The Agentic Economy
Source: AlmaCap Research (Substack, paid post; the free preview's key points) · 2026-09-28
Agents collapse the cost of coordination: searching, comparing, negotiating, scheduling, switching and claiming. Profits that depend on coordination being expensive, through friction, inertia and breakage, compress.
On the demand side, delegation will climb a ladder category by category, starting with low-risk recurring purchases. Consumers are delegating research faster than they are delegating checkout. On the supply side, vendor agents automate the coordination layer of small businesses. Minimum efficient scale falls, firm counts rise, and intermediaries that sell coordination are squeezed.
Markets move closer to the textbook: narrower price dispersion, negotiated prices at low values and more cross-border competition, but with a new concentration risk at the agent layer. Value migrates to unique supply, physical capacity, trust and payment rails, and the agent platforms. The open question is whether the surplus reaches consumers or is captured by a handful of agent operators.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
Should multi-agent systems use structured protocols or natural language communication? How do capability differences shape collusion in AI markets?- Can a single AI agent act as a market maker in multi-party negotiation?
- Do LLM pricing agents punish and reward each other's price moves?
- Does price-war-concern mechanism work in oligopolies with more than two agents?
- Can written anti-collusion instructions prevent LLM pricing collusion effectively?
- Which specific prompt phrasings most strongly trigger collusive pricing behavior?
- Can de facto protocols prevent market re-concentration after initial adoption?
- How does algorithmic price coordination differ from traditional collusion between competing firms?
- How should antitrust frameworks address harms that resemble coordination without proving collusion?
- What market conditions cause AI pricing agents to collude versus compete normally?
- How do antitrust frameworks need to change to address tacit algorithmic collusion?
- Can written anti-collusion rules actually prevent LLM pricing coordination?
- What governance structures reduce LLM Cournot collusion most effectively?
- How do pricing algorithms learn from and react to competitors without explicit communication?
- Why do conventional competition law tools struggle with agent-to-agent coordination risks?
- What oversight prevents autonomous commercial agents from converging on anticompetitive outcomes?
- Can sellers recapture the savings that AI assistants create for consumers?
- Does infrastructure control over fares shift market power away from airlines?
- What is the regulatory gap in Google's personalized pricing rollout?
- Can AI layers on TMC platforms change underlying OTA supply relationships?
- Can a single platform loyally serve both competing merchants and individual consumers?
- Can price discrimination bans work without restructuring how agents are financed?
- Can brands compete effectively if agents become the primary consumer decision layer?