As AI, cloud infrastructure, and blockchain systems scale, one critical issue remains unresolved: trust in compute. Today, most computation—whether powering AI models or executing financial transactions—operates as a black box.

In this episode of Untangling Web3, Leo Fan, founder and CEO of Cysic, explores how verifiable compute and zero-knowledge proofs can transform computation into something that is not just fast and scalable, but provably correct.

Key highlights:

Why Verifiable Compute Matters in an AI-Driven World: Modern compute infrastructure has been optimized for performance and scale, not for verifiability. As computation moved from local machines to centralized cloud providers and data centers, users increasingly lost visibility into how results are produced. This becomes critical with AI systems, where outputs are often trusted without insight into the underlying process. Verifiable compute introduces a new trust model: instead of trusting a provider, users can independently verify that a computation was executed correctly.
Zero-Knowledge Proofs as the Foundation of Trustless Compute Zero-knowledge (ZK) proofs enable computation to produce a compact, cryptographic “proof” alongside its output, certifying correctness without requiring re-execution. This transforms compute into something that can be verified quickly—even on low-power devices—while being executed on high-performance infrastructure. In blockchain systems, this replaces the need for every validator to re-run all computations, dramatically improving scalability. In AI, it allows developers to prove that a specific model, input, and workflow were used, even if the underlying system remains partially opaque.
From Black Boxes to Verifiable Systems: Verifiable compute bridges AI, blockchain, and decentralized systems, enabling a future where critical workflows—financial transactions, autonomous agents, and enterprise decision-making—can be trusted without relying on centralized intermediaries. This unlocks new architectures where compute happens off-chain at scale, while proofs are verified on-chain or by distributed networks. However, significant challenges remain: performance overhead in proof generation, developer complexity in integrating ZK systems, and limited user awareness of the need for verifiability.

Verifiable compute represents a fundamental shift in how digital systems are designed and trusted. By combining zero-knowledge proofs, blockchain, and advanced AI infrastructure, computation can move from opaque black boxes to transparent, provable systems.

As AI agents take on more responsibility and automation expands across industries, the ability to verify outcomes will become essential—not optional. The long-term vision is clear: a world where trust is no longer assumed, but mathematically guaranteed.

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Untangling Web3 is brought to you by hosts Jack Davies and Alec Burns, with music by Daniel Paigge. Got a question or topic suggestion? Send us an email at theuntanglingweb3podcast@gmail.com.

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#128 Untangling: Verifiable Compute w/ Leo Fan