Google and NVIDIA are both betting on it; after four months since its founding, it’s valued at $4 billion. What gives this AI company that edge?

Original Title: “Google and NVIDIA Bet Big: This $4 Billion Valued AI Company Wants to Cut Scientists Out of the Equation”

Original Author: Hualin Wuwang, GeekPark

In 1956, a group of scientists gathered at Dartmouth to officially discuss whether machines could think for the first time. They optimistically believed they could solve this problem in a summer.

Seventy years later, the question still remains unanswered. But one company, founded just four months ago, has secured $500 million in funding, with a valuation reaching $4 billion—simply because it claims to have found a way to make AI conduct research and evolve on its own.

This company is called Recursive Superintelligence.

Google Ventures GV led the investment, with NVIDIA participating as a co-investor. Their positions in the AI ecosystem need no elaboration. Both companies are making a move simultaneously, betting on a startup that hasn’t even released a product yet. The underlying logic behind this deserves careful analysis.

01 “Removing Humans from the Loop”

Let’s first talk about what Recursive Superintelligence is actually doing.

Founded by former Salesforce Chief Scientist Richard Socher, the core team comes from Google DeepMind and OpenAI. This is not an unfamiliar combination—over the past two years, engineers and researchers leaving top labs to start their own ventures have formed a clear wave.

Richard Socher’s X personal homepage, clearly of interest to Altman | Image source: X

Socher is not the typical “big tech alum” founder. Born in Germany in 1983, he studied under AI pioneer Andrew Ng and NLP authority Christopher Manning at Stanford University. He completed his PhD in 2014, winning Stanford’s Best PhD Thesis Award in Computer Science that year.

Richard Socher is one of the key figures who truly brought neural network methods into the natural language processing field—his early research on word vectors, contextual vectors, and prompt engineering directly laid the technical foundation for today’s BERT and GPT series models. His work has been cited over 180k times on Google Scholar.

In the year he graduated with his PhD, he founded the AI startup MetaMind, which was acquired by Salesforce two years later through a strategic merger. He then led Salesforce AI strategy as Chief Scientist and EVP for several years, overseeing enterprise AI products like Einstein GPT.

After leaving Salesforce, he founded AI search engine You.com in 2020, completed Series C funding in 2025 with a valuation of $1.5 billion. This time, his focus shifted from search to more fundamental questions.

Thinking Machines Lab, Safe Superintelligence, Ineffable Intelligence, Advanced Machine Intelligence Labs… each bears the label of “top XX large model core team,” each telling a story of “next-generation AI.”

But Recursive’s approach is more aggressive than most peers.

Its core proposition is “self-learning AI”—not just making AI better at answering questions, but enabling AI to autonomously complete the entire scientific research process: hypothesize, design experiments, evaluate results, iterate on directions. In other words, it aims to completely remove human researchers from this cycle.

This isn’t a new direction, but Recursive places it within an extremely practical business logic. Today’s top AI researchers earn between $15 million and $20 million annually. If a system can do the same work at lower cost and faster speed, the economic model for cutting-edge research could be fundamentally rewritten.

Investors clearly see this logic. The funding round reportedly was oversubscribed, with a final scale possibly reaching $1 billion.

02 Google and NVIDIA Bet Simultaneously

GV led the investment, NVIDIA followed. This investor combination itself is a signal.

Google’s logic is straightforward. DeepMind has long been a key explorer in “AI for Science,” with AlphaFold solving protein folding problems and AlphaGeometry beating top human competitors in math competitions.

But DeepMind’s path is to use AI to solve specific scientific problems. Recursive aims to do something more fundamental—allow AI systems to autonomously advance the process of scientific discovery itself. For Google, this is both a competitive relationship and a hedge worth betting on.

More importantly, earlier this month, Google announced a multi-generation AI infrastructure partnership with Intel. This indicates that Google’s layout in AI infrastructure is accelerating across the board. Investing in Recursive is a move within this larger strategic game—whoever leads the most advanced models, Google wants a stake.

