Electricity is a key raw material for artificial intelligence, but new processing techniques outstrip the ability of data center operators to manage their relationship with the power grid, forcing them to throttle down by as much as 30%.
The problem is significant enough that it caught the attention of Nvidia's leadership. During a keynote at Nvidia's annual GTC conference, CEO Jensen Huang highlighted the scale of waste, stating that there is enormous power being squandered in AI factories and that every unused watt represents lost revenue.
Niv-AI Emerges With $12 Million
Startup Niv-AI has now emerged from stealth with $12 million in seed funding to solve this problem by precisely measuring GPU power use with new sensors and developing tools to manage it more efficiently.
The Tel Aviv-based company was founded last year by CEO Tomer Timor and CTO Edward Kizis, and is backed by Glilot Capital, Grove Ventures, Arc VC, Encoded VC, Leap Forward, and Aurora Capital Partners. The company declined to share its valuation.
Why GPUs Waste So Much Power
As frontier labs operate thousands of GPUs in concert to train and run advanced models, there are frequent, millisecond-scale power demand surges as the processors switch between computation tasks and communicating with other GPUs.
These surges make it difficult for data centers to manage the power they draw from the grid. To avoid being left without sufficient electricity, data centers pay for temporary energy storage to cover surges, or throttle their GPU usage. Both cases reduce the return on investments in expensive chips.
Grove Ventures partner Lior Handlesman, who sits on Niv's board, summed up the urgency by saying that the industry simply cannot continue building data centers the way it builds them now.
How Niv-AI Plans to Fix It
The first step in Niv's roadmap is understanding what's going on. The company is now deploying rack-level sensors that detect power usage at the millisecond level on GPUs that it owns and alongside design partners. The goal is to understand the specific power profiles of different deep learning tasks and develop techniques that allow data centers to unlock more of their existing capacity.
The engineers expect to build an AI model on the data they collect, with the goal of training it to predict and synchronize power loads across the data center — essentially a "copilot" for data center engineers.
The Road Ahead
Niv expects to have an operational system in a handful of US data centers in the next six to eight months. The timing is attractive given that hyperscalers trying to build new data centers are facing difficult land-use and supply chain challenges. The founders see their ultimate product as a missing "intelligence layer" between data centers and the electrical grid.
CEO Timor explained to TechCrunch that the grid is actually afraid of data centers consuming too much power at specific moments. He described the problem as having two sides: one is helping data centers utilize more GPUs and make more of the power they're already paying for, and the other is creating more responsible power profiles between data centers and the grid.
As AI infrastructure spending accelerates globally, the companies that solve the power efficiency bottleneck won't just save money — they'll determine how fast the entire industry can scale. Niv-AI is betting that smarter power management, not just more power, is the real answer.
