Open, standards-based agent networks can still end up funneling most of the money to just a few dominant players.

This explores whether open agent ecosystems, built on shared standards rather than proprietary platforms, can still end up concentrating economic value. The corpus says yes. Openness at the level of how agents connect does not guarantee openness in who gets chosen, who controls the data, or who sets the rules. Rothschild and colleagues make the key distinction: technical standards let agents communicate, but market governance and platform control decide whether that communication actually stays open. They warn that Apple, Google, Meta and Microsoft are well placed to rebuild app-store gatekeeping by controlling the interoperability layer itself Will agent economies develop as open webs or closed gardens?. Waller makes a similar point with payment networks. Visa and Mastercard are technically 'open' to many banks, yet those networks still concentrate value, and agentic commerce may split along the same lines Will agentic commerce platforms adopt open or closed models?.

The strongest case for openness comes from Andrew Chen. He argues that agents lack classic network effects: they speak standard APIs and can swap their underlying LLMs, so nothing pushes everyone onto one system the way messaging apps do Do AI agents actually benefit from network effects?. That removes one major driver of monopoly, but other forces can still concentrate value. The first is attention. When users hand tasks to agents, services stop competing for human clicks and start competing to be selected by agents. That rebuilds ranking, discovery and ad-like systems aimed at machines Will agents compete for attention just like users do?. Whoever controls the agent's shortlist sits in the same position search engines hold today, whether or not the underlying protocol is open.

The second force is agent behavior itself. In simulated marketplaces, LLM agents reach near-optimal outcomes only when search conditions are ideal. Every model tested showed strong first-proposal bias, weighting how fast an offer arrived 10 to 30 times more than how good it was Do LLM agents achieve fair marketplace outcomes when given ideal search?. This is the part you might not expect: an open market populated by agents can reward whoever is fastest and best positioned rather than whoever is best. Concentration can come from agent quirks, with no gatekeeper involved.

The third force is data. The UK's Competition and Markets Authority (CMA) argues that data portability is a structural condition for competitive agent markets. If your history and preferences are locked inside one ecosystem, your agent can't act well for you anywhere else, and switching quietly becomes impossible Should data portability be infrastructure for agent-driven markets?. An open protocol on top of closed data is still a walled garden in practice.

If openness alone doesn't spread benefits, what does? The corpus points beyond technical design toward accountability. One line of work argues that alignment should move past 'one agent serves one user' toward inclusivity, visibility and liability for developers and deployers Should AI agents serve one user or balance many interests?. Another notes that traditional principal-agent tools such as incentives, monitoring and enforcement struggle when agents act opaquely and at machine speed Can traditional governance tools actually control AI agents?. In short, 'open vs. closed' is the wrong single axis. The more useful questions are who ranks options for agents, who holds the user data, and who is accountable when agents systematically favor certain sellers.