How Polyhedra is making AI trustworthy—with zero-knowledge proofs
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The case for trust in AI
AI is getting smarter, faster—and more powerful. Soon, agents won’t just recommend products or summarize articles. They’ll make payments. Trade assets. Manage workflows autonomously.
But here’s the catch: how do we trust the decisions they make?
Eric from Polyhedra believes the answer lies in verifiable AI. In this episode, he breaks down how zero-knowledge (ZK) proofs can ensure machine learning inferences are provably correct—without slowing them down.
“ZK lets you mathematically prove what your model predicted, without exposing the model itself.”
Bridges, but bulletproof
Polyhedra didn’t begin with AI. It started with a more fundamental problem in Web3: cross-chain bridges are vulnerable.
Most rely on multisigs, oracles, or validator sets—each introducing trust assumptions and attack surfaces.
Polyhedra’s answer? Use ZK proofs to verify messages mathematically, across chains.
This led to the creation of Expander, a proof system that’s fast enough to scale interoperability without compromising security. And it didn’t stop there.
From secure bridges to verifiable models
The breakthrough came when the team realized Expander could also power ZKML—zero-knowledge machine learning.
Their flagship tool, ZK PyTorch, turns any PyTorch model into a verifiable model. Inference outputs can now be paired with a proof, confirming they came from the original model and weren’t manipulated or faked.
No re-training required. No new architectures. Just compile and prove.
“If AI agents are going to spend real money, they need receipts. ZK provides those receipts.”
Why it matters: finance, agents, and accountability
The most urgent use case? Autonomous agents in financial applications.
Picture this: an LLM agent moves funds or initiates a trade. How do you ensure its action was legitimate, not hijacked or hallucinated?
ZK proofs can confirm that the agent’s action came from a known model, using valid inputs—without exposing the logic or user data.
“It’s not enough for AI to be smart. It has to be accountable.”
Enter EXP Chain: the ZKML blockchain
To bring this vision to life, Polyhedra launched EXP Chain—a new L1 built specifically to verify ZK proofs cheaply and in real time.
Think of it as a blockchain optimized for trust. It’s not trying to be another general-purpose chain. It’s tailored for the future of verifiable AI, where every output can be validated on-chain, instantly.
Building the ecosystem
Polyhedra isn’t just shipping code—they’re growing a community.
Through events like the ZKML Festival, they’re helping ML developers understand what’s possible when you combine ZK proofs with neural networks.
And with Eric’s background in content-driven growth, Polyhedra is also leaning heavily into domain-specific education—bridging the gap between AI researchers and cryptography engineers.
Where it’s all headed
The endgame? Sub-second ZK proofs for LLMs.
That means fast, provable machine learning, baked into every inference. Whether it’s a chatbot, a trading agent, or a recommender system—verifiable AI becomes the default.
And Polyhedra is building the infrastructure to make that happen.
How to get involved
If you're building in Web3, working on ML infrastructure, or just curious about how trust will work in the next era of computing—Polyhedra wants to hear from you.
Listen to the full conversation:
Spotify
Apple Podcasts
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