Generalist raises $400M to scale its general-purpose AI models

From left to right, Generalist’s co-founders: CEO Pete Florence, Chief Scientist Andy Zeng, and CTO Andrew Barry. | Source: Generalist

Generalist AI Inc., a company creating AI for a range of robot form factors, today said it has raised $400 million in new funding. This latest round brings Generalist AI’s total funding to more than half a billion dollars.

Millions of robots are operating in the world today. Generalist asserted that billions more are coming to factories, warehouses, laboratories, restaurants, farms, homes, and space. These robots will take many forms, but they will share one need: intelligence that can understand and act in the physical world.

Founded in 2024, Generalist AI said it is building embodied foundation models for general-purpose robots. In November 2025, the San Mateo, Calif.-based company released GEN-0, which it said “brought robots into the pretraining era.”

Generalist said its models, trained on large-scale, real-world data, demonstrated scaling laws in robotics. This proved that more physical experience and larger models can predictably produce more capable systems, it added.

In April, Generalist AI released GEN-1, its general-purpose AI model for robotics. The company asserted that the technology “unlocks commercial viability across a broad range of applications.”

Generalist claimed that GEN-1 improves average success rates to 99% on tasks where previous models achieved 64%. The model also completes dexterous tasks roughly three times faster than current approaches, and it requires only one hour of robot data for each of these results, according to the company.

In addition, GEN-1 demonstrated traction toward the practical thresholds required for real deployments, including the ability to learn complex new physical skills and to solve problems creatively through “emergent improvisational intelligence.”

“These results are not the product of a single idea,” said Generalist’s founders. “They are the compounding result of thousands of decisions, across data, models, hardware, infrastructure, operations, and deployment, made by a world-class team building at the frontier of AI and robotics.”

Since launching GEN-1, the company said a data flywheel has begun to take shape. It said scaling robot learning creates better models that can do more useful physical work, and data from real businesses will then drive the next generation of more capable models.