This is far bigger than people think.

So much advanced equipment is just sitting there in labs, waiting for humans to finally go and make experiments. Which they eventually get round to, sort of, when they can secure funding and when the grad student isn't ill or making mistakes or framing the problem the wrong way.

AI-driven labs can iterate 'good enough' hypotheses way faster than human R&D systems. Automated labs are going to be a major source of discovery.

> Eve independently screened some 1,600 chemicals and modelled how their structure related to their activity to predict which ones were worth testing. King and his group armed the robot with background knowledge and a machine-learning framework for developing hypotheses. Eve then used those elements to design experiments to test these hypotheses and, crucially, performed them itself.

> King plans to use the system — which occupies one-fifth of floor space than Eve does — to model how genes, proteins and small molecules interact in cells. Part of that will involve taking around 10,000 mass-spectrometry measurements each day.

The throughput here is astounding, especially when driven by researchers who really know how to chart a path. I feel every time a critical feedback loop is made both faster and cheaper, it makes everyone participating better. I wonder whether we will see many more "whiz kid" scientific researchers than we have today.

> So much advanced equipment is just sitting there in labs, waiting for humans to finally go and make experiments. Which they eventually get round to, sort of, when they can secure funding and when the grad student isn't ill or making mistakes or framing the problem the wrong way.

That's not really what the article is about though. Short of staffing it with humanoid robots, existing labs and their equipment will continue to be unused.

I don't really see why most existing equipment would be usable in this way. When you automate a thing you often have to rethink the entire problem. But more generally, automation is for _repeatable_ things and a lot of research is... not that.

The expensive equipment is usually a small (but crucial!) part of research activity, which involves things like talking to a lot of people, getting permission to do weird or new things, going out into the environment and collecting things in very specific ways, storing and transporting them carefully, observing, etc. Building or modifying existing lab instruments, doing various things with animals that are not co-operative ... and CLEANING. Who does all the cleaning?

Definitely use cases when you have a specific protocol you want to scale, but I'm also not sure how safe I would feel around AI with a license to experiment and access to dangerous reagents, high temperatures, etc. Or, god help us, an oligonucleotide synthesizer. Which is definitely going to happen (if it has not already).

> AI-driven labs can iterate 'good enough' hypotheses way faster than human R&D systems.

Is there evidence of that?