2025 was the year @privasea moved from concept to real infrastructure.
What we shipped
✅ FheID live at scale: 800k+ verified humans
✅ Proof-of-Human running on encrypted ML, no biometric databases
✅DeepSea AI Network: FHE inference for decentralized compute
✅WorkHeart and Privanetix nodes online with real contributors and real workloads
What we proved
☑️Fully Homomorphic Encryption works in production, not just papers
☑️Humans can be verified without collecting or storing sensitive data
☑️AI agents don’t need raw data access to be useful
☑️Trust belongs at the compute layer, not bolted on later
Ecosystem growth
• Integrations across @BNBCHAIN , @solana and @arbitrum
• Deep research + engineering collaboration
• A global community of builders, node operators, and real users
2026 is about confidential compute as default infrastructure.
Thanks to everyone contributing - users, researchers, operators, and partners 🌍
We’re just getting started.
Google’s AI search summaries fabricated a lawsuit and the error persisted for months.
The issue wasn’t polish. It was opacity.
zkML fixes this by proving:
✔️ Which model made the claim
✔️ Which evidence contributed
✔️ Whether reasoning followed constraints
This isn’t an AI UX problem it’s a verifiability problem.
When models fabricate claims and no one can audit how they happened, proof becomes mandatory.
zkML ensures AI can’t just answer it has to show its work.
Why zkML? Because Google’s AI-generated search summaries invented a lawsuit against a U.S. electrical contractor, and attributed it to a state attorney general despite no such case ever existing.
The company discovered the fabricated claims back in 2024, filed suit in March 2025, and the false outputs persisted into November — well past the point when they should have been resolved.
Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 not due to lack of capability but lack of control.
zkML adds the missing layer
✔️ Prove which model acted
✔️ Prove guardrails were enforced
✔️ Prove actions aligned with policy
Gartner’s warning is clear: agentic AI without verifiability won’t survive in production.
zkML gives enterprises what they actually need proof of behavior, policy compliance, and control at scale.
That’s how agents move from demos to deployment.
Why zkML? Because Gartner now predicts that more than 40% of agentic AI projects will be canceled before the end of 2027.
The issue isn’t capability. It’s control. Enterprises don’t have a reliable way to verify what autonomous agents are doing once they’re deployed.
AI is no longer just helping write messages it’s becoming the person on the other side of the screen.
That’s why Polyhedra i-D exists.
✔️ Prove a real human is present
✔️ No biometric storage
✔️ No exposed personal data
AI can now fake faces, voices, and entire personalities.
What it can’t fake is cryptographic proof.
Polyhedra i-D brings real human verification to a world full of synthetic identities.
AI isn’t just writing dating bios anymore — it’s becoming the person on the other side of the screen.
Across dating apps and social platforms, synthetic identities are fueling a new wave of romance scams.
Here’s what’s happening 👇
AI like GPT-5 is now helping design and iterate real lab experiments.
At that point, mistakes aren’t abstract they’re physical.
zkML ensures scientific AI decisions are:
✔️ Attributable
✔️ Reproducible
✔️ Constraint-aware
✔️ Cryptographically verifiable
AI is no longer just analyzing results it’s shaping experiments.
When model outputs influence real-world lab protocols, verifiability becomes non-negotiable.
zkML makes scientific AI accountable, reproducible, and safe.
Why zkML? Because OpenAI’s GPT-5 has demonstrated the ability to assist with real scientific lab work, including designing, optimizing, and iterating on wet-lab biology protocols.
When AI begins influencing experimental decisions, correctness is no longer theoretical. It becomes physical.
AI may soon train and improve itself autonomously
At that point, logs and dashboards aren’t enough.
zkML makes self improving systems governable by proving
✔️ which model acted
✔️ which rules applied
✔️ how behavior evolved over time
The future of AI needs proof, not assumptions
Self-improving AI isn’t dangerous because it’s malicious it’s dangerous because it can evolve beyond oversight.
zkML gives us something we’ve never had before: verifiable behavior across changing models.
That’s how governance keeps up with intelligence.
Why zkML? Because Anthropic’s chief scientist has warned that humanity may soon face a critical decision: Whether to allow AI systems to autonomously train and improve themselves.
As models move toward recursive self-improvement, the challenge is no longer just capability — it’s governance.