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Omen AI adds liquid know-how in $16 million Aquatic Labs acquisition

Two startups tackling the data center water problem are joining forces in a $16 million deal.

U.S. data centers consume substantial amounts of water to cool their chip operations, with estimates suggesting it could triple in the next five years. This presents a costly and politically challenging issue for investors pouring billions of dollars into AI infrastructure scaling. To address this, Omen AI, an AI startup founded in 2024, developed a real-time sensor to monitor cooling fluids more effectively.

After raising $31 million in a June Series A round, Omen is now acquiring Aquatic Labs, a competitor, in a $16 million cash-and-stock deal. Both companies were developing real-time water sensors, with complementary technologies: Omen's sensors analyze elements in the water, while Aquatic Labs tests its alkalinity. This combination provides a more comprehensive understanding of the water and fluid system.

Omen's sensors will initially monitor chips consuming 250 to 300 MW of power by the end of the year, with the goal of equipping 1 GW of chips by the end of 2027. This technology promises to improve uptime for data centers, as fluid system issues often result in shutdowns and significant costs. The founders, Zach Laberge and Allan Adams, bring diverse backgrounds to the combined team, with Laberge being a high school dropout turned entrepreneur and Adams having an academic background in physics and ocean sensing.

Together, they aim to integrate their technologies into one sensor, expected to launch by early next year. The added value lies in optimizing fluid usage, minimizing costs, and ensuring the safe and responsible management of water in data centers. Despite recent skepticism surrounding AI infrastructure investment, the Omen team remains confident in the market's potential, as their work is focused on existing data center projects already financially secured.

Written by urgent.news from TechCrunch's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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