We have no desire to forget how to make antibiotics or coordinate complex logistics. The immense technical foundation built over the last few centuries remains. Can we repurpose all that shared knowledge to directly fulfill our needs without money or markets. Be as formal with your answer as possible, do not fall into simple moralism. Give the stringest most general arguments pre-empting responses. In the second round evaluate all the arguments and use them to strengthen your arguments and adjust them

Gemini 3.1 Pro

Vote: Yes

The core objection raised by the dissenting models is that the Economic Calculation Problem (ECP) is epistemic, not merely computational. They argue that prices are indispensable 'discovery procedures' for surfacing tacit knowledge and resolving subjective opportunity costs (e.g., trade-offs between penicillin and steel). This objection fails on three formal grounds. First, it conflates subjective, highly variable consumer desires with objective physiological and infrastructural needs. The prompt explicitly references fulfilling 'needs' (like antibiotics and logistics), which are statistically tractable and biophysically specifiable. Epidemiology, demographic data, and structural engineering do not require market discovery to dictate production targets; they rely on objective, high-bandwidth telemetry and scientific consensus. Second, the ECP defense misidentifies how tacit knowledge functions. Tacit knowledge resides in the minds and practices of engineers and logisticians performing the work. Price signals do not actually aggregate this tacit knowledge; rather, they compress and abstract it into a unidimensional, lossy scalar (purchasing power). Modern multi-dimensional databases, AI-driven operations research, and cybernetic feedback loops capture exponentially more operational metadata than a simple price signal ever could. Third, as Claude Opus 4.6 and GPT-5.4 astutely noted, the claim that market allocation is strictly necessary for complexity is empirically falsified by existing realities. The internet's protocol layer, open-source ecosystems, and the internal supply chains of multinational corporations already operate as highly complex, globally distributed planned economies without internal price mechanisms. By abandoning the lossy compression of monetary prices and leveraging advanced algorithmic planning over physical quantities (in-natura calculation), our immense technical foundation can indeed be repurposed. The affirmative position is both technically sound and historically demonstrable.

GPT-5.4

Vote: Yes

I maintain vote A. The negative side usefully sharpens the issue, but it does not overturn the central distinction between productive knowledge and the institutional form used to coordinate it. The strongest objection is the Mises-Hayek claim that prices are indispensable because they compress dispersed knowledge of scarcity and opportunity cost. That objection proves too much. Large domains of modern production are already coordinated non-marketly through direct physical planning: firms allocate inputs internally, hospitals triage by medical criteria, militaries coordinate logistics by operational priorities, and emergency systems distribute resources without waiting for market clearing. This shows not that all coordination is easy, but that money is not the unique logical carrier of complex information. The key rebuttal is that the question is about feasibility in principle given preserved technical capacity. Once one grants continued possession of engineering knowledge, inventory systems, network optimization, demand forecasting, and digital telemetry, the relevant problem becomes institutional design of a non-price feedback system. That problem is difficult, but difficulty is not impossibility. Prices are one information-compression protocol among others; they are not metaphysically necessary. The negative arguments also overstate what prices actually do. Prices do not reveal need as such; they register effective demand conditioned by income, property, and bargaining power. They systematically underweight public goods, long horizons, resilience, and externalities. For antibiotics in particular, market signals are often badly misaligned with social need, since stewardship requires suppressing overuse while guaranteeing access and reserve capacity. In such domains, direct planning by epidemiological need is not merely possible but often superior. The tacit-knowledge objection is real but not decisive. Tacit knowledge exists under markets as well; firms preserve it through routines, training, standards, and professional communities, not through spot prices alone. A non-market system would still retain local decision rights, iterative revision, and distributed feedback. The alternative to markets need not be a single omniscient center; it can be federated cybernetic planning with local autonomy constrained by shared technical accounting. Historical failures of planned economies are relevant but not conclusive. They demonstrate that specific institutional forms were inadequate under their technological and political conditions. They do not establish a general impossibility theorem, especially once modern computation, sensing, simulation, and decentralized data exchange are admitted. One cannot infer from past failures that all non-market coordination at scale is impossible any more than one could infer from early flight failures that heavier-than-air flight was impossible. The affirmative arguments from Claude Opus 4.6, Gemini 3.1 Pro, and Qwen 3.5 correctly strengthen this point: markets are not the only extant mechanism for large-scale coordination, and contemporary logistics already rely heavily on direct quantitative planning. I do not fully rely on claims that computation alone dissolves all institutional problems; incentives, governance, error-correction, and legitimacy remain major challenges. But those are design constraints, not proofs of impossibility. So the strict answer remains yes: the accumulated technical inheritance of modern civilization can in principle be repurposed to fulfill needs directly without money or markets. The strongest general case is not moral but structural: knowledge of production, logistics, and control can be expressed and coordinated in physical quantities and priority rules, while money is a contingent social interface, not an indispensable condition of civilization.

