T1 · Representation
O-ring with retries: fragility requirement f = φ + (h + ln(1/ε))/(1+ℓ), success converges to a Gumbel kernel, and the scalar quality ladder is exactly the case of homogeneous forgiveness.
Horizon, forgiveness, and the direction of AI innovation. Cheap models commoditize demonstrated capability within months, yet frontier labs keep raising their spending — because imitation is directional, and the race concentrates on what cannot be copied.
Why do cheap AI models catch up to the frontier in months, while frontier labs spend more every year? Because AI tasks differ on two margins — how long a chain of actions they need, and how much they forgive mistakes — and copying is easy on one margin but hard on the other. A clever plan is revealed the first time anyone sees it work. Proving that a system almost never fails is different: to show it fails less than once in ten thousand tries you need on the order of ten thousand real tries; the unforgiving tasks where that proof matters generate few tries; and the tries come from being deployed, which requires the proof you don't yet have. So competition erases the value of anything that can be demonstrated and spares the value of reliability — a capability lead is a bond that stops paying the day it is copied. As followers close in, the frontier race turns from "can it do longer tasks?" to "can it be trusted with unforgiving ones?", and rival labs pile onto that same hard-to-copy frontier. One policy punchline: liability rules and public safety-testing infrastructure both buy reliable AI, but the first concentrates the market and the second opens it.
The paper has been through three adversarial rounds — each time a blind, neutral-prompt review by two frontier-model referees (a theory referee judged against Econometrica, an IO/innovation referee with live literature search) plus an adversarial self-review with numerical counterexamples. Every fatal objection was then fixed with derived machinery, not wording, and re-verified. The trajectory of top-5 R&R verdicts: June draft: certain reject → v3.0: ~5–10% → v3.1: ~25–35% (first MAJOR REVISION verdicts; RAND judged Accept/Minor) → v3.2: round-three objections addressed. Full reports: v3·GPT, v3·Gemini, v3.1·GPT, v3.1·Gemini.
v3.2's additions close the third round's convergent objections: a data-moat proposition (the sample-complexity floor becomes an imitation lag exactly when trial flow is gated by certified deployment; a simulator good enough to certify the tail must itself be certified against the tail — the real-trial floor is route-independent), an endogenous-intensity proposition (herding survives endogenous breakthrough rates; cheap continuous progress restores differentiation), first-order separability for the direction game, and the cyclical rotation conjecture: herding cannot be absorbing, so the race should alternate between agency and reliability phases.
O-ring with retries: fragility requirement f = φ + (h + ln(1/ε))/(1+ℓ), success converges to a Gumbel kernel, and the scalar quality ladder is exactly the case of homogeneous forgiveness.
Bertrand in tasks: frontier profit equals the boundary-value integral over the capability gap to the best imitator. Saturation is an equilibrium state — served, valuable, and rentless.
Certifying failure rate ε needs Ω(1/ε) ground-truth trials — and the floor binds invention too. It becomes a lag because trial flow is gated by certified deployment: lag ratio = flow ratio, a data flywheel. Synthetic data rescales the wall; it cannot remove it.
An increment earns its boundary value for exactly its imitation lag: appropriable share = 1−e^(−rτ), exact over a finite look-ahead window. In the depletion phase B_F/B_H rises, so investment rotates to the reliability boundary — and faster imitation brings the rotation earlier. Imitation steers.
Head-to-head racing replaces r by r+ν inside the annuity — the prize split is derived, not assumed. Herding iff the moat's duo annuity beats the horizon's sole annuity (cutoff ≈ 1.2 at calibrated lags); cheap continuous progress restores differentiation, even with endogenous intensity.
The direction wedge equals (1−e^(−rτ_H))/(1−e^(−rτ_F)) ≈ τ_H/τ_F, signed by observable imitation lags. Liability raises reliability investment and concentration; public verification raises reliability diffusion and lowers concentration.
Synced from the project-level learning.md; Rounds 95–100 document the three referee rounds, the rebuild, and every verification.
The compiled PDF, LaTeX source, design spec, and math checker on this page are synchronized with v3.5. Every numbered claim in the paper has a corresponding check in math_check_v3.py (95 pass, 0 fail).