> Obtaining the knowledge is a process of trial and error, bruteforce, observation, search, etc. Humans follow that same process. Machines can do that too. If they discover or are taught the same heuristics humans use, and a computational capacity greater than that of all humans combined, they will outpace us in this endeavour.
Sure, you can reduce the 99.9% of research labor that matters to "a process of trial and error, bruteforce, observation, search, etc.", but that's like saying that nuclear fusion is only a few technical details away from implementation. We already know the theory!
The part where you're closest to being correct is "trial and error" -- it would be great if a robot existed that could do any experiment, tirelessly, with the mechanical fidelity, intelligence and creativity of a human. That robot does not exist. Moreover, the fundamental techniques to do the kinds of observation necessary to unlock the parts of science we don't know about do not exist. They must be invented. So now we have two problems. The problems are recursive and interlocking.
Biology and chemistry are the sciences I know best, so I will use those examples -- every major breakthrough of the last 50 years has involved invention of some fundamental new mode of observation, such as crystallography, NMR, mass spec, electron microscopy, various kinds of light microscopy, DNA sequencing, PCR, etc. Someone invents some innovative technique, and a wave of progress happens. Expert practitioners in in the lab are probably the second rate-limiting step, but the part that LLMs can do -- taking data and turning it into hypotheses -- is the part that carries the least value. Any postdoc has enough ideas to keep a lab going forever.
The thing you linked about Anthropic creating a "robot standard" for operation of lab tools is great for Anthropic, but that's about all. There's tons of lab automation tooling already. Having LLMs run the microscope is maybe a cool automation technique if you have the kinds of experiments that benefit from it, but those are rare, and they're still ultimately limited by people doing the upstream work.
AI will certainly help people be more efficient at their current scientific jobs, make better methodology more universal, etc., but suggesting that it will replace actual scientists is just science fiction.