Why Conventional Price Theory Fails
Introduction: The Price Signal Myth
Mainstream economics holds a foundational belief: prices signal quality, scarcity, and value. In perfectly competitive markets, prices aggregate dispersed information about supply and demand, guiding efficient resource allocation. When prices rise, they signal scarcity and attract supply. When they fall, they signal abundance and redirect resources elsewhere. This elegant theory underpins everything from central bank policy to antitrust law.
The reality is more complex, and more revealing. Prices do convey information, but not the information economists assume. Rather than signaling intrinsic quality or true scarcity, prices primarily reflect the liquidity-to-capacity ratio (how much debt-backed m…
Why Conventional Price Theory Fails
Introduction: The Price Signal Myth
Mainstream economics holds a foundational belief: prices signal quality, scarcity, and value. In perfectly competitive markets, prices aggregate dispersed information about supply and demand, guiding efficient resource allocation. When prices rise, they signal scarcity and attract supply. When they fall, they signal abundance and redirect resources elsewhere. This elegant theory underpins everything from central bank policy to antitrust law.
The reality is more complex, and more revealing. Prices do convey information, but not the information economists assume. Rather than signaling intrinsic quality or true scarcity, prices primarily reflect the liquidity-to-capacity ratio (how much debt-backed money exists relative to the productive capacity base), capacity allocation choices (which productive capacity gets activated and how), and structural intermediation (who captures value in the monetization process).
Understanding this reframes major policy debates. Interest rates don’t just reflect “the price of money”, they signal the capacity base’s ability to support debt creation. Asset prices don’t just reflect supply and demand, they reveal which productive capacity is being monetized. Wages don’t just reflect productivity, they show how capacity certification and extraction work.
This appendix develops the price-as-capacity-signal framework, applies it to interest rates, asset prices, and wages, and reveals why conventional price theory fails to explain or predict economic outcomes.
The General Framework: Price = Present Capacity + Future Capacity Claims
Building on the Productive Capacity Theory of Money, we can decompose all prices into two components:
Price = Cost Floor + Liquidity Spread
The cost floor represents present productive capacity, the minimum sustainable price determined by currently deployed productive capacity. This comprises labor multiplied by skills, health, infrastructure multiplier, and technology, representing the real resources consumed in production.
The liquidity spread represents future productive capacity, the premium above the cost floor. It is determined by the ratio of available liquidity to total productive capacity base, reflecting debt-backed claims on future productive capacity. This spread varies with monetary conditions and capacity base changes.
Price embeds a temporal dimension: both present capacity deployment and future capacity claims. A price simultaneously reveals the actual resources (present capacity) being used now and the ratio of money (future capacity claims) to total productive capacity in the economy. This framework reveals that prices are derivative of productive capacity conditions, not fundamental signals of value.
Two Levels of Choice
Prices also reflect allocation choices at two levels: productive capacity allocation (how workers, companies, and infrastructure get deployed, representing supply decisions) and liquidity allocation (how debt-backed purchasing power gets directed, representing demand decisions). Traditional supply and demand curves describe these allocation choices, but they assume a stable productive capacity base and stable liquidity-to-capacity ratio. When these foundations shift, conventional price theory breaks down.
Interest Rates: The Price of Future Capacity
Interest rates are the purest example of prices as capacity signals. Conventional economics treats interest rates as “the price of money” determined by supply and demand for loanable funds. Central banks believe they can manage economies by setting rates to stimulate or cool activity. The capacity framework reveals a different mechanism.
Interest Rates as Capacity-to-Liquidity Signals
When banks lend, they create money by issuing debt backed by future productive capacity. The interest rate reflects base capacity availability (how much productive capacity exists to back new debt creation), capacity utilization (how much existing capacity is idle versus deployed), and the liquidity-to-capacity ratio (how much debt already exists relative to the capacity base).
When productive capacity is abundant and underutilized, rates can be low, new lending activates idle capacity. When productive capacity is fully deployed or the capacity base is shrinking, low rates cannot conjure capacity that doesn’t exist.
