The backwards AI pacing debate and how far business is from the frontier

Washington and Silicon Valley have found a new fight to pick over artificial intelligence in “pacing,” or the deliberate throttling of frontier model development until safety, alignment, and society at large can catch up. To its detractors, pacing is unilateral disarmament in the race with China. To its champions, pacing is the only responsible path for a technology whose own creators warn of catastrophic risk. 

Both camps have fallen prey to the “Compute-to-GDP Fallacy”—the mistaken belief that every incremental leap in AI model performance immediately translates into macroeconomic output. Every prior general-purpose technology took decades to diffuse into measurable productivity. AI is following the same curve at an accelerated pace, but everyone seems to buy the hype that the laws of history or of economics do not apply this time.

In reality, Corporate America is already years behind the AI frontier, and the labs’ commercial fortunes will be decided by trust and adoption, not raw capability. Pacing would cost the economy remarkably little. Racing ahead of alignment could cost far more. Here’s why we—whether out of arrogance or misdiagnosis—are simply having the wrong argument.

The pacing skeptics’ suspicions are not frivolous. Is pacing real, or a savvy marketing gambit by frontier labs and cybersecurity companies polishing their financials ahead of IPOs? Would pacing cede the U.S. lead in AI to China, or would Beijing reciprocate and pace in its own manner?

AI has plainly reached a critical capability milestone. Warnings of catastrophic or existential risk can no longer be dismissed outright, even if the near-term probability remains modest. Yet by focusing almost exclusively on cutting-edge models, frontier labs have mismanaged both their messaging and the public trust. More than 100 recent conversations with CEOs, policy leaders, and AI scientists for our coming book, When Machines Act, have convinced us that the pacing debate has lost sight of first-principles thinking.

The Alignment Problem

Since the release of ChatGPT in 2022, corporate leadership has scrambled with a speed unmatched in modern commercial history. Even so, while executive suites have mobilized with unprecedented urgency, the structural physics of enterprise architecture—fragmented data silos, legacy ERPs, strict compliance regimes, and basic data hygiene—make true economic absorption an inherently slow slog. As corporate budget shocks from runaway “tokenmaxxing” demonstrated, many daily enterprise workflows require far simpler models, and precious few tasks at the average Fortune 500 company demand a frontier system at all. Pacing, therefore, will neither harm economic output nor choke off the labs’ commercial revenues, because enterprises need time simply to assimilate the capabilities already on the table. 

Among high-performing companies, more than two-thirds identify data as the primary barrier to implementing AI, a figure that has proven stubborn even as the models........

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