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Physical Intelligence

Builds π (pi) general-purpose robot foundation models that run across many robot bodies

Scaleup · No humanoid status · Robot foundation model · San Francisco, California, United States · Founded 2024 · physicalintelligence.company

Series C amount and lead come from secondary trackers/filings; no company press release found. Prior round: $600M Series B Nov 2025 at $5.6B post (humanoid.observer). Source dates approximate.

Profile

Founded by robot-learning researchers from Google DeepMind, Stanford and UC Berkeley, Physical Intelligence trains vision-language-action models (π0, π0.5) that control many robot types. It sells models rather than robots. Its valuation rose from $5.6B (Nov 2025) to a reported ~$11B in a May 2026 Series C.

People

As of –. X handles are shown only when we could confirm they belong to the person.

Leadership on X

What leadership has posted about robotics, AI and tech in the last 30 days.

Karol Hausman @hausman_k
–
  • He congratulated Ultra's Jon Miller Schwartz on OP1, a robot Ultra says is built for scaled deployment, and called Ultra an awesome partner.1

1 post considered. Off-topic posts are skipped.

Replies are included only when they mention robots, robotics, humanoids or AI.

Sergey Levine @svlevine
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  • Shared new work that pairs a small reinforcement-learning policy with a large robot foundation model, so the robot can react to newer camera images and steer toward better behavior.1
  • Highlighted a hackathon built on Physical Intelligence's π-0.7 model, where teams ran it on unusual machines such as an excavator.2

2 posts considered. Off-topic posts are skipped.

Replies are included only when they mention robots, robotics, humanoids or AI.

Chelsea Finn @chelseabfinn
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  • Shared a video of a Physical Intelligence robot working fully autonomously at a chocolate maker, and lessons on the gap between a one-off demo and hours of useful work.1
  • Co-wrote a blog post on what robots need to be broadly useful, calling reliability one of the biggest open challenges in AI.2
  • Shared Stanford research on reinforcement learning for fast, dynamic robot tasks, where a small real-time policy corrects a large vision-language-action model.3
  • Promoted a new benchmark that tests whether AI models can spot which earlier papers could advance a research problem.4
  • Shared Mulligan, a method that focuses human help during deployment on the starting conditions where a robot tends to fail, noting that standard DAgger wastes a person's time once robots succeed most of the time.5

6 posts considered. Off-topic posts are skipped.

Replies are included only when they mention robots, robotics, humanoids or AI.

Chatter on X

Social posts, not verified facts. What X is saying about this company, rated for how far it can be trusted. Shows the last 14 days, plus anything still unresolved.

Items shown have cleared our internal credibility screen.

Labels

Confirmed
The company, an SEC filing or a major outlet backs it.
Likely
A credible first-hand source or named outlet reports it; nothing official yet.
Unverified
One source, or a source with a thin track record. Treat as a rumor.
Disputed
A credible party contests it.

Last checked Oct 10.

Flagship products

Funding and valuation

Latest round

$1.05B Reported

Series C · May 21, 2026

Led by Founders Fund

Also Lightspeed, Thrive Capital, Lux Capital.

Reported ~$1.0-1.05B; based on reports and filings, not a company press release

Hiring signals

Open roles
38all roles
Top location
San Francisco · 95%36 of 38 roles
Top role area
AI & foundation models12 roles · 35% of 34 engineering & ops

Trademarks

No US filings since 2025 (last checked ).

Source: USPTO Trademark Search, matched on the company’s legal names. Rechecked on the 1st and 15th of each month. A filing shows intent to use a name in the US, not a launched product.

Competitors

Companies with overlapping products. Direct = same customers and same job; Adjacent = overlapping technology or market, different focus.

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Recent news

Sources

Confidence: medium.

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