Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World

Why It Matters

By unifying hardware test data and embedding AI analytics, Nominal enables faster, safer product cycles, giving manufacturers a decisive advantage in the emerging re‑industrialization era.

Key Takeaways

• Hardware testing data bottleneck drives need for unified platform.
• Nominal offers AI‑powered data management across design, test, production.
• Defense primes adopting Nominal to replace fragmented, local test data workflows.
• Simulation‑real world gap narrows via continuous telemetry and AI analytics.
• Re‑industrialization fuels rapid hardware cycles, making AI tools essential.

Summary

The interview spotlights Nominal, an all‑in‑one AI and data platform designed to modernize hardware engineering by centralizing test data and accelerating development cycles. As the U.S. re‑industrializes, companies across aerospace, defense, robotics and autonomy are racing to shorten product timelines, but they lack the data infrastructure to reliably test physical systems. Key insights include a “pendulum swing” back toward intensive hardware testing, the compression of development timelines, and the stark contrast between software’s mature CI/CD ecosystem and hardware’s fragmented, locally‑stored test data. Nominal positions itself as the GitHub for hardware testing, offering a semantic layer that merges simulation outputs with real‑world telemetry, thereby narrowing the notorious gap between virtual models and physical performance. Notable examples illustrate the shift: SpaceX built proprietary test tools that accelerated its success, while most legacy primes still rely on PDFs, PowerPoints and isolated MATLAB scripts. The founders repeatedly emphasize that “physics gets a vote,” underscoring the necessity of rigorous physical validation even as AI tools proliferate. The broader implication is that firms adopting Nominal can transform raw sensor streams into actionable AI insights, automate repetitive data checks, and ultimately gain a competitive edge in a rapidly evolving hardware market where speed, safety, and data‑driven decision‑making are paramount.

Original Description

Nominal’s cofounders (Cameron McCord, Jason Hoch and Bryce Strauss) realized that the new age of reindustrialization requires a new approach to hardware engineering and testing that’s closer to how software is developed.

They founded Nominal with the insight that while SpaceX, Tesla, and Anduril built proprietary internal platforms for hardware testing, the thousands of new hardware entrants can't afford to replicate that work. Nominal serves as the system of record for hardware testing, helping companies move from PDF-based workflows to modern data infrastructure that catalogs telemetry from sensors producing millions of data points per second.

The platform enables engineers to author validation logic that follows hardware systems from initial testing through manufacturing and field deployment. We discuss their belief that all hardware companies will become physical AI companies, and why they think Nominal's role as the verification layer will be critical - because unlike a video game, physical products require rigorous validation before they enter the real world.

Hosted by: Alfred Lin and Sonya Huang, Sequoia Capital

00:00 Hardware Testing Returns

00:59 Meet Nominal and Mission

01:27 Reindustrialization Tailwinds

02:27 GitHub for Hardware Data

03:42 Why Hardware Is Hot Again

05:17 Simulation vs Reality Gap

08:06 Broken Status Quo Tools

10:55 AI Readiness for Primes

14:11 Why Start With Testing

33:14 Physical AI Future