TLDR: Astra has 8.6x better odds of doing a reasoning task without CoT than the
  next best model (Fable 5.1), and can do 7.2 serial arithmetic steps in a forward
  pass vs 4.1 for the next best model (Gemini 3.8 Flash/Fable 5.1)

[1] https://devforth.io/agents-for-code/?sortby=monthly-value And I can confirm the numbers, I subscribe to both and watch the numbers

I'm surprised. But it shouldn't be up to Tesla; it's illegal and they never get on the road in countries with proper regulations.

But the Hutter prize (of which I'm a big fan) is for ever-more-optimal compressors, and in fact many of the solutions don't generalise to other input data without stripping out various tricks.

It's amazing that LLM pretraining is both extremely data inefficient at learning concepts and cognitive functions from the training data compared to humans, while actually being quite efficient at learning facts, memorising things seen just a few times.

I used to likewise think that the resources required to run large transformers were absurd, but the architectures are far more efficient now than 3 years ago and I underestimated just massive the parallelisation advantage of transformers is, how many TFLOPS effective you can get. You can already run amazingly decent LLMs on PCs and phones.

I generally agree with you, but my view has shifted from "we need to augment or replace LLMs" to it there being far more efficient algorithms possible but it not actually being necessary for fulfilling most goals.

But this is attacking a strawman, amateur bioterrorists. AI is a force multiplier in the hands of an expert. If it took a team before, maybe a single malicious actor can accomplish it now that AI can fill in the parts they aren't well-versed in. And that dramatically increases the chance of it happening.

Being at risk of killing yourself also just makes success X% less likely, but if X < 90 that doesn't mitigate much.