AI介入編程反而讓效率降低
https://www.theatlantic.com/economy/archive/2025/09/ai-bubble-us-economy/684128/On the other hand, evidence is piling up that AI is failing to deliver in the real world. The tech giants pouring the most money into AI are nowhere close to recouping their investments. Research suggests that the companies trying to incorporate AI have seen virtually no impact on their bottom line. And economists looking for evidence of AI-replaced job displacement have mostly come up empty. None of that means that AI can’t eventually be every bit as transformative as its biggest boosters claim it will be. But eventually could turn out to be a long time. This raises the possibility that we’re currently experiencing an AI bubble, in which investor excitement has gotten too far ahead of the technology’s near-term productivity benefits. If that bubble bursts, it could put the dot-com crash to shame—and the tech giants and their Silicon Valley backers won’t be the only ones who suffer. Since the experiment was conducted, AI coding tools have gotten more reliable. And the study focused on expert developers, whereas the biggest productivity gains could come from enhancing—or replacing—the capabilities of less experienced workers. But the METR study might just as easily be overestimating AI-related productivity benefits. Many knowledge-work tasks are harder to automate than coding, which benefits from huge amounts of training data and clear definitions of success. “Programming is something that AI systems tend to do extremely well,” Tim Fist, the director of Emerging Technology Policy at the Institute for Progress, told me. “So if it turns out they aren’t even making developers more productive, that could really change the picture of how AI might impact economic growth in general.” The capability-reliability gap might explain why generative AI has so far failed to deliver tangible results for businesses that use it. When researchers at MIT recently tracked the results of 300 publicly disclosed AI initiatives, they found that 95 percent of projects failed to deliver any boost to profits. A March report from McKinsey & Company found that 71 percent ofcompanies reported using generative AI, and more than 80 percent of them reported that the technology had no “tangible impact” on earnings. In light of these trends, Gartner, a tech-consulting firm, recently declared that AI has entered the “trough of disillusionment” phase of technological development. Perhaps AI advancement is experiencing only a temporary blip. According to Erik Brynjolfsson, an economist at Stanford University, every new technology experiences a “productivity J-curve”: At first, businesses struggle to deploy it, causing productivity to fall. Eventually, however, they learn to integrate it, and productivity soars. The canonical example is electricity, which became available in the 1880s but didn’t begin to generate big productivity gains for firms until Henry Ford reimagined factory production in the 1910s. Some experts believe that this process will play out much faster for AI. “With AI, we’re in the early, negative part of the J-curve,” Brynjolfsson told me. “But by the second half of the 2020s, it’s really going to take off.” Anthropic CEO Dario Amodei has predicted that by 2027, or “not much longer than that,” AI will be “better than humans at almost everything.” That prosperity has largely yet to materialize anywhere other than their share prices. (The exception is Nvidia, which provides the crucial inputs—advanced chips—that the rest of the Magnificent Seven are buying.) As The Wall Street Journal reports, Alphabet, Amazon, Meta, and Microsoft have seen their free cash flow decline by 30 percent over the past two years. By one estimate, Meta, Amazon, Microsoft, Google, and Tesla will by the end of this year have collectively spent $560 billion on AI-related capital expenditures since the beginning of 2024 and have brought in just $35 billion in AI-related revenue. OpenAI and Anthropic are bringing in lots of revenue and are growing fast, but they are still nowhere near profitable. Their valuations—roughly $300 billion and $183 billion, respectively, and rising—are many multiples higher than their current revenues. (OpenAI projects about $13 billion in revenues this year; Anthropic, $2 billion to $4 billion.) Investors are betting heavily on the prospect that all of this spending will soon generate record-breaking profits. If that belief collapses, however, investors might start to sell en masse, causing the market to experience a large and painful correction.
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