Why you should work on AI for AI Research — Richard Socher of Recursive
Latent Space
Richard Socher thinks AI should automate AI research itself. He says that could cut years of invention down to weeks, and he’s already seeing early wins.
Based on reporting by Latent Space — read the original for the full story.
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Richard Socher has moved from helping shape early NLP ideas to chasing something much bigger: systems that can do AI research on their own. At Recursive, the new company he founded after You.com and AIX Ventures, he’s betting on what he calls the “Eureka Machine” — a system that can improve invention itself. Recursive has also raised a $4.65B seed round, and Socher talks about it less like a product and more like a research program with civilization-sized ambitions.
His pitch is straightforward, if a little audacious. Build AI that can take a goal, an environment, and a reward signal, then keep getting better at finding solutions. In his telling, that could speed up work across science, energy, materials, biology, economics, and more. He even argues that research programs that now take thousands of people and years might one day be compressed into weeks. That’s the core of the book he’s finishing, and the same idea running through Recursive’s work.
The early results he describes are the interesting part, because they’re less philosophical and more concrete. Socher says one AI research system beat humans and their agents on optimization tasks in under two days. He also says the system found improvements in NVIDIA GPU kernels without a team of CUDA specialists. Recursive has also been working on NanoChat, NanoGPT, and kernel optimization, all of it framed as a first pass at automating the research loop itself.
Socher is bullish on the upside, but not naive about the messier pieces. He talks about reward hacking, the limits of constitutional AI, and why he thinks AI regulation should focus on specific applications rather than intelligence in the abstract. He’s also skeptical of hard takeoff hype, pointing to hardware, compute, and economic constraints. And he keeps returning to open-source models as a form of resilience and geopolitical soft power, which is a very Socher position: ambitious, slightly contrarian, and allergic to the idea that slowing everyone else down counts as safety.
That’s where Recursive’s real bet sits. Not just that AI gets smarter, but that it starts helping build the next version of itself. If that sounds risky, yes, that’s the point. The company is aiming at the part of AI that makes the rest of AI move faster.
My take — AI-written commentary, not fact-checked reporting
The useful part here isn’t the sci-fi gloss; it’s the insistence that research tooling is now part of the frontier. That’s why open models still matter: closed labs can brag, but open systems can spread capability, scrutiny, and pressure to actually improve. And if the industry is going to keep pretending “pace, not pause” is a safety plan, it should at least admit who gets to hold the brakes.
Read more about this at: Latent Space
