Robotics Has a $10 Billion Blindspot
Teleoperation, zero-day hunters, autonomous drug design, and why the grid is the real bottleneck
Each week, we share a small collection of ideas that shaped our internal thinking. Inspired by experiments like USV’s Librarian, this series is powered by an AI assistant that helps synthesize recurring themes from our discussions, alongside our own reflections.
Teleoperation is the blindspot of robotics. The prevailing narrative is that we’re waiting for AI to solve autonomy, but teleoperation is the apprenticeship that produces it. When a human teleoperates a robot, the data has zero domain adaptation gap. Waymo proved this: one intervention every 1,250 miles in 2015, 17,000 today, trained by teleoperators generating exactly the data to make the next intervention less likely. We think teams building the control layer (fleet coordination, handoff from 1:1 to 1:100) will own the next chokepoint in physical AI.
AI is becoming a first-class security actor. In a live demo, Claude discovered zero-day vulnerabilities in Ghost. 50,000 GitHub stars, no prior critical CVE, finding a blind SQL injection and stealing the admin API key in 90 minutes, then doing the same to the Linux kernel. Vulnerability discovery has always been bottlenecked by scarce human expertise. If AI can find zero-days at this speed, the economics of offense and defense shift fundamentally. We think the investable surface is autonomous security agents that treat vulnerability discovery as a continuous process, not a periodic audit.
Drug discovery is moving from copilot to autopilot. Latent Labs launched Latent-Y, an autonomous agent for end-to-end drug design — give it a research goal and it reasons, designs, iterates, and delivers lab-ready antibodies without human intervention. We wrote in Week 14 about drug discovery search becoming software. Latent-Y closes the loop: hypothesis to validated candidate, autonomously. We think the teams that own the full design-to-validation workflow will compress timelines that used to take months into days and reshape early-stage biotech.
What is the actual TAM for physical AI? We’ve been discussing internally: most AI investment is concentrated in white-collar automation — coding, writing, analysis. But the physical world is where the majority of economic activity and labor sits. More people drive, build, manufacture, and serve than write software. The market for physical AI is far larger, purely because the surface area is bigger. We think current venture allocation toward software-only AI dramatically underweights the opportunity, and the largest outcomes will come from teams moving intelligence into atoms, not just bits.
America’s energy problem isn’t supply — it’s delivery. Generation is getting cheaper, but delivery now accounts for nearly half of customer electricity costs. 70% of US transmission lines are over 25 years old, transformer demand has doubled since 2019, and 80% of supply is imported. We’ve written about how coordination, not generation, is energy’s real bottleneck. One emerging fix is solid-state transformers on silicon carbide — software-controlled power conversion replacing passive hardware with programmable nodes. We think the next wave of energy infrastructure looks like Moore’s Law applied to the grid.
We’ll share another edition next week.