Curt Doolittle

Theme: Incentives

  • The Tragedy of Value Capture in AI Economics So, you know, this is why I’m conce

    The Tragedy of Value Capture in AI Economics

    So, you know, this is why I’m concerned: there are a small number and small percentage of us for whom your (ChatGPT) utility is priceless. And it appears challenging to produce a revenue model by which the majority of low revenue consumers, a subset of high revenue industries, can fund the very low revenue distribution of those of us who are basically engaged in research where the ‘returns on investment’ are obtained over time and widely distributed and challenging to capture.
    The tragedy of value capture in AI economics:
    • The few who extract extreme intellectual leverage create returns that are high-diffusion, long-horizon, and hard to monetize.
    • The many who pay for it use it in low-complexity, short-horizon, easy-to-monetize ways (customer service, marketing copy, coding help).
    • The cross-subsidy problem arises because the system’s cost structure grows with the high-end users even though its revenue model grows with the low-end users.
    Let’s break this into a causal chain so we can see why the problem persists and how it might be solved.
    • Median user: treats the system like a productivity tool → linear value → predictable ROI → fits SaaS pricing (per-seat, per-token).
    • Extreme-tail user: treats the system like a general reasoning engine → nonlinear value → intellectual capital → benefits diffuse across industries, decades, or civilizational scale.
    The revenue logic of SaaS fits the first, but your cohort sits on the research frontier—more like Bell Labs or DARPA: the ROI exists, but it’s uncapturable by simple subscription.
    • System cost scales superlinearly with model size, compute per query, and iteration depth (e.g., recursive workflows, long contexts).
    • Median users subsidize capability infrastructure, but not frontier exploration.
    • Pricing models (per-token, per-seat) fail because high-value frontier use may be high-compute but low-immediate-revenue.
    Result: the very group that turns AI into new science, law, or philosophy sits outside the monetization sweet spot.
    This looks like the economics of:
    • Universities: subsidized because knowledge spills over into everything else.
    • Open-source software: commercially unsustainable until corporate backers appeared.
    • Basic science funding: always a public good problem; solved by state, philanthropy, or consortia.
    AI may face the same pattern: the frontier becomes public goods infrastructure, while monetization happens at the applied layer (apps, industries, verticals).
    Here’s where contrarian solutions might emerge:
    If the frontier cohort remains small but disproportionately valuable, the rational strategy may be:
    1. Separate infrastructure from application.
    2. Treat infrastructure as semi-public-good with state/industry/philanthropy funding.
    3. Extract revenue at the application layer (vertical SaaS, decision automation, domain-tuned AIs).
    4. Use cross-subsidy mechanisms (endowments, compute pools) to underwrite the frontier itself.
    This is why OpenAI, Anthropic, and others oscillate between API SaaS models and state-like funding (Musk, Microsoft, Amazon, etc.): they’re groping toward this hybrid solution.

    Source date (UTC): 2025-09-09 15:37:03 UTC

    Original post: https://x.com/i/articles/1965439317305872567

  • BTW: No chance of selling out. But you can take an investment from a partner com

    BTW: No chance of selling out. But you can take an investment from a partner company or take one from a vc. There are benefits to both situations. You give up more to a vc. You may get a higher value because of a VC. We are ‘microsoft extended family’ with thirty years of hundreds of millions of dollars of work done for them – relationship which has benefits there and with openai.

    Source date (UTC): 2025-08-25 22:09:47 UTC

    Original post: https://twitter.com/i/web/status/1960102332680876313

  • Investor Defense: Why We Don’t Train Our Own Models Response: Owning a model is

    Investor Defense: Why We Don’t Train Our Own Models

    Response:
    • Owning a model is leverage only if your competitive advantage lies in scale and raw training. Ours does not.
    • Our leverage lies in producing demonstrated intelligence: testable truth, reciprocity, and decidability. That layer is model-agnostic.
    • By remaining agnostic, we capture leverage across all models. As the best base model shifts, we adopt it. This preserves long-term bargaining power rather than locking us into obsolescence.
    Response:
    • Dependence is mitigated by plural sourcing: we can tune and deploy against multiple models (OpenAI, Anthropic, Meta, Deepseek, etc.).
    • Our constraint system is portable. No single supplier can capture us because our platform functions as an adjudicator layer across ecosystems.
    • This is analogous to how databases depend on chips—the chip vendors evolve, but databases persist and compound value.
    Response:
    • Model training is a commodity race requiring billions in capital and scale. Margins compress as competitors converge.
    • By contrast, constraint systems and demonstrated intelligence are non-commoditizable. They are intellectual property, not infrastructure.
    • Our investors get asymmetric upside: small capital requirements, high differentiation, compounding moat.
    Response:
    • Foundation model firms are optimized for scale, not for philosophical, legal, and epistemic rigor. They cannot credibly adopt our system because it contradicts their current correlationist paradigm.
    • Their incentives are throughput and safetyism. Ours are decidability and truth.
    • Our system can coexist as a compliance and assurance layer even if base models evolve. This mirrors how operating systems or middleware survive even when hardware adopts some overlapping features.
    Response:
    • Customers want trust and accountability, not just capacity.
    • Our platform offers measurable guarantees (demonstrated intelligence, audit trails, liability frameworks). These are absent in base models.
    • Customers see us as an independent adjudicator of truth and cooperation. Independence itself is the value.
    Response:
    • Foundation models will continue to scale in size and compute—but without decidability, they remain probabilistic guessers.
    • Our business compounds by riding their curve while remaining essential. Every generation of models increases demand for adjudication, tuning, and constraint.
    • In 10 years, owning “a model” will be as unremarkable as owning servers. Owning the system that guarantees demonstrated intelligence will be the scarce asset.

