AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
The following article originally appeared on Venkatesh Rao’s Substack, Contraptions, and is being republished here with the author’s permission. The sole athletic achievement of my life came in 1993: winning the IIT Bombay freshman 50m freestyle race with a time of 41s. That got me into the college swim team (it was a bad recruitment year) and launched my brief and entirely undistinguished athletic career. By my senior year, however, my 50m time had improved to about 38s (not enough to get me off water-boy duty since the team had several exceptional swimmers with much better times). Interestingly though, it was easier for me to swim faster at the end of my career than it was to swim slower in the beginning. The reason was that in the interim, the coach had significantly improved my stroke and breathing technique. It was all about managed pacing, not raw intensity of effort. There is a fairly deep literature behind this apparently mundane lesson. Daniel Chambliss’s classic 1989 paper “The Mundanity of Excellence,” based on years of fieldwork studying competitive swimmers all the way from local clubs to the Olympic level, argued that excellence is primarily qualitative rather than quantitative. Elite swimmers do not simply do more of what mediocre swimmers do, or do it harder. They organize their activity differently: Strokes, turns, training habits, attention, and countless other small practices combine into a qualitatively different way of swimming. The route to excellence is not therefore reducible to maximizing effort along some obvious scalar dimension. The same insight is condensed in a maxim common in military and special operations circles: “Slow is smooth, smooth is fast.” In activities where speed really matters, trying to go fast naively is often an excellent way to go slowly. I never really stopped thinking about this problem. My 2011 book Tempo grew partly out of a long-standing interest in pacing across performance domains: how people experience time while making decisions, how rhythms of action emerge, and how timing relates to effectiveness. One of the ideas that has stuck with me since then is that tempo is something to be managed rather than maximized. There is no universally correct speed. There are only tempos appropriate or inappropriate to the dynamics of the situation. Which brings me, somewhat unexpectedly, to Dario Amodei. Amodei recently made the case that frontier AI development should be deliberately paced. His argument is primarily a safety argument. AI capabilities, he believes, are advancing quickly enough that the processes required to understand, evaluate, align, secure, and safely operate them are having trouble keeping up. This is not quite the old proposal for an AI “pause.” Pacing means continuing to advance the frontier while deliberately managing its rate, allowing safety work and institutional capacity to remain within striking distance of capability. Sam Altman has now endorsed the basic proposition, and Demis Hassabis has made closely related arguments about frontier capabilities outrunning scientific understanding and governance capacity. Elon Musk, more tersely, has said that Amodei is right. There is an obvious cynical reading of this emerging consensus. The leading frontier labs have powerful economic reasons to want a regulated frontier. A regime that requires enormous compliance budgets, restricts open-weight releases, discourages foreign models, imposes burdens that startups cannot afford, or legitimizes coordination among a small number of incumbents could turn “safety” into a remarkably effective mechanism for protectionism and regulatory capture. That suspicion is not paranoid. Open models increasingly constitute a competitive threat to proprietary frontier providers, and the politics around regulating them already feature explicit accusations of regulatory capture. There is an additional awkwardness: Coordinated pacing among nominal competitors looks uncomfortably like coordinated restriction of output, enough so that the legality of such arrangements under antitrust law is already being debated. I don’t think we need to resolve the question of motives. Perhaps these CEOs are sincerely terrified. Perhaps they are sincerely terrified and understand perfectly well that the regulations they favor would strengthen their competitive positions. Perhaps the mixture varies by person, company, and day of the week. It doesn’t matter much for my argument. The proposition that the frontier should be paced is worth considering independently of the political economy of the people proposing it. I also don’t share enough of Amodei’s safety premises to make his argument my own. In particular, I think a great deal of contemporary concern about runaway AGI, superintelligence, and “alignment” is badly framed, and often borders on the theological. But I increasingly agree with his conclusion. In fact, I think there is a strong case for frontier pacing even if you are an accelerationist and your objective is simply to make technological progress happen as fast as possible. I am not myself an accelerationist. My preferred framing is closer to managed tempo. But if I were one, I would still favor pacing the frontier right now, for a simple reason: Maximizing the instantaneous velocity of the AI capability frontier is no longer obviously maximizing the rate of technological progress. There is, however, an important difference between my conclusion and the emerging frontier consensus. Their natural solution is coordination at the top: labs agreeing upon thresholds, governments blessing the coordination, evaluators policing it, and eventually perhaps international agreements extending it. In other words, cartelization, hopefully of a benign sort. I would prefer to see how much frontier pacing can be produced from the bottom up through ordinary market mechanisms. The distinction matters. The objective should not be to decide