Mirendil, founded by two researchers who left Anthropic after barely a year, has raised $200m at a $1bn valuation. The pitch: sell the self-improving AI that the big labs build for themselves and guard from everyone else.

The biggest AI labs share one private conviction. The fastest way to build better AI is to point AI at the problem of building AI. They run that loop in-house, and their terms of service stop outsiders from doing the same. Two of their former researchers have now raised $200m to break the lock.

The startup is called Mirendil, and it announced its seed round on 24 June. The figure is striking for a company with no product: $200m at a $1bn valuation, one of the largest seed rounds the sector has seen. Andreessen Horowitz and Kleiner Perkins co-led it, with Nvidia joining in.

Its founders are Behnam Neyshabur and Harsh Mehta. The pair met at Google in 2019, moved to Anthropic in late 2024, then left in December 2025, soon after the launch of Claude Opus 4.5. Neyshabur, now chief executive, had spent more than five years at Alphabet co-leading reasoning research for Gemini.

Selling the engine the labs keep for themselves

Plenty of lab alumni have started their own shops. Mirendil is aiming at a different layer. It wants to build AI that does the work of an AI researcher: designing experiments, searching for the right settings, evaluating models, and running the next round of training. The idea is to package that as a platform other organisations can point at their own problems.

The more important shift is who that platform is for. Neyshabur frames it as “AI for AI for science,” not AI for science. A university biology lab could use it to build a drug-target model without a machine-learning team. He cites a model that predicts a person’s risk of Alzheimer’s as the kind of thing a customer might make. The work that takes labs months, the pitch goes, compresses into days.

The detail that gives the thesis its edge is the lock it tries to pick. As of May, Anthropic said its Claude model wrote more than 80% of the company’s own code. Yet its terms of service forbid using its tools to build competing services. Anthropic told the Wall Street Journal the policy is standard among model providers and helps keep frontier AI out of foreign adversaries’ hands.

That gap is the business. As Andreessen Horowitz’s Matt Bornstein put it to the Journal, the labs are being “rational economic actors” when they deny customers the means to supercharge their own models. “Structurally, there has to be an independent company,” he said. Mirendil wants to be it.

A $200m bet on recursive self-improvement

The technical name for this loop is recursive self-improvement, and it is contested ground. Anthropic has pointed to it as a potential danger, on the theory that a model rewriting its own code without oversight could slip beyond human control. The founders disagree. They call it the “shortest path” to faster science, and a problem that can be supervised rather than avoided.

That argument lands in a tense moment for Anthropic itself. The company recently pulled access to its most powerful Mythos and Fable models after the Trump administration imposed export controls. The same week, critics accused it of quietly degrading answers about AI development. Into that backdrop steps a startup whose entire reason to exist is handing that capability to others.

The team is small and senior. Mirendil runs on about 20 researchers and engineers drawn from Anthropic, xAI, Google DeepMind, and OpenAI. The founding group also includes Shayan Salehian, an early member of xAI, and Tara Rezaei, a 23-year-old MIT graduate. There is a neat irony in the line-up. Mehta built the first version of Anthropic’s internal AI-research platform, at times as a team of one. Now he is rebuilding that idea to sell it. The Information first reported some details of the round.

The money is chasing a structural bet

The valuation makes sense only against the flood of capital around it. AI took close to half of all global venture funding in 2025, some $202bn, up more than 75% on the year, according to Crunchbase. The AI infrastructure market alone ended 2025 near $337bn in revenue and is forecast to reach $1.2tn by 2030.

Mirendil also sits inside a specific cluster of lab spinouts, and the comparison flatters it. Ilya Sutskever’s Safe Superintelligence has raised $6bn at a $32bn valuation. Mira Murati’s Thinking Machines Lab took $2bn at $12bn. Both, like Mirendil, launched with no shipped product, on the strength of their founders alone.

The closer rhyme is Periodic Labs, another Andreessen Horowitz bet that raised $200m to aim AI at materials science. Mirendil is pitching the layer beneath that: the research-automation engine such companies would themselves run on. It is a harder thesis to prove, and a bigger prize if it holds. Venture firms have poured money into the field for two years, and this is their next structural wager.

The democratisation pitch

There is an ideological pull, too. The founders talk about prising AI research out of a few labs and handing it to thousands, the same democratising argument that has followed every open challenge to Silicon Valley’s frontier. Whether that vision survives contact with a real product is another matter, and it lands amid a steady run of nine-figure AI infrastructure rounds.

For now, Mirendil has a name from Tolkien, a billion-dollar valuation, and a model and product it says will arrive in the coming months. If AI can truly automate its own research, the advantages today’s labs hold, thousands of staff and years of accumulated knowledge, start to look less permanent. Whether Mirendil is on time or five years early is exactly what the $200m is there to find out.