Biology grad students can spend 40+ hours per week in the lab, on top of all their course work. It's not so bad though, since there's plenty of time to grade papers or work on your thesis while waiting for reactions to occur.

Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.

I think human review may screw things up. If you look at dimethyl fumarate[1], no card carrying medicinal chemist would have taken this serious. No pharma exec would have risked 100s of millions of dollars for clinical research trials.

  - 1950s people notices that a wood polish cleared psoriasis. Topical formulation worked. 
  - 1990s an oral formulation was created.
  - 2000s people noticed people treated for psoriasis that had MS, had fewer MS symptoms while taking this molecule. 
  - 2017 dimethyl fumerate is approved for MS.
  [1] https://en.wikipedia.org/wiki/Dimethyl_fumarate

You can automate the setup and mechanical process. You could increase the physical capacity for parallel tests. You could increase the rate of acquiring raw materials.

But how much you cut down the time depends on how much of the trial-and-error feedback loop depends on feedback and iteration vs cast a wider initial net.

(One of the interesting things to me about the dominant software development agent pattern across all of Claude, Codex, and Cursor is that the approach is still extremely local and iterative, human-mimicking, rather than big-brain-one-shotting a novel or perfectly-fit-for-purpose software design. The performance of the agents on the initial "jump to conclusions" step does not appear superhuman in the same way that the speed-of-iteration is clearly superhuman.)

Care to elaborate, explain?

That is utterly untrue. It stems from the notion that biology only needs big data for us to solve all its mysteries.

Beyond that, while automated labs for cell biology are plausible and active, and the same fit organoid biology, the same really isn’t true at organism level. Labs already are artificial environments that make organisms have odd phenotypes, and odd interactions which bias the data away from what you’d find in the natural world.

You add in robotic manipulation, and you add a new confound.

Drones in the wild are an obvious counterbalance to this, though even in that scenario, the drone can be a confound unless well hidden.

In the end though, this kind of systemic breakdown of biology, while much needed raw material to improve our theories, won’t solve the whole of biology. Human hands and human minds will be needed for a good long while.

How many of these "we sandboxed it and said not to do Y, but then it went and did it!" stories would you consider necessary to disprove that?

It may be good at following rules 99.99% of the time but if someone launches a million times more parallel agents than they would hire humans, it might still be much worse in practice...

"We've been told we must find an effective drug candidate here, and to follow this process for evaluating the quality of the candidate, but actually we could subtly change the analysis like this to make this candidate look favorable enough..."

(And then compare that to deterministic algorithms...)