administratively how fast AI is allowed to improve. It should be to stop artificially rewarding frontier velocity after frontier velocity has ceased to be the most important form of progress. Getting inside the loop A useful way to understand the distinction comes from another idea that startup culture has borrowed, and mostly misunderstood, from the military: John Boyd’s OODA loop. OODA theory says that you win by “getting inside the adversary’s decision cycle,” which is usually glossed as making decisions faster than the other guy. If you observe, orient, decide, and act faster than he can, the story goes, you eventually overwhelm him. But inside does not mean faster. The objective is to operate within the decision dynamics of the system you are engaging in a way that lets you shape them. Against a human adversary, that may indeed sometimes involve accelerating until his ability to orient collapses psychologically. But it may also require waiting, withholding action, changing rhythm, or deliberately slowing down. In nonadversarial situations, the goal may not be collapse at all but harmonization for resonant support. What matters is the right tempo at the right phase, not speed for the sake of speed. Something analogous applies to scientific and technological progress. There is no enemy psychology to collapse, but there are still loops to get inside: observation, experimentation, interpretation, investment, construction, deployment, feedback, learning, and recombination. The useful question is not how rapidly one component of that system can be made to move. It is whether the tempo of development allows those loops to close. If one subsystem changes faster than the surrounding system can observe, understand, absorb, and respond to it, pushing that subsystem still faster can reduce rather than increase effective progress. It can induce fragility and collapse. This, I think, is approximately where AI is now. The simplest evidence is personal and almost embarrassingly mundane. Frontier AI is already overpowered for nearly everything I use it for. In my most advanced projects I may use the strongest model available (Fable for my coding projects) to plan an approach or make critical strategic decisions, but I can generally hand the resulting specification to a cheaper model (such as Opus or Sonnet) to do the routine work. For ordinary uses I don’t need anything close to the frontier. In ChatGPT, I no longer even know exactly which model I am talking to much of the time. Whatever the “think harder” control does is sufficient model selection for my purposes. The situation increasingly reminds me of smartphones. There was a period when getting the newest iPhone produced a noticeable improvement in everyday life. Eventually the hardware got good enough that the upgrade cycle ceased to matter much. I kept an iPhone XS for almost a decade before replacing it with a 16. The frontier continued advancing; I simply fell off the frontier because my demand curve had stopped following it. Something similar is beginning to happen with AI, except that the supply curve is moving incomparably faster. Six months ago I routinely maxed out token allotments. Now I don’t. Some weeks I barely use coding agents. This isn’t because I’ve become less interested in AI. It is because my own capacity to productively absorb AI output has become the constraint. I have projects to think about, things to read, people to talk to, and work to do in domains where AI cannot help me yet, or perhaps ever. I am already pacing myself at my own tiny personal frontier. That is a significant change in the technological situation. The binding constraint is migrating. When the bottleneck moves Broader AI deployment is increasingly blocked by things other than model intelligence. Robotics has long been constrained by actuators, power, reliability, dexterity, manufacturing, and the sheer recalcitrance of the physical world. Those constraints are beginning to move, but making the model smarter does not make them disappear. AI in education is constrained less by whether a model can explain calculus than by our lack of sufficiently rich classroom experimentation about what happens when students and teachers actually use these systems. Current mid-tier models are probably capable enough to power almost any educational experiment worth trying in a high-school or undergraduate classroom. We do not need another order of magnitude of intelligence before conducting them. This pattern should become more common as AI improves. Once intelligence ceases to be scarce, its complements become more important. Model capability can be abundant while classroom knowledge is scarce. Model capability can be abundant while actuators are scarce. Model capability can be abundant while electrical infrastructure is scarce. It can be abundant while organizational competence, human attention, scientific understanding, military doctrine, security practices, and good judgment are scarce. This is not peculiar to AI. Capability-maxxing the coolest new weapon is bad military doctrine. The United States has enjoyed extraordinary technological superiority over its adversaries for decades and has nevertheless repeatedly discovered that superior equipment does not automatically produce strategic success. Logistics, doctrine, morale, training, political understanding, industrial capacity, and orientation matter. A force that neglects those complements because it possesses the best weapons can become remarkably fragile. From Vietnam to Iran, the US military has been repeatedly forced to relearn the lesson. AI may now be entering the same regime. Fragility from neglect of everything non-AI is becoming a bigger risk than failure to token-max. There is a further reason to suspect that continuing to redline the existing frontier may yield diminishing returns. The major labs increasingly appear to be competing along broadly the same technological S-curve. One su [truncated for AI cost control]