Brain
Generalist AI (founded 2024 in San Mateo, California by chief executive Pete Florence, a former DeepMind senior research scientist, with Andy Zeng, also ex-Google DeepMind, and Andrew Barry, ex-Boston Dynamics, and backed by Nvidia) builds embodied foundation models trained directly on raw physical-interaction data. Its GEN-0 model, published in a November 4, 2025 research blog, was tested at one to ten-plus billion parameters on more than 270,000 hours of real manipulation trajectories growing by roughly 10,000 hours a week, and reports a scaling phase transition around 7 billion parameters; a successor, GEN-1, followed in April 2026. The models are proprietary with no open weights. The registry records the brain at research maturity because its capability is demonstrated rather than deployed: it runs across 6-, 7-, and 16-plus-degree-of-freedom semi-humanoid robots in lab and benchmark settings, but there is no named hardware partner, customer, or production deployment, and the robot wiring is in-house and captive. The artifact is real and substantive enough to clear the registrable bar even without peer review, but its benchmark numbers, including a 99-percent task-success claim, the 270,000-hour figure, and the scaling claims, are self-reported and not independently reproduced, there is no arXiv paper or formal model card, the funding amount and valuation are undisclosed, and there is no named robot-platform partnership.
Foundation model · Maturity: Research · Closed
Machine-readable surfaces
- Markdown mirror: /brains/gen-0.md
- RSS feed: /brains/gen-0/feed.xml
- JSON-LD: embedded in this page’s head
- REST API: /v1/brains/41097187-969e-4df7-adb6-330d1c60e981
- Data documentation: /data
Architecture
Embodied/manipulation foundation model (generalist VLA-class) trained on raw physical-interaction data; ~7B-param scaling phase transition reported.
Key facts
- Model
- GEN-0 (Nov 4 2025): embodied foundation model trained directly on raw physical-interaction data; tested at 1B/6B/7B/10B+ params; >270,000 hrs of real manipulation trajectories growing ~10,000 hrs/week; reports a 'scaling phase transition' at ~7B params. GEN-1 (Apr 2026) successor. Proprietary, no open weights.
- Deployed wiring: demonstrated, NOT deployed
- Runs across 6-DoF, 7-DoF, and 16+-DoF semi-humanoid robots in lab/benchmark settings. NO named hardware partner, customer, or production deployment. Robot wiring is in-house/captive.
- Provider
- Generalist AI (founded 2024, San Mateo CA; founders Pete Florence (CEO, ex-DeepMind), Andy Zeng (ex-Google DeepMind), Andrew Barry (ex-Boston Dynamics); Nvidia-backed).
- Cap-flag
- Benchmark numbers (99% task success, 270k hrs, scaling claims) are SELF-REPORTED, not independently reproduced; no arXiv/peer-reviewed paper or formal model card (self-published research blog). Funding amount/valuation undisclosed; no named robot-platform partnership; GEN-1 SOTA-comparison claims unverified.
Developed by (1)
Common questions
- What is GEN-0 (and GEN-1)?
- Generalist AI (founded 2024 in San Mateo, California by chief executive Pete Florence, a former DeepMind senior research scientist, with Andy Zeng, also ex-Google DeepMind, and Andrew Barry, ex-Boston Dynamics, and backed by Nvidia) builds embodied foundation models trained directly on raw physical-interaction data. Its GEN-0 model, published in a November 4, 2025 research blog, was tested at one to ten-plus billion parameters on more than 270,000 hours of real manipulation trajectories growing by roughly 10,000 hours a week, and reports a scaling phase transition around 7 billion parameters; a successor, GEN-1, followed in April 2026. The models are proprietary with no open weights. The registry records the brain at research maturity because its capability is demonstrated rather than deployed: it runs across 6-, 7-, and 16-plus-degree-of-freedom semi-humanoid robots in lab and benchmark settings, but there is no named hardware partner, customer, or production deployment, and the robot wiring is in-house and captive. The artifact is real and substantive enough to clear the registrable bar even without peer review, but its benchmark numbers, including a 99-percent task-success claim, the 270,000-hour figure, and the scaling claims, are self-reported and not independently reproduced, there is no arXiv paper or formal model card, the funding amount and valuation are undisclosed, and there is no named robot-platform partnership.
- What type of AI is GEN-0 (and GEN-1)?
- GEN-0 (and GEN-1) is a foundation model on the DEPLOY registry. It is proprietary.
- Who developed GEN-0 (and GEN-1)?
- GEN-0 (and GEN-1) is credited to Generalist AI on the DEPLOY registry. Each developer attribution is verified via primary sources.
- Which robots run on GEN-0 (and GEN-1)?
- No robot models on the DEPLOY registry are recorded as running GEN-0 (and GEN-1). DEPLOY wires brain-to-model connections only when the wiring is verifiable from primary sources; absence may reflect pre-deployment or unverified manufacturer claims.
- Is GEN-0 (and GEN-1) open source?
- No. GEN-0 (and GEN-1) is recorded as proprietary on the DEPLOY registry. Model weights and source are not publicly available.
- What is GEN-0 (and GEN-1)'s maturity stage?
- GEN-0 (and GEN-1) is at the research stage on the DEPLOY maturity ladder. Research stage means active development without commercial deployments on file.
Sources (3)
Canonical ID 41097187-969e-4df7-adb6-330d1c60e981