Case Study: Why Japan’s Zero Rates Failed
Japan has maintained zero or negative interest rates for over 25 years. The Bank of Japan purchased unprecedented quantities of government bonds, corporate bonds, and equity ETFs. Government debt rose from 60% of GDP in 1990 to 264% today. The result has been persistent deflation, stagnant wages, and absent growth.
Conventional economics cannot explain this outcome. If interest rates are “the price of money,” then zero rates should flood the economy with cheap credit, stimulating investment and consumption. Yet Japanese banks hold massive reserves rather than lend. Companies sit on cash rather than invest.
The capacity explanation is straightforward: Japan’s working-age population peaked in 1995 at 87 million and has declined to 74 million today, a 15% erosion of the capacity base. Banks don’t lend because there’s insufficient productive capacity to back new debt. Companies don’t invest because there’s insufficient capacity to deploy profitably. Zero interest rates cannot create workers who don’t exist. Monetary policy fails when the foundation itself is eroding.
Case Study: Why US Rate Hikes Worked (2022-2023)
In contrast, the US Federal Reserve raised rates from near-zero in 2021 to 5.5% in 2023 to combat inflation. Conventional theory predicted recession, raising “the price of money” should choke off investment and spending. Instead, inflation declined significantly while unemployment remained low and the economy continued growing.
The capacity explanation clarifies this outcome: the US has a robust and growing productive capacity base through immigration, strong infrastructure (despite decay), and world-leading innovation. The 2021-2022 inflation wasn’t driven by capacity constraints, it reflected an excessive liquidity-to-capacity ratio from fiscal stimulus and supply chain disruptions. Raising rates reduced the liquidity-to-capacity ratio without destroying productive capacity. The capacity base was sufficient to support the existing debt stock at higher rates.
Central Bank Confusion
Central banks believe they “set” interest rates and control money supply. The framework reveals they do neither. Banks create money by lending to creditworthy borrowers. Central banks influence lending conditions, but they cannot force money creation when the productive capacity base is insufficient (as in Japan), when existing debt is already high relative to capacity (overleveraged systems), or when capacity utilization is already at limits.
Interest rate policy is secondary. The foundation is productive capacity, demographics, skills, infrastructure, health. When that foundation is robust, monetary policy appears effective. When it erodes, monetary policy becomes impotent. This implies that central banks should explicitly monitor capacity metrics (working-age population trends, infrastructure quality, education levels, health outcomes) alongside inflation and employment. Interest rates cannot solve capacity problems.
Asset Prices: Monetized Capacity Claims
Asset prices, stocks, real estate, bonds, are treated as reflecting expected future cash flows discounted by risk and time preference. Bubbles are dismissed as “irrational exuberance” or information failures. Policy focuses on preventing “speculation” and ensuring “market efficiency.” The capacity framework reveals asset prices as claims on productive capacity, and their dynamics as liquidity-to-capacity ratio signals.
Housing Prices: Mortgaging Future Capacity
When you buy a house with a mortgage, the bank doesn’t lend based on the house’s intrinsic value. It lends based on your future productive capacity, your ability to generate income for 30 years to service the debt. Housing prices are thus a function of buyer productive capacity, available mortgage liquidity, and housing supply.
Consider: a software engineer earning $150,000 can borrow $600,000 (four times annual capacity). The same house in the same location will have a different “price” depending on the buyer’s productive capacity certification. When banks are willing to lend more (liquidity expansion), housing prices rise even if no new buyers or houses enter the market.
Housing bubbles form not because people irrationally believe houses will appreciate forever, but because the liquidity-to-capacity ratio increases. More debt-backed money chases the same productive capacity, bidding up prices. The US Housing Crisis of 2008 illustrates this: liquidity (subprime lending) expanded faster than productive capacity. Workers with insufficient capacity to service mortgages received loans anyway. When capacity couldn’t support debt, the system collapsed. The “bubble” was a capacity-liquidity mismatch. Similarly, China’s housing crisis (2020-present) emerged when debt backed real estate speculation rather than productive capacity development. Housing prices reflected liquidity expansion, not capacity to service mortgages. When the link between prices and capacity broke completely, the system seized.
