodharted and given too much weight. That's not a very controversial statement, I'm happy to say it has merit, but I can also say that these improvements are noticeable:

Metrics and statistics were supposed to be a tool that would aid in the interpretation of reality, not supercede it. Just because a salesman with some metrics claims that these models are better than butter does not make it true. Even if they manage to convince every single human alive.

You say:

Besides which, your logic cuts both ways. Rates of change are not constant. Moore's Law was a damn good guarantee of processors getting faster year over year... right until it wasn't, and it very likely never will be again. Maybe AI will keep improving fast enough, for long enough, that it really will become all it's hyped up to be within 5-10 years. But neither of us actually knows whether that's true, and your boundless optimism is every bit as misplaced as if I were to say it definitely won't happen.

I think that blindly extrapolating lines on the graph to infinity is as bad an error as thinking they must stop now. Both are mistakes, reversed stupidity isn't intelligence.

You can see me noting that the previous scaling laws no longer hold as strongly. The diminishing returns make scaling models to the size of GPT 4.5 using compute for just model parameters and training time on larger datasets not worth the investment.

Yet we've found a new scaling laws, test-time compute using reasoning and search which has started afresh and hasn't shown any sign of leveling out.

Moore's law was an observation of both increasing transistor/$ and also increasing transistor density.

The former metric hasn't budged, and newer nodes might be more expensive per transistors. Yet the density, and hence available compute, continues to improve. Newer computers are faster than older ones, and we occasionally get a sudden bump, for example, Apple and their M1

Note that the doubling time for Moore's law was revised multiple times. Right now, the transistor/unit area seems to double every 3-4 years. It's not fair to say the law is dead, but it's clearly struggling.

Am I certain that AI will continue to improve to superhuman levels? No. I don't think anybody is justified in saying that. I just think it's more likely than not.

  1. Diminishing returns!= negative returns.
  2. We've found new scaling regimes.
  3. The models that are out today were trained using data centers that are now outdated. Grok 3 used a mere fraction of the number of GPUs that xAI has, because they were still building out.
  4. Capex and research shows no signs of stopping. We went from a million dollar training run being considered ludicrously expensive to companies spending hundreds of millions. They've demonstrated every inclination to spend billions, and then tens of billions. The economy as a whole can support trillion dollar investments, assuming the incentive was there, and it seems to be. They're busy reopening nuclear plants just to meet power demands.
  5. All the AI skeptics were pointing out that we're running out of data. Alas, it turned out that synthetic data works fine, and models are bootstrapping.
  6. Model capabilities are often discontinuous. A self-driving car that is safe 99% of the time has few customers. GPT 3.5 was too unreliable for many use cases. You can't really predict with much certainty what new tasks a model is capable of based on extrapolating the reducing loss, which we can predict very well. Not that we're entirely helpless, look at the METR link I shared. The value proposition of a PhD level model is far greater than that of one as smart as a high school student.
  7. One of the tasks most focused upon is the ability to code and perform maths. Guess how AI models are made? Frontier labs like Anthropic have publicly said that a large fraction of the code they write is generated by their own models. That's a self-spinning fly-wheel. It's also one of the fields that has actually seen the most improvement, people should see how well GPT-4 compares to the current SOTA, it's not even close.

Standing where I am, seeing the straight line, I see no indication of it flattening out in the immediate future. Hundreds of billions of dollars and thousands of the world's brightest and best paid scientists and engineers are working on keeping it going. We are far from hitting the true constraints of cost, power, compute and data. Some of those constraints once thought critical don't even apply.

Let's go like 2 years without noticeable improvement before people start writing things off.

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