Expert takes Sep 13, 2026

This quarter · retrospective, originally posted August 23 to 24

Two people, neither of them an industry spokesman, used arguments that do not overlap at all and reached the same policy conclusion in the same week: blocking data centers does not slow AI.

This column has no new named view this week; the pick is an older item that matters. Arvind Narayanan, a professor of computer science at Princeton and co-author of AI Snake Oil, proposed a conversion: translate "how many gigawatts of data centers were blocked" into "how long was AI progress delayed." His answer is that a typical one-year state-level building ban in the United States is worth about 5 to 10 hours, and a total ban across New York State less than a day (Arvind Narayanan, 2026-08-24). The conversion is clever because nobody has an intuition for "1 gigawatt blocked," while "how long until efficiency gains claw that compute back" has one. ⚠️ Three qualifiers travel with it. He writes plainly that he "used AI for the analysis and did some spot checks," so 5 to 10 hours is an order of magnitude, not a precise value. He also says explicitly that the local environmental effects are "a highly contested topic that I won't comment on here," so this is not evidence that bans are unjustified. The most fragile assumption is that 90% of blocked capacity gets rebuilt elsewhere. We checked his key parameter ourselves. He puts inference efficiency at 5x per year, and his text does refer to the inference side. Against an inference-side price benchmark, the unit price of GPT-4-level capability fell from $50 per million tokens to $0.14 in about two and a half years, an annualized rate of roughly 11x, so his description of 5x as conservative holds. The training-side benchmark is a different figure, measured by Epoch AI: the compute needed to reach the same performance halves roughly every 8 months. Even if you misapplied that benchmark, the delay would only grow by a factor of 1.55. The order-of-magnitude conclusion is not sensitive to this parameter. The same week, Miles Brundage, formerly head of policy research at OpenAI and now founder of an organization for external AI audits, reached the same conclusion from the other direction: a national building ban is a bad idea, for safety reasons. A ban would hurt the "basic safety, security, and auditing requirements," which he notes "we essentially still don't have" (Miles Brundage, 2026-08-23). ⚠️ His "it will not happen" refers to a national ban. State-level bans have already happened, and his post does not separate the two layers. Which of our calls it supports or rebuts: all four pieces of material on our data-center-ban thread are counts of political events. This is the first one to give an effect size. The operational conclusion is direct: delete "regulation will slow my competitors down for me" from your deck. The system-level effect is measured in hours. What is actually rising is the probability that your own project fails to get a permit, and that has to be modeled as a permitting probability, not as schedule slack.