Matt Bornstein
Frontier Insights
Frontier Thesis
AI models have become the fourth compute pillar. Because probabilistic systems absorb core logic, coding agents thrive on objective feedback loops (linters, tests), dramatically expanding total software production rather than shrinking developer headcounts.
Strategic Decisions
Back vertical user-experience moats over raw foundational weights—exemplified by Cursor out-executing incumbents through context engineering and rapid enterprise enterprise adoption—while anchoring open-source infrastructure (e.g., vLLM) to secure hardware-agnostic inference, data sovereignty, and performance control.
Risks & Warnings
Thin wrappers face extinction. Long-term defensibility requires deep systems engineering, enterprise switching costs, and resilient licensing models capable of surviving shifting open-source economics and relentless hyperscaler bundling.
Key Views & Dialogues
The Chip That Could Unlock AGI.
Unconventional is pursuing physical learning circuits, arguing that analog dynamics could complement digital computing for stochastic, time-dependent workloads rather than simply extend matrix multiplication. The thesis targets an energy wall: U.S. data centers use roughly 4% of the national grid, while some estimates call for 400 additional gigawatts over the next decade. Commercial significance depends on finding a manufacturable paradigm within five years, with TSMC partnership, a potentially very large analog prototype and unresolved AGI claims creating major execution risk.
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Naveen Rao’s thesis is that AI’s energy wall requires changing the computer, not merely scaling today’s architecture. U.S. data centers already consume roughly 4% of the national grid, he says, while some estimates call for 400 additional gigawatts over the next decade. The bottleneck is becoming physical: “The current paradigm, as good as it is and as far as it’s taken us, is not going to take us to that level.”
Unconventional AI is starting with a theory of physical learning, not a conventional chip roadmap. Rao wants to use circuits whose own dynamics perform useful intelligence rather than relying on layers of digital abstraction: Bornstein summarizes the idea as “Intelligence is the physics,” and Rao agrees. Human brains operate around 20 watts, while a squirrel or cat may use roughly a tenth of a watt—an efficiency gap large enough to justify revisiting first principles.
Analog is not meant to replace digital computing; it targets stochastic, time-dependent workloads that may benefit from physical dynamics. Rao’s candidates include diffusion, flow, and energy-based models because their dynamics can be written as ordinary differential equations and potentially mapped onto physical systems. His goal is an “intelligent substrate” alongside conventional computation, preserving digital precision for problems that require it.
Rao thinks dynamics might supply the causality missing from current AI, but he repeatedly labels the AGI argument “hand wavy.” His intuition is that systems built from elements with real time evolution will provide a better basis for understanding cause and effect than systems whose basis does not include such dynamics. Today’s models contain intelligence and are highly useful, he says, but remain “nowhere close to AGI” because they still make elementary errors and do not feel like working with a person.
The commercialization test is whether Unconventional can find an intelligence-like physical paradigm within five years and make it manufacturable at massive scale. Rao sees TSMC as a necessary partner for prototyping and eventual scale; he characterizes Google as having everything internally and pursuing lower-risk, continual TPU improvements, while Nvidia has built the dominant programming platform. Unconventional is trying to build “a better substrate than matrix multiply,” though Rao leaves open collaboration rather than assuming direct conflict.
Execution risk is extreme, but Rao sees several partial proofs rather than a blind leap. The brain provides an existence proof, more than 40 years of academic work supplies evidence of promise, and dynamical-systems and neuroscience theory offer pieces that engineers can combine and “sand down.” The first prototype may be one of the larger, possibly the largest, analog chips ever built, making talent across theory, systems, algorithms, analog circuits, and digital circuits central to the thesis.