Where HyperC thinks out loud: the long-range vision, research as it publishes, new computable markets as members discover them, and what changes in the product. Written for operators, not for a press cycle.
Margin is the price of not knowing. HyperC is building a global economics platform — a logistics and risk system precise enough that businesses can operate at virtually zero margin and still produce stable wealth and sustainable growth. It starts with a foundational model that scores real deals, for real businesses, on computable markets — and it ends, we hope, with an economy that pays for every robotic movement and every watt with a calibrated, risk-adjusted, humanity-aligned judgement.
Finance theory allows two sources of excess return in a market without price discovery: be cheaper to operate, or be right where others are wrong. The seven reasons a deal gets rejected sort between them, and 67 newly screened markets show which side each family lives on.
Read →67 markets outside the catalogue, scored on nine criteria. Ten clear the bar today; one rule predicts fit better than any criterion: anyone can register, few can execute.
Read →The theory behind computable markets: seven mechanisms from Grossman–Stiglitz to the winner's curse, which side of the edge each one sits on, and the statement that can be falsified.
Read →Every market we have screened but not catalogued, with scores, evidence and a first experiment. Proposed blueprints, not catalogue entries.
Read →One scenario for how economic bot wars develop: once the obvious deals are gone, traders defend a market by generating noise — and filtering noise is itself a barrier to entry. Whether you run out of compute or run out of surviving evidence, it is the same computational efficiency problem, which is why fast models on small compute may decide who can still operate.
Read →A business only knows what happened to the deals it took. Training on that history produces a model that is confidently wrong exactly where the evidence is missing. How we train against synthetic worlds whose hidden truth we hold, label the model on whether it was right about its own predictions, and make competing interpretations of a market compete.
Read →The model requires an explicit volume per deal. On markets where a deal is one indivisible thing, sizing leaves the regularized path and selection falls back to portfolio-level risk calibration — and adversarial action on the individual deal is lost entirely. What that costs, and the three approaches we are weighing.
Read →Why the bottleneck was never production, why we started by scoring a single deal, and why the alignment target has to widen from one business surviving to an ecosystem of collaborating businesses compounding together. To be updated and expanded.
Read →The P34 Membership opens to builders: the decision model, a managed agent workspace, market intelligence and the community — at founding terms, with the profit-share rate fixed by registration order for the first cohorts.
Membership details →The operator-relative theory of computable markets, the Menu-Sales-Description data contract proposed for the category, application templates across ten-plus verticals, and a falsification protocol — with the notebook results, including the selection-bias trap measured end to end.
Research & evidence →The technical report behind the model class: benchmark construction under partial observability, selection bias and regime change; baseline tuning; results and stated limitations, with notebooks published for inspection.
computablemarkets.com →This section fills up as research publishes, markets are discovered and open problems are found. Subscribe below and you will not have to check.
Market experiments as we run them, model and agent-tool releases, member findings and research on the emerging AI economy — plus first access when the P34 Community opens. Free forever, unsubscribe anytime.
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