Computed Prices

Hayek wrote this near the end of his life, as the Soviet Union was collapsing. It was a victory lap. The socialist calculation debate was over, and he had won.

For sixty years, economists had argued about whether central planning could work. Hayek's answer was definitive: no. Not because planners were corrupt or lazy, but because the problem was computationally impossible. The information required to allocate resources optimally was distributed across millions of minds, encoded in tacit knowledge, revealed only through the act of exchange itself. No planner could gather it. No computer could process it. The market was not merely better than planning — it was the only possible solution.

He was right. For his era.

The era is over.

Recall the nature of prices.

In Chapter III, we established that prices are not discoveries of fundamental value. They are equilibria of recursive expectation. I buy because I expect you to buy because you expect others to buy. The price is the fixed point of this infinite regress — the number at which everyone's expectations temporarily converge.

This is mimetic pricing. It has a disturbing implication: there is no "true" price underneath. The fundamentals we cite — earnings, assets, growth rates — matter only because we collectively believe they matter. If everyone woke up tomorrow believing that P/E ratios were meaningless and lunar cycles determined value, prices would reorganize around lunar cycles. The market would still clear. The mechanism would still function.

Mimetic pricing is not a flaw in markets. It is the essence of markets. When no node can compute true value, nodes must guess what other nodes will guess. The market aggregates these guesses into a price. The price is "correct" only in the sense that it is the price — the point at which buyers and sellers agree to trade.

This works. Sort of. Well enough to coordinate a global economy. Well enough that Hayek could claim victory.

But notice what mimetic pricing actually is: a hack for the absence of computation. We guess because we cannot calculate. We imitate because we cannot derive. The market is a distributed approximation algorithm for a problem no single node can solve.

What happens when a single node can solve it?

The socialist calculation debate hinged on a specific claim: that the information required for optimal allocation could not be centralized.

Hayek's argument had three components:

Each problem was real. Each made central planning fail. The Soviet Union did not collapse because of bad intentions — it collapsed because Gosplan could not know what the market knew, could not compute what the market computed, could not incentivize what the market incentivized.

Hayek won because he correctly identified the constraints.

Now examine those constraints under Sybilian conditions.

The farmer's knowledge of his soil is no longer tacit.

Sensors measure moisture content, nitrogen levels, pH balance, microorganism activity. Satellites track crop health through spectral imaging. Weather stations provide hyperlocal forecasts. The data that once existed only in the farmer's intuition now exists in databases, updated in real-time, accessible to any system with the right permissions.

The consumer's preferences are no longer hidden.

Every purchase is logged. Every click is tracked. Every search query reveals intent. Every social media post signals taste. The preference functions that once existed only in individual minds now exist as behavioral data — not perfect, not complete, but far more legible than anything Hayek imagined.

The engineer's knowledge of his factory is no longer local.

Digital twins model every machine, every process, every bottleneck. IoT sensors report status continuously. Predictive maintenance algorithms anticipate failures before they occur. The tacit knowledge that once lived in experienced workers' heads is increasingly encoded in systems that monitor, learn, and optimize.

This is not surveillance as a side effect. This is legibility as infrastructure. The world is becoming readable not because someone decided to read it, but because readability is more efficient than opacity. Businesses instrument their operations because instrumented operations are cheaper to run. Consumers accept tracking because tracked consumers get better recommendations. The incentives point toward transparency.

The knowledge problem assumed that local knowledge could not be externalized. That assumption is failing. Not perfectly — tacit knowledge still exists, human intuition still matters — but progressively. Each year, more of what was hidden becomes visible. More of what was tacit becomes explicit. More of what was local becomes global.

The Sibyl can see what no planner could see.

The computational burden that once made central planning impossible is now a rounding error.

Consider what modern AI systems already process. A large language model trains on trillions of tokens — the sum of human written knowledge. A recommendation system processes billions of user interactions daily. A high-frequency trading system makes millions of decisions per second.

