A friend of a friend had a grant project written up for the next 10 years, with habilitation on the horizon.

His end goal was one of the things released in the recent OpenAI publications. It took a model 3.5 hours to do.

Things are wild now on math and theoretical faculties now.

And for theoretical fields labs don’t need human data - synthetic one works well if not better.

The best thing you can do if you’re a scientist, imho - wrapup whatever grant you have now asap with ai, use ai to get more grants if possible, and spend the rest od the year learning what new science you can do with the new tools and participate in creating the new paradigm in your field.

That’s when you’re an established scientist. If you’re new in the field then this is the most exciting time you could wish for - an opportunity to make a name for yourself.

That's insane advice. That's like telling CS students to never use a cloud LLM.

Sometimes HN is crazy and out of touch.

But how do they expect to sustain on that?

One natural step of AI usage, the way I see it, is that even mediocre researchers can use AI as a harness to become prolific researchers.

So if you're part of a pure "human only" researchers that publish 1/10th of what the rest are doing, how are you going to survive?

The more prolific researchers will eat your grants for lunch.

This is exactly the same thing a lot of software devs are going through now. AI models have lifted up EVERYONES ability to produce code, so there's simply no premium anymore for those that will only code by hand. And the people paying their salary are asking why they aren't more productive, when even business analyst Joe is pumping out new products left and right.

That's going to be a very short term solution, no?

You can't really swear off looking at problems solved by AI, or keep moving away from fields whenever AI starts contributing, and have a viable career.

Today's frontier models, are everybody's local models in the near future.

If this were a change that had expected stalls or reprieves it would be different.

Doing mathematics looks to be the next chess, er, I mean protein folding challenge. The latter's computation demands have dropped significantly.

That might be like telling early biologists to throw away their microscopes because they are unfair competition to human eyes.

I think we should apply AI into human, so we could be better and then start doing more advanced stuff required for the Universe explorations. Current AI/LLMs can be considered as first step to that. Take and look about our history. We could never achieve so many if we stopped doing great things because of <any reason>.

Why do we ever use PCs, smartphones, Internet, electricity, medicine and so on? Why do we live? For what?

So others will write their PhD thesis faster, will publish more, you will not publish so you will perish. The rules of the game in this business are brutal. One can count on inventing something entirely new that AI will not be able to figure out, but this is a really high bet.

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