Building the verification layer for Physical AI data. An open market for provably correct data processing.
Joined September 2026
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Physical AI has a data problem:
You can buy footage, annotation, and compute. But none guarantees usable results.
We're building a new way to coordinate, verify, and reward the work between raw data and model training.
Introducing the PAGI Network ҉
“In one customer deployment, analysis of data from more than 40 sensors across 65 wind turbines identified new previously unknown failure modes, revealing critical problems before they cause downtime.”
Exactly the problem space PAGI addresses: forbes.com/councils/forbeste…
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Define the rules before the work begins.
Verify against those rules ⟶ reward the result.
That's the foundation of accountable data processing ҉
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An open network needs a way to hold independent participants accountable.
PAGI uses performance-linked deposits to secure participants' obligations.
• Accepted work earns rewards
• Missed assignments forfeit rewards for that work
• Proven misconduct can trigger deposit slashing, with evidence and an appeal process
The network will incorporate a native token into its incentive and governance design. Token issuance supports networks as they establish demand, while governance is designed to transition toward token-holder voting.
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The vision: an open network where physical AI teams can access specialized data-processing services without rebuilding the infrafor every new requirement.
Full Litepaper ↓
pagifdn.org/pagi-litepaper.p…
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Physical AI is creating an entirely new market for data.
Consider these announcements from just the last 30 days:
• Figure reportedly reached 37.5M real-world video uploads
• Scale AI and Hugging Face released FineART, with 533K+ annotated robot subtasks
• NVIDIA introduced Human 1M Dataset, featuring synchronized multi-view footage and 3D hand tracking
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Collecting data is only the beginning.
Qualification, annotation, evaluation, and verification are becoming critical infrastructure for physical AI.
The next step is making that infrastructure open, shared, and reusable.
pagifdn.org/
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You can buy training data. You can’t prove it’s good.
That's the problem space we're building for ҉
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The internet gave AI its first great training set ◌
The physical world will provide the next one ҉҉
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҉҉ ᴘʜʏꜱɪᴄᴀʟ ᴀɢɪ ꜰᴏᴜɴᴅᴀᴛɪᴏɴ ʟᴏᴀᴅɪɴɢ... ҉҉
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Raw data is not training data.
And what happens between capture and training matters just as much as the capture itself.
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