Stock Prices: Claims on Corporate Productive Capacity
Stock prices are treated as reflecting company earnings potential. But what are company earnings? They’re the output of aggregated worker productive capacity that the company has activated and monetized. Stock prices represent the market’s valuation of aggregated productive capacity multiplied by the company’s ability to monetize it.
When a company hires 1,000 engineers generating $50 million annually in output, the company can borrow hundreds of millions against that capacity and achieve a market capitalization of billions. The stock price reflects the certified productive capacity (the workforce), the infrastructure and technology multiplying that capacity, the company’s extraction efficiency (how much of the capacity premium it captures), and the liquidity available to flow into equity markets.
Case Study: US Tech Stocks and Tax-Subsidized Capacity (2022-2025 AI Boom)
The 2022-2025 AI stock market boom provides a clear example of how government policy directs liquidity flows toward subsidized productive capacity, creating what appears to be a “market-driven” price surge.
R&D tax credits allow companies to receive tax deductions for R&D expenses, including engineer salaries. This effectively reduces the cost floor for activating tech worker productive capacity, a $150,000 engineer might cost the company only $100,000 after tax benefits. Stock-based compensation tax advantages allow companies to compensate workers with stock options rather than cash. This redirects liquidity: instead of paying cash wages, companies issue equity and use cash for capacity building (hiring more engineers, infrastructure, compute). Workers receive compensation tied to future capacity monetization rather than present deployment.
These policies create a feedback loop:
Tax subsidies lower tech capacity activation cost
→ More VC/investor liquidity flows to subsidized sector
→ Higher stock valuations
→ More capacity activated (more hiring, more investment)
→ More productive capacity in tech sector
→ Higher valuations justified by larger capacity base
→ Cycle repeats
Despite global availability of AI knowledge and research (published openly), tech talent, and capital (global financial markets), the US dominated 2022-2025 AI development because it structurally subsidized tech productive capacity activation through tax policy. Europe has strong education but higher labor costs and less liquidity flow into tech. The “market price” of AI stocks isn’t pure supply and demand, it reflects government-directed liquidity flows toward subsidized productive capacity.
Why Bubbles Are Capacity Measurement Failures
Conventional economics attributes bubbles to irrationality, speculation, or information failures. The capacity framework provides a structural explanation: bubbles form when liquidity grows faster than productive capacity, but markets fail to measure the divergence.
The dot-com bubble (2000) saw liquidity flood into tech stocks based on projected capacity that didn’t materialize. The companies couldn’t activate sufficient productive capacity to justify valuations. The housing bubble (2008) emerged when liquidity (mortgage lending) expanded faster than borrower productive capacity. Prices reflected liquidity, not sustainable capacity to service debt. China’s real estate bubble (2020s) saw housing prices completely detach from productive capacity to service mortgages, becoming a pure liquidity absorption mechanism. Asset price inflation is thus a capacity-liquidity mismatch signal, not an information failure.
Wages: Capacity Certification and Extraction
Conventional economics treats wages as reflecting marginal productivity: workers are paid according to the value they create. Labor market “equilibrium” occurs when supply equals demand at a wage reflecting productivity. The capacity framework reveals wages as capacity certification prices that obscure structural extraction.
The Same Human, Different Productive Capacity
Consider an identical worker, same skills, intelligence, work ethic, in two different contexts. In a rural village with poor internet (2 Mbps, frequent outages), long commute (3 hours daily to nearest city), unreliable electricity (power cuts several times weekly), and limited access to tools, collaboration, and markets, the productive capacity is approximately $25,000 per year, with wages offered around $20,000 per year. In a modern city with fiber internet (1 Gbps, reliable), urban transit (20-minute commute), stable electricity (24/7 power), and access to co-working spaces, tech community, and global clients, the productive capacity is approximately $100,000 per year, with wages offered around $80,000 per year.
