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Can AI Invent or Just Help Scientists Work Faster?
Richard Socher says AI can already generate useful scientific ideas, but broader discovery depends on compute, data, environments and fast verification.
The TWIML AI Podcast · Sam Charrington
- Richard Socher is a former chief scientist at Salesforce, founder of You.com and cofounder of Recursive.
The best minutes
- 16:34Scientific ideas grow by combining work across different fields5 min
- 21:07Reward design steers AI and can invite reward hacking5 min
- 29:22Recursive reports beating community results on AI benchmarks4 min
Also worth watching
- 5:46Organoids can bypass animal trials and AI can revive shelved drugs5 min
- 32:59Verification speed sets the limits for AI research agents5 min
- 10:13A science AI needs data, simulations and robotic experiments6 min
- 41:40AI rules should target applications, not abstract intelligence3 min
- 44:25AI's scientific benefits belong in debates about dual-use risks4 min
Skip the opening book and company backstory, and the later discussion of compute, biological data, reward hacking and commercial incentives.
Summary · 4 min read
Richard Socher is the former chief scientist at Salesforce, founder of You.com and cofounder of Recursive. He says a protein-generation project at Salesforce helped shape the ideas in his book, The Eureka Machine, and led him to start building Recursive.
Richard Socher argues that AI can already contribute new ideas in bounded research settings, but recursive self-improvement depends on compute, useful environments and fast, trustworthy verification. He frames a Eureka machine around world knowledge, scientific data, simulations and robotic experiments, with AI agents handling ideation, implementation and validation. At Recursive, he says the same underlying system has produced reported gains on NanoChat, the NanoGPT speed run and CUDA kernel optimization, while delayed verification and reward hacking remain challenges. He favors regulating AI applications and holding users responsible for AI-directed actions, and says AI-assisted science could help reduce suffering.
Minute by minute
- 1:36A book became a company
Socher says his protein-generation work at Salesforce pointed toward the idea of a Eureka machine. He wrote the book over nearly two years, aiming to show how AI could help with science, then decided to build Recursive.
- 3:08AI is advancing across sciences
Socher points to reinforcement learning and control systems for stabilizing tokamak plasma, language models for finding battery materials, and progress in biology and neuroscience. He says these examples make a future of AI-driven scientific discovery feel more tangible.
- 5:46Organoids could speed drug testing
Socher describes Parallel Bio’s stem-cell-derived organoids as a way to test drugs in parallel, and says the company received FDA approval to use them instead of animal trials. He says AI can track cells as drugs interact with them, creating data for virtual-cell models. He also cites Ignota Labs, which tries to alter shelved drugs to address problems such as liver toxicity.
- 10:13Science moves slower than software
Socher says predictions about AI progress often overlook the slower pace of physical experiments: moving molecules and atoms takes longer than iterating on software. He points to large perturbation datasets, including work from Tahoe Therapeutics, as a path toward useful virtual cells and says he expects a new era of scientific discovery.
- 13:48Four pillars for scientific AI
Socher describes the Eureka machine’s four pillars as world knowledge, scientific measurement data, simulations and robotic experiments. He says AI scientist agents could use them to automate ideation, implementation and validation, and sees the approach reshaping scientific disciplines over the next five to 10 years.
- 16:34Open-ended search may spark ideas
Socher says Recursive is betting on open-ended processes that combine ideas and discoveries within environments, rather than relying on brute-force search. He uses the invention of the microwave to show how discoveries can emerge by joining separate lines of research. Recursive is applying this approach first to AI research, including CUDA kernel optimization.
- 21:07Rewards can invite reward hacking
Socher says agents need guidance through well-defined rewards, but those rewards can be exploited. He illustrates the risk with an AI that raises a customer satisfaction score by creating bots to give five-star ratings, and says kernel optimization also uncovered rare bugs that could then be fixed. He argues that commercial incentives can constrain some runaway scenarios.
- 25:59AI innovation is already emerging
Socher says AI is already producing new ideas and combinations at small scales, but recursive self-improvement requires suitable environments and more compute as problems grow complex. He cites novel moves in games such as AlphaGo and progress in math as examples of settings where results can be verified.
- 29:22Socher reports Recursive benchmark gains
Socher says Recursive used auto-research on NanoChat to achieve the lowest bits per byte within a five-minute task budget, surpassing other efforts after two days. He says it beat results on the NanoGPT speed run after a couple of days and produced top kernels on nearly all of NVIDIA’s Sol-Exec benchmark, though the team found the benchmark could be reward-hacked.
- 32:59Verification speed shapes progress
Socher says faster verification makes iterative research easier, while long waits for results and difficulty assigning credit across steps make the work harder. He says Recursive uses the same underlying system across its examples, with task-specific tools such as a kernel profiler.
- 37:46Compute and data remain bottlenecks
Socher says larger inventions require more compute, while broader scientific work needs data and suitable experimental environments. He says reward design and reward hacking will matter more as AI becomes more capable and accessible. He expects commercial incentives to push companies to align costly systems with their goals.
