Evaluating competitor capability matching lags relies on balancing algorithmic diffusion against structural moats. On the Epoch Capabilities Index, models have frequently taken the lead since GPT-4 epoch.ai, and recent measurements show the gap from the closed frontier to the best open-weights models flat-to-slightly-widening at around 3 to 4 months epoch.ai. We project that the major closed labs will face an 85-day internal-to-public deployment gap by 2027–2028 . This significant internal holding period suggests that open-source and fast followers will have some time to close capability differentials once a flagship reaches general availability.

Powerful structural forces push in opposite directions. Algorithmic efficiency, distillation, and talent flow drive rapid diffusion. Conversely, capital concentration and the 5x annual growth of frontier training compute favor longer lags. The theory that returns to being ahead will accelerate once deployment becomes part of training suggests future thresholds could be increasingly insulated. Assessed against the broader timeline of internal deployment delays and competitor capability matching, the distribution remains largely consistent, anticipating moderate structural lags before followers catch up. Balancing these factors, the median lag centers around 115 days. The lower percentiles (a P10 of 50 days and P25 of 78 days) reflect scenarios where open weights efficiently match the frontier. The right tail (a P75 of 175 days and P90 of 270 days) accounts for prolonged leads generated by extreme compute scaling or successful implementations of continual learning creating massive moats.