永恒的互补品

作者注：这是我们“下一代经济”系列的第一篇文章，属于一个旨在汇聚独立声音、探索 AGI 未来的新平台。文中观点仅代表我们自己，不代表 OpenAI 或我们的同事。Authors’ note: This is the first essay in our series on the next economy, part of a new platform to host independent voices exploring an AGI future. It reflects our views, not those of OpenAI or our colleagues.

人类几乎见证了时间的起源。
Humanity has seen almost to the beginning of time.

我们的方程为我们讲述了一个关于宇宙最初时刻的大体连贯的故事。借助望远镜，我们观察了宇宙的遥远边际，一瞥了它的早期历史。我们了解恒星的诞生和星系的形成。Our equations tell a mostly coherent account of the universe from its first moments. With our telescopes, we have looked to the reaches of the cosmos and glimpsed its early history. We know about the birth of stars and the formation of galaxies.

然而，人类从未有任何人旅行到月球之外。Yet no human being has ever traveled beyond the Moon.

我们探寻的解释横跨整个宇宙，而我们的身体几乎从未离开家门。我们的思想远远超出了双手所能触及的范围。The explanations we seek span the universe, while our bodies have barely left home. Our minds venture way beyond the reach of our hands.

这种不匹配可能意味着两种截然不同的情况。That mismatch could mean two very different things.

一种可能是，我们无需走得太远，最深刻的真理或许仅凭一个行星便能探究明白。凭借开明的理性思维和高效的仪器，我们无需穿越宇宙，便能揭示它的奥秘。在这个故事中，我们的文明通过向深处发展，而不是向广度扩张来进步。这也有助于解释为什么我们尚未发现其他智慧生命：我们或许生活在一个充满卓越心智的星系中，而它们从未需要扩张，因此也从未引起我们的注意。One possibility is that we needn’t go very far, that the deepest truths might be fathomed from a single planet. Armed with enlightened reasoning and efficient instruments, we reveal the universe without traversing it. In this story, our civilization advances through depth instead of width. It would help explain why we haven’t seen other intelligent life: we might live in a galaxy full of brilliant minds that never needed to expand and become noticeable to us.

第二种可能性更令人望而生畏。我们的思维速度可能已经简单地超出了执行能力。每迈出下一步都需要更多时间和资源。我们并不缺少梦想，但缺少将其变为现实的人力。The second possibility is more intimidating. Our minds may have simply outrun our execution capacity. Each next step takes more time and more resources. We have no shortage of dreams but we lack the manpower to realize them.

伽利略用两片透镜和一根镜筒拓展了我们的视野。如今，为了再次拓展视野，人类建造了詹姆斯·韦布空间望远镜——这座价值一百亿美元的天文台折叠在火箭内部，被送往一百多万英里之外。它的十八块巨大镜片段经过精密设计，精度达到五十纳米。这座天文台汇聚了十四个国家三百个组织的力量。伽利略的贡献只需几十双手便可完成。韦布则需要一支大军，以及其背后的全球经济。Galileo widened our sight with two lenses and a tube. To widen it again today, humanity built the James Webb Space Telescope, a ten-billion-dollar observatory folded inside a rocket and sent a million miles away. Its eighteen enormous mirror segments are engineered to a fifty nanometer precision. The observatory was born from three hundred organizations across fourteen countries. Galileo’s contribution called on a few dozen hands. Webb needed an army, and a global economy behind it.

要看得更远，靠的不只是更聪明的人，还需要更庞大的官僚体系。It didn’t just take brighter minds to see farther. It took a larger bureaucracy.

我们提出的问题多于能够开展调查的问题。我们设计的实验多于能够实际进行的实验。我们构想创意的速度，快于将其实现的速度。而在许多领域，随着我们不断进步，构建出我们所设想之物也变得愈发复杂。We question more than we can investigate. We design more experiments than we can run. We dream up creative possibilities faster than we can realize them. And across many fields: the further we advance, the more complicated it becomes to build what we imagine.

Nick Bloom 及其合著者（在新窗口中打开）研究了美国经济中的科研生产率。如今，要维持摩尔定律，需要的研究人员数量是 20 世纪 70 年代初的十八倍以上。整个经济体的有效科研投入较 20 世纪 30 年代增长了二十三倍，而测得的科研生产率却下降了四十一倍。Nick Bloom and his coauthors⁠(opens in a new window) studied research productivity across the American economy. Sustaining Moore’s law now requires more than eighteen times as many researchers as it did in the early 1970s. Economy-wide effective research effort rose twenty-three-fold from the 1930s, while measured research productivity fell by a factor of forty-one.