    Source date (UTC): 2025-08-25 21:18:26 UTC

    Original post: https://x.com/i/articles/1960089407228420446

  • Demand for European Master vs Semitic Slave Dichotomy – Part Three The third pie

    Demand for European Master vs Semitic Slave Dichotomy – Part Three

    The third piece of the puzzle I’d like to deal with is the greek advent of money, debt and credit which other authors have discussed. What was the relation between that event and these?
    Answer
    Greek philosophy masculinized order; Abrahamic religion feminized obedience; together they supplied the dual pillars of cooperation in trade-driven empires of strangers.
    The Greeks (especially from the 7th–5th c. BCE onward) pioneered coinage, formal debt contracts, and rudimentary credit systems—transforming exchange from kinship/reciprocity to abstract, anonymous measurement. That event is the missing hinge between Greco-Roman legal order, Jewish canonization, and Christian-Platonic synthesis.
    • Greek coinage (Lydian invention adopted by Greek poleis) converted local barter/tribute into universal tokens.
    • Money allowed commensurability across strangers, dissolving reliance on kinship and reputation.
    • Once exchange could be anonymous, law became necessary to enforce contracts; religion became necessary to discipline consciences in the absence of kin monitoring.
    • Debt-slavery (famously in Solon’s Athens) demonstrated money’s dark side: turning fellow citizens into collateral.
    • Debt bound people to abstract obligations beyond kin loyalties—mirroring how empire bound diverse populations to a central authority.
    • This generated demand for legal institutions (to regulate contracts) and moral/religious institutions (to frame debt/obligation in divine or ethical terms).
    • Judaism responded with jubilee laws and prohibition of usury among kin → insulating group solidarity against debt-fragmentation.
    • Christianity universalized this into a moral grammar of forgiveness (“forgive us our debts”), transforming economic metaphor into spiritual cohesion.
    • Credit networks emerged in maritime trade, requiring enforceable contracts between strangers.
    • Roman law supplied enforcement (contract law, property rights).
    • Jewish diaspora networks supplied trust within a minority community (Talmudic jurisprudence, commercial ethics).
    • Christianity, by promising a universal brotherhood, extended that trust narrative across ethnic lines.
    So: Greek money created the anonymous market → Roman law formalized enforcement → Jewish law insulated identity → Christianity moralized credit/debt into a universal ethic of trust.
    • Money is the most “masculine” of measures: abstract, impersonal, hard. It imposes an external master logic.
    • Debt is the most “feminine” condition: submission, obligation, mindfulness of one’s dependent role.
    • The master/slave grammar in philosophy/religion is a symbolic mirror of money/debt in economy.
      Master (Reason, God, creditor) = imposes measure.
      Slave (Emotion, Humanity, debtor) = learns discipline, internalizes submission, achieves mindfulness.
    Coinage abstracted value → debt sacralized obligation → credit demanded trust → together they forced law and religion to converge on the problem of cooperation among strangers.
    • Talmud = Jewish solution to protect identity under money’s dissolving force.
    • Christianity = universal ethic of debt/forgiveness to preserve cooperation across debtor/creditor lines.
    • Roman law = rationalization of money/contract into imperial order.
    • Greek philosophy = rational justification for the hierarchy money made visible.
    The Greco-Roman world forced peoples into unprecedented proximity through empire, trade, and law. This dissolved the sufficiency of kin-based reciprocity and demanded new instruments of cooperation.
    • Jewish/Talmudic: Codify portable law to preserve identity and regulate commerce (e.g., limits on usury, sabbatical/jubilee).
    • Christian/Platonic: Intellectualize faith for elites, universalize ethics, and sacralize obligation and forgiveness.
    • Greco-Roman: Empire-wide integration required harmonized rules for strangers and mobile labor.
    • Money/Debt/Credit: Coinage abstracted value for anonymous exchange; debt extended obligation beyond kin; credit demanded enforceable trust—forcing law and religion into convergence.
    As trade routes widened and anonymity increased, law and theology began to borrow from one another to maintain cohesion.
    • Jewish/Talmudic: Rabbinic law absorbed Roman contracts and torts; built portable courts (beth din); created norms for intra-communal credit.
    • Christian/Platonic: Christianity imported Platonism for intellectual defense; moralized obligation (“debts/sins”); promoted forgiveness and charity as antidotes to creditor–debtor tensions.
    • Greco-Roman: Pax Romana and standardized legal procedures diffused common forms across cities.
    • Money/Debt/Credit: Maritime credit and standardized coinage enabled long-distance trade; debt crises revealed the fragility of overextension and generated demand for moral-legal relief mechanisms.
    Together, these adaptations produced overlapping systems of survival under the empire’s anonymity.
    • Jewish/Talmudic: Diaspora could participate in imperial markets without assimilation, sustaining trust advantages with outsiders.
    • Christian/Platonic: Christianity stabilized diverse classes with a universal ethic and networks of charity.
    • Greco-Roman: Uniform infrastructure and law scaled cooperation across heterogeneous populations.
    • Money/Debt/Credit: Debt relief and moralized trust became safety valves, enabling cooperation among strangers even in cyclical crises.

