Mirendil signs $100 million-plus Google Cloud deal to build AI that improves itself

Key points

• Mirendil, an AI startup, has signed a deal worth more than $100 million with Google Cloud to access computing power for its research.
• The deal covers access to Google's TPUs (Google's own custom chips) and Nvidia GPUs (specialised processors used to train AI models), giving Mirendil flexibility to run different workloads on different hardware.
• Mirendil raised seed funding at a $1 billion valuation in late June 2026, making the Google deal worth roughly half that total raise.
• The startup is building what researchers call recursive self-improvement, meaning AI that can keep getting better at a task without humans rewriting it each time.
• Mirendil's co-founders previously worked at Anthropic, the AI safety company behind the Claude assistant.

A little-known AI startup called Mirendil has quietly locked in one of the larger cloud computing commitments seen among early-stage AI companies. First reported by TechCrunch, the multi-year deal with Google Cloud is worth more than $100 million.

That figure sits alongside a striking data point: Mirendil raised seed funding at a $1 billion valuation in late June 2026, meaning this single infrastructure contract accounts for roughly half of everything the company raised. It's a pattern we've been tracking: when we covered Recursive Superintelligence's $410 million compute deal with Amazon on 28 July 2026, the same logic applied, committing most of the raise to raw infrastructure before shipping a product.

What is Mirendil actually building?

Mirendil is working on self-improving AI, sometimes called recursive self-improvement, which refers to AI systems that can refine their own performance over time without humans updating the code after every step.

A standard AI tool is fixed at the moment it ships. You can ask it questions, but it doesn't get better at your specific problem the longer it runs. Mirendil wants to build systems that behave more like a scientist who keeps experimenting and learning. Point the system at Alzheimer's disease research and it should keep making measurable progress on its own.

"How can we have an AI system that keeps doing research, keeps improving its own knowledge and performance on Alzheimer's disease?" Mirendil CEO Benham Neyshabur said. "This technology allows us to set goals that are ambitious for AI, and the AI would keep making progress."

Neyshabur and co-founder Harsh Mehta both previously worked at Anthropic, where recursive self-improvement is an active area of study. Other startups are chasing the same idea: Recursive Superintelligence and Ricursive Intelligence have both emerged recently around the same goal.

Why does this need so much computing power?

Building AI that teaches itself is enormously expensive. Training an AI model, the process of feeding it data until it learns patterns, demands huge amounts of specialist computing hardware.

The Google Cloud deal gives Mirendil access to two chip types: TPUs, Google's own custom processors built for AI training, and Nvidia GPUs, the industry-standard chips most AI research relies on. Mehta said the ability to mix and match workloads across chip types should lower costs for Mirendil and, eventually, for its customers.

Google gets something in return. Mirendil's software layer is designed to help customers squeeze more performance out of Google's hardware, making Mirendil a useful partner for Google when pitching enterprise clients who want advanced AI without building the infrastructure themselves.

Should you worry about self-improving AI?

Mirendil has no consumer product. Near-term customers will be large research institutions and enterprises, not individuals.

The longer arc matters, though. If self-improving AI works as its proponents claim, it could accelerate research in medicine, biology, or materials science, fields where progress is currently slow and expensive. Faster drug discovery or a better understanding of diseases like Alzheimer's would affect everyone eventually. That's the bet, at least. Whether the systems Mirendil builds actually compound in the way a human scientist does is something we won't know until they're running at scale.

For now, watch for other early-stage AI startups announcing similarly large cloud commitments. Securing computing capacity has become as important to AI companies as raising cash.