Podcast Summary: a16z Show

Episode: The 80-Year Bet: Why Naveen Rao Is Rebuilding the Computer from Scratch
Date: December 8, 2025
Host: Andreessen Horowitz (A16Z, Matt Borenstein)
Guest: Naveen Rao – Co-founder and CEO, Unconventional AI

Overview

This episode features a deep and visionary conversation between Matt Borenstein and Naveen Rao—a leading AI entrepreneur who previously founded Nirvana (acquired by Intel) and Mosaic, and was head of AI at Databricks. Rao is now launching Unconventional AI, a startup that aims to fundamentally rethink computer architecture, betting instead on analog approaches inspired by biology and neuroscience, to address a looming compute and power crisis driven by AI’s exponential demands. The discussion explores why Rao believes now is the time for this radical “80-year bet,” the inefficiencies of current digital computing, the case for analog hardware, and the generational opportunity to transform the future of intelligence and technology.

Key Discussion Points & Insights

1. The Case for Rebuilding Computers (00:20–05:06)

• Not Just Another Chip Company: Rao clarifies that Unconventional AI isn’t “just a chip company,” but is re-examining the very principles of how learning and intelligence manifest in physical systems.

  • “Most of what we're doing is at the beginning is theory and really kind of looking at first principles of how learning works in a physical system.” (03:37, Rao)

• Energy Crisis in AI: The energy demands of AI data centers are unsustainable—currently using 4% of the US power grid, projected to increase dramatically. The bottleneck isn’t power generation, but computing paradigms.

  • “Our AI data centers now consume 4% of the entire US power grid … We need 400 more gigawatts in the next decade.” (00:52, Matt Borenstein)

• Why This Matters: Biological brains are orders of magnitude more energy-efficient and dynamic than silicon-based computers. Rao’s passion is translating these lessons from biology into new hardware designs.

  • “Biology is exquisitely efficient … When you're chilling out, you don't use much energy, but you're still aware of other threats...” (03:37, Rao)

2. Digital vs. Analog: Rethinking the Foundations (05:06–10:33)

• Historical Context: Digital computers won out because they were easier to scale and manufacture, even though analog systems were often more efficient.

  • “We went that direction actually very early on because we couldn't scale up computation...analog computers … worked really well, they were very efficient, but they couldn't be scaled up because of manufacturing variability.” (06:30, Rao)

• Analog Computing Defined: Computing by direct physical analogy to a process (like wind tunnels), rather than by abstract numeric simulation. Analog is inherently more efficient for certain problem classes.

  • “You're effectively using the physics of the underlying medium to do the computation.” (07:54, Rao)

• The Brain as Model: The human brain (20W) and mammalian brains are existing proofs of highly efficient, dynamic computation that current digital architectures cannot approach.

  • “Intelligence is the physics. They're one and the same. There's no, you know, OS and ... API …” (09:25, Rao)

3. Why Now? Power, Scale, and AI’s Unique Demands (10:33–14:36)

• AI’s Power Crisis: The growth of AI workloads is outpacing existing infrastructure. Even if more power is built, transmission grids are not ready.

  • “The US is about 50% of the world’s data center capacity [putting] about 4% of the energy grid into those data centers…we need 400 gigawatts additional capacity over the next 10 years to power the demand for AI.” (10:40, Rao)

• Workload Suitability: Analog/dynamical approaches are ideal for intelligence and perception problems—tasks brains are especially good at, like integrating numerous “fuzzy” real-world inputs and making high-precision decisions.

  • “I actually don’t see it as ... digital or analog. It doesn't work like that. I think there are certain types of workloads that are amenable to these analog approaches, especially ... dynamical system dynamics...” (12:13, Rao)

• Neural Precision in Messy Environments: Human and animal brains make very precise decisions in highly dynamic and unpredictable environments—a feat current digital computers struggle to emulate.

  • “Steph Curry, when he shoots a ball, is never going to shoot it under ideal circumstances in a game...Brains are exceptionally good at this.” (14:00, Rao)

4. The Path to AGI and the Role of Causality (16:06–18:23)

• Dynamics and Causality: Rao speculates that AGI (Artificial General Intelligence) will require systems with inherent time dynamics and causal reasoning—a property of brains, much less so of current digital AI.