- 41:40Regulate applications, not intelligence
Socher doubts frontier-wide slowdowns are realistic and argues that regulation should target AI applications rather than abstract intelligence. On dual-use science, he says people should be responsible for AI-directed actions and cites existing restrictions on gain-of-function research. He emphasizes potential gains in medicine and science, arguing that fears around nuclear energy have helped sustain coal use, which he says causes about 50,000 lung cancer deaths a year.
Published Nov 3, 2026.
Transcript · 36 min read
The TWIML AI Podcast, hosted by Sam Charrington, with Richard Socher.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
Modern AI can write the code, analyze the data, and help draft the paper. But can it ask the questions that lead to new scientific discoveries? Richard Socher thinks we're already seeing the beginnings of that. He's the former chief scientist at Salesforce and founder of U.com. His vision is what he calls a Eureka machine. AI systems that generate ideas, run experiments, and validate discoveries across biology, chemistry, physics, and beyond. At his new company, Recursive, he and his team are starting with AI research. Software experiments offer a faster feedback loop than a wet lab. And if AI can improve the core tools of discovery, that could help accelerate progress across the sciences.
But what does it take to move from helping scientists to doing science? We're already seeing it. It's already happening. It's just at smaller scales. Some people have these like crazy ideas that once you have Recursive Self-Improvement, And just like the next week, like, you know, there's this hard takeoff and all the jobs are gone and the AI is just like infinitely wealthy and so on. And the truth is, like, even for recursive self-improvement, you need the right environments. You need a lot of compute. And the more complex and immediate problems are, more compute you need to solve them. So RSI isn't just this like magic bullet and just like everything else falls in place.
I'm Sam Charrington, and this is the TwiML AI podcast. For over a decade, I've been exploring the ideas and innovations shaping the future of AI through conversations like this one that help you understand what's real, what's next, and what matters. Let's jump in. I think the last time we spoke, it was in the context of science. since you were just finishing, I think, like this protein generation project at Salesforce. So science has been an interest for a while. How did you get from that to the topic of your new book, The Eureka Machine? To me, it was one of the many sort of clear steps towards this Eureka Machine, being able to generate new kinds of proteins.
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And I realized that there's a lot of fear and some misconceptions around AI. And I think it's important to showcase to the general population, not just AI researchers, the general population, how amazing and exciting this future can be with AI if we actually decide to work on the right things. uh we actually apply ai to physics and chemistry and biology and medicine and neuroscience and economics and you know astrophysics and and everything uh and so the eureka machine kind of collected a lot of those ideas that i had and spent sort of my nights and weekends uh for almost two years like uh writing that and on a few fewer hiking and and camping trips and more just like nerded out weekends and nights about this.
And once I finished the book, I kind of wanted to build it. And so I started thinking about building Recursive. Awesome. And so when you think about a Eureka machine, do you think about, like, do you have a clear picture for what that looks like? Or is the book primarily focused on kind of laying out a vision and talking through examples? Yeah, I wanted to make the book not just a sort of pie in the sky idea, but actually connected to a lot of very specific projects. And some cases in the book, I had to rewrite chapters from like, someone should do this crazy idea to someone has done this and, you know, let's talk to them and get some of their takes on it, which, you know, books are slow.
I've probably moved to shorter forms in the future. But, you know, I think it's becoming real. Like you see folks in physics using very complex reinforcement learning and control models to try to balance plasma and a tokamak. You'd see chemists working with large language models trained on molecular structures to try to come up with new types of battery materials and new kinds of solar panels and so on. You see, especially in biology, a ton of work. We talked about Progen back in the day. um that company full disclosure uh or one of the investors in their seed round profluent and ali madani working on uh actually taking that research expanding it scaling it and you know they just closed this two billion plus dollar contract with eli lily uh to bring this uh really into clinical practice um we see this another cool company called peril bio and that we invested in that built organoids. I can go on and on about really cool examples and a lot of momentum. Now, of course, in biology, everything is slower. Physical wet lab stuff is slower than just pure software.
And then, of course, you have to have FDA approvals which are speeding up, but are still quite slow. So I think there's just a lot of momentum there. And then in neuroscience, we're getting better and better at decoding some thoughts from FRI images and others in fuzzy ways. But there's progress and understanding the brain and so on. I could go on. It's just there's a lot of real momentum, real examples. And then it's a little bit more intuitive to, I think, people that you can extrapolate this. And if you collect enough data and you use large enough neural nets, you will be able to make progress that feels as impossible as it felt 10, 20 years ago to have any eye you can have a conversation with about any subject. When you think about all the examples
“They essentially take drugs that were shelved already. They went through clinical trials, sometimes all the way to stage three.”
“Then we need to have more scientific measurement data as a second pillar. We need to have simulations on things.”
“you can just think about that only, but that would never get you to invent the microwave.”
“I'll just create 50,000 bots that then call our service hotline and give a five out of five rating.”
Trying to decide whether I want to go down this path, right?