NVIDIA’s logic is more direct. The core bottleneck for self-learning AI isn’t algorithms, but computing power. If AI is to autonomously run experiments and iterate models, the GPU clusters needed will grow exponentially. By investing in Recursive, NVIDIA is essentially betting on its future orders.

Both companies’ simultaneous moves also send a more subtle signal—that this track may have reached a point where “not investing means missing out.”

03 Is a $4 Billion Valuation in Four Months Reasonable?

When most people first see the figure of $4 billion, their initial reaction is likely “here we go again.”

AI startup valuation bubbles have been a hot topic for the past couple of years. A PDF, a demo, a few slides, and a few top lab names can move hundreds of millions of dollars—this is no longer a myth in Silicon Valley and London, but everyday reality.

But a closer look at Recursive reveals some differences from typical “PPT unicorns.”

First, the credibility of the founding team. Richard Socher has genuine academic credentials in NLP, not just a “big tech halo.” The experience from DeepMind and OpenAI also means they have firsthand exposure to the pain points of cutting-edge research.

Second, the fact that the funding was oversubscribed. This indicates market demand far exceeds supply, and investors are rushing in rather than being persuaded.

However, a $4 billion valuation for a company only four months old with no publicly available product is based on expectations, not reality. Essentially, it’s paying for a direction, not a product or revenue.

This valuation logic is becoming increasingly common in the AI era, driven by investors’ deep fear of “missing the next OpenAI.” Safe Superintelligence also achieved a sky-high valuation with almost no product, with Ilya Sutskever’s name being the most valuable asset.

Recursive is following the same path. This isn’t criticism, just an objective observation.

04 What’s Behind the “Self-Learning” Door?

The name Recursive Superintelligence clearly states the company’s ambition.

“Recursive” means recursion— in computer science, recursion is a function calling itself, a core mechanism in many complex algorithms. In AI research, “recursive superintelligence” hints at a system capable of continuously optimizing itself, spiraling upward.

This concept isn’t new; its extreme version is “intelligence explosion”—once a system surpasses a critical point, it can autonomously accelerate its evolution, ultimately reaching levels of intelligence beyond human comprehension. This has long been a core concern in AI safety.

But what Recursive is doing probably isn’t at that level yet. A more realistic interpretation is that it’s trying to build a system capable of autonomously driving the cycle of scientific exploration, aiming to drastically reduce the human labor and time costs of AI research.

If it truly succeeds, the impact won’t be limited to the AI community. It could mean a new era in drug discovery, materials science, physics, and other fields—where progress can be made rapidly without human scientists’ direct involvement.

Of course, this is still an “if.”

The gap between claiming and achieving has never been linear in the AI industry.

05 The Tide’s Logic

Since late 2025, waves of startups emerging from top labs have been flooding in. Thinking Machines Lab, Safe Superintelligence, Ineffable Intelligence… the list keeps growing.

Recursive is the newest and currently the highest valued among them.

The structural reason is simple—competition among OpenAI, Anthropic, and Google DeepMind has made these top labs increasingly resemble large corporations, with KPIs, compliance, and politics.

Researchers eager to bet on the most radical directions find more freedom in starting their own ventures.

Meanwhile, the capital market is reinforcing this trend. For top researchers backed by big tech, the window to start a company might be the best in history—investors are more willing than ever to pay for “directions.”

The core question of this wave isn’t “who will succeed,” but “what does success look like?”

If Recursive ultimately proves the feasibility of self-learning AI, it will rewrite the fundamental paradigm of AI research. If not, after burning through $500 million, what remains will be another overhyped concept.

Both possibilities are real.

Four months, a $4 billion valuation—this number excites and warns at the same time. The AI arms race has reached a point where even “how to do research” has become a battlefield.

Scientists debated this question at Dartmouth all summer; now, someone plans to answer it with AI—researching AI with AI, rushing toward superintelligence recursively.

Where this path leads, no one truly knows. But it’s clear that Google and NVIDIA have already decided that, no matter where it goes, they cannot be absent.

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