  • “Anything where the basis is dynamic, which has time and causality as part of it, will be a better basis than something that's not.” (16:33, Rao)

• Innate Causality in Intelligence: Human intelligence may be built upon this kind of dynamic causal understanding.

  • “Children kind of innately understand causality in some ways ... So I think there's something innate about the way our brains are wired, built out of primitives that do understand causation.” (17:50, Rao)

5. Industry Landscape and Collaboration (18:23–20:15)

• Big Tech as Partners, Not Just Competitors: Rao envisions working with manufacturing partners like TSMC and potentially collaborating—even with giants like Nvidia—rather than direct competition.

  • “TSMC is absolutely going to be a partner ... Google, Nvidia, Microsoft, all these guys are … at the forefront.” (18:36, Rao)

• Building at Scale: New analog paradigms must be manufacturable at scale to be viable—proof-of-concept is not enough.

  • “We need to have something to say, okay, go build 10 million of these things.” (18:41, Rao)

6. Motivation, Culture, and Advice for Innovators (20:15–28:37)

• Personal Drive: Rao is driven both by the “dopamine hit” of building functioning hardware and the potential generational impact.

  • “When you work on a piece of hardware and you turn that thing on, that's a big dopamine hit...For me personally, we have this opportunity now that we can really change the world of computing and make AI ubiquitous. I'm the opposite of an AI doomer. I think AI is the next evolution of humanity.” (20:25 & 21:06, Rao)

• Building Teams for Ambitious Problems: He seeks polymaths and risk-takers—engineers, theorists, and builders willing to traverse the stack from math theory to circuit design, combining skills rarely found together.

  • “We need theorists … system architecture level … people actually physically building this stuff, like analog circuit people … it's going to be probably one of the larger, maybe the largest analog chip people have ever built.” (24:06, Rao)

• Advice to Young Talent: Startups give broader exposure and skills than big tech roles; Rao’s own career full-stack journey enabled Unconventional’s vision.

  • “The reason I can think across the stack is because I did all those things very early in my career ... in big companies, you get hired to do a thing and you do that thing over and over again ... being really good at one thing is probably less valuable than being ... slightly good at a lot of things.” (26:17, Rao)

• Empowering High-Agency Teams: Rao emphasizes giving staff ownership and space to try new things.

  • “What decisions can I make as a leader to increase agency of the org overall? ... If more people have agency ... we will do better.” (27:42, Rao)

Notable Quotes & Memorable Moments

• “I think AI is the next evolution of humanity. I think it takes us to a new level, allows us to collaborate and understand the world in much deeper ways.” (00:00, Rao)
• “Our AI data centers now consume 4% of the entire US power grid … we need 400 more gigawatts in the next decade just to keep up.” (00:52, Matt Borenstein)
• “Brains are existence proof.” (22:28, Rao)
• “You need some crazy in there. So it's okay, I'm fine with that.” (23:54, Rao)
• “This is like an opportunity to do something that is generationally will be felt...if we are successful here, the world will not forget this.” (28:39, Rao)

Highlighted Timestamps

• 00:20–05:06: Why digital computing is hitting physical/energy limits and the rationale to start Unconventional AI
• 09:25–10:11: Biological brains as the ultimate analog intelligent systems
• 11:33–13:34: The scale of AI’s impact on the power grid; urgency in changing computing paradigms
• 16:06–17:50: The necessity of dynamics and causality for real intelligence and AGI
• 20:25–21:06: The thrill of hardware innovation and RAO’s drive for generational impact
• 24:06–25:17: The kind of talent Unconventional AI is seeking—full-stack, theory-to-hardware visionaries
• 28:39–29:10: Rao’s closing reflection on aiming for a generational, history-book impact

Tone & Conclusion

The conversation is visionary, candid, and technically ambitious—conveying both urgency about AI’s energy crisis and excitement about radical innovation. Rao brings a blend of rigor, daring, and optimism, championing open-ended research with real-world impact and empowering those bold enough to think across boundaries. For those who haven’t listened, the episode is a masterclass in rethinking the fundamentals and an invitation to help invent the next epoch of computing.