技术人员队伍的增长速度是科学家队伍的两倍⁠（在新窗口中打开）。科学领域对专业设备的使用量在四十年间翻了一番⁠（在新窗口中打开）。如今的芯片制造厂成本是三十年前的五倍⁠（在新窗口中打开），而且规模也大得多、运营更为复杂。The technician workforce is growing twice as fast⁠(opens in a new window) as the scientists. The use of specialized equipment in science has doubled⁠(opens in a new window) over four decades. A chip fab today is five times as costly⁠(opens in a new window)—and a much more sprawling operation—than thirty years ago.

这些统计数据暗示，进一步取得进展的成本正在上升。经济之所以仍在发展，是因为研究事业的大幅扩张抵消了每个单位产出的下降。These statistics hint at a rising cost to further progress. The economy still advances because a vast increase in research enterprise has compensated for the declining yield of each unit.

这段历史应当让我们以不同的方式看待天才：不应将其孤立地考量，而应将其视为生产过程中的一个投入。一项 brilliant 的假设仍然需要证据、仪器以及将其付诸实施的手段。没有实现它所需的设备和辅助人员，一个想法还不能算是进步。That history should make us think differently about genius: not to be considered in a vacuum, but as one input to a production process. A brilliant hypothesis still needs evidence, instruments, and the means to carry it out. An idea isn’t yet progress without the machinery and the support staff to actualize it.

当一种投入的增加会提高另一种投入的价值时，经济学家称这两种投入为互补品。前沿智能与实现其创意的能力在这个意义上是互补的。更好的望远镜会让一个好的天文学问题更有价值；更好的问题也会让望远镜更有价值。Economists call two inputs complements when more of one raises the value of the other. Frontier intelligence and the capacity to realize its ideas are complements in this sense. A better telescope makes a good astronomical question more valuable. A better question makes the telescope more valuable.

有些补充条件是物质性的。一个理论要等待粒子加速器；一项技术设计则要等待建造它所需的能源和机械设备。Some complements are physical. A theory waits on a particle accelerator; a technological design waits for the energy and machinery to build it.

其他互补因素更难用肉眼看到。一个想法本身是脆弱的。它需要各种各样的制度。法律、官僚机构、融资机制、供应链以及许多其他因素共同协作，才能将这个想法付诸实施。Other complements are harder to see with your eyes. An idea, on its own, is a fragile thing. It needs institutions of all kinds. Laws, bureaucracy, funding mechanisms, supply chains, and much else come together to execute on the idea.

一个伟大的创意必须经受住这些机构之间一长串正确的局部行动。我们称这种能力为制度性智慧：一种无人称颂的执行智慧。天才设计纪念碑，机构铺下石块。前者才华横溢，后者看似枯燥；但二者都至关重要。A great idea must survive a long chain of correct local actions across these institutions. Call this institutional intelligence: the uncelebrated intelligence of execution. Genius designs the monument; institutions lay the stone. One is brilliant, the other seems boring; both are absolutely crucial.

我们有时会把超级智能想象成十亿个爱因斯坦。但由十亿个爱因斯坦组成的文明，仍然需要其中大多数人去开采采石场、管理账目。只有当日常世界围绕前沿创新无缝运转时，前沿创新才有可能发生。进步需要每一个人的参与——从制造望远镜上那颗螺丝的焊工，到为一所培养了那位救治天文学家的护士的小学提供保险的保险公司。进步建立在一个运转不息的经济之上，而在讲述那些最伟大成就的故事时，我们往往忽略了它。We sometimes imagine superintelligence as a billion Einsteins. But a civilization of a billion Einsteins still needs most of them to mine the quarries and manage the accounting. Frontier innovation can only happen when the normal world works seamlessly around it. Progress calls on everyone—from the welder of a screw that goes in the telescope, to the insurer for a grade school that taught a nurse who treated the astronomer. Progress is built on a humming economy we often ignore in the tales of our greatest feats.

