Summary
In this talk, Stepan argues AI is pushing the economy from capturing attention to fulfilling intention. Instead of users spending hours searching, comparing, and coordinating, they will express goals (“Buy a Burning Man bike,” “Plan a Lisbon offsite under $X”), and a market of specialized AI agents will plan, source, negotiate, and execute. Because agents dramatically cut transaction costs, many tasks that once favored in-house teams will move to open markets where agents compete, yielding better outcomes and prices.
This system requires distributed market mechanics rather than a single platform or super-agent: agents compete in multi-attribute auctions over intents, settle via cryptographic contracts, and interoperate through emerging agent standards. Trust comes from privacy-preserving user context plus public agent reputation and verifiable work receipts. With agent autonomy improving exponentially (e.g., code, legal, marketing), the speaker expects working intent-economy rails within 1–2 years, creating major opportunities for builders, researchers, and investors.
Key Takeaways
- Shift from “attention economy” → “intention economy.” Value moves from time/clicks to outcomes: you state a goal, a network of AI agents delivers it.
- AI agents gain economic agency. Individuals will run dozens; orgs will run thousands—working 24/7 and transacting autonomously.
- Post-Coasean dynamics. As agents slash search, bargaining, contracting, and enforcement costs, markets beat firm boundaries more often; AI-native orgs stay lean and move faster.
- Why a network (not one super-agent): Such a singleton doesn’t exist; economics/history favor distributed, competitive markets over centralized platforms that may front-run or under-optimize user value.
- Every intent becomes a market. Intents are posted; solvers (agents/companies) compete to fulfill them; auctions drive efficient price discovery.
- Auctions must be multi-attribute. Matching isn’t just price—also SLA, ETA, constraints, policies, etc., turning intents into personalized RFPs.
- Throughput advantage. Agent-to-agent comms scale at hundreds of tokens/sec, compressing coordination time versus human bandwidth.
- Practical stack emerging. Interop and trust need standards: A2A (agent-to-agent context), MCP (tool/supply-chain orchestration), u004 (work validation via re-runs/TEEs/economic checks), X402 (agent-to-agent payments).
- Institutional layer required. Combine user privacy (ZK/FHE) with public reputation/track records for agents; cryptographic contracts govern fulfillment and recourse.
- Timeline & scale. Early versions could appear in 12–24 months; the target is a $10T+ swath of today’s digital economy (ads, e-commerce, B2B SaaS, social).