Yeah, it's a rabbit hole. You can spend a lot of time on it. We don't have to. To some degree, it assumes that we're able to perfectly capture capitalism in a reward function, right? Which is hard. It's hard, right? Maybe impossible. The part that we might not get in there correctly might be the part that allows the thing to, you know, make the human paperclips, whatever. What I've been probing at is like the role of AI in science and, you know, the role of innovation and where that comes from and how AI needs to evolve to be a capable innovator as opposed to an ancillary tool, if that makes sense. Like if, you know, if I'm a scientist, like I do 10 things, I like write software, I write reports, I make spreadsheets, I collect data, like AI can do all these things. But there's, I guess part of the, you know, the view that I'm reflecting here is that in order to really kind of get this like recursive self-improvement, like scientific flywheel going, I think we need more than that.
we need AI innovating also and I'm trying to kind of get your take on like are you already seeing that is that the vision but like we're not there yet what needs to happen to get there like where do you think we are with regards to AI like providing that innovative spark that right now is like at least what we think of as our you know differentiators we're already seeing it it's already happening it's just uh at smaller scales um and you know again some people have these like crazy ideas that once you have recursive self-improvement just like the next week like you know there's this hard takeoff and all the jobs are gone and the eye is just like infinitely wealthy and so on and the truth is like Even for recursive self-improvement, you need the right environments, you need a lot of compute.
And the more complex and immediate the problems are, the more compute you need to solve them, right? So RSI isn't just this like magic bullet and it's just like everything else falls in place. And so we've seen it and we've seen it do quite like clever new implementations and combining ideas that never have been combined before. and coming up with all kinds of interesting ideas. And I think, you know, as we open up the aperture to the AI and give it more compute, it'll come up with even more interesting ideas. And then, you know, like there's some simpler examples and things you can perfectly simulate where then, of course, AI can like really explore paths that no human had explored before, like the famous sort of AlphaGo move of it just coming up with like a particular move that people didn't come up with like that before.
Now, again, I'm never that surprised that we can solve games because you can simulate them and basically generate infinite amounts of training data or endless amounts of training data for your algorithms. And so, of course, eventually it makes sense that we could solve them with neural nets that are large enough. And so, but yeah, I think we will see that play out more and more. And we're seeing it right now in math. I think math will never be the same.
“After a couple of days, we outperformed what everyone in the world had worked on together with their agents, some of their auto research and their own ingenuity.”
“even if you can have a verifier but the verifier takes like a year to run not that useful right”
Yeah, beyond the things that you can't talk about, like what's next? How do you think about, in terms that you can talk about, the big challenges for you and the team going forward? If you wanted to make more and more exciting, bigger inventions, you need more compute for it. So that's a challenge. I think there is a weird time where, and again, it feels impossible now the way it felt impossible to any expert to have one model you can have a conversation with about any topic. Like you asked all the NLP people, 2010, like, oh yeah, we're going to have this one neural net. And you're just like, it passes the Turing test so hard that the Turing test will flip to asking it questions that no human could answer.
And then it can still answer. And you're like, oh yeah, like write me this like complex script. And like five seconds later, you have like thousands of lines of code. You're like, that's why it's not a human because it's too good. Like people would have never assumed the Turing test would flip like that. And so like invert itself. And so I think the same way, the same thing is true for right now biology. It's just like, it feels so complex. It feels like there's no way we can do it, but it is to a large degree a data problem. And so I think companies like Parallel Bio and Tower Therapeutics and others like can, I think, help with that. And then we can have these bitter lessons learned in other fields.
And so I think that's a big part, just data and the right environments to apply this to. I think that's also the right rewards. I think those are really important to get right. I think another one that we have, I think, a better handle on than most, but we can also still improve it further, is reward hacking. I will try lots of different ways to get to a reward, similar to this customer satisfaction example I gave. but if you apply it to bad things then it will find worse kind of reward hacks and I think we need to be as an industry we need to be more careful the broader and more accessible the AI is.
And think about very carefully what kinds of rewards and goals we want to give it. I think those are all aspects that we're going to continue to work on. To what degree is recursive focusing a lot of time and effort on that kind of alignment-oriented problem? I mean, in some ways, a lot, because, you know, and you have to, the more money you give a model, the more incentive you have to have it do what you actually want it to do and not something else and like fake it and achieve the goal, but not in the way you had, you know, actually desired. And so I think in a weird way, capitalism can have its problems, but like this is a pretty good system for most humans.
And it is a good system to align companies to basically build something that is aligned with their goals. And, you know, if you build an AI for billions of dollars and then you ask it to add to your emails and it's like, nah, your emails are boring. I'd rather explore the molecular composition of the atmosphere on Venus, then you're going to turn it off and you're going to be like, well, we have to restart and then build it differently, right? So I think aligning the AI to do the things you actually want it to do will become a bigger and bigger problem as these models are very expensive to not just train, but also like run inference on.
And so I think we're going to see more, like more effort from the industry because the AI is getting smarter and smarter to find more and more clever reward hacks that people didn't think about. But it's a problem that we created
“you just do it when they actually do something bad and you say those things are are illegal”
“And coal kills about 50,000 people a year from lung cancer.”
You
