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The TWIML AI Podcast · 5 min · Nov 3, 2026

Organoids can bypass animal trials and AI can revive shelved drugs

Richard Socher · Co-founder, Recursive

Richard Socher cites Peril Bio’s FDA approval to test drugs on organoids instead of animals, then describes AI tracking cells and a strategy for changing shelved drugs.

In this episode

Full episode
  1. 16:34Scientific ideas grow by combining work across different fields5 min
  2. 21:07Reward design steers AI and can invite reward hacking5 min
  3. 29:22Recursive reports beating community results on AI benchmarks4 min
  4. 5:46Organoids can bypass animal trials and AI can revive shelved drugs5 min
  5. 32:59Verification speed sets the limits for AI research agents5 min
  6. 10:13A science AI needs data, simulations and robotic experiments6 min
  7. 41:40AI rules should target applications, not abstract intelligence3 min
  8. 44:25AI's scientific benefits belong in debates about dual-use risks4 min
“They essentially take drugs that were shelved already. They went through clinical trials, sometimes all the way to stage three.”
Richard Socherat 9:22 · The TWIML AI Podcast
9:22

Takeaways

  1. AI can track cells as drugs interact with organoids.
  2. Ignota Labs makes minimal changes to shelved drug molecules.
  3. The approach could return some failed candidates to later trials.

About this segment

Richard Socher cites Peril Bio’s FDA approval to test drugs on organoids instead of animals, then describes AI tracking cells and a strategy for changing shelved drugs. These approaches could improve testing and reopen drug candidates.

Richard Socher

Richard Socher is a former chief scientist at Salesforce, founder of You.com and cofounder of Recursive.

Pioneered neural networks in NLP, invented widely used word and contextual vectors, and leads Recursive's self-improving AI.

From “Can AI Invent or Just Help Scientists Work Faster?”, Nov 3, 2026. Watch the full episode

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 5:48

    that you've come across what one or two you know very specific examples most leave you with the feeling of the of progress i think uh one that i particularly love is this maybe we'll like double click into it is peril bio and they build uh they use stem cells uh human stem cells which you can now derive from like blood or skin cells and other things get them into pluripotent stem cells then create different kinds of organoids, like tiny little mini organs. And you can study the effect of different drugs on those mini organs. And that they got full FDA approval for to skip animal trials and just test things in these Petri dishes. And so you can do this in parallel at scale.

  2. 6:38

    and just one company could save a lot of animal lives. Animals that have only been bred to get a disease and then be tested upon. So you don't need to be a hardcore like PETA, human like animal rights activist to like appreciate that there's less suffering on the planet when we can do that in little Petri dishes instead. And what's the role of AI there? Bunch of places like one, of course, like creating them, but then also actually tracking every single cell as like the drugs interact with those organoids.

  3. 7:13

    And then when you collect all this data, you can eventually go even further and create first versions of sort of virtual cells, which are still a little bit further away to getting a full realistic virtual cell, but you need to have that kind of data collection to get there. The idea of a virtual cell is kind of like a digital twin of an actual cell. It's going to be fully like a simulation of the cell.

  4. 7:41

    That's exactly right. I think there are a couple of things that are very clear in AI. If you can have sheer unlimited data about a space or you can have a simulation of a space or you can have a verifier for the solutions. and sort of a logical step to get there. In any of those three scenarios, AI will get to superhuman capabilities. And so we got that through language because humanity has kind of collected together tons of data on language on the internet.

  5. 8:16

    We got there in chess and every other simple, quote unquote, simple game that you can simulate incidentally many times and verify in the end that you win or not. And we see this in math, which will never be the same. um and we'll see many more uh complex theorems be be proven might not always be the most beautiful proof minus some of them might be just like brute force but it will the whole field will change forever because you can verify it and hence give a clear reward to an rl algorithm and so now what we look at is like well biology is not in any of those three right now uh and so we need to collect a ton more data that we can then build simulations that then, and then, you know, be basically experimented inside the simulation on instead. So Parabio is one example. Were you about to mention another? I guess we, I mentioned Profilind already also, also incredibly exciting.

  6. 9:13

    They basically create proteins for DNA manipulation and helping people with genetic diseases. One that I particularly liked is called Ignota Labs. They essentially take drugs that were shelved already. They went through clinical trials, sometimes all the way to stage three. Pharma companies spent hundreds of millions of dollars on them and then they didn't work because they had some kind of toxicity on the liver, for example.

  7. 9:44

    And so what they do is they go back to those molecules and then see if they can minimally change the molecule to prevent that one downside and then go directly back into sort of later stage two trials. And so that is, I think, a really clever idea. You need to use a ton of understanding of the molecules and the literature and then get many more drugs into the later stage trials much more quickly than in the past in traditional pharma and biotech.