Exploring a New Settlement Structure for AI × Web3:
Some thoughts on where AI × Web3 may be heading
As the DAO moves toward AI × Web3 as a strategic focus, many conversations naturally start at the tooling layer:
- AI helping write smart contracts
- Agents automating on-chain interactions
- Code, content, or transaction generation
These are all meaningful directions and will continue to improve efficiency.
But during the past months, while building an agent-native settlement primitive and discussing with several ecosystem partners, a deeper question started to emerge:
When AI stops being just a tool and begins acting as an economic participant,
the system may need a different kind of settlement structure.
The goal of this post is to share a conceptual frame that might help the DAO explore this direction more clearly.
1. Events are verifiable because they are closed systems
Most of Web3 today runs on events:
- a payment
- a signature
- a transaction
- an on-chain write
These actions have clear boundaries and deterministic results.
They belong to what we might call Minimum Verifiable Events (MVEs).
Because events are closed systems, blockchains can easily support:
event → proof → state → settlement
The v0 prototype I recently deployed on Base mainnet validates that this sequence can hold in a minimal, live setting.
2. Economic outcomes, especially from AI agents, belong to open systems
AI agents don’t just produce events—they produce economic outcomes (short: outcomes):
- quality of task execution
- a recommendation
- a collaborative result
- a contribution to a public good
Outcomes usually exhibit:
- context dependence
- incentive dependence
- evolving behavior
- no single “correct” answer
In other words:
Outcomes cannot be “verified” the way events can,
because they arise from open, non-deterministic systems.
This isn’t a limitation of blockchains—it’s the nature of open systems.
And once AI agents scale, event-level settlement frameworks reach their limits.
3. This suggests the need for outcome-aware settlement (assurance)
If outcomes cannot be verified, then settlement cannot rely solely on correctness checks.
A future system may need:
- risk-aware settlement
- conditional execution
- delayed or rejected settlement
- multi-signal assessment
- programmable accountability
- definitions of acceptable outcome ranges
The goal becomes:
not verifying correctness, but determining whether an outcome can be safely settled.
This feels like a new layer, somewhere between execution and economic finality.
4. The v0 mainnet prototype is a primitive, not a product
The Base-mainnet v0 I recently released is intentionally minimal.
Its purpose is to:
- demonstrate that event → proof → state → settlement can run on-chain
- show a minimal loop for agent-native public goods contribution
- validate that settlement primitives can be executed in a realistic context
It is not an application.
It is a foundational primitive — a starting point for exploring outcome-level structures.
5. Why this might matter for the DAO
If the DAO wants to explore agent economies, a key question will emerge:
How should the economic consequences of AI behavior be settled?
This is not only an engineering challenge;
it is fundamentally a protocol architecture question.
Right now, the broader ecosystem lacks:
- a shared vocabulary for outcomes
- assurance primitives
- responsibility models for agent interactions
- composable trust signals
- frameworks for settlement under uncertainty
This means the DAO is well-positioned to explore and potentially shape this emerging area.
6. A simple mathematical analogy:
Closed systems can be verified; open systems must be assured
This analogy has helped many discussions crystallize:
Closed systems → Verifiable
- finite state
- deterministic behavior
- clear correctness conditions
This aligns with event-level settlement.
Open systems → Assurable
- non-deterministic
- path-dependent
- context-dependent
- no strict correctness condition
This aligns with outcome-level settlement.
In this framing, Web3’s role is not to prove outcomes true.
Instead, Web3 provides the structure that makes outcomes executable and economically safe:
- assumptions
- constraints
- incentives
- accountability mechanisms
These enable an inherently unverifiable outcome to become:
executable, settle-able, and governable.
This may be where AI × Web3 converge most deeply.
Looking forward to exploring this direction together, any thoughts or perspectives are welcome.
