Notwithstanding the duplication of these posts across social media, OpenAI, and Anthropic ("it's all the model, they just tell it to keep going" if you beat anything with RL enough ... it's going to do the thing)

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.

> 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.

> 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.

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.

> 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.

The moat is very limited. Harnesses aren't crazy hard to engineer. Open models are quite capable.

Divide the compute of your argument (pre-training, training, post-training) through everything this model now can do and it will not look that bad at all.

The training costs get rolled into the usage prices. It's not hiding anything to talk about the cost of usage without the cost of training, any more than I'd be hiding something by talking about the price of a $1,000 CPU without mentioning the tens of billions of dollars in R&D and infrastructure needed to create it.

What a weird thing to be butthurt about. You don't have to pay them. Do it the old fashioned way, with elbow grease and pots of coffee. Or invest in local AI.

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?