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.
- •AI framed as a civilizational shift, not just a product category
- •Naveen Rao positioned as an early, contrarian builder across AI and systems
- •Unconventional AI introduced as a new approach to compute for intelligence
- •Core tension: current compute paradigm may not scale (especially on energy)
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.
- •Specialized hardware historically enabled real-time workloads (e.g., compression, wireless)
- •Efficiency and form factor constraints often force hardware innovation
- •Rao’s cross-training: industry hardware + PhD in neuroscience
- •Perspective: if something is too slow/inefficient in software, reconsider the substrate
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.
- •“Full stack” as silicon-to-application understanding
- •Hardware/software boundary is a design choice, not a natural law
- •Right-sizing: decide what must be fixed vs. programmable for the problem
- •Breadth across layers helps identify the best leverage points
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.
- •Company focus begins with theory and first-principles learning in physics
- •Biology as inspiration: efficiency, adaptivity, dynamic energy usage
- •We’ve largely built the same digital-computing paradigm for ~80 years
- •AI demand is pushing computing into an energy-limited regime
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.
- •Digital computing: numeric representation with finite-bit precision and error
- •General-purpose simulation drove digital dominance
- •Analog computers were efficient but hard to scale due to variability
- •Historical parallel: ENIAC-scale tube counts resemble today’s “scale-out” training clusters
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.
- •Neural networks as stochastic machines; brains implement dynamics physically
- •Digital stacks add lossy abstractions (OS/APIs) compared to “intelligence as physics”
- •Analog approaches can compute via the medium’s native dynamics
- •Goal: find circuit-level mappings that naturally support learning and inference
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.
- •Data centers already consume a meaningful share of grid energy; AI is accelerating demand
- •Rising risk of brownouts and transmission constraints
- •Estimates imply massive new generation capacity is needed over the next decade
- •Efficiency gains in compute are framed as essential, not optional
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.
- •Hybrid view: digital remains vital; analog helps where dynamics/time matter
- •Computers “simulate time” numerically; physical systems embody time directly
- •Brains integrate many messy variables yet achieve high precision (athletes as examples)
- •Positioning: build an “intelligence substrate,” not just faster arithmetic
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.
- •Near-term compatibility with transformers/diffusion rather than discarding SOTA
- •Interest in diffusion/flow/energy-based models due to ODE/dynamics structure
- •Hypothesis: map model dynamics onto physical-system dynamics for efficiency
- •Expectation of open-source releases to let others experiment with mappings
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.
- •AGI discussion acknowledged as inherently uncertain and “hand-wavy”
- •Claim: dynamics/time-evolution may support better causal reasoning
- •Physical irreversibility and temporal structure as important priors
- •Current AI: useful tools, but not yet comparable to working with a person
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.
- •Two-phase plan: paradigm proof → scalable manufacturable systems
- •Manufacturing scalability is required to impact energy constraints globally
- •TSMC positioned as a key partner for prototyping and production readiness
- •NVIDIA/Google/Microsoft seen as application-frontier players; competition vs collaboration depends on how the paradigm lands
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.
- •Hardware bring-up as a powerful motivator; “turning it on” moment matters
- •AI viewed as enabling deeper collaboration and understanding, despite tradeoffs
- •Confidence sources: brains + 40+ years of research + emerging theory
- •Culture: prioritize existence proofs; avoid premature manufacturability constraints while keeping product focus
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.
- •Hiring across theory, architecture, and analog/digital mixed-signal implementation
- •Acknowledgment: first prototypes are unpredictable; pushing scale is inherently hard
- •Career advice: prioritize breadth early; startups accelerate cross-stack learning
- •Leadership: recruit for hard challenges, then increase agency and let teams own outcomes
