Brett Adcock

Adcock’s core claim is that general-purpose autonomy—not manufacturing volume—unlocks a labor market the hosts size at roughly $50 trillion. Figure could finance and build 100,000 robots, he says, but that means little if they require teleoperation or replay fixed motions; “If you don’t solve that, none of this matters.” Conversely, he believes a truly general humanoid could attract demand for a billion units immediately.

Helix 2 is Figure’s claimed architectural break: the remaining 109,000 lines of C++ are gone, leaving an end-to-end neural stack for more than 40 degrees of freedom. Its learned System Zero controller coordinates full-body movement while System 1 integrates cameras, fingertip touch and palm cameras; onboard inference drives motor torques hundreds of times per second. The resulting room-scale kitchen behavior—including using a hip and foot—was something Adcock said, “You could never code.”

Figure’s prospective moat compounds through fleet data rather than task-specific software libraries. One neural network handles logistics, dishes and other work, with Adcock reporting positive transfer as diverse data is added: “Once one robot learns how to do a task, every robot in the fleet knows it.” Figure designed Helix 2 around the pre-training set, Figure 03 around Helix 2, and is bringing 3,000 B200s online for pre-training.

The strongest operating evidence offered was one error across 67 hours of continuous package work over multiple robots. The robots reportedly worked at human speed, found and positioned barcodes and even patted packages flat for the scanner; a six-month BMW deployment also ran every workday. Adcock’s real benchmark, however, is autonomous work lasting days in an unseen location—not karate, backflips or videos with “a guy in Tennessee driving it.”

Manufacturing is being scaled in parallel, with robots being targeted for Figure’s own production lines during 2026. The current facility can support four lines at roughly 12,000 units each, just under 50,000 annually, while a near-term target is one robot every 30 minutes. Longer term, Adcock discussed $10,000-$20,000 robots and hopes that within 24 months “all the robots will build all the robots,” although one billion units at $20,000 would still require $20 trillion of working capital.

The home roadmap is aggressive but explicitly staged rather than a mass-market promise. Adcock expects that by the end of 2026 Figure might place a robot in an unseen home for fairly long-horizon work, measure interventions per hour, day or week, and begin limited user deployments the following year; “I don’t want to ship slop.” He also said Figure is not yet ready for fully autonomous operation freely around his children or to hold his newborn, making that personal threshold the readiness test.

Adcock expects “far less than 10” global humanoid winners and views China collectively as Figure’s only serious competitive threat today. He nevertheless argues that closed-loop autonomy remains scarce worldwide and that every major technology company will enter because “you have no choice.” Figure intends to keep its model tied to its own vertically integrated hardware, declining to license it on safety grounds and describing safe deployment as a “fiduciary duty to our civilization.”

Adcock’s core thesis is that humanoids are the “ultimate deployment vector for AGI,” because intelligence trapped on a server must ask humans to affect the physical world. A mechanical-human form factor can operate across human environments, while one foundation model can transfer across tasks without hardware changes. He says solving the key challenges would put Figure in the right decade for the space’s “iPhone moment.”

The near-term commercial thesis is repetitive workforce labor, not the far more complex home. Adcock puts human labor at roughly half of global GDP, which Diamandis translates into a $50-$60 trillion TAM against $110-$120 trillion of global GDP. Adcock says that if Figure had 100,000 functioning robots today, its two first customers would take them, while home operation remains “the Wild West.”

Figure now has robots working daily at BMW’s Spartanburg plant, autonomously placing sheet metal onto fixtures with what Adcock calls “no faults, no failures.” The BMW task took about a year to execute fully end to end at high speeds; Figure 01’s cycle fell from roughly four minutes last summer to 40 seconds. With Helix, Figure completed an unnamed logistics customer’s use case from nothing in under 30 days and believes a repeat could take less than 48 hours.

Helix is the claimed AI inflection, turning robot programming into speech-directed generalization. Trained with about 500 hours of data, two robots used an English instruction to put away groceries deliberately withheld from training. Adcock calls it “probably the most important AI update for robotics in human history,” though the evidence presented is Figure’s own demonstration.

The hardware road map combines rapid iteration with a mass-market cost target. Figure designs a new platform every 12-18 months; Figure 3 took 18 months and is described as “90% cheaper,” smaller and lighter, with improved sensors and neural-net-oriented hands, head and feet. Diamandis raises a future $20,000-$30,000 price point; Adcock says bill-of-materials work does not indicate that the product should be extremely expensive, but does not explicitly reaffirm that range. Diamandis frames a $30,000 robot as roughly $300 monthly, $10 daily or $0.40 hourly when continuously available.

Adcock has moved the home timeline forward by “multiple years,” but makes it conditional on data, generalization and safety. Figure will start internal home alpha testing this year, with robots expected in homes “in the coming years.” The goal is a robot that understands spoken requests and performs hours-long tasks without repeated prompts or fixes—not merely one that manipulates familiar objects.

Execution depends on an unusually intensive, vertically integrated organization built for hardware speed. Adcock initially removed near-term financing risk with his own capital, reached a $1 million monthly burn within six months and recruited around an “iPhone moment” despite telling candidates the success probability was “pretty low.” Figure’s office-based, five-to-seven-day culture is organized around one reward: “We want to ship product”; Adcock says he does not do 1-on-1s.