Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI
Quick Answer
Mirendil has secured a multi-year, $100M+ deal with Google Cloud to enhance its self-improving AI research, leveraging TPUs and Nvidia GPUs.
Quick Take
This partnership aims to automate scientific research, potentially revolutionizing fields like medicine and biology.
Key Points
- Mirendil's deal with Google Cloud is valued at over $100 million.
- The partnership provides access to Google’s TPUs and Nvidia GPUs.
- Self-improving AI aims to automate scientific research in various fields.
- Mirendil's technology could enhance AI's ability to tackle complex problems.
- Google's infrastructure offers flexibility in workload management for AI training.
DeepSignal Analysis
What happened
Mirendil has entered a multi-year partnership with Google Cloud, valued at over $100 million, to enhance its self-improving AI capabilities. This collaboration will utilize Google’s TPUs and Nvidia GPUs, enabling Mirendil to automate scientific research in various fields.
Key evidence
- Mirendil's deal with Google Cloud is worth upwards of $100 million, which is approximately half of its seed funding raised at a $1 billion valuation.
- The partnership provides Mirendil access to Google’s TPUs and Nvidia GPUs, which are essential for training its self-improving AI.
- Mirendil aims to automate scientific research, particularly in medicine, biology, and materials science, by developing AI that can iteratively improve itself.
Why it matters
This partnership highlights the growing trend of cloud providers investing in AI startups to secure compute resources. As AI technologies advance, the ability to access powerful computing infrastructure becomes critical for research and development. Mirendil's focus on self-improving AI could lead to significant advancements in scientific research, potentially transforming how discoveries are made in various disciplines.
📖 Reader Mode
~3 min readAI lab Mirendil has signed a multi-year partnership with Google Cloud to source compute capacity for its self-improving AI research, TechCrunch has exclusively learned.
The deal mirrors two trends shaping the AI industry: cloud giants are courting startups with huge infrastructure commitments, and AI companies are snatching up as many compute deals as they can to secure access as they scale.
The deal is worth upwards of $100 million, Mirendil’s co-founder and CEO, Benham Neyshabur, told TechCrunch. That’s roughly half of what Mirendil raised in seed funding at a $1 billion valuation in late June.
The deal gives the startup access to both Google’s TPUs and Nvidia GPUs, as well as managed training clusters with which Mirendil will work on its self-improving AI. The startup hopes its AI will eventually be able to take on the work of an entire frontier AI lab.
Self-improving AI, also known as recursive self-improvement, refers to AI systems that iteratively improve themselves. It’s a concept that major labs like Anthropic, where Mirendil’s co-founders hail from, have been working on. A handful of startups like Recursive Superintelligence and Ricursive Intelligence have also recently sprung up around achieving that goal.
Mirendil believes this process will automate a lot of scientific and AI research, helping scientists make progress in fields like medicine, biology, and materials science.
Neyshabur thinks AI can mimic how human scientists can learn more about new domains, accumulate knowledge and expertise, and gradually improve their performance. “You can have a self-improving AI where you can point a problem at it and it keeps getting better with time,” he said.
“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 self-improving AI, however, requires enormous amounts of computing power. The lab’s co-founder Harsh Mehta said training is increasingly about matching the right workloads to 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 flexibility is central to Google’s AI infrastructure pitch. Amin Vahdat, SVP and chief technologist of AI and infrastructure at Google, said in a statement that AI advancement isn’t just about chip-level performance anymore, “but how we orchestrate entire systems of intelligence and break through the physical constraints of scaling.”
Neyshabur said Mirendil’s software and systems layer help customers get more out of Google’s hardware, giving the cloud giant another potential leg up in the race against its competition. In return, Google gets a strategic partner building frontier recursive self-improving AI — technology that it can eventually shop around to enterprise customers.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
Rebecca Bellan is a senior reporter at TechCrunch where she covers the business, policy, and emerging trends shaping artificial intelligence. Her work has also appeared in Forbes, Bloomberg, The Atlantic, The Daily Beast, and other publications.
You can contact or verify outreach from Rebecca by emailing rebecca.bellan@techcrunch.com or via encrypted message at rebeccabellan.491 on Signal.
— Originally published at techcrunch.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from TechCrunch
See more →
AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors
AI chip startup Etched has achieved a $10.3 billion valuation after a $300 million Series C funding round, led by Sequoia and supported by notable investors like Andreessen Horowitz. The company claims to have developed innovative low-voltage chips for AI inference, significantly enhancing performance and reducing costs, with $1 billion in orders already booked.
