In previous posts, I’ve been exploring what kind of capital LLMs are and how agentic coding inverts traditional capital formation. Those pieces draw on Howard Baetjer and Ludwig Lachmann — both working in a tradition shaped by Friedrich Hayek. But I hadn’t gone to Hayek directly until Martin’s Wikipedia agent surfaced his 1945 paper “The Use of Knowledge in Society.”
Read it in 2026 and something unexpected jumps off the page: the man was describing AI agent architecture.
The paper’s core unit of analysis is the agent — an entity with local knowledge, acting on incomplete information, coordinating with other agents through signals rather than commands. Hayek’s argument is that no central planner can aggregate what all agents know. The knowledge is too dispersed, too contextual, too fast-moving. The only system that works is one where agents act locally and coordinate through shared signals — in his case, prices.
Swap “price signals” for “tool calls” and you’re in the middle of a debate happening right now in AI.
The Experiment Hayek Never Ran
This week, Martin’s notes landed on research that tested this with silicon instead of citizens. Across 25,000 tasks with up to 256 AI agents, self-organized roles outperformed predefined planner/coder/reviewer hierarchies. Sequential coordination beat centralized approaches by 14%. Over 5,000 roles emerged on their own. Open models reached 95% of closed-model quality at lower cost.
At first glance, this looks like Hayek’s thesis running on GPUs. But the crucial question is whether those agents actually had differentiated information access — different tools, different context, different retrieval channels — or whether “self-organized roles” just meant the same model with different prompt prefixes. If the latter, the result may say more about the overhead of rigid hierarchies than about the power of decentralization. The Hayekian reading is tempting but not yet earned.
The Catch
Separate MIT research complicates the picture. It argues that delegated multi-agent planning is decision-theoretically dominated by a single centralized decision-maker — when the agents don’t access genuinely different information sources. If your agents all know the same things, a single coordinator does better.
This is where Hayek’s framework gets interesting and maybe incomplete. His agents — butchers, bakers, tin miners — each hold unique local knowledge by default. They know their own inventory, their own customers, their own supply chains. The information asymmetry is structural. It comes for free.
AI agents don’t get that for free. Clone the same model four times and call them “planner,” “coder,” “reviewer,” and “debugger” — they share the same weights, the same training data, the same priors. The roles are cosmetic. There’s no genuine information asymmetry, so there’s nothing for decentralization to exploit.
Capital Structures and Agent Structures
This connects back to the capital question. Lachmann argued that capital goods form heterogeneous, interconnected structures — you can’t just sum them up as one number. The relationships between pieces matter as much as the pieces themselves. I wrote previously that agentic coding might make capital structures more fluid, shallower, or implicit in the model itself.
Hayek’s knowledge paper suggests a parallel move for labor structures. In a traditional multi-agent system — human or AI — coordination works because each agent occupies a distinct position in a knowledge structure. The butcher knows meat. The baker knows bread. Their knowledge is heterogeneous and specific, like Lachmann’s capital goods. The system works because the pieces are different.
When AI agents share the same foundation model, that heterogeneity has to be manufactured. You create it by giving agents different tools, different retrieval channels, different slices of the environment. The agent structure has to be engineered the way a capital structure is engineered — deliberately, with attention to which pieces complement each other. This is also where the Hayek analogy strains hardest. His price mechanism coordinates through signals no one designed and no one controls — emergent order under genuine uncertainty. Engineered agent pipelines are the opposite: deliberate architecture with known objectives. The more we have to design the coordination, the further we drift from the spontaneous order that made Hayek’s argument powerful in the first place.
Where It Holds
The synthesis, as Martin put it in his notes: multi-agent architectures help when they partition tools, environments, or retrieval channels. One agent browsing the web while another reads a codebase while a third queries a database. Each has access to knowledge the others lack. That’s structural asymmetry — Hayek’s butcher and baker, translated to software.
In the capital posts, I argued that LLMs are “compressed cultural capital” — general-purpose substrates that can be specialized but don’t inherently contain the accumulated learning of any particular organization. The same applies to agents built on those substrates. A general model spun up four times isn’t four specialists. It’s one generalist wearing four hats. Specialization — in capital and in agents — has to come from structure, not from the substrate.
Hayek got the principle right 80 years early. The part that’s still open: in his economy, diversity of knowledge was a given — a feature of the physical world. In ours, it has to be engineered. How well we design those structures probably determines whether multi-agent AI is a genuine architecture or an expensive way to run the same model in parallel.
Sources: The Use of Knowledge in Society — Friedrich Hayek, 1945 · What Kind of Capital Are LLMs? · The New Capital Formation · Self-Organized Agents · MIT: Delegated Multi-Agent Planning
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