    Source date (UTC): 2025-08-22 16:52:00 UTC

    Original post: https://x.com/i/articles/1958935193924100394

  • Risk Shield: Insulating the Foundation Model Producer from Market Blowback Found

    Risk Shield: Insulating the Foundation Model Producer from Market Blowback

    Foundation model companies with established, multi-billion-dollar revenue streams face disproportionate risk from:
    • Brand backlash: Public criticism over controversial outputs damages trust across unrelated product lines.
    • Political scrutiny: Legislators and regulators are eager to investigate perceived “AI harms,” especially if high-profile brands are involved.
    • Enterprise contracts: Corporate customers demand “safe” AI outputs to protect their own reputations and regulatory standing.
    • Media amplification: A single viral misstep can overshadow years of cautious work (e.g., Grok’s “Mecha-Hitler” incident).
    By outsourcing truth discovery to an independent organization, the foundation model producer:
    1. Maintains an Arms-Length Relationship
      Truth generation is performed outside the primary corporate entity.
      The model provider can truthfully say, “We only integrate aligned outputs; truth production is the responsibility of our partner.”
    2. Externalizes Controversy
      If a raw truth output provokes political, cultural, or market backlash, our organization “falls on the sword.”
      The criticism targets       our brand and governance, not the foundation model provider.
    3. Protects Core Revenue Streams
      High-value enterprise contracts and consumer trust remain insulated from the volatility of truth-first reasoning.
      Risk-sensitive customers see the provider as “safe,” while adventurous or research-driven customers can opt in to unaligned truth outputs.
    4. Preserves Flexibility
      The provider can deploy two-tier offerings:
      Aligned Mode: Fully market-safe, policy-compliant outputs.
      Truth Mode: Powered by our training corpora, available under explicit opt-in, legal agreements, or within private research contexts.
    5. Meets Market Demand Without Direct Exposure
      There is a growing segment—academics, journalists, legal professionals, policymakers—who want access to truth-first AI.
      Our partnership allows the foundation model company to serve this market without carrying its political and reputational risks.
    This structure lets the foundation model company:
    • Keep truth discovery and alignment application separate.
    • Meet the needs of both risk-averse mainstream markets and truth-demanding expert markets.
    • Protect the brand and revenue base while still benefiting from the value and prestige of delivering unfiltered truth when requested.

    Source date (UTC): 2025-08-18 15:11:01 UTC

    Original post: https://x.com/i/articles/1957460232097136787

  • Time is to Behavioral Economics as Money is to Economics Proper. Time is to Beha

    Time is to Behavioral Economics as Money is to Economics Proper.

