CHAPTERS
Trailer: AI as humanity’s next evolution and the “new chip” premise
The episode opens with a bold framing: AI as a transformative step for humanity, and Naveen Rao as a builder who’s repeatedly been early to major waves. The trailer tees up the central bet—rethinking compute hardware to make AI more efficient and more ubiquitous.
Why hardware matters for AI workloads: Rao’s “shrink it, speed it up” mindset
Rao explains how his early career trained him to translate algorithms into efficient, real-time systems by building specialized hardware. That background shapes his view that the right hardware substrate can unlock entirely new applications and form factors for AI.
Full-stack before it was trendy: why he built software inside “hardware companies”
Rao argues the boundary between software and hardware is artificial—just a line drawn around what’s configurable. He describes an older definition of “full stack” spanning silicon, architecture, low-level software, and applications, and why that breadth enables better system design decisions.
Why start Unconventional now: first principles, brains, and the energy wall
Rao says Unconventional isn’t “a chip company” first—it’s an exploration of how learning works in physical systems. He ties the motivation to biology’s efficiency and the growing reality that global compute is becoming fundamentally energy-constrained.
How computing went digital: scalability beat analog’s efficiency
The conversation contrasts digital and analog: digital represents numbers with bits and trades precision for generality and scalable manufacturing. Analog computing was early and efficient, but variability and scaling challenges pushed the industry toward robust digital abstractions.
Why intelligence may fit analog/mixed-signal: stochastic brains vs deterministic substrates
Rao argues neural networks are inherently stochastic and distributed, yet we run them on highly deterministic, precision-oriented hardware built for arithmetic. He suggests there may be an “isomorphism” in circuits that better matches intelligence, using physics directly rather than layered abstractions.
The real constraint: AI at grid scale and the infrastructure bottleneck
Rao and Bornstein connect AI’s growth to data-center energy use, grid strain, and the difficulty of building generation and transmission fast enough. This creates urgency for compute paradigms that dramatically improve efficiency rather than assuming unlimited power.
Tradeoffs and where analog wins: dynamical systems, time, and “intelligence substrates”
Rao rejects a binary digital-vs-analog framing and instead argues for workload fit. He highlights dynamical systems—problems with time evolution—as especially amenable to physical computation, while conventional numeric computing remains important elsewhere.
Best-fit modalities and models: transformers today, dynamics-forward models tomorrow
Unconventional plans to start from what works—transformers and diffusion—while exploring models that explicitly encode dynamics, like diffusion/flow and energy-based approaches. Rao argues transformers exploit GPU-friendly structure but may be parameter-inefficient relative to alternative formulations.
Does this move toward AGI: causality, time evolution, and richer primitives
Rao cautiously argues that systems grounded in dynamics, time, and causality could be a better basis for more general intelligence than static function approximators. He notes today’s models are powerful yet still make “stupid errors,” suggesting gaps in causal understanding.
Ecosystem strategy: partners, competitors, and manufacturing-scale reality
Rao outlines a phased ambition: prove a new intelligence paradigm within five years, then ensure it scales in manufacturing to matter at global energy scale. He describes TSMC as a likely partner, and leaves open whether NVIDIA/Google become collaborators or competitors.
Motivation, confidence, and the “practical research lab” culture
Rao describes the unique thrill of hardware bring-up and his optimism about AI’s societal upside, positioning himself as strongly non-doomer. He grounds confidence in biology as existence proof plus decades of academic work, and emphasizes an open-ended early culture focused on existence proofs before optimization.
Hiring, career advice, and leadership style: agency, breadth, and big hard problems
Rao details the interdisciplinary team needed—model theorists, systems architects, mixed-signal circuit designers—and the risks of building one of the largest analog chips attempted. He advises young engineers to start in startups for breadth, and explains his leadership approach: attract people to hard problems and maximize agency across the org.
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