π model family
Vision-language-action models for general robot control.
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Robotics · Robotics Foundation Models
Physical Intelligence
Builds general-purpose AI systems for robots operating in the physical world.
- Research status
- Research coverage
- Headquarters
- San Francisco, CA
- Coverage
- 2 product lines mapped
- Last reviewed
- September 2026
Product map
What the company builds
Products and operating platforms help connect company-level news to the revenue pools, customers, competitors, and supply chains it may affect.
Robot learning platform
Data, training and evaluation stack for physical intelligence.
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.
Explore founderCo-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.
Explore founderCo-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.
Explore founderSelected 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.
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.
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.
Company overviewphysicalintelligence.companyFounding & leadership backgroundphysicalintelligence.company
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.
Generalization across tasks rather than narrow, separately programmed automation.
Private-company information is incomplete, episodic, and difficult to verify. Treat every data point as a starting point for diligence—not a substitute for company-approved materials, legal review, or investment analysis.
Add to my private currentSignals to monitor
Task generalizationRobot-platform coverageDeployment partnershipsRisks to underwrite
Research-to-product gapData scarcityCapital intensity Underwriting map
What an investor should actually diligence
Measure useful work delivered, not demonstrations. The key bridge is from a supervised pilot to repeatable deployments with improving utilization, reliability, service burden, and customer payback.
Buyer map
01
Manufacturers and warehouses
02
Logistics operators
03
Automotive and industrial firms
04
Research and development partners
Economics to request
01
Paid deployments and contracted units
02
Hours between intervention
03
Bill-of-materials trajectory
04
Deployment and support labor per unit
05
Robot-as-a-service contribution margin
Moat tests
01
Performance generalizes beyond staged tasks
02
Data flywheel improves deployment speed
03
Hardware supply chain can scale
04
Customer payback survives labor and financing assumptions
Milestone map
01
Pilot-to-production conversion
02
Manufacturing ramp
03
Autonomy reliability gain
04
New task or environment deployment
Comparable lensesIndustrial automationWarehouse roboticsAutonomous vehiclesAI software and sensors
Underwriting framework
How to diligence Physical Intelligence
A company-specific starting point based on the operating realities of robotics. Replace assumptions with verified company materials, customer evidence, legal documents, and expert work.
Hardware gross margin
Service and software revenue per deployed unit
Manufacturing cost curve
Customer payback period
Paid deployments and renewals
Robot-hours without intervention
Task reliability across environments
Serviceability and fleet uptime
Industrial automation
Machine vision
Warehouse robotics
Labor and fixed-automation alternatives
Does the demo generalize?
Who carries maintenance and downtime risk?
Which component controls cost?
How much new data improves performance?
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SMYC does not represent that shares are available, transferable, appropriately priced, or suitable for any person. Private securities are illiquid, speculative, subject to transfer restrictions, and may result in total loss.