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Robotics / COMPANY INTELLIGENCE

Physical Intelligence

Builds general-purpose AI systems for robots operating in the physical world.

01 / THE BUSINESS

How Physical Intelligence creates value.

Builds general-purpose AI systems for robots operating in the physical world. The research focus is whether customer adoption can support attractive economics after development, delivery and ongoing service costs.

SectorRobotics
Founder coverage3 profiles
Research checkedSep 18, 2026

02 / PEOPLE BEHIND THE COMPANY

Meet the builders.

Co-founder

Karol Hausman

Karol Hausman is a co-founder of Physical Intelligence. The company’s founding and leadership material provides context for its work in robotics foundation models.

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Co-founder

Chelsea Finn

Chelsea Finn is a co-founder of Physical Intelligence. The company’s founding and leadership material provides context for its work in robotics foundation models.

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Co-founder

Sergey Levine

Sergey Levine is a co-founder of Physical Intelligence. The company’s founding and leadership material provides context for its work in robotics foundation models.

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Selected founders; historical founding roles are distinct from current management positions.

03 / CAPITAL & OWNERSHIP

Follow the financing.

Selected disclosed events, newest first. Announcement values describe that transaction and date; they are not current share prices. Debt, equity and secondary transactions are identified separately.

A financing figure is not established here.

Use the original company sources below to check new announcements. We have not substituted an estimate for a disclosed round.

04 / IN THEIR OWN WORDS

Watch. Listen. Form your view.

A direct interview has not yet been verified for this profile. The company’s official materials are linked below.

Interviews reflect the speaker’s perspective at publication. Company statements are not independent verification.

05 / EDITORIAL ANALYSIS

The opportunity. The open questions.

Our interpretation of the business model and cited sources, not a valuation or a recommendation.

What could work

The case to investigate

  • Generalization across tasks rather than narrow, separately programmed automation.
  • A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
What could go wrong

Pressure-test the thesis

  • Research-to-product gap.
  • Data scarcity.
  • Capital intensity.

What to watch next

01

Task generalization — what changed in the latest original disclosure?

02

Robot-platform coverage — what changed in the latest original disclosure?

03

Deployment partnerships — what changed in the latest original disclosure?

YOUR RESEARCH WORKSPACE

Keep the questions that matter.

Use this checklist to record what you have investigated. Your notes stay in this browser.

06 / RESEARCH TRAIL

Go straight to the source.

Research checked Sep 18, 2026. Selected public disclosures, not a complete capitalization table or securities-filing search. Missing information is left unconfirmed. No investment availability or IPO date is implied.