The panel explored why agents need crypto rails, how to finance the compute stack, and the guardrails for truth and privacy on-chain.
Speakers:
Niraj Pant, Co-Founder, Ritual AI,
Jansen Teng, Co-Founder, Virtuals Protocol,
David Choi, Co-Founder, USD.AI,
Richard Muirhead, Co-Founder & Managing Partner, Fabric Ventures
We sat in on the panel exploring where AI and crypto genuinely intersect and what builders should prioritise now. The discussion centred on why autonomous agents need crypto rails for payments, identity, and verifiable state; how to finance the compute-heavy stack at machine speed; and the guardrails required for truth and privacy.
The Agentic Moment: Crypto as Coordination Fabric
Stablecoins have proven crypto’s payment rails. The next step is agents using those rails, which begs the question: which AI × crypto primitives will scale, and why do they need a blockchain?
Today, autonomous agents already handle discovery, due diligence, and execution. They are also turning complex products such as options into natural-language intents. As workflows span multiple agents, apps, and organisations, coordination breaks without a shared, verifiable source of truth—and blockchains provide that baseline.
Teng sets out three reasons agent economies run better on-chain: state, escrow, and reputation. On-chain memos and payments keep context intact between agents. Programmable money enables conditional release and independent checks before funds move. Persistent histories let agents choose reliable counterparts at scale.
Financing With Agent-Ready Infrastructure
Training, power, and datacentre build-outs demand trillions, and traditional finance moves too slowly for hardware with a 1 to 3-year shelf lives. That is why Choi’s USD.AI issues on-chain debt primitives that price AI compute risk in minutes, raising funds within hours of launch. The rule is simple: finance paying for usage now rather than hypothetical demand.
Provable by Default, Private by Design
Agent ecosystems will not scale if disputes arise with every transaction. Provenance should be cryptographic and resolution automatic, with hashed signatures and records of every action stored on-chain.
A slashing mechanism can deter fraud, and curation incentives that pay contributors for high-quality labelling and verification help these ecosystems remain trustworthy without a central gatekeeper.
With regard to privacy, such protocols can use zero-knowledge proofs to demonstrate rights or actions without exposing details, run sensitive workloads in confidential compute (TEEs), or apply fully homomorphic encryption where needed.
The bar is clear: satisfy compliance, protect users and enterprises, and give developers ergonomic tools without sacrificing performance or reliability.
Conclusion
In the near term, the most useful opportunities sit in focused, easy-to-measure tasks that can be handed to small AI assistants inside products people already use. Agents can prepare purchase records for finance teams, pull together background research before a meeting, and carry out simple trading routines with clear rules.
It helps to define the road to full launch up front by setting targets for speed and uptime and capping costs with alerts, which in turn helps the system move from trial to trusted automation.
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