The backwards AI pacing debate and how far business is from the frontier
· Fortune
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.
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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?
The Frontier Problem
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 themselves have leaped forward. Only 7% describe their data as “completely ready” for AI; fewer than a quarter have a data strategy at all; and 63% either lack AI-suitable data management or are unsure whether they have it.
The Fallacy Problem
As McKinsey Senior Partner Asutosh Padhi emphasized on air with Fareed Zakaria, technical availability is fundamentally different from economic transformation. General-purpose technologies have historically required decades to reorganize workflows and generate broad-based productivity gains. Electricity took 75 years to lift productivity economy-wide. Computers required 50 years, and the Internet and mobile devices demanded 25. The underlying models may be ready, but the systemic organizational restructuring they demand will take substantial time. When McKinsey surveyed the business community, the firm found that only 6 percent of companies reported a “significant” impact and modest earnings attribution.
Companies are concentrating on the high-reward, low-risk automation tasks that models one or two generations old can already solve. As one highly respected former Wall Street CEO told us, these systems will run in parallel with legacy systems for years to confirm they operate correctly and that no regulatory risk is unknowingly absorbed.
A parallel dynamic has emerged in the economics of silicon. Older-generation chips, initially cast aside in the scramble for cutting-edge accelerators, are finding a second life as workhorses for the practical inference tasks that dominate enterprise demand. As Growth Protocol founder and CEO Miro Dimitrov noted at last week’s Yale CEO Caucus, deploying neuro-symbolic architectures has allowed his enterprise reasoning platform to slash inference costs by roughly 80-fold in live client deployments, largely by shifting workloads off ultra-expensive GPUs and onto everyday enterprise CPUs.
The Three Phases of AI Adoption
Corporate AI adoption is best understood in three phases, distinguished by how much work a company can responsibly hand over, which is gated by data readiness and the trust systems have earned. The first phase, assistance, consists of off-the-shelf copilots that ride atop enterprise platforms such as Salesforce, connecting data across existing applications and enabling employees to work faster with minimal re-architecting. Payback arrives quickly and risk stays modest, since a human still performs much of the work. The second phase, orchestration, covers agentic workflows that demand real investment—structuring proprietary data and connecting far-flung data lakes never meant to meet—with a human in the loop approving each consequential step. The third phase, autonomy, brings end-to-end agentic operations across seamlessly interconnected systems, with humans supervising by exception.
Reward compounds with each phase, but so do the risk and trust required, which is why the average Fortune 500 CEO remains in the first phase, making sizable but early investments to prepare for the second. Nor do the phases advance in lockstep across an enterprise. Most companies will oversee a multi-phased portfolio, piloting orchestration in select forward-leaning departments as the rest of the organization becomes comfortable with basic assistance.
Underlying all three phases is the need for CEOs to trust that AI will perform as any other employee would—following the guidelines spelled out in the employee handbook, obeying the rule of law, and maintaining a foundational layer of human values and judgment. So when markets, media commentators, or investors fret that pacing for AI alignment will hinder progress in frontier models, they misdiagnose how enterprise value is created. Never mind the confusion pervading the vague promises and threats of “AGI” with the sublime opportunities and genuine catastrophic risks of reaching the “singularity.” If AI technologies are meant to automate human tasks, they should be held to the same standards of values, judgment, and moral alignment as any current or prospective employee. If the most advanced frontier models cannot meet those standards in a testing environment, they are not ready for release—which is exactly why the U.S. has always maintained laws protecting consumers against such risks.
As former FTC Chair Lina Khan reminded the public on X: “There is an extensive set of laws that govern dangerous and defective products… releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an ‘unfair or deceptive’ act or practice under the FTC Act (and analogous state laws).” Those laws hold companies responsible for harm done to consumers, employees, investors, patients, competitors, and the markets and financial systems on which they all depend.
America vs. China, and Speed vs. Trust
Wherever one lands on the China distillation debate, the fact remains that China now fields models rivaling the frontier systems on the market today. Indeed, the Chinese Communist Party has signaled that it is turning its energies to diffusing AI through the economy instead of continuing to push the frontier. The frontier labs must recognize that two races are underway at once: one to reach “AGI” or “superintelligence” and the other for share of wallet. As their most established customers, mostly Fortune 500 enterprises, can attest, recapturing a customer after the decision is made is extraordinarily difficult. China understands the dynamic well, one that powered its victory in the global telecommunications race the U.S. largely lost.
The CCP has also expressed deep concern over alignment. Beijing’s domestic alignment prioritizes state control, party orthodoxy, and narrative consistency, whereas Western alignment centers on fiduciary reliability, consumer safety, and product liability. Yet beneath the ideological gulf lies an identical commercial reality in both systems—unpredictable, “hallucinating” agents that fail to adhere to organizational rules, judgment, and institutional guardrails cannot be trusted to run mission-critical workflows or drive durable economic growth.
The Trump administration should still pursue avenues to collaborate and coordinate with President Xi to ensure that no mass destruction or catastrophe, intentional or accidental, issues from AI. Whether Trump will raise the matter is another question. At the CEO Caucus, 93 of the roughly 100 CEOs surveyed did not believe the president was correct to classify warnings about AI’s dangers as a “hoax.” Almost 90 percent said the president should press the need for joint AI-safety guidelines with China during the state visit, yet nearly three-quarters did not expect him to do so. Based on preparatory discussions between Treasury Secretary Bessent and his Chinese counterpart, those 75 business leaders may soon be gladly proven wrong.
A pacing interval is no passive holiday or an economic ceasefire but an active defensive hardening window. Both Washington and Beijing need intentional breathing room to allow their critical infrastructure to build resilient defenses against autonomous agentic exploits before the next generation of frontier capabilities is unlocked. And instilling human alignment is only half the battle. The immediate priority during such a period must be fortifying the institutions that serve as the bedrock of civilization. Financial, healthcare, and education systems should be probed for vulnerabilities by the most advanced models, as Anthropic demonstrated through its restricted deployment of Mythos under Project Glasswing. Those models breach defenses through impressive engineering ingenuity, but their exploits succeed only because of systemic weaknesses in corporate digital infrastructure.
The frontier labs believe they are running a single race toward superintelligence. However, the race that will decide their fortunes—earning the trust of the enterprises, regulators, and citizens who must live with what they build—is slower. Speed may win headlines, but trust earns share of wallet. This race is a marathon, not a sprint. As past runners ourselves, we know that the first half of any long-distance race is for pacing and the second half is for passing. In every technological revolution, from the railroad to the Internet, the greatest fortunes went to those who understood that a frontier is worthless until the settlers arrived.
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