According to many true-believers, the biggest promise of AI is its potential to accelerate scientific discovery; once-in-a-generation breakthroughs could one day become routine, thanks to algorithms. By extracting patterns from troves of data far too vast for any human mind to fathom, so the thinking goes, AI scientists could eventually help solve some of humanity’s most dire technical problems: climate change, cancer, even—according to diehard transhumanists—death itself.
But science is a crowdsourced enterprise, dependent upon a global community of researchers who can freely access and build off each other’s work. The AI industry, in contrast, is currently dominated by a handful of research labs whose proprietary code is closed off from one another, and from the wider world.
A fast-rising startup called Mirendil is now hoping to bridge that gap between scientific discovery and frontier AI access.
The company recently raised $200 in seed funding, bringing its total valuation to $1 billion. Funding was provided by VC firms Andreessen Horowitz and Kleiner Perkins, as well as by Nvidia. Based in downtown San Francisco, it currently has a technical staff of around twenty researchers. Its website features several job postings with starting salaries of up to $500,000.
The startup—whose name means “friend of precious things” in Elvish, adding to a growing list of tech companies names inspired by The Lord of the Rings—has set out to build something that’s long been a sought-after technical goal, and occasionally a source of anxiety, within Silicon Valley: AI that can build increasingly more capable versions of itself, a process known within tech circles as recursive self-improvement.
All AI and machine learning algorithms fundamentally have some capacity for self-improvement, since they’re trained to learn from their mistakes over time and adjust their outputs accordingly. But some of the latest and most advanced models have taken that process to a new level by largely replacing human software engineers and revising much of the code it runs on. It points to a possible future in which each new version of a model builds its own successor, a feedback loop that could either usher us into a post-scarcity utopia or a hellscape dominated by misaligned superintelligent AI overlords, depending on whom you ask.
Even Anthropic and OpenAI, the two current frontrunners of the AI race, have publicly called for the formation of a global oversight committee to keep tabs on recursively self-improving AI, and if it should ever become necessary, to (somehow) enforce a unilateral slowdown to prevent humans from losing control. Two of Mirendil’s cofounders, Behnam Neyshabur and Harsh Mehta, previously worked at Anthropic; they left the company in January.
Microsoft, meanwhile, is trying to turn the trend towards recursive self-improvement into a sales pitch for enterprise AI. In an X post earlier this month, company CEO Satya Nadella wrote that “agentic systems that improve over time” could soon become an important asset for businesses. “I think of it as a hill climbing machine,” he wrote.
Anthropic’s Fable 5, which was publicly released earlier this month, only to be swiftly shut down in response to an order from the Trump administration, comes with security guardrails that prevent it from responding to queries on potentially dangerous topics like cybersecurity and chemistry. Its restrictions were so stringent, however, that it would often refuse to engage with harmless scientific research questions.
Mirendil believes the problem isn’t recursively self-improving AI per se but rather the fact that access to such frontier capabilities is currently gated by a small number of deep-pocketed labs, like Neyshabur’s and Mehta’s former employer. The company is therefore setting out to build self-improving AI systems specifically for open source developers.
“Today, any lab trying to use AI in drug discovery, chemistry, biology, or robotics must also become a frontier AI lab,” Mirendil writes on its website. “Our goal is to democratize frontier AI R&D and make it widely accessible. Our work will accelerate every scientific and technological effort that depends on AI.”
The idea, in other words, is to put frontier-level recursively self-improving AI into the hands of as many independent laboratories as possible, with the end goal of supercharging scientific progress. “The most direct path to maturity and massive impact for the AI industry is to let engineers and researchers outside the labs to do real AI work, i.e. to push the frontier in their own domains of expertise,” Andreessen Horowitz wrote in its Mirendil investment announcement. “Call it vibe research.”