Turning AI Data Centers Into Grid Allies with Emerald AI

Feb 10, 2026

Varun Sivaram, founder and CEO of Emerald AI and energy infrastructure veteran, builds software that makes AI data centers power-flexible. He discusses treating data centers as controllable loads, shifting compute across time and place, coordinating with batteries, and a Phoenix field trial showing large grid relief without breaking compute.

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INSIGHT

Treat Data Centers As Flexible Grid Assets

Emerald AI treats AI data centers as controllable, power-flexible assets rather than fixed loads on the grid.
This reframing lets data centers support grid stability and connect faster without huge new buildouts.

ANECDOTE

Founder’s Shift From Supply To Demand

Varun shifted from building large energy projects to focusing on demand-side solutions after seeing limits to just adding supply.
He founded Emerald AI to use compute flexibility to squeeze more value from existing grids.

INSIGHT

Conductor Connects Grid And Compute

Conductor is a software layer that connects power grids and AI data centers to co-optimize both networks.
It enables data centers to modulate consumption based on grid needs while keeping compute customers satisfied.

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Varun Sivaram is Founder and CEO of Emerald AI, a company building software that makes AI data centers power flexible. As AI data centers become one of the fastest-growing sources of electricity demand, grid constraints are emerging as a critical bottleneck for compute deployment.

In this episode, the conversation focuses on why power availability — not GPUs — is increasingly the limiting factor for AI. Data centers concentrate massive electrical loads in specific locations, creating grid stress, long interconnection delays, and rising electricity costs for surrounding communities. Traditional grid expansion alone is too slow to meet near-term AI demand.

Emerald AI’s response is to treat AI data centers as flexible loads rather than fixed ones. Its software coordinates compute with grid conditions by shifting workloads across time, geography, and on-site energy resources like batteries. The episode walks through real-world demonstrations, including a published field trial showing a 25% power reduction during grid stress without breaking compute performance. The discussion frames flexible load as one of the fastest ways to unlock power for AI while improving grid stability.

Episode recorded on Feb 2, 2026 (Published on Feb 10, 2026)

In this episode, we cover:

(0:00) Intro
(1:36) What Emerald AI is and how it works
(6:41) Varun’s background and why he founded Emerald
(10:59) Emerald’s software for power-flexible data centers
(19:04) The three types of flexibility: temporal, spatial, and resource
(23:29) How much control customers give Emerald
(28:20) Coordinating compute with on-site energy like batteries
(31:27) Off-grid vs. grid-connected data centers
(35:39) Why exiting the grid creates political and systemic risk
(37:12) Emerald AI’s open roles

Links:

Varun Sivaram on LinkedIn: https://www.linkedin.com/in/varunsivaram
Emerald AI: https://www.emeraldai.co/
AI data centers as grid-interactive assets paper

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*Editing and post-production work for this episode was provided by The Podcast Consultant