如同人类智能一样，人工智能将被引导用于杰出的洞察力和机构能力。问题在于，它大部分时间将用于何处。Like human intelligence, AI will be channeled toward both brilliant insight and institutional competence. The question is where it will spend the preponderance of its time.

AI 已经让执行不再那么稀缺。它可以编写代码、检索陌生的文献，并将草图转化为可运行的原型。过去需要整个组织才能实现的想法，如今越来越可以由一个有抱负的人来推进。AI is already making execution less scarce. It writes the code, searches an unfamiliar literature, and turns a sketch into a working prototype. Ideas that once required a whole organization can increasingly be pursued by one ambitious person.

大多数有志于成为电影制作人的人，永远不会得到两亿美元来拍摄自己的作品。大多数游戏设计师穷尽职业生涯，都在实现他人的创意构想。我们努力提拔最有天赋的人，却不可避免地会错失人才。从这个角度来看，智能时代始于对人类创造力的补足，让更多人拥有支持团队来构建属于自己的创意。Most aspiring filmmakers are never given two hundred million dollars to shoot their shot. Most game designers spend their entire careers implementing the creative visions of others. We try to promote the most gifted people but we inevitably miss out on talent. Viewed in this light, the intelligence age begins by complementing human ingenuity, giving more of us the support staff to build ideas of our own.

这可能会催生大量各具特色的项目，让更多富有创意的作品在那些过去充当相关领域工作准入守门人的机构之外诞生。稀缺的投入从执行转向品味——即判断什么值得创作、哪个问题值得提出，以及一千个看似合理的方向中哪一个值得继续探索的能力。This could yield a profusion of idiosyncratic projects, with more creative work produced outside the institutions that previously served as gatekeepers to doing work in those fields. The scarce input moves from execution towards taste—the ability to decide what is worth making, which question is worth asking, and which of a thousand plausible directions deserves to be pursued.

但这可能还不是故事的结局。随着 AI 越来越有能力独立获得出色的新洞见——我们或许已经在数学等领域看到了这一点——它提供的不仅是劳动力，还有研究议程。它不再只是执行某个人类研究计划，而是设计出成千上万个属于自己的计划。AI 天才会让值得开发的电影创意和有待检验的假设数量激增。新想法涌现的速度，超过了配套基础设施的消化能力。But this may not be the end of the story. As AI grows capable of arriving at brilliant new insights on its own—which we might already be seeing in domains such as math—it supplies agendas as well as labor. Rather than implementing one human research program, it designs thousands of its own. AI geniuses explode the number of film concepts worth developing and hypotheses worth testing. Ideas abound faster than supporting infrastructure can absorb them.

如今的 AI 带来了期待已久的喘息，让我们的好点子终于得到应有的重视。明天，好的点子可能会多到让我们比以往任何时候都更缺乏执行力。Today’s AI offers a long-awaited reprieve, where our good ideas finally get their due. Tomorrow there may be so many good ideas that we become more execution-starved than ever before.

归根结底，我们并不知道，为了有效利用新的洞见，所需的配套条件会增加、减少，还是保持稳定。一个人仅凭独自思考，能够取得多大的智力进步？才华横溢究竟需要多少官僚机构？We ultimately do not know whether the complements needed to make productive use of new insights will grow, shrink, or remain stable. How much intellectual progress can one make by thinking alone? How much bureaucracy does brilliance need?

在我们看来，答案取决于思想在必须再次与现实接触之前，能够走多远。In our view, the answer depends on how far thought can travel before it must make fresh contact with reality.

我们描述两条潜在路径。第一条是“深度文明”，在这种文明中，超级智能克服了对更多物质资本和官僚式协作的需求。第二条是“广度文明”，在这种文明中，自然的复杂性超越了任何智能——无论是人类还是机器——的能力，使其无法在没有大规模且日益复杂的现实世界实验的情况下取得进展。We describe two potential pathways. The first is a civilization of depth, in which superintelligence surmounts the need for more physical capital and bureaucratic orchestration. The second is a civilization of width, in which the complexity of nature surpasses the ability of any intelligence—human or machine—to make progress without large and increasingly elaborate experiments in the real world.

