The rise of flexible data centers

Catalyst with Shayle Kann43mApril 9, 2026

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AI-Generated Summary

In this episode of Catalyst, host Shayle Kann revisits Varun Sivaram, founder of Emerald AI, to explore the rapidly evolving landscape of data center flexibility in response to the massive energy demands driven by AI. Since their last conversation in 2025, data centers have become an even more dominant force on the grid—projected to account for 94% of PJM’s peak load growth and up to 17% of U.S. electricity by 2030. The episode unpacks how data centers, traditionally seen as inflexible loads, can become flexible assets through compute orchestration and behind-the-meter resources. Sivaram argues that the real bottleneck isn’t on the compute side—where companies like Google and Anthropic are already offering flexible inference tiers—but on the utility side, where differentiated service tiers (like faster interconnection or larger capacity for flexible loads) are still absent. The discussion highlights how workload flexibility, when combined with on-site generation and storage, can create a dynamic 'mini dispatch curve' that optimizes grid performance, reduces costs, and accelerates AI adoption—all while maintaining reliability. Emerald AI’s upcoming 100-megawatt commercial-scale flexible AI factory, set to launch in 2026, is presented as a pivotal test case for this vision. Key takeaways include: 1) Data center flexibility is no longer theoretical—it’s essential for grid affordability, reliability, and speed of deployment. 2) The most powerful incentive for flexibility isn’t cost savings, but faster and larger grid connections. 3) Workload flexibility is the cheapest form of grid support and should be prioritized before relying on behind-the-meter generators. 4) A unified orchestration platform is needed to coordinate multiple stakeholders—from utilities and data center operators to cloud providers and end users. 5) The future lies in hybrid AI factories that integrate compute flexibility with on-site energy resources, creating true grid-responsive infrastructure. The episode ends on an optimistic note, suggesting that with the right policy and market incentives, data centers could become the hero of the energy transition rather than the villain.

Key Takeaways
1

Data centers are projected to consume up to 17% of U.S. electricity by 2030, making flexibility essential for grid stability and affordability.

2

The biggest barrier to data center flexibility is not technical—it’s the lack of differentiated utility service tiers that reward flexibility with faster interconnection or larger capacity.

3

Workload flexibility (e.g., delayed inference, batch processing) is the cheapest and most immediate form of grid support, and should be prioritized over behind-the-meter generation.

4

Emerging compute tiers like Google’s flex and priority inference show that the industry is ready for flexibility—what’s missing is the grid’s ability to respond with equivalent incentives.

5

Hybrid AI factories that combine compute orchestration with on-site energy resources can act as dynamic, grid-responsive assets, creating a 'mini dispatch curve' for optimal performance.

Chapters
0:00
5 min

The Rise of the AI Grid Challenge

Data centers now account for 94% of PJMs projected peak load growth. And by 2030, EPRI forecasts that data centers could use up to 17% of America's power.

Highlight
5:00
10 min

From Inflexible to Flexible: The Paradigm Shift

The conversation explores the historical perception of data centers as inflexible loads and how that is changing. Varun Sivaram explains that while data centers have long been seen as 'always-on' consumers, the reality is that many workloads have inherent flexibility that can be leveraged.

15:00
15 min

The Missing Incentive: Utility Service Tiers

If you agree to curtail a certain amount for certain times, we will interconnect you faster or we will give you a larger interconnection. That's the thing that's missing.

Highlight
30:00
15 min

Workload Flexibility in Practice

There are many different AI workloads... there's a lot of inherent flexibility. It's just a call it 50 or 100 or 200 hours a year that you'd have to curtail.

Highlight
45:00
15 min

The Hybrid AI Factory: Flexibility at Scale

We'll be able to, with one solution, we'll be able to basically get the grid we want and the AI adoption that we want. It's that really rare holy grail solution.

Highlight
High-Impact Quotes
Data centers now account for 94% of PJMs projected peak load growth. And by 2030, EPRI forecasts that data centers could use up to 17% of America's power.
Varun Sivaram5:55
Viral: 90.0
We'll be able to, with one solution, we'll be able to basically get the grid we want and the AI adoption that we want. It's that really rare holy grail solution.
Varun Sivaram42:23
Viral: 90.0
The most critical thing that has to happen is power utilities saying, if you are willing to be power flexible, we want you in our state.
Varun Sivaram48:30
Viral: 88.0
Speakers

Host

Shayle Kann

Guest

Varun Sivaram
Topics Discussed
data center flexibility95%utility service tiers92%hybrid AI factories90%grid reliability and stability90%AI workload flexibility88%compute orchestration85%behind-the-meter resources80%electricity market dispatch curves75%
People & Brands

Varun Sivaram

person

25xPositive

Emerald AI

organization

18xPositive

Shayle Kann

person

12xPositive

NVIDIA

organization

10xPositive

Google

organization

8xPositive

PJM

organization

4xNeutral

Fishtank PR

organization

4xPositive

Energy Hub

organization

4xPositive

EPRI

organization

4xPositive

Anthropic

organization

3xPositive

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