Mirendil Lands $100M+ Google Cloud Deal to Power Self-Improving AI Research
By admin | Aug 06, 2026 | 2 min read
This arrangement highlights two major forces currently shaping the AI landscape: major cloud providers are luring young companies with massive infrastructure commitments, while AI firms are locking in as much computing capacity as possible to fuel their growth. The funding amount represents roughly half of what Mirendil secured during its seed round, which valued the company at $1 billion in late June. As part of the deal, the startup gains access to both Google's TPUs and Nvidia GPUs, along with managed training clusters that Mirendil will use to develop its self-improving AI systems.
The company’s long-term ambition is for its AI to eventually handle the workload of an entire frontier AI lab. Self-improving AI—also called recursive self-improvement—describes systems that continuously refine their own capabilities. This is an area that major research organizations like Anthropic, where Mirendil’s co-founders previously worked, have been actively exploring. Recently, a handful of other startups, including Recursive Superintelligence and Ricursive Intelligence, have also emerged with similar goals.
Mirendil believes this approach could automate significant portions of scientific and AI research, accelerating breakthroughs in fields such as medicine, biology, and materials science. Co-founder Neyshabur draws a parallel to how human scientists expand their understanding of new domains, accumulate expertise, and steadily enhance their performance over time. “You can have a self-improving AI where you can point a problem at it and it keeps getting better with time,” he explained. “How can we have an AI system that keeps doing research, keeps improving its own knowledge and performance when it comes to Alzheimer’s disease,” he continued. “This technology allows us to set goals that are ambitious for AI, and the AI would keep making progress.”
Training such advanced systems, however, demands vast computing resources. Co-founder Harsh Mehta noted that the key to training is increasingly about aligning the right tasks with the right hardware. “These models are really good at working with different workloads and chips, and assigning the right workloads to the right chips,” Mehta said. “[Google] provides multiple kinds of chips […] this flexibility allows us to ultimately mix and match workloads with the right kind of accelerators, and then lower the cost not just for us, but also for our customers using our systems.”
That adaptability sits at the heart of Google’s infrastructure strategy. Amin Vahdat, SVP and chief technologist of AI and infrastructure at Google, said in a statement that progress in AI now goes beyond chip-level performance, “but how we orchestrate entire systems of intelligence and break through the physical constraints of scaling.”
Neyshabur added that Mirendil’s software and systems layer helps customers extract more value from Google’s hardware, giving the cloud provider another edge in its competitive landscape. In return, Google gains a strategic partner working on frontier recursive self-improving AI—technology it can eventually offer to enterprise clients.
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