Tera AI is pioneering a software-centric approach to robotics, moving away from traditional hardware-dominated solutions toward a unified operating system for robotic platforms. After raising $8 million and transitioning from insurance applications to robotics, the company is building what founder Tony Zhang envisions as "a general purpose operating system for robot platforms" powered by spatial foundation models. In this episode of Category Visionaries, Tony shares his journey from Google X to founding Tera AI, including hard-won lessons about market validation, customer discovery, and the critical importance of understanding buyer priorities.

Topics Discussed:

• Tera AI's evolution from geospatial foundation models in insurance to robotics applications
• The challenges of customer discovery in regulated industries like insurance
• Tony's experience at Google X and the ChatGPT moment that sparked entrepreneurial action
• First Round's Product Market Fit program and structured customer discovery methodology
• The transition from hardware-centric to software-centric robotics architecture
• Fundraising strategies and developing instincts for investor feedback
• Building a team of top-tier AI researchers in a competitive talent market

GTM Lessons For B2B Founders:

• Lead with priority validation, not pain discovery: Tony learned the hard way that not every pain point can be solved on a VC timeline. His breakthrough insight was asking upfront: "Tell me if this is one of your top three priorities. If not, tell me what are those three priorities." He discovered that many insurance prospects liked their solution but had more pressing infrastructure problems unrelated to AI. B2B founders should qualify buyer priorities before presenting solutions to avoid getting trapped in lengthy sales cycles for non-critical problems.
• Understand regulatory constraints early in enterprise markets: Tera AI spent nearly a year in insurance before realizing that regulatory barriers made technology adoption extremely difficult, regardless of product-market fit. Tony explains: "Because of the regulations in America, it is incredibly difficult for an insurer or carrier to adopt new technology, especially technology that was as new as the stuff that we were building." Founders entering regulated industries should map compliance requirements and adoption timelines before committing significant resources.
• Structure customer discovery to eliminate waste: Through First Round's PMF program, Tony discovered they were doing discovery calls inefficiently, often requiring multiple meetings with the same prospects. The key insight was asking the right qualifying questions upfront rather than leading with solutions. This approach eliminated unnecessary follow-up meetings and accelerated their discovery process by 5x. Founders should develop structured discovery frameworks with clear qualifying criteria before scaling outreach efforts.
• Market timing requires both technology readiness and buyer urgency: Tony's "ChatGPT moment" wasn't just about technological possibility—it was about recognizing the convergence of technical capability and market readiness. He emphasizes: "It wasn't too early, it wasn't too late." The key was understanding that spatial AI could finally deliver value that buyers were ready to adopt. Founders should evaluate both technical feasibility and market timing when deciding on startup opportunities.
• Attract talent with novel technical challenges, not just compensation: Despite intense competition for AI talent in Silicon Valley, Tera AI successfully recruits top researchers by offering genuinely innovative work. Tony explains: "We genuinely try to innovate across the entire stack. We build our own models, we build our own datasets, we can write papers on the things we're doing." They target researchers who are "bored to death by the LLM world" and want to work on groundbreaking spatial AI problems.