Exploring a New Settlement Structure for AI × Web3:
关于 AI × Web3 的一些结构性思考
DAO 最近将 AI × Web3 作为重点方向，这对整个生态都是一个新的探索机会。
很多讨论目前集中在工具层，比如：
- 用 AI 写智能合约
- 让智能体自动执行链上操作
- 生成内容、代码或交易逻辑
这些能力都非常实用，也会持续推动效率提升。
但在过去几个月构建一个 agent-native settlement primitive（并和生态内外不同伙伴交流）时，我逐渐看到另一层可能性：
当 AI 不再只是“工具”，而是参与经济行动的主体时，我们可能需要一种新的结算结构。
下面尝试分享一些更底层的观察，希望有助于 DAO 在未来讨论方向时参考。
1. Event（事件）是可验证的，因为它是闭合系统
今天绝大部分 Web3 基础设施都运行在事件层：
- 一次支付
- 一次签名
- 上传数据
- 完成一笔交易
这些操作都有共同点：
- 边界清晰
- 输入确定
- 可重复验证
它们属于 最小可验证事件（Minimum Verifiable Events, MVEs），因此我们能在链上跑通：
event → proof → state → settlement
我最近上线的 Base 主网最小原型（v0）正是验证这一结构可以最简化地成立。
2. Outcome（经济结果）与 event 本质不同，是开放系统
AI agents 的行动，不再是一次性的 event，而是 economic outcome（在文中简称outcome）：
- 执行某项任务
- 做出一个推荐
- 完成一项协作
- 产生一次贡献行为
这些结果往往具有：
- 不确定性
- 多路径
- 依赖环境与激励
- 没有单一正确答案
因此 outcome 的性质更像：
开放系统，无法通过传统的“验证正确性”来结算。
也就是说，event-level 的结构在 agent 大规模出现后，会自然达到它的边界。
3. 这意味着未来可能需要一种“结果可结算结构”（Outcome Assurance）
如果 outcome 无法像事件一样被验证，我们可能需要的是：
- 风险判断
- 条件执行
- 对结果的可接受范围
- 延迟或拒绝结算
- 信号组合
- 可编程的责任结构
不是要判断 outcome “是否正确”，而是判断：
这个结果是否可以被安全结算（settle）。
这是一个新的协议层，类似基础设施。
4. v0 主网原型的意义不是功能，而是原语（primitive）
我上线的 v0 showcase 不试图展示完整的产品。
它只验证：
- event → proof → state → settlement 的原语可以在主网上成立
- 公共物品贡献的经济闭环可以最小化地跑通
- agent-native 的结算可以拥有一个可执行的起点
这为我们提供了一个“稳固的起点”，未来可以在其上叠加更丰富的 outcome assurance 结构。
5. 为什么这在 DAO 的方向讨论中可能有意义？
因为未来的 AI × Web3 如果真的想发展成 “agent economy”，
我们迟早会遇到一个核心问题：
AI 的行为带来了经济后果，那这些后果要如何结算？
这不仅是工程层的问题，而是协议层的问题。
而行业目前对这部分的讨论还很少，框架也未完全成型。
如果 DAO 想在这个领域建立自己的理解与路线，现在是很好的时间窗口。
6. 数学类比：闭合系统可验证，开放系统可保障
这是我在与不同伙伴交流时最能清晰表达的类比，也分享给 DAO：
闭合系统（closed system） → 可验证（verifiable）
- 状态有限
- 边界明确
- 可以判断“对/错”
对应 event-level settlement。
开放系统（open system） → 可保障（assurable）
- 状态不封闭
- 路径依赖
- 没有固定正确答案
对应 outcome-level settlement。
Web3 在这里的价值，不是让 outcome “可验证”，而是在一个开放系统中提供：
- 假设（assumptions）
- 边界（constraints）
- 激励（incentives）
- 责任结构（accountability）
帮助一个无法验证的结果：
变得可执行、可结算、可治理。
这可能是 AI × Web3 的深层交汇点。
期待与大家一起继续探索这个方向，欢迎任何讨论、想法或补充。