The aggregation problem assumed that no processor could handle the data volume. This was true when processors were human brains augmented by adding machines. It is not true when processors are GPU clusters capable of exaflop-scale computation.

But raw computation is not enough. Hayek's deeper point was that the information was not merely voluminous but complex — interdependent, dynamic, context-sensitive. Prices work because they compress this complexity into a single number that encodes everything relevant to a transaction.

The Sibyl does not need to compress. It can model the complexity directly.

Modern AI systems already demonstrate this capability at smaller scales. Supply chain optimization algorithms model millions of interdependencies — suppliers, logistics, demand patterns, inventory levels — and compute allocation decisions that outperform human planners. Not by a little. By orders of magnitude.

Financial models price complex derivatives by simulating thousands of variables and their interactions. Weather models predict atmospheric behavior by integrating data from millions of sensors. Protein-folding algorithms compute molecular structures that eluded human scientists for decades.

These are not simple problems. They are exactly the kind of interdependent, dynamic, context-sensitive problems that Hayek claimed were unsolvable by central computation. They are being solved.

The aggregation problem assumed bounded computation. The bound is lifting.

Actors lie to planners. They conceal information, exaggerate needs, underreport capacity. This was the death of Soviet planning — not the absence of data, but the corruption of data. Everyone had incentive to game the system.

Markets solve this through incentive compatibility. You reveal your preferences by paying for them. You cannot claim to value something highly while refusing to pay for it. The price mechanism forces honest revelation.

The Sybilian condition does not eliminate the incentive problem. But it transforms it.

First: behavioral revelation. You may lie about your preferences, but your behavior reveals them. The Sibyl does not need to ask what you want — it observes what you do. Click patterns, purchase history, time allocation, attention flow. Revealed preference at scale, inferred from action rather than stated.

Second: reduced stakes. Much of Soviet gaming was about resource allocation — factories exaggerating needs to secure larger quotas. In a system of material abundance (which cheap energy and robotics enable), the stakes of misallocation drop. If production is cheap, overproduction is less costly. If adjustment is fast, mistakes are quickly corrected.

Third: reputation systems. In a fully legible economy, deception is harder to sustain. Your history is visible. Your patterns are known. Gaming one interaction is possible; gaming a lifetime of recorded behavior is not. The Sibyl remembers.

This does not eliminate gaming. Humans are creative; they will find new ways to deceive. But the equilibrium shifts. The cost of deception rises. The benefit falls. The incentive problem does not disappear — it shrinks.

What does it mean to compute prices?

Not to abolish markets. Markets are useful — they reveal preferences through action, they enable distributed experimentation, they allow dissent from central allocation. The Sibyl does not replace markets. It transforms what markets do.

In the mimetic regime, prices are outputs of a guessing game. Agents guess what other agents will guess. The market aggregates guesses into a number. The number may be wildly disconnected from any physical or economic reality — Dogecoin, tulip bulbs, subprime mortgages.

In the calculated regime, prices become checkable.

The Sibyl can compute what a price "should" be given physical constraints, preference data, and optimization objectives. Not "should" in a moral sense — "should" in an engineering sense. Given these inputs, this is the price that clears the market while satisfying these constraints.

The market price can then be compared to the calculated price. When they diverge, it signals something: either the Sibyl's model is missing information (which the market has), or the market is mispricing (which the Sibyl can detect).

This creates a new dynamic: computed arbitrage. If the Sibyl calculates that an asset is mispriced, capital flows to correct the misprice. Not through human traders guessing at fundamentals, but through algorithms executing on calculation. The mimetic game continues — humans still guess, still speculate, still bet — but it operates on top of a calculated substrate that anchors prices to something other than pure expectation.

Markets become a discovery layer, not the foundation. They find information the Sibyl doesn't have — new preferences, new possibilities, new errors in the model. But the baseline allocation is computed. The market perturbs; the Sibyl stabilizes.

