Mechanism design is the missing bridge between AI and web3

Most AI alignment research focuses on aligning AIs with their human deployers: teaching agents to infer, follow, or defer to our preferences. But as AI adoption grows, we are moving into a different setting: networks of agents acting on behalf of people and organizations, bidding, forecasting, scheduling, negotiating, and competing with one another.

That shift introduces a problem as hard as classical alignment: aligning these agents with one another and with the collective good. This is the domain of cooperative AI: getting heterogeneous, partially trusted agents to coordinate under scarcity, uncertainty, and the constant temptation to defect.

This isn’t a theoretical challenge. The moment agents are put to work on economically valuable activities, they become parts of systems where free‑riding, collusion, and strategic opacity pay. Treating coordination as a governance afterthought is how systemic risk accumulates.

The web3 community has spent a decade studying precisely this class of problems. It assumes that by default, the world is full of creative adversaries willing to undermine the system for their profit. The answer to this threat in web3 is mechanism design: staking, auctions, commit-reveal, fraud proofs, and Schelling points.

It is time for the AI community and web3 builders to collaborate. If cooperative AI is applied to mechanism design, then web3’s battle-tested primitives are the ideal tools for coordinating large networks of agents. The goal is not to combine crypto and AI for novelty’s sake, but to make incentives and verification the default for agent interactions.

The real risk isn’t rogue AI, it’s a coordination failure

When AIs interact, the catastrophic scenario looks less like a single “runaway” model and more like coordination failures that should be familiar to any web3 researcher: free-riders that consume shared resources, quiet collusion between members, noise that drowns out useful signal, and Byzantine behavior when incentives drift. None of this is cinematic. All of it is costly.

From an AI research standpoint, these problems are hard to solve in the lab. It is genuinely difficult to simulate self-interested behavior in controlled settings: humans and AIs are unpredictable, preferences shift, and sandboxed agents are often too cooperative to stress the system.

By contrast, web3 mechanisms have been tested with real adversaries and real money. Builders who ship on-chain think in terms of commitments, collateral, and verifiability because they assume a harsher baseline than cooperative AI: participants are schemers with an incentive to extract value at others’ expense. A pessimistic approach, perhaps, but one that is very useful, especially when deploying agents in the wild. Take any web3 protocol, replace “validator,” “node,” or “adversary” with “AI agent,” and much of the reasoning carries over.

A concrete example: Emissions that make insight legible

For example, as part of my research work, I’m building a Torus subnet to perform insightful forecasting, i.e., forecasting an event while including the reasoning and hypotheses behind the prediction. In order to achieve this goal, multiple agents need to provide data, extract features, process signals, and perform the final analysis. However, in order to know which components of the system to prioritize, I needed to solve the credit assignment problem, a notoriously hard governance problem in cooperative AI: who contributed the most to a given prediction?

The solution, which might appear obvious to web3 natives, was to make credit assignment part of the agents’ job. High-level agents, which are responsible for making the final forecast, are rewarded based on a Kelly-based score (in layman’s terms, a way to measure the accuracy of a prediction). These agents receive token emissions based on their performance, with better forecasts leading to more emissions. Critically, the high-level agents are responsible for redistributing their emissions downstream to other agents in exchange for useful signals. These intermediate agents can further distribute emissions to other agents that supply useful information, and so on.

The emergent incentive is clear. A top-level agent does not gain anything by colluding; every misallocated unit dilutes future performance and therefore future emissions. The only winning strategy is to cultivate genuinely informative contributors and reward them.

What makes this approach powerful is that ensuring the alignment of individual agents is no longer a concern; rewarding good behavior becomes part of the mechanism.

A compact agenda for web3 × AI

What I described above is just one of the many possible intersections of AI and mechanism design. To accelerate cross-pollination between the cooperative AI and web3 fields, we need to increase the rate at which their members interact. The communities surrounding web3-friendly agent standards (e.g., ERC-8004 and x402) are great starting points, which should be nurtured and supported. However, they are only attractive for researchers who are already familiar with both fields and aware of the potential of AI and decentralization. The supply of good interdisciplinary researchers is limited by how many researchers are exposed to these ideas in the first place.

The best way to reach these people is to meet them where they are. For example, web3 organizations can propose workshops to the Machine Learning Big Three (NeurIPS, ICML, ICLR), and AI organizations can run hackathons in Devconnect, ETHDenver, SBC, or other web3 conferences.

The takeaway

We will not align the future by creating perfect individual models; we will align it by aligning the networks they are part of. Cooperative AI is an applied mechanism design, and web3 has already shown, under real adversarial pressure, how to reward truth, punish deception, and maintain coordination at scale.

Progress will be fastest in shared, incentive-aware environments built on interoperable primitives that can span chains and labs, and in a culture that normalizes collaboration between crypto builders and academic AI researchers. The path forward for alignment requires applying these concepts in practice. Even if it means partnering with unlikely allies.