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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
2:00:44
1:04:29
12:34
1:11:49
1:08:44
1:11:58