In this episode of WaveSession #1, Thrun joins Jeff and Ilya for a wide-ranging conversation on physical AI, the collapse of the hardware-software divide, and why the next decade of innovation will look nothing like the last.

Thrun opens by revisiting the 2005 DARPA Grand Challenge, where his team's self-driving car, Stanley, navigated 130 miles of Mojave Desert terrain using little more than a trunk-mounted computer and roughly 100 parameters, a stark contrast to today's trillion-parameter models. He explains how that early work exposed the hard tradeoffs of physical AI: unlike a hallucinating chatbot, a hallucinating self-driving car can kill someone, which is why reliability, not just capability, became his team's obsession. That same thread carries into his account of building Waymo, now 17 times measurably safer than human drivers.

From there, the conversation moves into Thrun's years running Google X, where he helped incubate Google Brain, Google Glass, and a string of moonshot projects built on the belief that technology follows an exponential curve and pessimists are almost always wrong. He's candid about failure here: Google Glass didn't fail because the underlying vision was wrong, he argues, but because the form factor and go-to-market were wrong, a distinction he credits to a passage in Mark Pincus's book about how entrepreneurial intuition tends to be right even when specific ideas are wrong. Thrun connects this directly to his current obsession: building an "AI engineer" capable of collapsing the fragmented, over-engineered way hardware and software teams work today into a single, radically more efficient design process.

The conversation also digs into what AI means for careers, education, and the workforce. Thrun argues that specialists, lawyers, pilots, accountants, are the most exposed to automation, while generalists who can adapt across domains will thrive, a shift he says is already visible in his own companies, where engineer-to-product-person ratios have flipped. He's equally direct about the societal stakes: this isn't a technology revolution, he says, it's a societal one, requiring redistribution, dialogue, and deliberate effort to keep opportunity from concentrating in too few hands, grounded in his experience scaling Udacity across the Middle East, including a measurable GDP impact in Egypt.

Toward the end, the conversation turns personal. Thrun talks about his hiring philosophy (he wants to hear what you screwed up, not just what you're proud of), his belief that blame is corrosive and self-reflection is a competitive advantage, and his own admission that even after decades of building companies, he still feels like an eager undergraduate. He closes with two practical rituals for staying oriented amid constant change: "non-pessimist Tuesday" and "no-blame Thursday."

This episode covers:

• Building Stanley and winning the 2005 DARPA Grand Challenge with roughly 100 parameters
• Why physical AI has almost zero tolerance for error compared to language models
• Inside Google X: moonshots, Google Brain, and why Google Glass failed as a product but not as a concept
• Why specialists are most exposed to automation while generalists will thrive
• Why Thrun calls this a societal revolution, not just a technological one
• His hiring philosophy: asking candidates what they screwed up, not what they're proud of
• "Non-pessimist Tuesday" and "no-blame Thursday": simple rituals for staying oriented amid constant change
• And more!

Takeaway: Two decades at the frontier of AI taught Thrun the same lesson over and over: intuition is usually right even when the specific idea is wrong, reliability matters more than raw capability, and the real risk of this moment isn't what AI can do, it's who gets left out as it reshapes everything.

About the Guest

Sebastian Thrun is a serial entrepreneur, AI researcher, and educator who has spent over two decades at the forefront of applied artificial intelligence. He led the Stanford team that won the 2005 DARPA Grand Challenge, founded and led Google X (Alphabet's "moonshot factory"), and co-founded Waymo, now one of the world's leading autonomous vehicle companies. He also founded Udacity, an online education platform focused on making technology training accessible globally, with measurable economic impact in markets including Egypt, Saudi Arabia, and Palestine. Thrun previously served as a professor at Stanford University and Carnegie Mellon University, and continues to work hands-on building AI-driven robotics and engineering tools.

Chapters
0:00 — Opening: AI Access for Everyone

0:52 — The Stanley Era: Early Autonomous Vehicles

5:25 — From Chauffeur to Waymo: Physical AI Safety

7:54 — Google X & the Moonshot Factory

12:21 — Transforming Organizations: AI 1.0 vs. 2.0

15:33 — The 20x Productivity Shift in Software

17:20 — Democratizing Education & Commoditized Intelligence

22:30 — Google Glass: Intuition, Failure & Project Wingman

29:21 — Specialists vs. Generalists in the AI Era

33:55 — Advice for New Engineers & Graduates

38:21 — Intuitions vs. Ideas: Lessons from Failure

42:39 — Frontier Models Eating Vertical SaaS

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

• 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"

The people setting the direction of the open AI ecosystem are making calls right now. Founders, researchers, builders, capital providers. They're deciding what gets built, how it scales, and who gets a preview of what's coming next.

wavesession is where those conversations happen.

Each episode moves across the stack. From the chip to the grid, the software to the firmware, the language to the physics underneath it. These are the people making those decisions in real time.

This isn't a highlight reel. There are no polished talking points. It's an honest account of what it actually takes to build open AI infrastructure at this scale, where the technical, financial, and human problems don't come with a playbook.

This is a technological revolution happening in the open. It comes with consequences we're all going to live with. wavesession is an invitation to listen in, challenge what you hear, and help shape where it goes.

New episodes coming weekly, starting September 30th.