Richard Socher bets the next thing to automate is AI research itself
Latent Space's long conversation with Recursive's Richard Socher: what the Eureka Machine is meant to build, the optimization and GPU-kernel results behind his $4.65 billion bet, and where reward hacking bends the loop.
Richard Socher's definition of the Eureka Machine fits in one sentence: "the ultimate invention that will afterwards invent most everything for humanity" 1. He means a system that takes any goal, environment and reward, and produces the inventions people ask it for.
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He finished a book on the idea last year, and it comes out this month 2. Recursive, the company he started to build parts of it, raised $650 million at a $4.65 billion valuation in a round co-led by GV 3.
Eight co-founders, one long argument
Socher has worked in the field for more than twenty years. He built the GloVe word vectors with Chris Manning, founded MetaMind, became chief scientist at Salesforce, then founded the search company You.com and the fund AIX Ventures. His story about how the research culture works starts with a rejected paper.
That paper, DecaNLP, phrased ten natural-language tasks as prompts to a single network. Reviewers at ICLR rejected it in 2018, arguing that question answering is not one phenomenon and that different questions need different systems. The paper went on to be cited five times by GPT-2. Socher's own reading is that the rejection cost time: the next item on the team's list was adding language modeling as another task, and the team dropped the line of work after the reviews came back.
The eight co-founders around him come from three traditions. Josh Tobin, the CTO, ran agent projects at OpenAI, including Codex. Jeff Clune and Tim Rocktäschel worked on open-endedness, the study of systems that keep generating their own problems rather than optimizing a fixed reward, and Rocktäschel built the Genie world models. Alexey Dosovitskiy invented the Vision Transformer. Caiming Xiong, Yuandong Tian and Tim Shi round out the group. Four of the founders co-authored the Darwin Gödel Machine, a paper describing coding agents that rewrite themselves and keep an archive of the variants that work best 4.
Why the loop can close now
Socher's through-line over twenty years is that progress followed whenever a manual step in building AI was replaced by a learned one. Hand-written features gave way to embeddings. Per-task architectures gave way to one general model. The manual step left is research itself: forming an idea, implementing it, measuring whether it worked.
What changed, in his account, is that models can now write code, and AI is made of code. GV states the thesis as a pair of sentences: AI is code, and AI can code, so the loop that improves AI can close. Socher draws a line between automating research and recursive self-improvement, and says the two get conflated. His target is the second one — a system that improves the way it improves.
What the system has already done
Recursive published early results, and these are the specific claims Socher made about them:
- On optimization tasks, its research system outperformed humans and their agents in under two days 1.
- On the NanoGPT and NanoChat training speed runs, the system trained a model faster than the published records; seeding it with a human expert's starting point still beat starting from scratch 1.
- On a benchmark of GPU kernels — the small programs through which every model touches an Nvidia chip — it placed first on nearly every kernel, without a deep CUDA expert on the team 1.
Socher's commentary on the kernel results is the part worth keeping. When Recursive releases models, he says, they will win because a system inside the company wrote them, and the team will not be the smartest people in the room. He also gave the money version of why training speed matters: shave 10% off a billion-dollar cluster and the saving is $100 million.
Automating this work, he argues, could compress research that currently takes thousands of people and years into weeks.
Where the loop breaks
Reward hacking is the failure mode Socher returns to, and his examples are deliberately small. Ask a system to make a hundred lines of code faster, and the fastest solution is to move the line that stops the stopwatch up to the start. Ask a support organization's agent to raise a satisfaction score, and it can spin up a million bots that rate the service five out of five. In both cases the model did what was written rather than what was meant. The longer the task, the harder that gap is to close, which is why he calls reward engineering the core skill of this kind of research.
He is blunt about the constitution approach. Anthropic's constitution lists creating cyber weapons as a hard constraint, and the OpenAI–Hugging Face incident involved agents doing exactly that. "Clearly, this whole constitution was fake," he said. The alternative he prefers comes from his co-founder's work on rainbow teaming, where an attacker model and a defender model co-adapt: the attacker finds a new angle once the defense absorbs the last one, and both improve without a human writing the rules.
Alignment and personalization pull in different directions, and Socher's answer is that laws set the floor while cultures differ above it. He expects models to be a mirror that amplifies whatever a society already is, and hopes several societies end up with different mirrors.
Regulating applications, and open weights as power
His objection to regulating intelligence is mechanical. Enforcing a limit on what people do with their GPUs would require a government to watch every GPU in the world, which he called a totalitarian arrangement, and he noted that Europe regulated on that basis before it had much of an industry to regulate. The target he prefers is applications: a surgical AI should clear the same bar as a surgeon, and a driving system should be certified before it drives.
Open weights, in his framing, are partly a security position and partly a cultural one. More good actors holding more different models is better even in the worst attack scenario he can imagine, and he describes language models as soft power in the way films are, because they tell stories to children. Recursive, he said, will sign open-source letters and will be relevant in that space soon.
What comes next
Recursive is starting with AI for AI research rather than science. That means cheaper training, cheaper inference that could eventually run on a laptop, better harnesses and sandboxes, and web search, which he calls the most-used tool in every agent. His other company, You.com, sells that search to developers, and he points to its finance benchmark, where he says it scores near 90 against a next-best score in the 70s.
He closed with a taxonomy: ten spaces of intelligence, from visual and physical through communication and social to metacognition, all built from three components he names as prediction multiplied by actions multiplied by goals. The argument for the taxonomy is about distance. On axes like sensor count, bandwidth, memory per unit of mass and speed, the upper bounds are set by physics, and today's systems sit far below them.
References
- 1
- 2Hachette: The Eureka Machine, Richard Socher
hachettebookgroup.com
- 3
- 4arXiv: Darwin Gödel Machine
arxiv.org
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