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).