AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
OpenAI Intelligence Age The eternal complement Why genius machines might prove their greatest value doing monotonous work. By Hemanth Asirvatham and Elliott Mokski Loading… 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. Humanity has seen almost to the beginning of time. 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. Yet no human being has ever traveled beyond the Moon. The explanations we seek span the universe, while our bodies have barely left home. Our minds venture way beyond the reach of our hands. That mismatch could mean two very different things. 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. 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. 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. It didn’t just take brighter minds to see farther. It took a larger bureaucracy. 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 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. 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. 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. 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. 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. Some complements are physical. A theory waits on a particle accelerator; a technological design waits for the energy and machinery to build it. 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. 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. 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. 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 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. 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. 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. 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. 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. 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? In our view, the answer depends on how far thought can travel before it must make fresh contact with reality. 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. 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. 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. 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. 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. Neither pathway will perfectly describe our actual future, but they can be thought of as two directional possibilities for our civilization. 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. 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. 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. 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. 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. 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. 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. 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 [truncated for AI cost control]