When AI Improves Itself: Richard Socher (Recursive)

Source: The MAD Podcast with Matt Turck · 2026-09-10

Yeah, it's a somewhat surprising fact. And you may argue, clearly not. We're making so much little progress on so many different things. But when you think about how much progress have we made on antibiotics, bacterial infections went from a death sentence and the plague to a nuisance. We now have antibiotics for almost all the different bacteria, and we truly solved that. And we have clearly not solved viruses or cancer the same way we solved bacterial infections. When you think about foundational novel things like E=mc2 and general relativity, we clearly have not made progress on many theories in physics either when it comes to such foundational things that then literally led to nuclear energy.

And fusion and fission and other kinds of research that could be conceptually done. And so a lot of the fields have kind of gone through from we understood some foundational pieces to we can now do a lot of engineering, but they've also split up into thousands of different subfields. It is almost impossible nowadays to be this sort of generalist genius that can dabble in all of these different fields because each field takes years and years and years to get really deep into. And so what we found is that as there are more and more subfields and niches, it's actually hard to have enough people in each of these subfields.

Yeah, you have a great expression. You talk about how we evolved from a body of knowledge to a labyrinth of knowledge: 34,000 journals that might as well have no-trespassing signs.

Yeah, 100%. People often—the way careers work is, you want to be kind of novel, but if you're too novel, if you're too far out there, then your papers will get rejected. And that certainly happened to me a lot in the early days, like 2010, of neural networks for natural language processing, where the majority of my first couple of papers got rejected from NLP conferences when they got accepted in some small sub-niches and subgroups. I still remember the first sort of deep learning workshop at NIPS back in the day, now NeurIPS, that was basically like 30, 40 people, all the now super famous folks.

That's exactly right. Even, like, a good example again in biology is that you study not all of biology anymore. You study either biology at the cell level, or the tissue sort of medical level, or the biochemistry level, or the protein level. And if you ask a PhD in biology about, like, a deep question in one of the other layers, they often don't know either.

The high-level idea is that science has gotten really good at understanding smaller and smaller pieces, but to bring them back up and bring them together, we actually have to use AI. And AI is kind of what calculus did for physics. AI will do for biology in the sense that it'll help us weave back together lots of very complex pieces that build these complex systems that then have certain properties. So concretely, for instance, your microbiome or the brain, they have so many different pieces.

There's still a good number of people out there that think of generative AI as a next-token predictor, as a chatbot, as something for language. Here, the claim is very different. It's predicting protein structures. Why is the technology that's good at being the best chatbot in the world also good at scientific discovery?

But now I think once you showed that we didn't have to actually truly understand and have perfect rules for every single aspect of translation or question answering, we will see similar things in, and have seen already in, for instance, the language of proteins. So you have just sequences of amino acids. No human has been sort of evolutionarily trained and has learned to speak the language of proteins. But just like the language of natural language, of English and so on, AI doesn't really care if it's English or a sequence of amino acids.

We can already create new materials. We can already help with economic questions of how much to tax or subsidize certain populations if you have a certain objective or reward that you're looking to achieve inside your economy. All of these things are already within our grasp.

I think we can basically predict where AI will certainly have superhuman capabilities. And those are all scenarios and all domains where we can either have a simulation and/or a verification tool. Any kind of domain that we can simulate will basically result in a world where the AI can essentially infinitely many times experiment inside that simulation. And assuming this simulation doesn't take years to run every time you want something useful from it, you then know that you can solve the problems in that domain.

Terence Tao and the most famous and most sort of frontier mathematicians are already fully aware of it. The whole field will change, just like the field of AI has changed. And a lot of sort of skills that used to be useful, where you manually feature engineer, and then you manually architecture engineer, and you manually do these things, are not that useful anymore. That will be true for a lot of mathematics also. And so I think that's a great sort of situation if you cared about solving as many things, proving as many theorems as possible in math.

And we do need to collect a lot more data in various robotic forms. And those are essentially the four columns that I talk about in the Eureka Machine too. You start with human knowledge and LLMs, then the second pillar are all the measurements we can take, and more and more of those that we already have, and we should incorporate that into the model. The third thing is a simulation. And the fourth thing, fourth pillar, is essentially robotic process automation to collect even more data and verify whether the inventions really made sense.

So at Recursive, we are fairly sure that we have to start with AI for AI and then make it really good at doing research on creating better AI so that it has the equivalent of 50,000 PhDs in terms of knowledge and its own capabilities, and only then go after the physical natural sciences like physics, chemistry, and biology. Especially biology, I think, will be most interesting. I do believe that in the next two or three years, while we're focused on recursive self-improvement, we will also have more and more data collection.

There is another counterintuitive idea in the book that I thought was fascinating, which is that when it comes to AI-based science, hallucination might be a feature rather than a bug. Can you explain?

It's becoming an engineering science. And that's often the case, I think, in sort of the transition of different sciences. Once you've understood most of the basic pieces, you now want to learn how to put them together in novel ways such that they are useful for you. And there's, like, low-hanging fruit when a field transitions into that becoming sort of an engineering science. And I think biology is in that state right now, where we know, okay, this protein does this, but if we change that protein a little bit, maybe it can do something else.

It's called ProGen. Ali Madani is the first author of that paper. That was back in the day when I was the chief scientist at Salesforce still. And he's since started Profluent. They've now closed, like, multibillion-dollar contracts with Eli Lilly at Profluent, his company, because they've created new kinds of proteins that are, for instance, even better than CRISPR-Cas9 at gene editing and being even more specific and targeted for changing certain genes inside living people, potentially, and creating new kinds of therapies from that.

And so proteins, being such an important piece of all the building blocks of life and disease and health, making them programmable will unlock very, very obviously many, many exciting use cases. And I think you're starting to see this sort of in this recent trial that is making a lot of progress, where they basically created a different drug for every different patient in the trial. And this is a first for the FDA, too. And more will happen there. It's actually unfortunate how hard it has become in the US and certainly in Europe to run clinical trials.

It's a good question and sort of touches upon what some people call the hard takeoff too, where some people think once we have RSI, and generally with AI, there will be this really hard takeoff, and then everything will just happen very quickly. And as bullish and excited as I am about AI, I'm not a believer in this crazy hard takeoff. I think, yes, things will accelerate, but there are certain things that will just require time because of physics and constraints in the real world, such as long-term trials that you want to know whether people have some issue, like three years after they stop taking the drug, and things like that.

And each cancer often is also not one homogeneous thing. It has different types of subcancers in it and so on.