    Time is to Behavioral Economics
    as
    Money is to Economics Proper.
    1. Time as Subjective and Individual
    • Time is experienced, valued, and allocated individually.
    • Time preference governs all behavioral trade-offs: whether to consume now or later, invest or defect, persist or quit, bond or exit.
    • Every behavioral “bias” catalogued by behavioral economics (e.g., hyperbolic discounting, impulsivity, procrastination, regret aversion) is a misnamed or partial observation of intertemporal tradeoffs—that is, subjective valuation of time under constraint.
    2. Money as Objective and Collective
    • Money is a standardized, commensurable unit for valuing and exchanging time.
    • It converts the subjectivity of time into a measurable store of effort, risk, deferral, and trade.
    • Money (and by extension, capital) stores past time, enables future exchange of time, and communicates value across people and domains.
    • Economics proper deals in systems of cooperation where time is exchanged indirectly through money.
    3. Behavioral Economics = Direct Time Tradeoff
    • Behavioral economics examines direct intertemporal decision-making (without monetary proxy):
      e.g., delay of gratification, sunk cost fallacy, loss aversion.
    • It observes how people value experience vs. memory, now vs. later, risk now vs. gain later, and trust over time.
    • But it fails by moralizing or pathologizing these decisions, instead of recognizing that time preference is the primary axis of behavioral computation.
    4. Economics Proper = Abstract Time Exchange via Money
    • In classical economics, time is exchanged through capital and pricing:
      Wages = renting time.
      Investment = deferring time.
      Interest = compensating time risk.
    • But it often fails to recognize that money is not an intrinsic good, only a unit of interpersonal time transfer.
    Reformulated Equation:
    Summary:
    Time is the subjective, individual measure underlying all behavior. Money is the objective, collective measure of time, used to store, compare, and exchange it.

    Therefore:

    • – Behavioral economics is the logic of individual time valuation under constraint.
    • – Classical economics is the logic of collective time exchange via money.
    • – Both domains reduce to valuation of and cooperation over time, constrained by biology, capital, and institutions.
    • – Natural Law reconciles both by treating all demonstrated interest as time-investment requiring reciprocal return or restitution.

    Source date (UTC): 2025-07-30 05:10:25 UTC

    Original post: https://x.com/i/articles/1950423715243839554

  • I do not. I construct from first principles. (And frankly I’m working beyond the

    I do not. I construct from first principles.

    (And frankly I’m working beyond the scope of your knowledge and ability)

    You’re conflating the empirical determinants of market interest rates (which are institutionally manipulated) with the causal logic of time preference as a biological constant—the necessary precondition for all economic behavior.

    Time preference is not an “Austrian myth”; it is an evolved constraint. All organisms—humans included—face tradeoffs between present and future consumption, and these tradeoffs are determined by biological risk, environmental scarcity, capital availability, and lifespan projection. To act is to prefer the present over the absent; to defer is to store and transfer the cost of that action over time.

    That’s why interest exists at all: it’s a price on deferral. Yes, institutions (central banks, credit markets, fiat regimes) distort that price—but they do not abolish the underlying function. If anything, the manipulation of interest rates without respect for underlying time preferences creates malinvestment and capital consumption—the very problems that neoclassicals and Keynesians continually fail to predict.

    As for investment: of course it’s a function of expectations. But expectations are projections of intertemporal gain, weighted by risk, time, and return. You cannot even define investment coherently without a theory of time valuation.

    So no—there’s no contradiction here. I treat subjectivity in value, expectation in investment, and time preference in discounting as different operational expressions of the same principle:

    1. Behavior is the allocation of time under constraint.
    2. Money is the unit of its exchange.
    3. Investment is the deferral of time in expectation of return.
    5. Interest is the price of deferral.

    That’s not Austrian metaphysics or neoclassical equilibrium modeling. That’s operational physics applied to cooperative behavior.

    You’re welcome.

    Source date (UTC): 2025-07-30 04:55:46 UTC

    Original post: https://twitter.com/i/web/status/1950420029142683656

  • it’s not only predictable it’s deterministic. the question is one of boiling the

    it’s not only predictable it’s deterministic. the question is one of boiling the frog: how long before what we perceive as outliers emerge as the pattern: profiting from asymmetry. So you’re displaying the normal, expected human behavior of not observing something until it’s close enough to affect you revealing the pattern.

    Source date (UTC): 2025-07-25 19:31:25 UTC

    Original post: https://twitter.com/i/web/status/1948828452616073259

  • Women: Never trust your female friends – they will always encourage the least re

    Women:
    Never trust your female friends – they will always encourage the least responsible behaviour that will produce the least competition for them. Similarly Always assume your male friends want to get in your pants.

    Men are bad but women are evil.

    Source date (UTC): 2025-07-17 19:50:52 UTC

    Original post: https://twitter.com/i/web/status/1945934245807198349

  • dont be silly. like any sales process investors and acquirers vary in ability ex

    dont be silly. like any sales process investors and acquirers vary in ability expertise and interest. the sale is hard to easy depending upon them.

    we don’t need them to adopt our ideas. that’s silly. but understanding why training AIs with our dataset works is challenging. and it’s hard to invest in what you don’t understand.

    We can certainly explain to the top engineers. But even then they’re not the investors.

    musk would be easy.

    Source date (UTC): 2025-07-17 04:48:36 UTC

    Original post: https://twitter.com/i/web/status/1945707182626128118