The history of artificial intelligence is a history of constraints.

For years the limit was data, until the internet and a wave of data providers supplied enough labeled examples to learn from. Then it was compute, until GPUs and the cloud made it easier to train the models at scale.

Today, we believe the constraint in AI is the pace of research itself. The industry can only move as fast as a finite number of researchers can dream up experiments, run them, and read the results manually.

It's only a matter of time until AI becomes the primary driver in improving itself. And when a machine can do research as well as the people building it, the domain experts stop being a bottleneck.

We believe the team that gets there first will set what AI can do for medicine, energy, materials science, and everything else that relies on faster discovery.

Today, we're proud to announce that USVC has invested in Recursive Superintelligence, a startup built to produce self-improving AI, alongside GV (formerly Google Ventures), Greycroft, NVIDIA, and AMD.

Most of the progress in AI over the last decade can be traced back to a few dozen people. Eight of them started Recursive.

Recursive’s founding team has laid the groundwork for many of the tools we use today. One co-invented retrieval-augmented generation (RAG), the technique that allows virtually every modern AI system conduct research before it answers a user. Another co-authored the Vision Transformer, a 2020 paper that sits underneath today's image models. Another ran Meta's Fundamental AI Research lab. Another spent his career on AI that rewrites and improves its own code, which happens to share characteristics with the problem Recursive is trying to solve at scale.

It's one of the strongest concentrations of AI research talent we've seen assembled outside the leading AI labs. And the leader who pulled them together, Richard Socher, is a rare breed of researcher-entrepreneur with the credibility to lead a team this dialed in.

In 2013, when the best systems still couldn't reliably tell you whether a movie review was positive or negative, Socher was a Stanford PhD student who thought the field was reading language wrong. The paper he published that year with Christopher Manning and Andrew Ng taught machines to read a sentence as a structure, where "isn't bad" means something different from "bad." It became a foundational step toward machines understanding meaning like humans.

He went on to found MetaMind (acquired by Salesforce in 2016), served six years as Salesforce's Chief Scientist, and built You.com, an AI-first search engine most recently valued at $1.5 billion.

Socher has been working on the foundational problems in AI for more than fifteen years, and so have many of his co-founders. Today, the company is 25+ people across San Francisco and London.

The current playbook for AI progress is well documented: larger models, more data, and more compute. This methodology has produced GPT, Gemini, Claude, coding agents, and image generators. But it also has a ceiling that researchers are bumping up against.

Progress now moves at the speed of how fast human researchers can think of experiments, run them, and read the results. Yet, there are only so many qualified researchers, and they only have so many hours.

Recursive's plan is to hand that loop to AI. Their systems generate their own hypotheses, run their own experiments, and build on their own results, without a person at every step. The first goal is to create a system with the research capability of "50,000 PhDs," pointed at the science of AI itself. Once that engine runs, they intend to aim what they call a "Eureka machine" at the harder quantitative problems behind it: drug discovery, battery chemistry, fusion physics.

If that sounds circular, one helpful precedent is the compiler. Before compilers, people wrote machine code by hand. Then, they built software that wrote the low-level code for them, and the result wasn't fewer programmers. It was faster development, more ambitious software, and whole categories of programs that weren't worth attempting before. AI that does AI research could be the same process applied to science. The constraint on what AI can do for medicine, materials, and energy isn't a shortage of ideas. It's the pace at which ideas can be tested.

USVC is joining Recursive’s recently announced $650 million round at a $4.65 billion valuation, led by GV and Greycroft with NVIDIA and AMD Ventures participating.

This appears to be an extraordinary price for a company founded only one year ago with no publicly accessible product, and it deserves to be addressed directly. We don't think it is appropriate to underwrite Recursive with the same methodology used to evaluate a traditional SaaS business. We underwrote this investment primarily to the potential outcome if its thesis is right.

If the Recursive team can build AI that meaningfully accelerates its own research, the outcome may be more than a successful startup. It could become the foundation for future discovery in most fields. At those stakes, the question stops being whether $4.65 billion is expensive and starts being whether this is one of the few teams capable of getting there. We think it is possible.

USVC's investment thesis starts with the founders. We believe that, at the earliest stages, the best businesses may often appear indistinguishable from mediocre ones on paper. What can separate them is the team who is building and whether they have the specific depth required to keep solving the problem long after the initial insight stops feeling exciting.

We believe Recursive is on that trajectory. It is a rare case where the team alone could justify entering before there's substantial revenue. By the time a research program at this frontier is obvious to everyone, the entry point may look very different than it does today.

The risks are real, though, and worth stating plainly. Recursive is early-stage and working in one of the most technically demanding and competitively intense corner of technology. There's no guarantee the research succeeds on the timeline they project, or that they outpace labs chasing the same goal with larger teams and deeper balance sheets.

We made the investment because we believe self-improving AI may be the most consequential direction in technology, and that this is among the few teams equipped to pursue it. Owning a piece before it's the obvious winner is exactly what USVC was built for.

USVC invested in Recursive, alongside GV, Greycroft, NVIDIA, and AMD.

A company at Recursive's level of investor demand typically has its pick of capital and keeps its cap table short and institutional. We negotiated this allocation in Recursive to make it accessible to our investors.

Historically, individual investors have struggled to get a primary position in such a company at this stage. The people who do are often industry insiders, large venture funds, and strategic backers like the chipmakers whose hardware Recursive runs on.

Our investment is a direct position with no underlying management fee and no carried interest. When this investment creates value, it flows to USVC without an extra layer of cost in between.

To be precise about what you would own through USVC: you would not hold Recursive shares directly. You would own USVC shares, a regulated fund that holds Recursive as one investment alongside other private companies and emerging managers.

What that would give you is economic exposure, through your USVC shares, to one of the most ambitious bets in AI, as part of a broader portfolio.

U.S. investors can get started with as little as $500. No accreditation required.

Learn more: usvc.com

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