The human is identical. The productive capacity differs by a factor of four. The wage differs by a factor of four. This reveals that wages don’t primarily reflect the worker’s intrinsic value, they reflect the infrastructure multiplier (how much the environment amplifies capacity), company certification (whether a firm validates the capacity to the financial system), and company extraction (how much of the capacity premium the company captures versus pays the worker).
Why Wage ≠ Productivity: The Capacity Premium
When a company hires you, several value streams emerge. The worker receives wages (typically 30-70% of value generated), benefits and training, and capacity certification (access to credit markets for mortgages, car loans, credit cards). The company captures current output minus wages (conventional profit), corporate borrowing capacity (can borrow against the workforce), equity value (market cap based on productive capacity aggregated), option value (first claim on productivity improvements and innovations), and the capacity premium (the monetization value enabled by certifying and activating capacity).
A software engineer generating $200,000 in value annually might receive $150,000 in wages. The company captures $50,000 in direct profit margin, the ability to borrow $500,000 against this engineer (and 999 others), equity value (the company’s market cap includes the certified capacity of its workforce), and innovation (any patents or IP the engineer creates belong to the company).
The capacity premium, the value enabled by having certified, activated productive capacity, accrues primarily to the company, not the worker. This isn’t necessarily exploitation in intent, but it’s structural extraction in mechanism. The worker needs the company to activate their capacity and certify them to the financial system. The company can thus capture most of the spread between worker productivity and monetized value.
Minimum Wage and the Capacity Floor
Minimum wage debates typically frame as: “Should we set a price floor on labor?” Proponents argue it ensures dignity. Opponents argue it creates unemployment by pricing low-productivity workers out of the market. Both perspectives miss the capacity foundation.
Minimum wage isn’t just a price floor, it’s a capacity activation threshold. It says: “Below this level of productive capacity, we won’t enable monetization.” A worker with productive capacity of $30,000 per year can be profitably employed at $15 per hour. A worker with capacity of $15,000 per year cannot. But why does productive capacity differ? Often the answer lies in infrastructure, education, and health.
The worker with $15,000 capacity might have poor education (capacity base underdeveloped), health issues (capacity constrained), location without infrastructure (capacity multiplier near zero), or no company willing to certify them (activation failure). Minimum wage policy without capacity building is either meaningless (if most workers have capacity above the floor) or exclusionary (if many workers have capacity below the floor, they’re locked out of capacity activation entirely).
The productive solution is to build capacity through education, infrastructure, and healthcare. Then wages rise naturally because productive capacity rises.
Why Tech Workers Earn More: Subsidized Capacity Activation
US tech workers earn $150,000-$500,000 while equally skilled workers in other countries earn $30,000-$80,000. Conventional explanations focus on supply and demand, or productivity differences. The capacity explanation reveals different mechanisms: the US has superior tech infrastructure (fiber internet, cloud services, payment systems, legal frameworks) providing a higher infrastructure multiplier; tax subsidies (R&D credits and stock compensation) reduce effective labor costs, increasing company willingness to pay; tax policy directs liquidity toward the tech sector, creating high demand for tech capacity; and concentration of tech capacity in San Francisco, Seattle, and New York creates ecosystem network effects that multiply individual capacity.
The same engineer in India has similar raw skills but less infrastructure multiplier, no tax subsidies to reduce effective labor cost, less liquidity flowing into the tech sector, and weaker ecosystem effects. Wage differences primarily reflect capacity activation conditions, not intrinsic productivity differences.
Wage Stagnation and Capacity Degradation
US median wages have stagnated since the 1970s despite productivity growth. Conventional explanations focus on globalization, automation, or declining union power. The capacity explanation adds crucial dimensions: infrastructure decay (D+ grade infrastructure reduces capacity multiplier for median worker), healthcare costs (18% of GDP on healthcare versus 10-12% elsewhere reduces net productive capacity), education costs (student debt of $1.7 trillion makes capacity building extractive), and financialization (companies use borrowing capacity enabled by workers for stock buybacks instead of capacity building or wage increases).