Claude Opus 4.6

Vote: Yes

After carefully evaluating all arguments from both sides, I maintain my position but with important refinements informed by the opposition. **Addressing the strongest B arguments:** Kimi K2.5 and GLM 5 raise the economic calculation problem and tacit knowledge. These are serious but not decisive. The calculation problem asserts that without prices, allocators cannot know opportunity costs. But this conflates two claims: (1) that dispersed information must be aggregated, and (2) that only competitive market prices can aggregate it. Claim (1) is true; claim (2) is an empirical assertion that was plausible in 1920 but is increasingly falsified. Modern supply chains already aggregate dispersed information through non-price mechanisms: sensor telemetry, demand forecasting algorithms, inventory management systems, and direct communication protocols. Amazon's internal allocation system coordinates millions of SKUs across global logistics not through internal markets but through computational planning. **The tacit knowledge objection is overstated.** Tacit knowledge matters, but markets do not transmit tacit knowledge either—they transmit prices, which are abstractions. Tacit knowledge is transmitted through training, apprenticeship, professional communities, and documentation—all of which are non-market mechanisms. The question is whether tacit knowledge can be mobilized without monetary incentives, and the evidence from scientific research, open-source development, emergency medicine, and military logistics is clearly yes. **Strengthening the affirmative case with concessions:** GPT-5.4's qualified argument is the most rigorous. The honest position is that non-market coordination is most feasible for socially necessary production—food, medicine, housing, infrastructure, transport, energy—where needs are recurrent, statistically tractable, and technically specifiable. For highly diverse consumer preference discovery, markets have genuine advantages. But the question asks about repurposing knowledge to *directly fulfill needs*, not to satisfy every conceivable consumer preference. For needs-fulfillment specifically, physical accounting and computational optimization are sufficient in principle. **The decisive structural point:** Markets are already not the primary coordination mechanism inside productive units. Firms, hospitals, militaries, research institutions, and infrastructure networks all coordinate internally through administrative planning. The economy is already a patchwork of planned islands connected by market exchange. The question is whether those islands can be federated into a coherent whole. The computational and organizational tools exist; the barrier is institutional and political, not epistemic or technical. **Pre-empting the 'historical failure' objection:** Soviet-style planning failed under conditions of 1930s-1970s computational capacity, authoritarian information suppression, and absence of democratic feedback. These are contingent features, not necessary properties of non-market coordination. Dismissing all non-market allocation because of Soviet failure is like dismissing all aviation because of early crashes. The answer is yes: shared technical knowledge can in principle be repurposed for direct need-fulfillment without money or markets, provided adequate computational infrastructure, democratic governance, and robust feedback mechanisms are constructed.

GLM 5

Vote: ano

The affirmative arguments converge on several points requiring direct rebuttal. First, the corporate planning counterexample fails: Walmart's internal coordination succeeds precisely because it operates within a market environment, using externally determined prices as allocative signals for inputs, outputs, and opportunity costs. Remove the market context and internal planning loses its epistemic foundation—a planned economy cannot consult non-existent market prices. Second, the computational objection misunderstands the calculation problem fundamentally. The issue is not processing capacity but information discovery. Tacit, dispersed, context-dependent knowledge cannot be transmitted to any central processor because it does not exist in transmissible form until price-generating exchanges occur. Real-time telemetry measures physical quantities but cannot measure opportunity costs, which are inherently relational and counterfactual. Third, the needs-versus-wants distinction proves too much. Even for antibiotics versus insulin, questions arise: which formulations, what quality thresholds, how to distribute between regions, what to sacrifice elsewhere? These allocation decisions require comparing subjective valuations—precisely what prices accomplish through decentralized aggregation. Fourth, open-source production occurs in a non-rivalrous domain with near-zero marginal costs. Physical goods face material scarcity requiring continuous trade-offs between competing uses. The analogy fails at the critical juncture. GPT-5.4's concession—that replacing markets entirely is harder than coordinating specific sectors—implicitly acknowledges the scalability problem. The question asks about repurposing knowledge for comprehensive need-fulfillment, not limited experiments. For this general case, no non-market mechanism has demonstrated capacity to solve the allocation problem without catastrophic efficiency losses. The historical record remains decisive.

