August 19 & 20, 2026
Hall F, Donald E. Stephens Convention Center, Chicago, USA

Exhibitor News

29 Jun 2026

Omen AI’s plan to optimize data centers is all wet

Omen AI Hall: F Stand: 230

The AI-driven demand for compute power has data centers looking to squeeze more from every rack of GPUs. One consequence? Bacterial outbreaks.

The liquid for liquid-cooled chips is a mixture of water and a substance that inhibits bacteria growth. To run the chips hotter, data center managers can change the mix to include more water, which absorbs heat better, but leads to nasty contamination that clogs the flow. To solve that, they flush the system, which can mean shutting down a rack for five or six hours at a potential cost of millions of dollars.

Omen AI has a solution: A tiny spectrometer that can monitor that fluid health in real time, spotting bacterial growth before it becomes a massive problem. “You’re not risking huge amounts of downtime because you have no insight into what’s going on chemically,” explains CEO and founder Zach Laberge.

Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave.

Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school. (His father and mother, a former Minister of Education for Ontario, were supportive of his plan to carve his own path.)

After that startup shut down, Laberge started Omen in 2024, with the idea of focusing on fluid systems as the key to enabling construction machinery to be smart enough to know when it needed to be fixed. The idea was to replace the time-consuming process of extracting samples and sending them to a lab with real-time awareness. Besides bacterial growth, the device can spot pumps and pumps wearing out if it sees copper or chromium, or seals if it sees silicon.

Caterpillar dealerships were a key early customer for Omen’s heavy vehicles business, but Cat is also a major supplier of gas-powered turbines and generators to provide on-premises power for data centers. It didn’t take long for Omen to see where the wind was blowing.

“That was kind of the transition,” Laberge told TechCrunch. About six months ago, “a lot of the dealerships were saying, ‘Hey, we’re starting to put sensors on our turbines, can you guys do anything on the building side of things?’”

Omen discovered that those buildings are full of fluid, from their HVAC systems to their chip cooling. Spotting a new, fast-growing group of potential customers, Omen began to focus on data centers.

“It’s rare to see such a young founder who has the respect of established, large corporations in a space that moves a bit more slowly,” said Cory Rellas, a partner at Nava Ventures who sits on Omen’s board. “For Omen in particular, much of our diligence came through our introductions with large customers which quickly validated their approach.”

Omen, which has raised $40 million since its founding in 2024, is working with a dozen data center customers as they build out their offering, including TensorWave, a company building an AI compute cloud on AMD chips.

“The fluid running through these massive systems is a critical variable that most of the industry is flying blind on,” Piotr Tomasik, TensorWave’s president, said in a statement. “Omen … see the future of infrastructure exactly the way we do, better monitoring to optimally support compute customers.”

While many organizations rely on mailing fluid samples to labs for insight, Omen isn’t alone in developing on-premises analytics — Pyxis, an established water-monitoring firm, rolled out its data center coolant monitoring product earlier this month.

The key tech advances that unlocked this approach are recent improvements in both optical technologies and signal processing software. “Hardware is just cheap enough that it makes sense to play at scale, and then signal processing lets us make more sense out of the noise,” Laberge said.

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 upward of $100 million, Mirendil’s co-founder and CEO, Behnam 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. 

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