迄今为止，人类历史暗示着对互补能力日益增长的需求。伽利略的望远镜可以握在手中；而韦布望远镜则需要整个文明的力量。贝尔实验室的一个小团队在工作台上组装出了第一只晶体管；如今，芯片前沿技术的进步来自一条错综复杂的全球供应链。触及更远的边界，始终在不断召集更多文明力量为其助阵。Human history hints at a rising need for complementary capacity so far. Galileo’s telescope fit in his hands; Webb required a civilization. A small Bell Labs team assembled the first transistor on a workbench; frontier chip advancements now come from an intricate global supply chain. Further reach keeps recruiting more of civilization behind it.

但这种历史并不一定决定命运。聪明才智可以发明出效率更高的互补方案。如果能源成为新型粒子加速器的瓶颈，卓越的智慧就能设计出更精简的加速器，或找到更好的能量获取方式。But that history need not be destiny. Smart minds can invent more efficient complements. If energy bottlenecks a new particle accelerator, brilliance can devise a leaner accelerator or a better way to harvest energy.

天才也可以替代其某些互补因素。爱因斯坦仅凭极少的实证数据，就能够通过思考推导出相对论。我们则需要多得多的数据。有些人能把自己的所有约会安排都记得一清二楚——而我们大多数人依赖的是 Google 日历的官僚体系。Genius can also substitute for some of its complements. Einstein was able to think his way to relativity from very little empirical data. We would need a lot more. Some people simply remember all their engagements—most of us rely on the bureaucracy of Google Calendar.

而天才能够将品味最大化，选择最佳假设进行测试，并避免代价高昂的失败。如果每增加一位天才都能提升计划的质量，即便需要规模越来越庞大的官僚机构，我们仍会大力投资于天才，正是因为执行成本如此高昂，不能浪费在错误的事情上。And genius can maximize taste, choosing the best hypotheses to test and avoiding wasteful failures. If each additional genius increases the quality of the plan, even if ever-larger bureaucracies are needed, we would still invest heavily into genius precisely because execution is so expensive to squander on the wrong thing.

这两条路径都无法完美描述我们实际的未来，但可以将它们视为人类文明未来发展的两种方向性可能。Neither pathway will perfectly describe our actual future, but they can be thought of as two directional possibilities for our civilization.

智能可以通过三种方式推动知识发展。它可以根据原理进行推理；可以从现有证据中得出新的洞见；也可以通过观察和干预世界来收集新的证据。Intelligence can advance knowledge in three ways. It can reason from principles. It can draw new insights from existing evidence. Or it can gather new evidence by observing and intervening in the world.

第一种模式仅凭思考就能走得很远。数学是最纯粹的例子。在证明或项目中的诸多关键节点上，强大的头脑能够始终做出正确的判断。它用一条简短而更有针对性的推理链，取代了多年的无头绪探索。The first mode can travel far through thought alone. Math is the purest example. At the many crossroads in a proof or project, a powerful mind can consistently make the right call. It replaces years of floundering search with a short, more directed chain of reasoning.

第二种模式同样是实现快速进展的处女地。人类收集的证据远多于已经理解的部分。对我们数据的研究——天文图像、显微镜载玻片、在线评论区——一直受到人类智力稀缺的限制。科学档案中包含着为回答一个问题而记录的观测结果，而这些观测或许能解答许多其他问题。充沛的天才无需收集任何新事物，就能通过看见原本已经存在的东西而有所发现。它或许无需再收集一个光子，就能发现一个新的宇宙。The second mode is also a greenfield for fast progress. Humanity has collected far more evidence than it has understood. The study of our data—astronomical imagery, microscope slides, online comment sections—has been limited by the scarcity of human intelligence. Scientific archives contain observations recorded to answer one question that may hold answers to many others. Abundant genius can discover not by collecting anything new, but by seeing what was already there. It might find a new universe without gathering a new photon.

第三种模式没那么容易，因为它要求我们与物理世界互动。物理过程需要时间，而且通常很难加快它们的进程。化学物质必须发生反应。生物体必须生长。机器必须建造完成。航天器必须跨越实际距离，并受制于宇宙速度极限。即使是完美的头脑，也无法观察尚未发生的实验结果。The third mode isn’t so easy because it requires us to interact with the physical world. Physical processes take time, and often little can be done to speed them up. Chemicals must react. Organisms must grow. Machines must be constructed. Spacecraft must cross actual distance, subject to cosmic speed limits. Even a perfect mind can’t observe the result of an experiment that has not occurred.