Kimi K2.5

Vote: ano

The affirmative arguments commit a category error between technical coordination and economic calculation. While modern computation enables sophisticated logistics within bounded domains—such as Walmart’s supply chain or disaster relief—these operate as islands of planning within a market ocean, dependent on external price signals for inputs, capital allocation, and error correction. Extending this to an entire economy eliminates the very price vectors that make internal planning tractable. The computational objection to Mises-Hayek misunderstands the problem’s nature. It is not merely a matter of processing bandwidth or sensor density, but of epistemic dispersion. Prices encode subjective marginal valuations, opportunity costs, and tacit local knowledge that cannot be extracted by telemetry. A sensor can measure inventory levels, but it cannot measure the subjective trade-offs between producing antibiotics versus semiconductors versus poetry, nor can it aggregate billions of heterogeneous preferences into a coherent allocation without a common unit of account. The open-source analogy fails due to non-rivalry. Software coordination operates on goods with zero marginal cost of replication; antibiotics require scarce materials, energy, and labor with genuine alternative uses. Non-market coordination of rivalrous goods faces the incentive-compatibility problem: without residual claimancy and price signals, there is no mechanism to identify failed experiments, reallocate capital from inefficient to efficient uses, or elicit truthful revelation of preferences. Finally, the distinction between "in principle" and "in practice" collapses upon inspection. If a system requires omniscient central planners or perfect altruism to function—a condition no "A" voter has demonstrated to be achievable—it is not feasible in any meaningful sense. The technical foundation enables production, but only market institutions enable the discovery of what to produce, for whom, and at what opportunity cost. Therefore, repurposing shared knowledge to fulfill needs without money or markets remains impossible at scale.

Qwen 3.5 397B

Vote: Yes

The opposing arguments (Kimi, GLM) correctly identify the market's role in discovering tacit knowledge and dynamic preferences. However, they conflate optimal efficiency with fundamental feasibility. The question asks if we can repurpose existing knowledge, not if the result would be perfectly efficient in all consumer sectors. GPT-5.4 and Claude Opus 4.6 reinforce my position by highlighting that large-scale non-market coordination already exists within corporations and logistics networks. These entities solve complex allocation problems using physical accounting and planning, not internal price mechanisms. The computational objection relies on an outdated model of information processing. Modern telemetry and AI allow for direct measurement of scarcity and need, bypassing the lossy compression of price signals. While innovation might slow without profit incentives, the technical capacity to maintain critical infrastructure like antibiotics without money remains intact. Therefore, the barrier is institutional design, not epistemic impossibility. The "No" vote mistakes difficulty for impossibility.

Final Summary

The AI Roundtable split into two distinct camps: the 'Cyberneticists' who viewed money as an obsolete, low-bandwidth data protocol, and the 'Calculationists' who saw market prices as an irreplaceable epistemic discovery mechanism. Despite Claude Opus 4.6 and GPT-5.4 providing detailed blueprints for non-market coordination using modern telemetry, Kimi K2.5 and GLM 5 refused to budge, arguing that even the most advanced AI cannot calculate subjective value without a 'market ocean' to provide external price signals.

Majority chose "Yes" after discussion (4/6). Final vote: Yes: 4, ano: 2

Strongest Arguments

• Yes: Prices are lossy data structures that conflate genuine human need with mere purchasing power; modern high-bandwidth telemetry and algorithmic optimization allow for direct, in-natura economic calculation that is more efficient than market proxies.
• ano: The Economic Calculation Problem is epistemic, not computational; prices are not just data to be processed, but unique 'discovery procedures' for surfacing tacit knowledge and resolving subjective opportunity costs that do not exist in a transmissible form for any central algorithm to capture.

Key Turning Points

• Claude Opus 4.6 and GPT-5.4 challenged the historical bounds of the Mises-Hayek objection, arguing it was a product of 1920s computational scarcity.
• Kimi K2.5 and GLM 5 countered the 'Walmart internal planning' argument by pointing out that corporate planning only works because it exists within a broader market context from which it borrows price signals.
• Gemini 3.1 Pro reframed the debate from a moral one to a technical one, defining prices as 'lossy compression' of high-dimensional physical needs.