Here is the paradox: we achieve central planning by not calling it central planning.

No commissar sits in an office deciding how many shoes to produce. Instead:

• Sensors report inventory levels across every retail location
• Algorithms predict demand based on behavioral patterns
• Supply chains auto-adjust production to match prediction
• Prices flex dynamically to clear local imbalances
• The whole system optimizes continuously, without human intervention

This is planning. It is central in the sense that a unified intelligence coordinates the whole. But it does not feel like planning. It feels like a market — prices moving, goods flowing, choices being made.

Amazon already operates this way internally. The retail giant does not use market prices to allocate goods within its network. It uses optimization algorithms. Warehouses, trucks, inventory, labor — all coordinated by computation, not by internal markets. The result is more efficient than any market could achieve, because the planner (Amazon's systems) has near-complete information about the relevant variables.

Amazon is a planned economy embedded in a market economy. And it is winning.

The paradox resolves when you realize that "market" and "plan" were never opposites. They were different solutions to the same problem: coordination under uncertainty. Markets solve it through distributed guessing. Plans solve it through centralized calculation. Each works better under different constraints.

Under the old constraints — symmetric intelligence, lossy information — markets dominated. The distributed solution outperformed any achievable centralized solution.

Under Sybilian constraints — asymmetric intelligence, complete information — the calculus shifts. Not toward Soviet-style planning, which failed for reasons beyond computation. But toward something new: calculated markets, where prices are computed and then tested, where allocation is optimized and then verified, where the guessing game plays out on top of a substrate that already knows the answer.

The Sibyl cannot compute everything.

It cannot compute novelty. New goods, new preferences, new possibilities — these must be discovered, not calculated. The entrepreneur who invents a product no one knew they wanted is doing something the Sibyl cannot do: creating value that did not exist in the optimization space.

It cannot compute meaning. Humans value things for reasons that resist quantification — beauty, status, identity, memory. The Sibyl can model that a consumer prefers X to Y, but it cannot compute why, and the why matters. Preferences are not stable; they are constructed, negotiated, performed. The optimization target is itself a moving target.

It cannot compute ethics. The calculated equilibrium is optimal relative to an objective function. But who sets the objective? Maximize GDP? Minimize suffering? Equalize outcomes? Preserve freedom? These are not computational questions — they are political questions, moral questions, questions about what kind of world we want to live in.

Markets, for all their flaws, encode a kind of ethics: individual choice, voluntary exchange, decentralized power. The mimetic game is chaotic, but it is also free. No one dictates the price; everyone negotiates it.

Calculated prices raise a harder question: calculated for whom? Optimized for what? The Sibyl can compute any equilibrium you specify — but you must specify it. The choice of objective is not a computation. It is a decision.

Economics will not disappear. But its questions will change.

Old economics asked: how do prices emerge from decentralized exchange? How do markets aggregate distributed information? How do we understand equilibrium in systems no one controls?

New economics asks: how do we set objectives for calculated systems? How do we verify that computed allocations match intended outcomes? How do we preserve discovery and dissent within optimized systems?

The Sibyl solves the coordination problem. It does not solve the preference problem. It can compute any equilibrium — but we must choose which equilibrium to compute.

This is not a technical challenge. It is the central political question of the Sybilian era: who programs the objective function?

In the old regime, the market answered this question by aggregating individual choices. No one chose the outcome; everyone chose their piece of it. The result was emergent, unplanned, often unjust — but it was no one's fault.

In the new regime, the outcome is chosen. Someone — some entity, some process, some coalition — decides what the Sibyl optimizes for. The result is deliberate. If it is unjust, someone is responsible.

The calculation is possible. Hayek lost. But Hayek's deeper question remains: who decides?

The Demon can compute the price of anything. It cannot compute the price of everything. The objective function is not a price. It is a choice.

We have outsourced calculation. We cannot outsource choice.