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Discussion (9 Comments)Read Original on HackerNews
Here's my issue with this post:
> True to the spirit of the challenge, they didn’t use millions of dollars in computer power. They used Fable 5.1, working within Claude Science, a platform scientists can pay to use.
Okay, billions of dollars have been poured into these agentic LMs, right? Each training run to get the next increment is costing millions of dollars?
This feels like an obvious jab at Navier-Stokes, but where we get to shift the numbers around to hide where the compute actually is being spent ... compute is being spent. It's either being spent in amortization to make the search smarter ahead of time, during training, or its being spent after.
Also love: scientists get to pay Anthropic to work within their special science harness to do science. That's exactly what I dreamed of doing when I pursued physics in undergrad, one or two companies holding the keys to "progress" for a monthly subscription price.
I get this anxiety, and am largely an AI skeptic, but at one point there were only a handful of computers in the world too (same for batteries, or engines, or crucibles, or stills -- it goes way back), and the organizations that had them had a stranglehold on progress in the field, as did the small number of companies who knew how to make them. It got better as they got cheaper and more plentiful.
I guess my point is that there are more important anxieties to feed when it comes to LLMs and the current state of the world.
I don't see how that makes the state of affairs any better.
The moat is very limited. Harnesses aren't crazy hard to engineer. Open models are quite capable.
I think that argument is recursive? These posts aren't very complicated for either of us, but they're written on devices that are fabricated with billions of dollars of semiconductor equipment. At what point do we just acknowledge that we stand on the shoulders of giants?
To me, the distinguishing factor is that the expense not special-purpose but upfront. The model here is trained without foreknowledge of what problems it will solve. Solutions like nine loops are genuine expressions of a pre-existing model capability, even if that capability has not pre-existed for very long.
Or embrace the future and realize that you couldn't imagine everything that would unfold, when you pursued your undergrad.
How you gonna get mad about all this?
It's interesting to note that the expensive part of this experiment was the Claude operations expense. For me I find that Claude is a small fraction of my cost with most of the bill attributed to computers to run simulations instead of the AI to monitor and tweak the simulations.
This gets me wondering why ai labs aren't proposing their own millenium prize type challenges.
No human-only was close to Navier-Stokes. The team that was close was also using AI.
>While it’s possible that this is just a much more AI-friendly problem, I don’t think it’s just that: I think the technology has genuinely gotten better.
It has gotten better imo. The blog author mentions 2 cases at the beginning -- users who think that AI will be capped and those who think it will be uncapped. From my perspective, both are technically right -- AI is capped or technically has usually reached some sort of cap, until human innovation improves it. AI doesn't really improve itself on a grand scale so much as humans improve it.
In other words, AI can and does iteratively improve, but every single ceiling we've spotted and broken through so far came from human ingenuity or effort. It will likely continue to require it, regardless of how much it can do on it's own. In that regard, it seems as though all of this will inevitably be "uncapped, until it reaches a cap, and then likely it will eventually be uncapped by humans (again)". Because of this, AI will never perfectly fit neatly into an 'uncapped' or 'capped' bucket, as long as time continues moving and we continue solving issues as they crop up.
In fact I will even admit it, yes AI has gotten better... but also how could it NOT? It has literally all the resources it can has, namely attention from everyone, everybody talks about it, a lot of workers, from state of the art researchers to annotators, all the material resources, from dedicated chips to networking to water to electricity, the entire available dataset of Human written and said thoughts, literally everything published and thus categorized.
AI literally has everything humanity can provide, it better be "better".
I also liked the article and the author before this post. But let's be honest that this was paid work as part of Anthropic's PR campaign, not just a random blog post.
There are thousands of people figuring out how to make AI better for their particular thing. Way more than that walking the AI through taking things from problem to solution.
This is framed as an "or", as if they're contradictory.
IMO, Both of these statements are true.
but the author explicitly distinguished between "AI" and "LLM-based AI", and they work for an AI company, where making these distinctions are really important
And how many runs did it take before this one? They say "in one shot" with nothing more than "keep going." But we only see the successful run, reported by the people who ran it.
https://arxiv.org/abs/2609.24454