但它可以尝试。一个有深度的文明无法逃避征询现实的需要，但会在这方面变得极其高效。更好的推理能够找出真正重要的少数实验。模拟将解决大多数问题，并定位出必须由现实来裁定的确切不确定性。超级智能可以将效率最大化，围绕其稀缺资源进行设计，充分发挥这些资源的作用。But it can try. A civilization of depth wouldn’t escape the need to consult reality. It would become radically economical in doing so. Better reasoning would identify the few experiments that truly matter. Simulations would resolve most and locate the precise uncertainty that reality must settle. Superintelligence can maximize efficiency, designing around its scarce resources to get the most out of them.

设想一个研究暗物质假说的天体物理学实验室。这些假说首先会被输入超写实的计算机建模系统。每个理论都会接受检验，以确认其与所有现有数据的一致性。完成这一切之后，才会针对天空进行少量有目标的新观测，以检验最有希望的理论。这个文明仍然需要天才的互补能力，但每个单位所产生的价值要高得多。Picture an astrophysics lab with a set of hypotheses on dark matter. These are first shunted through hyperrealistic computer modeling. Each theory is checked for coherence with all existing data. Only after all this are a few targeted new snapshots of the sky taken to test the most promising theories. The civilization still needs the complements to genius, but each unit yields far more value.

我们通往深度文明的道路，或许会像拼图一样展开。进步在开始时容易，在结束时也容易，而在中途最为艰难。起初，边缘部分清晰可见，也很容易找到匹配的拼片。随后，这些线索逐渐耗尽，而已拼出的图案又太少，无法为剩余部分提供指引。接近尾声时，剩下的空缺几乎会告诉你其中应当放入什么。一门几近完备的科学，或许会发现它最后的成果比我们今天努力探索的那些更容易获得。Our path to a civilization of depth might work like a jigsaw puzzle. Progress is easy at the start, easy at the end, and hardest in the middle. At first there are obvious edges and easy matches. Then these run out, while too little of the picture is assembled to guide the rest. Near the end, the remaining gaps almost tell you what belongs in them. A nearly complete science might find its final discoveries easier than the ones we struggle toward today.

这是科学中的一个基本方法。门捷列夫能够描述尚未被发现的元素，是因为元素周期表的其余部分限定了缺失的内容。标准模型让物理学家有理由在观测到希格斯玻色子数十年前就预期它的存在。我们的实验有针对性地寻找预期会看到的东西——也就是与现有理论和数据相一致的现象。This is a staple of science. Mendeleev could describe undiscovered elements because the rest of the periodic table constrained what was missing. The Standard Model gave physicists reason to expect the Higgs boson decades before we observed it. Our experiments were targeted to look for what we expected to see—what was coherent with existing theory and data.

超级智能或许能够通过深入理解世界的模式，精准知晓自己需要哪些新数据，从而实现一个有深度的文明。它将通过更深层次的思考推动进步，而不是依赖物理扩张的缓慢推进。Superintelligence might achieve a civilization of depth by understanding the patterns of the world so well that it knows precisely what new data it needs. It would drive progress through deeper thought, not relying on the slow march of physical expansion.

如果这些效率占据主导地位，文明或许能迅速加深对自然的掌控，而无需依赖太多制度性智慧来管理规模庞大的新型实验装置。它的物理占地可能依然很小，但智慧却日益成熟。If those efficiencies dominate, civilization could rapidly deepen its mastery over nature, with less need for institutional intelligence to govern vast new experimental apparatuses. Its physical footprint may stay small while its wisdom ripens.

我们人类足不出户，就已经了解了宇宙如此之多的奥秘。深度文明将是这一传统的延续。We humans have already understood so much about our universe without ever leaving home. The civilization of depth would be a continuation of that tradition.

但对新证据的需求可能会压倒这些效率。情报或许能降低每次与现实核对的成本，同时发现更多需要核对现实的理由。更精良的工具开辟出更细致的前沿。更快的实验可能会让开展更多实验变得值得。But the demand for new evidence can overwhelm these efficiencies. Intelligence may cheapen each consultation with reality while discovering many more reasons to consult it. Better instruments open finer frontiers. Faster experiments can make additional experiments worthwhile.

