Brett Adcock, CEO of robotics startup Figure AI, is making a bold claim: humanoid robots represent nearly half of global GDP in untapped economic opportunity, and the company that solves the artificial intelligence problem first will build the biggest business in the world. Rather than viewing robotics as a manufacturing challenge, Adcock frames it as an intelligence problem, a perspective that separates his approach from competitors like Tesla's Optimus program.

Why Does Humanoid Robotics Matter So Much?

The economic logic is straightforward. Human labor accounts for roughly 30 to 40 trillion dollars in annual wages globally, representing just under half of the world's gross domestic product. If robots can replicate human work at scale, they unlock access to that entire market. Adcock explained the stakes clearly: "The meta problem in robotics is to be able to solve a humanoid robot. If you can solve this, it'll build the biggest business in the world by a large factor".

Adcock

This isn't theoretical. Figure is already shipping robots to commercial customers. The company achieved record production in March and planned to triple that output by May, with deployments at automotive manufacturer BMW and plans to reach one million units per year in production capacity.

What Makes Figure's Approach Different From Competitors?

Figure's competitive strategy rests on two pillars: vertical integration and in-house artificial intelligence development. Unlike companies that outsource component manufacturing, Figure designs virtually every part internally, from motors and rotors to sensors, joints, and battery packs. This control extends to the supply chain itself, a critical advantage in an industry where component shortages have historically slowed progress.

But the real differentiator, according to Adcock, is not manufacturing prowess. When asked about Tesla's superior manufacturing capabilities, he pivoted immediately to the intelligence layer. "In my mind, this is not a manufacturing problem. This is an intelligence problem," Adcock stated. Figure built its own AI models, called Helix 2, after learning from a six-month deployment of robots at BMW. The company extracted detailed feedback from that real-world operation and completely refactored its approach to commercializing software and AI systems.

"In my mind, this is not a manufacturing problem. This is an intelligence problem," stated Brett Adcock.

Brett Adcock, CEO of Figure AI

Remarkably, Adcock claims his internal team, composed of engineers with over a decade of robot learning backgrounds, outperformed OpenAI on robotics-specific AI tasks within a year of collaboration. This decision to bring AI development fully in-house rather than relying on external partners proved decisive in Figure's competitive positioning.

How Is Figure Building Its AI Advantage?

Figure's approach to AI training differs fundamentally from how general-purpose AI models are developed. Rather than training on broad internet data, the company uses real-world robot deployments as a feedback loop. Small batches of robots work in actual commercial environments, and engineers log every moment a human operator had to take control. This data feeds into reinforcement learning systems that continuously improve the AI model's decision-making.

The company's stated goal is ambitious: Figure wants to be "the first place where we see AGI in the physical world," referring to artificial general intelligence, or AI systems capable of performing any intellectual task a human can do. This framing positions Figure not just as a robotics company but as an AI research organization that happens to test its models on physical robots.

Steps to Building a Robotics Competitive Moat

• Vertical Integration: Figure designs motors, sensors, joints, batteries, and kinematics in-house, controlling the entire supply chain and avoiding dependence on external vendors who may prioritize other customers or face shortages.
• In-House AI Development: Rather than licensing AI models from foundation model labs like OpenAI, Figure built its own robotics-specific AI team, claiming superior performance on tasks that matter for physical robots operating in unpredictable environments.
• Real-World Feedback Loops: The company deploys robots commercially, collects data on human interventions, and uses that information to train AI models continuously, creating a compounding advantage over time.
• Avoiding Hype Cycles: Adcock deliberately stays off the founder media circuit and avoids traditional PR, focusing instead on product engineering and real-world deployments rather than narrative control.

What Does Adcock's Leadership Philosophy Reveal About Deep-Tech Startups?

Adcock's approach to running Figure differs sharply from typical startup culture. He self-funded the company from zero to one million dollars per month in burn rate within four months, giving him control and speed that institutional capital might have slowed. This early self-funding phase allowed him to make decisions without board pressure or investor timelines.

He also made a deliberate structural choice to protect his time on product engineering. After his previous company, Archer Aviation, went public, Adcock found himself trapped in board meetings, analyst calls, and executive staff management. He made a decision to remove everything on his plate except product engineering and family time.

This ruthless prioritization extends beyond work. About five years ago, Adcock stopped attending annual golf trips and social dinners with acquaintances, deciding to spend all his time on either family or his companies. This binary approach to time allocation, while extreme, reflects a philosophy common among founders building capital-intensive, technically complex businesses.

Why Does the Public Underestimate Progress in Robotics?

Adcock argues that the robotics industry suffers from a visibility problem. Most people haven't seen a functioning humanoid robot in person, so they underestimate how far the technology has advanced. "Our hot take for robotics is it's kind of really difficult to see what's really happening in the space without coming on site and really seeing stuff," he noted.

Adcock

"Humanoid robots are working now. It's pretty simple. Like we're seeing robots do everyday things," Adcock stated, pushing back against the perception that humanoid robots remain five to ten years away from practical utility.

Brett Adcock, CEO of Figure AI

This gap between actual progress and public perception matters for fundraising, recruiting, and regulatory attention. Companies building robots face skepticism because the technology seems perpetually five to ten years away, even as deployments accelerate. Figure's strategy of focusing on commercial deployments rather than media visibility may actually serve the company better in the long run, as real-world success becomes harder to dismiss than press releases.

The humanoid robotics market is projected to reach trillions of dollars in economic impact by 2050, with over one billion robots potentially in operation, according to Morgan Stanley's research. Supply chain bottlenecks in actuators, sensors, wiring, connectors, and power electronics remain significant barriers to scaling, but companies like 1X Technologies have been supplying these components for over a decade, suggesting the infrastructure for mass production is already in place.

Adcock's vision positions Figure at the intersection of three converging trends: advances in artificial intelligence, maturation of robotic hardware supply chains, and growing commercial demand for automation in labor-intensive industries. If his bet on AI as the primary bottleneck is correct, the company that cracks robotics-specific artificial intelligence first will indeed build something extraordinary.