[epolanski]

I'll share something as a former solar researcher.

Scientific progress is heavily influenced by how many bodies you can throw at a problem.

The more experiments you can run, with more variety and angles the more data you can get, the higher the likelihood of a breakthrough.

Several huge scientist are famous not because they are geniuses, but because they are great fundraisers and can have 20/30/50 bodies to throw at problems every year.

This is true in virtually any experimental field.

If LLMs can be de facto another body then scientific progress is going to sky rocket.

Robots also tend to be more precise than humans and could possibly lead to better replication.

But given that LLMs cannot interact with the real world I don't see that happening anytime soon.

[joe_the_user]

Seems you're burying the Lede in your post - yes, AIs aren't scientists.

What can be said about scientists and bodies is interesting but ultimately irrelevant.

Edit: I'd add that various LLMs/neural-nets have turned out to be great tools for research. I simply find the scientist-equivalent position problematic.

[hiddencost]

I liked this proof of concept:

https://arxiv.org/abs/2509.06503

They set up scoreable computational science problems and do search over solutions.

[bonoboTP]

> But given that LLMs cannot interact with the real world

What type of interaction do you envision? Could a non-domain-expert, but somewhat trained person provide a bridge? If the LLM comes up with the big ideas and tells a human technical assistant to execute (put the vial here, run the 3D printer with this file, put the object there, drive in a screw), would that help? But dexterous robots are getting more and more advanced, see CoRL demos right now.

[epolanski]

> If the LLM comes up with the big ideas and tells a human technical assistant to execute (put the vial here, run the 3D printer with this file, put the object there, drive in a screw), would that help?

No, because the bottleneck isn't the thinking but running experiments.

I worked in solar research, assembling a cell to test implied 40 different steps and from beginning to testing it was around 4 to 5 days.

This means that in one year working full time I will realistically run 40ish different experiments. Many of those will need to be done multiple times, and when you have 40 different steps that can go wrong and kill your efficiency this further compounds.

Thus realistically are running 5 to 10 different experiments (or better, a handful plus their variations).

At no point in this process you're like "yeah, if only LLMs could provide ideas", it's just not true, you get millions of ideas, time and bodies are the limit.

[_0ffh]

In biochemistry there are multiple vendors that sell semi-to-fully automated setups that do large numbers of experiments in parallel.

I have no idea what solar research experimentation looks like in detail, is it theoretically possible to build similar setups for that use case? Where exactly is the bottleneck?

[nosianu]

This may be a problem of scale. biochem is a much wider field, and I guess, depending on what devices exactly you mean, a lot of it is usable in other bio fields (like https://www.faulhaber.com/en/markets/laboratory-automation/l... - the pictures showing a common general bio-lab scenario, there can be thousands of such assays to test)?

So it depends on if the same machinery can be used for more general material science research and testing work.

[epolanski]

Of course robotics can do a lot, it's process dependent.

[alphydan]

Not to worry. With the millions unemployed by AGI, you can get hundreds of thousands of unskilled "hands" for your IA. Even with reasonable failure rates you will get a few hundred experimenters past the 40 steps.

Not sure if I should finish with /s or /fear or /uncertainty

[kjkjadksj]

Someone needs to evaluate the big ideas spat out by the llm is the big issue. Lab work can already be automated. And bs holders are even cheaper than an automated machine.

[marbro]

Can these robots move a chess piece from one square to another?

[ragequittah]

Probably: https://www.senserobotchess.com/

[hodgehog11]

I agree that there is power in numbers for science, but not all science is lab work. Sometimes the bottleneck is purely computational.

[kjkjadksj]

Computational can be a huge bottleneck. Some steps they really do take dozens of hours to run on cluster. And you are not the main character, others might be using the cluster and your jobs might be waiting in a queue. You might not be able to appreciate parameters need adjustment until the run is over and you evaluate output.

Another delay point is getting collaborators schedules to align for meetings on progress or potential directions.

Placing the results in context takes some time but not so much as you might guess if you are constantly reading and writing sourced paragraphs and skeleton papers needing only results plopped in when they are ready and some exposition in the discussion section.

Writing the code might be the fastest step in the process already.

[a_bonobo]

In my computational niche the bottleneck was always writing up the results :) And wow does AI help there.... It's not hard to get a decent first draft written by AI based on my existing results.

[ekianjo]

> But given that LLMs cannot interact with the real world

Pair LLMs with machines and robotics and you are getting closer

[fennecbutt]

>But given that LLMs cannot interact with the real world

Yes they can...VLAs exist.