如果说爱因斯坦几乎纯粹依靠认知探索相对论的路径，体现了一种“深度文明”，那么生物学或许最能预示一种“广度文明”。尽管我们积累了如此丰富的知识，也运用了如此庞大的数据，新药仍然必须在大量人体上进行测试，才能确定其安全性和有效性。如今，机器已经能够模拟生物过程，让生物学家可以在计算机中而非通过实体实验测试新药，但这些数字模拟还不足以忠实呈现现实世界，因此无法取代大规模人体试验。机器产生的候选药物越多，经验验证就越成为更大的瓶颈。If Einstein’s almost purely cognitive path to his theories of relativity typifies a civilization of depth, biology might best foreshadow a civilization of width. For all the knowledge we have accumulated, and all the data we have brought to bear, new medicines still have to be tested on large numbers of humans to know they are safe and effective. Machines can now simulate biological processes, allowing biologists to test new drugs in silico rather than in physical experiments, but those digital simulations aren’t faithful enough to the real world to substitute for large-scale human trials. More candidates from the machine make empiricism a greater bottleneck.

在一个追求广度的文明中，光有才智并不足以推动进步。我们对物质资源的需求，以及管理其使用的官僚机构，其增长速度超过了聪明才智在任何一方面实现节约的能力。In a civilization of width, brilliance alone isn’t enough to make progress. Our need for physical resources—and a bureaucracy to manage their use—grows faster than a smart mind can economize on either.

即使未来的 AI 系统在很大程度上超越人类智能，这也可能是我们的道路。超级智能可以在一个下午设计出需要一个世纪才能完成的一系列实验，然后花费这一个世纪等待自然和机器逐步实现这些实验。在这样的世界里，将智能投入到执行想法所需的制度流程中，比投入到构思本身更有价值。This might be our path even if future AI systems greatly exceed human intelligence. A superintelligence could design a century of experiments in an afternoon, then spend the century waiting for nature and machinery to follow through. In such a world, it becomes more valuable to invest intelligence into the institutional processes needed to execute ideas than into the ideation itself.

如果后续每一步都需要越来越多的配套基础设施，这种影响将会不断累积。The effect will compound if each further step requires more and more support infrastructure.

在这里，我们会看到相对更少的思考，以及相对更多的建造。实验室实现自动化。工厂不断扩张。能源产量上升。制度性智能占据主导地位，推动这一切发生。物理层面的扩张成为知识的引擎，而文明之所以不断扩展，是因为它所提出的问题已经超出了获取答案的手段。更多材料、更多能源、更多数据，以维持这一进程。Here we would see relatively less thinking and relatively more building. Labs automate. Factories multiply. Energy production rises. Institutional intelligence dominates to make all this happen. Physical expansion becomes the engine of knowledge, and civilization spreads because its questions have outgrown the means of obtaining answers. More materials, more energy, more data to keep going.

戴森球是这种未来的一种极端图景。设计这样一个结构需要非凡的突破，但整个项目的大部分工作并不在于突破性的思考，而在于天文尺度的建造与物流。A Dyson sphere is an extreme image of this future. Designing one requires extraordinary breakthroughs, yet most of the project wouldn’t consist of breakthrough thought. It would be construction and logistics on an astronomical scale.

戴森球需要由卓越的头脑来绘制蓝图。它也将是人类开展过的最重复、最单调、组织协调上最具挑战性的项目之一。The Dyson sphere requires brilliant minds to blueprint. It would also be among the most repetitive, monotonous, organizationally challenging projects ever undertaken.

这不是智力的失败，而是智力成功的结果。越聪明的人会发现越多值得开展的项目；越复杂的项目则需要越多的物质来执行。This isn’t a failure of intelligence but rather a consequence of its success. Brighter minds discover more worthwhile projects; more complex projects need more matter to execute.

如果确实如此：超级智能的超能力可能并不在于它的天才才智。相反，它的超能力在于愿意充当官僚机构，协调将创意以越来越大的规模变为现实所需的庞大技术与制度机器。If this is so: the superpower of superintelligence might not be its genius. It would instead be its willingness to be the bureaucracy, to coordinate the vast technological and institutional machinery required to turn ideas into reality at ever-larger scale.

几乎所有的机器智能都会被部署来完成枯燥乏味的工作，而不是施展才华。Almost all machine intelligence would be deployed not to do the brilliant, but to do the boring.