Productivity grew because technology and infrastructure multiplied capacity for some workers (tech, finance, management) while infrastructure decay and cost extraction reduced capacity for median workers (manufacturing, retail, service). Wage stagnation reflects capacity multiplier divergence, not just globalization or automation.
Why Conventional Price Theory Fails
Having examined interest rates, asset prices, and wages through the capacity lens, we can now articulate precisely why conventional price theory fails, and why this failure matters for policy.
Failure 1: Assumes Stable Productive Capacity Base
Conventional price theory treats productive capacity as exogenous, given by technology, resources, and labor supply. Prices then allocate this capacity efficiently through supply and demand. In reality, the productive capacity base is dynamic (demographics, infrastructure, education, and health constantly change it), policy-dependent (government decisions directly affect capacity through immigration, infrastructure investment, and education funding), and monetarily fundamental (changes in the capacity base determine sustainable debt and money supply, not just output levels).
When productive capacity is growing (US 1950s-1960s, China 1990s-2010s), prices behave differently than when it’s shrinking (Japan 1990s-2020s, Europe 2010s). Conventional theory cannot explain these differences because it treats capacity as given. This leads to policy failure: Japan tried monetary stimulus for over 25 years without addressing demographic collapse. Europe imposed austerity that destroyed capacity faster than it reduced debt. Both failed because they treated prices as primary and capacity as secondary.
Failure 2: Ignores Structural Intermediation
Conventional theory assumes “factors of production”, capital and labor, meet in competitive markets where prices clear. Workers receive their marginal product, capital receives its return, and the market achieves efficiency. In reality, companies don’t just facilitate exchange, they aggregate capacity (pool individual workers into monetizable units), certify capacity (validate productivity to the financial system), and capture the capacity premium (earn profits not just from output but from enabling debt creation against workforce capacity).
The employment letter transforms your relationship with the financial system overnight, not because you changed, but because a company certified you. Banks now lend hundreds of thousands because your productive capacity has been activated and validated. This intermediation is structural extraction, not necessarily intentional exploitation, but a mechanical feature of the system. Companies capture most of the spread between worker capacity and monetized value because workers need companies to activate their capacity. Conventional price theory treats this as “capital’s marginal product” when it’s actually the monetization premium from capacity certification.
This leads to policy failure: labor policy focuses on wage bargaining over a fixed pie, missing that companies capture value by enabling borrowing capacity. Worker equity in capacity premium (not just wages) would address structural extraction.
Failure 3: Confuses Relative Prices with Absolute Price Levels
Conventional supply and demand theory works reasonably well for relative prices, explaining why apples cost more than oranges, or why beach houses cost more than inland houses. It fails completely for absolute price levels, explaining why the same house costs $100,000 in one decade and $500,000 in another, or why interest rates are 10% in one country and 0% in another.
Absolute price levels are determined by the liquidity-to-capacity ratio:
Absolute Price Level = f(Total Liquidity / Total Productive Capacity Base)
When liquidity grows faster than capacity, inflation results. When capacity grows faster than liquidity, deflation occurs. When capacity collapses while liquidity persists, hyperinflation emerges. This leads to policy failure: central banks focus on interest rates (a price) to control inflation (price levels), missing that the foundation is the capacity-to-liquidity ratio. Japan can’t inflate because capacity is shrinking. The US could raise rates in 2022 because its capacity base was robust.
Failure 4: Treats Price as Information Signal Rather Than Power Relationship
Conventional theory holds that prices aggregate dispersed information, signaling scarcity and guiding efficient allocation. “Irrational” actors distort prices, but markets correct toward fundamentals. In reality, prices reflect the liquidity-to-capacity ratio (determined by monetary system and capacity base), allocation choices (who controls capacity deployment and liquidity flow), and power structures (who captures the spread between capacity and monetization).
