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Typically, AI data centers are large, inflexible loads that the grid has to build around, which is one reason utilities take so long to connect them. Emerald AI has designed a “digital brain” that can ramp down, move, or delay computing jobs in a data center on demand, making a data center a flexible asset to the grid. I talk with CEO Varun Sivaram about how the software works, what flexibility costs the compute, why it beats just installing batteries, whether utilities can enforce it, and what it would mean for clean energy.

Chapters:

00:00 – Introduction

03:05 – Temporal, spatial, and resource flexibility

06:26 – What Emerald Conductor touches on site

08:35 – Who decides which workloads can flex

10:55 – Who is liable when a job slows down

13:45 – Who actually signs the contract

14:49 – Larger and faster grid connections

18:02 – Enforcing the flexibility promise

19:05 – What broke in the demos, from bad nodes to slow telemetry

26:41 – What flexing costs the compute jobs

29:21 – Why not just use batteries, and the demand merit order

35:44 – Training, inference, and substation-scale data centers

40:23 – PJM, ERCOT, and legal enforceability

44:50 – Renewables, gas, and consumer bills