Full episode · 22 min
Only AI Can Build the Next AI | Richard Socher
Richard Socher argues that AI research advances through open-ended systems that generate, test and recombine ideas rather than chase fixed goals.
MTS · Theo Jaffee & Sophia Dew
- Richard Socher is co-founder and CEO of Recursive.
The best minutes
- 5:41Open-ended search can find ideas direct optimization misses3 min
- 11:05Banning self-improvement would require invasive surveillance4 min
Also worth watching
- 0:29AI researchers helped build Recursive's first coding model5 min
- 15:00A rejected NLP paper anticipated prompted language models4 min
Skip the roughly three minutes of sponsor reads and the short discussion of evaluating creative writing.
Summary · 2 min read
Socher leads Recursive, which is building self-improving AI to automate knowledge discovery. The company launched Flint, an open-source local coding model that Socher says was developed with help from AI and a small team of researchers.
Socher says Recursive's Eureka machine uses AI to ideate, implement and validate research, and argues that open-ended search can uncover useful combinations that direct optimization may miss. He describes Flint as an early product built with substantial AI help and says coding is a key capability for recursive improvement. Socher opposes banning self-improvement, arguing that authorities should regulate risky applications rather than intelligence itself, while emphasizing safety work. He also reflects on the field's shift after ChatGPT and recalls early attempts to unify NLP tasks in one prompted model.
Minute by minute
- 0:29Socher calls Flint the best, fastest local model
Socher describes Flint as a fully open-source local coding model that users can download. He says it is better than other models in its category, most of which he says are Chinese, and calls it a first step toward shipping. He says AI did much of the development alongside a small, strong team of researchers.
- 1:23AI helps ideate, implement and validate
Socher says Recursive's Eureka machine brings together AI scientists to generate ideas, implement them and validate them. He says AI helped with nearly every aspect of Flint, and argues that automating all three steps remains uncommon among companies.
- 3:33Novel ideas can come from recombination
Socher says AI ideation can search combinations across fields, but must learn which combinations are viable rather than try them all. He points to the transformer paper: attention predated it, while a key insight was that recurrent neural-network layers were not needed.
- 5:41Open-ended search finds unexpected paths
Socher argues that pursuing a goal directly can miss ideas from other fields, using the microwave's development from work on radio waves as an example. He says evolutionary systems can let different branches develop and recombine. He cites the Darwin-Gödel machine and rainbow teaming, where attacker and defender models co-evolve.
- 9:40Coding comes before harder-to-score tasks
Asked about creative writing and long-term planning, Socher says Recursive is focusing on coding because it is crucial to self-improvement. He expects open-ended systems to help with tasks that lack verifiable rewards, including creative writing, where a fixed gold standard can constrain creativity.
- 11:05Socher opposes banning self-improvement
Socher says AI development must be safe and that Recursive is working on reward hacking and reward engineering. He argues that banning self-improvement would require intrusive surveillance, citing a local model that updates its prompt with facts learned from interactions. He favors regulating applications such as self-driving cars and AI-created drugs, not intelligence itself.
- 15:00Socher sees AI's shift with cautious optimism
Socher says the field has shifted from an intellectual effort to understand intelligence toward a mix that includes people seeking commercial opportunities. He welcomes AI applications and offloading tedious tasks, while expecting short-term pushback and saying education will need to adapt. He says he is optimistic about the long term.
- 17:21Socher backed unified models early
Socher says he favored combining tasks in one model early in his career, though publishing standards made that difficult. He says his DecaNLP paper showed one neural network could answer prompts for sentiment, summarization and translation. The paper was rejected, but Socher says early GPT papers later cited it.
Published Oct 26, 2026.
Transcript · 15 min read
MTS, hosted by Theo Jaffee and Sophia Dew, with Richard Socher.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
Right now, we're focused a lot on coding because AI is code. And now that this year, really only AI can code, you know, you really want to focus on that capability because that's very crucial to self-improvement. All right, we are back. We are live with Richard Socher, who is the co-founder and CEO of Recursive, a company building self-improving superintelligence to automate knowledge discovery. They just released their first model, Flint. So congratulations on the launch, Richard, and welcome to MTS.
“you have a community of AI scientists that essentially ideate, implement and validate ideas in an open ended fashion.”
“And once it figured out how to do the attack, you then inoculate it, and then you ask it again.”
Read the whole transcript
that is the Eureka machine than just having like pure RL where you like go chase after this exact goal. I want to shout out our sponsor Lovable. You know, the app you've been meaning to build the internal tool side project or product you'd otherwise lose a weekend to with Lovable. You get software you can ship today that includes an editable code base. You can inspect and change plus two-way github sync lovable also handles the back end and infrastructure including managed postgres auth storage hosting payments and more so you don't have to wire it all up yourself and through lovable's mcp server you can create and deploy your project directly from the agents you already use turn ideas into software people love with lovable lovable.dev now onto the episode
so how would this research direction help address like these specific shortfalls of existing models like for example creative writing um which is like a little bit hard to generate like a good like reward signal for because you know it's like it comes down to taste uh it's not explicitly formally verifiable in the way that like math or coding are so do you think there's anything that recursive could do to help models write better to help them become better long-term strategic planners or you know do anything else that models can't really do today yeah i think that's definitely uh possible uh right now we're focused uh a lot uh on coding because the ai is code and now that this year really only ai can code you you know you really want to focus on that capability because that's very crucial uh to self-improvement um and so uh but but generally open-ended uh systems can't have helped uh and and will help more in the future um on on these kinds of unverifiable tasks as well where you might want to co-evolve a system versus just saying like this is the gold standard because you're right creative writing is like it's like part of it is that you cannot actually uh just say this is the exact way you do creative writing
almost by definition then it's less creative after a while so you're pretty explicitly working on
“I think it absolutely makes sense to regulate applications of intelligence, but not intelligence itself.”
“the reviewers thought it was a ridiculously terrible idea to try to unify every single task of NLP”
having you on richard congrats again on the launch of flint very exciting stuff thanks so much for coming on mts thanks for having me thanks for listening absolutely a huge thanks to mts sponsors 11 labs ai that communicates at human level across every channel and modality 11 labs.io slash mts scale your startup on neon millions of developers and startups have already chosen neon for their back end start on the free plan or get up to a hundred thousand dollars in credits for
your startup at neon.com slash mts special thanks to our sponsor kong the ai connectivity platform connect api as LLM's agents and systems with serious security and governance. KongHQ.com. Finally prove what your AI investment is worth with Lairdyn. Head to Lairdyn.com today to start measuring your AI's bottom line. MongoDB, trusted to run critical applications by 75% of the Fortune 100, the intelligent data platform for the era. Apply today for access to up to $15,000 in Atlas credits dedicated technical support and exclusive events and go-to-market opportunities mongodb.com chargebee billing and monetization infrastructure for the ai era chargebee.com blitzy autonomous software development for enterprise code bases ship 5x faster blitzy.com adquick make your brand a billboard out-of-home advertising as easy to scale as digital adquick.com arena measuring ai performance in the real world. Arena.ai. Support for the show comes from VCX, the public ticker for private tech. The U.S. stock market started history's greatest wave of wealth creation.
From factory workers in Detroit to farmers in Omaha, anyone could own a piece of the great American companies. But today, our most innovative companies are staying private longer, which means everyday Americans are missing out. Until now. Introducing VCX, a public ticker for private tech. visit getvcx.com for more info that's getvcx.com carefully consider the investment material before investing including objectives risk charges and expenses this and other information can be found in the innovations fund prospectus at getvcx.com this is a paid sponsorship