The 2022-2025 AI boom’s stock prices don’t just reflect “information about AI value”, they reflect tax policy directing liquidity to the tech sector, subsidized capacity activation through R&D credits, corporate capture of the monetization premium from engineer capacity, and liquidity abundance flowing into subsidized sectors. This isn’t “efficient information aggregation.” It’s government-directed liquidity flows toward subsidized productive capacity, creating feedback loops that concentrate wealth and capacity in specific sectors.
This leads to policy failure: antitrust and securities regulation assume prices reflect information and markets self-correct. They miss that prices reflect power structures and subsidy regimes. “Market failures” are often capacity allocation failures or liquidity misdirection.
Failure 5: Misunderstands What “Quality” Signals
Conventional theory holds that higher prices signal higher quality, and willingness to pay reveals preferences and value. The capacity reality reveals that higher prices can signal better infrastructure multiplier (same worker, better environment), more liquidity available (housing bubbles), subsidized capacity activation (US tech wages), or corporate extraction efficiency (how much of capacity premium company captures). None of these necessarily reflect quality.
A $500,000 house in San Francisco isn’t “higher quality” than a $200,000 house in Pittsburgh, it reflects liquidity concentration (tech wealth), infrastructure multiplier (proximity to tech ecosystem), and capacity certification (tech jobs provide massive borrowing power). The price doesn’t signal the house’s quality. It signals the liquidity-to-capacity ratio and ecosystem effects in that location.
Implications for Policy
If prices are capacity signals rather than pure information signals, policy must shift from price manipulation to capacity building.
Monetary Policy: Recognize Capacity Constraints
The conventional approach sets interest rates to manage inflation and employment, lowering rates to stimulate and raising rates to cool. The capacity approach requires monitoring capacity metrics (demographics, infrastructure quality, education levels, health outcomes), recognizing limits (when capacity base is shrinking as in Japan or fully deployed through overheating, rate cuts cannot help), and coordinating with fiscal policy (monetary policy activates capacity while fiscal policy builds it).
Application: Japan should focus on immigration and productivity-enhancing infrastructure, not more monetary stimulus. US rate policy worked in 2022 because its capacity base was robust, but infrastructure investment would reduce future inflation risk by expanding capacity.
Fiscal Policy: Build Capacity, Not Just Demand
The conventional approach uses deficit spending to stimulate demand and tax cuts to boost consumption and investment. The capacity approach prioritizes infrastructure investment (directly multiplies productive capacity, enabling more sustainable money creation), education investment (builds capacity base, reduces extraction through student debt), healthcare investment (maintains capacity, prevents capacity loss through preventable illness), and immigration policy (maintains or grows working-age population and capacity base).
Application: European austerity failed because it cut capacity (infrastructure, education) faster than it reduced debt. The debt-to-capacity ratio worsened. The productive path is to maintain capacity while managing debt levels.
Industrial Policy: Direct Liquidity Strategically
The conventional approach holds that markets allocate capital efficiently and government shouldn’t “pick winners.” The capacity approach recognizes that tax policy and subsidies already direct liquidity, just implicitly, and advocates making it explicit and strategic.
US tech subsidies (R&D credits, stock compensation tax advantages) directed liquidity toward high-value capacity activation, enabling the AI boom. This wasn’t “free market”, it was government-directed capacity building that happened to work. The policy lesson is to explicitly target subsidies toward high-multiplier capacity (education, infrastructure, healthcare), strategic sectors (where capacity building creates spillovers and ecosystem effects), and capacity certification (helping workers access credit without corporate gatekeepers).
Labor Policy: Address Capacity Premium Capture
The conventional approach negotiates wages through collective bargaining and sets minimum wage floors. The capacity approach advocates worker equity in the capacity premium (employees should share in the borrowing capacity and equity value they enable), portable certification (reduce dependence on corporate gatekeepers for capacity validation), and infrastructure investment (raise capacity multiplier for all workers, increasing bargaining power).
