Data Centers Don't Waste Water. An Ex-Google Engineer Explains.

Could today's data centers produce twice the AI without building more? Former Google data center lead and DeepMind alum Jim Gao, CEO of Phaidra, talks about power, cooling, water use and the metric that matters most now: tokens per watt.

Data centers are at the center of this month's news. Long before anyone called them AI factories, Jim Gao was designing the cooling systems inside Google's data centers. Back then, the whole team was about 80 people, and a 5-megawatt shipping container counted as cutting edge.

In this episode of WaveSession, recorded at the Yotta Conference in Las Vegas, Gao joins Jeff and Ilya. They talk about how data centers grew from containers to gigawatts, why power, cooling and compute need to run as one system, and why he believes existing infrastructure could produce twice as many tokens.

Gao takes on the public perception problem head-on, including being heckled at New York Climate Week. He argues that data centers have become a physical stand-in for people's anxiety about AI. He explains why modern closed-loop facilities use far less water than most people assume. He also points to towns like Pryor, Oklahoma, and Quincy, Washington, that data centers helped revitalize.

He also goes back to his early days at Google, where he became responsible for the company's PUE, the core measure of data center efficiency. In 2012, fresh off Andrew Ng's Coursera machine learning course, he snuck an extra slide into a quarterly review. It pitched using ML to make sense of the tens of thousands of data streams in a single data center. That idea led him to DeepMind and eventually to co-founding Phaidra.

From there, the conversation turns to why Jensen Huang's "AI factory" framing is right, and why tokens per watt is now a business imperative. Power, cooling and IT have historically run in near-total isolation, a setup Gao calls "bonkers" at today's scale. Working with NVIDIA, Phaidra's AI agents predict thermal spikes from AI workloads and cool ahead of them, eliminating 80% of those spikes. He explains how raising liquid cooling temperatures by 10°C can cut chiller energy in half, and how dynamic oversubscription could turn stranded cooling capacity into compute.

This episode covers:

Takeaway: Power is now the limit on how much intelligence we can produce. The biggest gains won't just come from building more. They'll come from running power, cooling and compute as one intelligent system.

Did the water numbers surprise you? Tell us in the comments.

About the Guest
Jim Gao is the co-founder and CEO of Phaidra, which builds AI agents for AI factories and mission-critical industrial facilities. He spent over a decade at Google as a mechanical engineer responsible for the company's global PUE. He pioneered the use of machine learning in data center operations before joining DeepMind. 

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