We spent two years worrying about GPU supply. That worry is over, and almost nobody has caught up to what replaced it.
In the premiere episode of wavesession, Jeff and Ilya cut through the noise around AI's future and land on the one constraint nobody's pricing in: energy. Not silicon. Not compute. Power. This conversation makes the case, in detail, for why the grid, not the chip, is now the hard ceiling on how far AI can scale.
The thesis starts with a simple historical pattern: energy intensity has always tracked human prosperity. Jeff and Ilya argue that compute, the ability to deliver intelligence at scale, is becoming its modern successor. They trace how decades of deferred maintenance on the U.S. power grid, much of it running on infrastructure from the 1950s and 60s, is now colliding head-on with a demand curve nobody designed for. A decade ago, a 30-megawatt data center was considered large. Today's hyperscale campuses are pushing past 100 megawatts, built for a load the grid was never built to carry.
There's a culture problem underneath the technical one, too. Utilities run on a "five-nines" reliability mindset: slow, cautious, built for stability over decades. Data center operators run on mission-critical, can't-go-down urgency. Two industries, two completely different clocks, now being forced to speak the same language at the exact moment it matters most.
This episode covers:
- Why chip supply solved itself while energy infrastructure never caught up
- The historical link between energy abundance and prosperity, and why compute may be its modern equivalent
- A Duke study showing the U.S. grid could free up roughly 100 gigawatts of hidden capacity if data centers curtailed demand by just 2.5 to 3% at peak, no new power plants required
- How hyperscalers kept power demand flat for nearly a decade during the cloud buildout through efficiency gains, and whether that pattern can repeat for AI
- The moment betting on unproven AI infrastructure stopped looking risky and started looking inevitable
- Why efficiency and sustainability have to be built in now, not deferred to some future AGI breakthrough that may never show up
- And more!
Takeaway: The AI industry solved its chip problem. It hasn't solved its power problem, and that gap, not GPU supply, is what will actually determine how far and how fast AI can scale from here.
Chapters
Chapters
- 0:00 — Cold Open: Kevin O'Leary & the AI Hype Problem
- 1:54 — The Computing Stack: GPUs, CUDA & FPGAs
- 10:50 — The FPGA to GPU Pivot & Going All In on AMD
- 16:31 — AI's Impact on Society & Public Fear
- 22:03 — Energy Intensity as a Prosperity Indicator
- 24:37 — The Power Bottleneck: GPUs Abundant, Power Scarce
- 25:22 — Grid Complexity & Data Center Curtailment
- 33:44 — Efficiency Opportunities in AI Data Centers
- 43:01 — Ilya's Vision: Building the Fourth Utility
- 44:11 — Jeff's Vision: Bridging the Adoption Gap
- 50:10 — From "What Can We Compute" to "What Should We Compute"