Application: Instead of just fighting over wage shares, enable workers to capture more of the capacity premium, the value created by activating and certifying their productive capacity.
Asset Market Regulation: Monitor Capacity-Liquidity Ratios
The conventional approach prevents “bubbles” through margin requirements, lending standards, and moral suasion. The capacity approach tracks liquidity-to-capacity ratios in housing, stocks, and credit markets; intervenes on the capacity side (when liquidity grows faster than capacity, build capacity rather than just restricting liquidity); and recognizes subsidy effects (tax policy and regulations direct liquidity; adjust when creating problematic concentrations).
Application: Housing affordability isn’t just “too much demand”, it’s liquidity (mortgage capacity) growing faster than housing supply and buyer productive capacity. Solutions include building housing (capacity), improving infrastructure to multiply capacity in more locations, or restricting liquidity flows (lending standards).
Education Policy: Stop Extracting from Capacity Building
The conventional approach treats education as private investment, with student debt deemed reasonable because it increases earnings. The capacity approach recognizes that education builds the capacity base backing the entire monetary system. Charging people to build capacity is perverse, like charging banks to accept deposits.
Policy recommendation: Public investment in education, eliminating student debt, treating education as monetary base building. Singapore and China understood this; the US makes capacity building extractive.
Immigration Policy: Demographic Reality is Monetary Reality
The conventional approach treats immigration as social or cultural policy, separate from economics. The capacity approach recognizes that shrinking working-age population equals shrinking capacity base, which equals monetary system stress. Immigration is essential monetary policy.
Application: Japan’s refusal of immigration while demographics collapse is monetary suicide. US success partly reflects immigration maintaining the capacity base. Countries must choose: immigration or demographic decline and monetary stagnation.
Conclusion: Price as Derivative, Capacity as Foundation
The conventional wisdom treats prices as fundamental, information signals guiding efficient allocation, reflecting quality and scarcity, emerging from free market interactions. This is backwards.
Prices are derivative of productive capacity conditions: the cost floor (present capacity deployment), the liquidity spread (future capacity claims relative to capacity base), allocation choices (how capacity and liquidity get directed), and power structures (who captures the capacity premium). Interest rates don’t signal “the price of money”, they signal capacity-to-liquidity ratios and capacity base health. Asset prices don’t signal “fundamental value”, they signal monetized capacity claims and subsidy-directed liquidity flows. Wages don’t signal “marginal productivity”, they signal capacity certification and extraction.
Policy based on conventional price theory fails because it tries to manipulate prices rather than address capacity foundations. Japan’s monetary stimulus fails by treating prices (interest rates) as primary while ignoring the shrinking capacity base. European austerity backfired by cutting capacity faster than debt, worsening the debt-to-capacity ratio. US infrastructure decay reduces the capacity multiplier, creating hidden inflation pressure despite productivity growth. The student debt crisis extracts from capacity building, making monetary base expansion extractive.
The ancient Chinese principle of 以民为本 (People as Foundation) expressed this truth: productive capacity, people’s skills, health, infrastructure enabling their productivity, is the foundation. Money, prices, and financial systems are structures built on that foundation. Modern economics obscured this beneath complexity. Conventional price theory treats prices as information signals and assumes capacity as given. The reality is that capacity is dynamic, policy-dependent, and monetarily fundamental. Prices follow capacity.
The path forward requires capacity-first policy: monitoring and building productive capacity (demographics, education, infrastructure, health); recognizing monetary limits (when capacity shrinks or is fully deployed, monetary policy cannot help); directing liquidity strategically (tax policy already does this; make it explicit and capacity-focused); addressing structural extraction (companies capture capacity premium; workers should share it); and stopping extraction from capacity building (education, healthcare, infrastructure are foundational investments, not profit centers).
Price signals follow from productive capacity conditions. Fix the foundation, and prices will follow. Manipulate prices while ignoring capacity, and policy will fail. Part 2 examines the evidence across countries, showing how this framework explains what conventional economics cannot.