I would really take those benchmarks with a pinch of salt. We already know benchmaxxing is a thing for evals that are objectively verifiable like code, and coding models that seem to perform well on benchmarks don't pass the vibes test, imagine all the caveats that come with such a subjective test like creative writing, not to mention control issues (are they the same human writers across benchmarks? Are they the same testers?)

>Capital can get you a lot: access to wetlabs, historical medical data, ability to run simulations, creation of autonomous bio labs which run thousands of experiments in parallel. An LLM, in training on this flywheel of data can take in more information than a human biologist would be able to read in multiple lifetimes.

I don't disageee and am in fact excited to see the advances, what I am arguing is that it is going to be slow because the hypothetic feedback loop that would improve the model is bottlenecked. There are actual physical and biological limits in our universe. Unless VCs find a way to make mice embryos and crops grow 10x faster (among the many, many other things that would be required for acceleration) the trickle of data is not going to be anything like the accelerating returns imagined for math and coding. And that's assuming biological RSI is possible which has yet to be proven - current foundation models are interesting but they are still specialized and there is no clear, obviously uniform way to encode the many, many modalities that exist in biological data.

>The better an LLM gets at simulating human behavior, the more accurately you can simulate economic policies and their effects via running simulations of agents in virtual societies. There are many routes that models can take to create a broader sense of the world without needing the world to provide data.

You wouldn't need to 'just' simulate human behavior (good luck with that btw) but the entire political and economic conditions in which they live. Even assuming this is possible the amount of compute you'd need to run would be astronomical. Given how all RAM capacity for '27 and '28 is near sold out, hyperscalers are running out of cash flow to finance buildout and Sam Altman complains when users say 'please' to LLMs I don't think this is in the near horizon.

Anecdotally, creative writing communities do report drastic differences in quality between models (it is my understanding that Gemma 4 is especially praised) so there is something to it being verifiable but it's a far cry from being something that can be distilled into objective math/code-like evals. One could even say it comes down to vibes.

>For example, the life sciences, an LLM could be given access to data about an organism, and then make predictions about how a drug or gene therapy will affect that organism.

Yes, biotech companies are doing this right now but ultimately they are just predictions and you still need cold hard biological data to ground them and iterate on. That's slow and expensive, especially for the juicy fields (human biology, food and crops, clinical trials) and you can't just plug billions of VC capital into the pipeline and hope for RSI. Physical (gotta procure all those labs and their equipment), human (gotta hire and pay specialists to run experiments), biological (gotta wait for organisms to reproduce, drugs to take effect, crops to grow), regulatory (gotta convince agencies that your fancy new drugs are legit) bottlenecks get in the way.

And biology is one of the easier fields where a path to RSI (if not accelerating) is conceivable. Good luck iterating on macroeconomics data where there are no replicates and 'experiments' take literal years if not decades.

>I think this is because a lot of people genuinely like the claudisms, even though a small minority of technical people don't.

My uncharitable take is that the abtruse jargon makes people feel smart for understanding it and gives a sense of belonging (as a closed circle of initiates who understand LLM cant), just like rationalists love to repackage old or unsavoury ideas under new nerdy smart sounding names.

>The most passionate, intelligent people are usually a decent indicator of where culture is going to go.

That's a naive view of cultural determinism. The software landscape would have been very different if a handful of lawsuits had gone one way or another. Hell, even Unix and Linux were hobby projects that accidentally got big.

Also crypto is a grift lol, one of those is definitely not like the others

Funnily enough, two common retorts against criticism of user-centric open source software are:

>stop complaining and feel free to open a PR if you're unsatisfied

Seems like many maintainers are now getting what they wished for and they are somehow unhappy about it. Ooops

>feel free to start your own project

Seems like the artcraft dude literally just did that and people are somehow unhappy about it. Ooops

If anything, in some domains frontier models have become worse - claudisms and chatgpt idioms are making them notoriously bad at prose without a considerable amount of prompting and tweaking.

>It used to be that you compiled an executable, copied it onto the target computer and it worked.

Java had been marketed last century as a 'write once, run anywhere' language. Tells you a lot about how portable executables in other languages were.