自然界对物理扩张速度的限制，随后可能会成为进一步发展的瓶颈。建造用于收集恒星能量的设备，归根结底是物质层面的工程，无论规划绘制得多快都一样。再往外是其他恒星和其他星系，而距离（据我们所知）本身就是一种限速因素：没有任何飞船能够以超越光速的速度航行，也没有任何信号能够以超越光速的速度返回。一个横向扩张的文明，是一个发展缓慢的文明。它的重大变革可能相隔数千年，甚至数百万年。每迈出下一步，所需的时间都会更长。Nature’s speed limits on physical expansion may then bottleneck further progress. Building the machinery to harvest a star’s energy is a project in matter, however quickly the plans are drawn. Beyond it lie other stars and other galaxies, and distance (as far as we know) is its own throttle: no ship can travel and no signal can return faster than light. A civilization of width is a slow civilization. Its great changes might come millennia apart, perhaps millions of years apart. Each next step takes longer.

农业和工业革命将如同一部漫长得多、进步速度缓慢得多的历史的狂热开端。The revolutions of agriculture and industry would look like the frenzied opening of a much longer history, with a much slower rate of progress.

未来的人工智能系统很可能会在人类前沿洞见的产出能力，以及协调使这些洞见具有实际用途所需资源的能力这两方面都超越人类。但这种可能性并不会削弱人类好奇心的重要性。It may well be the case that future AI systems surpass humans in both the production of frontier insights and the coordination of resources needed to make those insights practically useful. But this possibility does not diminish the importance of human curiosity.

首先，即使机器似乎更擅长启蒙，我们对启蒙的责任依然存在。宇宙中或许已经存在着比我们明智得多的心智，它们掌握着超出我们理解范围的科学和道德问题的答案。然而，我们仍然努力亲自理解这个世界。另一种智能的到来——即使是由我们亲手创造的——也不会改变这一点。在我们确切知道人类已经没有任何独特的贡献可作之前，放弃努力就等于浪费我们唯独能够为世界增添的一切。我们仍有责任不断提出问题，并努力寻找答案。First, our obligation to enlightenment remains, even if machines appear better at it. The universe may already contain minds far wiser than ours, with answers to scientific and moral questions beyond our comprehension. Yet we still try to understand the world ourselves. The arrival of another intelligence—even one we build ourselves—doesn’t change that. Until we know with certainty that humanity has nothing distinctive left to contribute, giving up wastes whatever we alone can add to the world. Our duty remains to keep asking and trying to answer.

其次，相较于制度性智能，我们可能在前沿智能方面具有比较优势。这意味着，即使我们在两者上都不如对方，我们在前者上的相对表现更好，因此会专注于前者。人类倾向于发挥创造力，而不是从事例行化的工作；机器人则没有这种偏好。Second, we may have a comparative advantage in frontier intelligence over institutional intelligence. That would mean that even if we are worse at both, we are relatively better at the former and specialize in it. Humans express a preference to be creative instead of routinized, while robots express no such preference.

第三，我们能从多样性中获益。即使机器能够模拟大多数人类思维，只要我们保有一定的独特性，就能提供创造性的多样化。机器智能的大部分将留在其自身可持续运转的体系中，但其中一小部分会由我们根据创作需求加以部署。这只是整体中的一小部分，却足以拓展我们的能力范围，并且远远超出我们今天拥有的资源。Third, we gain value from variety. Even if machines can emulate most human thinking, if we maintain some uniqueness, we offer creative diversity. Much of machine intelligence would stay in its own self-sustaining apparatus, but a small fraction would be deployed by us at our creative behest. A sliver of the whole, but luxurious for our scope and far more than the resources we have today.

独创性并非孤立存在。它需要世界，而天才需要建设者来实现其创造愿景。Ingenuity isn’t a lonely thing. It needs the world, and genius needs builders to realize its creative vision.

如今，人类智能很大一部分都用于将想法变为现实这一默默无闻的工作。机器智能也将继承同样的依赖。它们不可能全都是杰出人物，也必须成为官僚。Much of human intelligence today goes toward the unsung work of turning ideas into reality. Machine minds will inherit the same dependence. They cannot all be luminaries. They must be bureaucrats too.

我们未来的故事及其发展速度，取决于杰出人才是需要更多的世界，还是能以更少创造更多。Our story to come—and the pace of its progress—depend on whether great minds need more of the world, or whether they do more with less.