伦敦，10月3日：从未有如此巨额的资金涌入某项新技术领域——目前投入人工智能领域的资金规模已远超当年铁路或互联网革命时期吸引到的资本总量。
LONDON, Oct 3 : Never has so much cash flowed into a new technology as is pouring into AI, eclipsing the sums splurged on railways or the internet when those technological revolutions sucked in capital.
根据普华永道的预测，到2050年全球用于数据中心建设的投入总额有望突破30万亿美元，这一数值几乎相当于美国未偿还国债的总额。即便经过通货膨胀因素调整，这一数字仍远远高于铁路建设热潮及互联网泡沫时期的总投入规模。
Cumulative spending globally on data centers alone could top $30 trillion by 2050, according to a projection by PwC, almost matching the value of outstanding US Treasuries. It "dwarfs" what was spent in the railroad or dotcom booms, even after adjusting for inflation, PwC said.
据路透社看到的IPO招股书显示，作为人工智能领域的领军企业之一，Anthropic计划在未来几年内投入5180亿美元，这一数额相当于其2025年预计营收的100多倍。该公司的支持者认为，人工智能技术所带来的变革意义将超越蒸汽机的发明及其所推动的工业化进程。
Meanwhile, Anthropic, just one of the major firms in the AI race, plans to spend $518 billion in coming years, according to the IPO prospectus seen by Reuters, which is more than 100 times its 2025 revenue. Its backers say AI technology will be more transformational than the advent of steam engines and the industrialization they powered.
然而经济学家指出，在这些令人咋舌的投入预测与估值背后，其实隐含着一个缺乏实证依据的假设：即人工智能技术能够带来广泛且显著的生产力提升并催生巨额未来利润。目前尚无任何证据或历史先例能够证实这一假设能够成真。
Yet lurking behind the dizzying projections and huge outlays by AI companies, alongside sky-high valuations, lie assumptions about vast broad-based productivity gains and future profits with little evidence so far - or historical precedent - to be sure they can deliver, economists say.
生产力提升仍难以实现摩根大通8月份曾指出，在人工智能领域处于领先地位的美国，广泛范围内的生产力提升“依然难以实现”，这一现状令人工智能相关估值的可持续性受到质疑。
PRODUCTIVITY GAINS 'REMAIN ELUSIVE' JP Morgan wrote in August that broad-based productivity gains in the US, which leads the AI race, "remain elusive", raising questions about the sustainability of AI valuations.
贝恩公司的一项研究指出，仅靠现有市场的生产力提升幅度尚不足以支撑当前的巨额投入，因此“必须催生全新的市场才能填补资金缺口”。这些新市场可能涉及人工智能引导的机器人应用，也可能涉及电池及半导体新材料的研发。
A Bain & Company study said productivity gains from existing markets would not be enough to justify current outlays and "entirely new markets must emerge to close the funding gap", suggesting those could range from using AI-guided robots to developing new materials for batteries and semiconductors.
贝恩公司称，包括谷歌、亚马逊、微软等在全球范围内布局基础设施的美国大型科技巨头，以及其他人工智能领域的参与者，未来五年内需要创造超过4.2万亿美元的新增营收，才能为相关基础设施建设提供资金保障。
US hyperscalers - the companies rolling out infrastructure around the world like Google, Amazon and Microsoft - and others in the AI race needed to find more than $4.2 trillion of new revenue in the next five years to fund the buildout, Bain said.
这份上个月发布的报告指出：“关键问题在于，能够带来回报的应用能否及时问世。”
"The question is whether the applications arrive in time to pay for it," according to the study, published last month.
几乎没有人怀疑人工智能彻底改变从办公室工作到实验室研究的一切的潜力，正如过去的革命曾将出行时间从数天缩短至数小时，或让人们轻触键盘便能与世界相连。
Few doubt the potential of AI to transform everything from work in an office to research labs, just as past revolutions shrank journey times from days to hours or connected the world at the touch of a keyboard.
似乎更难以改变的是确保投资回报的数学规律，以及偿还贷款的期限，这留给经济学家一个问题：在投资周期的起伏之外，人工智能将如何影响全球经济。
What seems more immutable is the maths behind securing a return on investment or the deadlines for repaying loans, leaving economists to work out the implications for the global economy beyond the ups and downs of investment cycles.
“历史先例表明，由技术推动的繁荣往往会在基础设施建设无法继续带来足够回报时告终。”摩根大通写道。
"Historical precedent suggests that technology-driven booms often end when infrastructure buildouts cease to deliver sufficient returns," JP Morgan wrote.
以英伟达为例，这家美国公司的芯片是人工智能革命的支柱。摩根大通估计，未来10年美国生产率增幅需达到每年3%至5%，才能证明其估值合理。这将远高于美国国会预算办公室对该时期美国生产率年增长1.75%的基准预期。
Using the example of Nvidia, the US company whose chips are the backbone of the AI revolution, JP Morgan estimated US productivity gains would need to be 3 per cent to 5 per cent annually over the next 10 years to justify its valuation. That would be a substantial increase from the baseline expectation of the US Congressional Budget Office of 1.75 per cent annual productivity growth for that period.
仅美国一国——据一些估计，其占全球人工智能投资总额约四分之三——从2025年至2032年的投资额就将高达约9万亿美元，相当于每年美国国内生产总值的3.2%。哥伦比亚商学院经济学家斯廷·范·纽维尔堡表示。
For the US alone - which according to some estimates accounts for about three quarters of the global AI investment total - investment will run as high as about $9 trillion from 2025 to 2032, equivalent to spending 3.2 per cent of US GDP each year, according to Columbia Business School economist Stijn Van Nieuwerburgh.
他估计，到2032年，美国人工智能行业每年必须创造约3.55万亿美元收入，才能实现10%的投资回报。而目前该行业的收入仅相当于这一水平的一小部分。
He estimates the US AI sector would need to generate about $3.55 trillion in annual revenue by 2032 to earn a 10 per cent return on investment. It earns a fraction of that now.
为人工智能基础设施提供融资的大量债务采用杠杆结构，这也意味着，“需求下降、项目延期或资产价值下跌，即便程度相对轻微，也可能因此造成大得多的损失。”他在一份于10月修订的会议论文中写道。人工智能与“新奇迹的发生率”这些令人咋舌的数字并未阻止美国人工智能行业的高管们以超凡的热忱谈论正在发生的变革。
The leveraged structure of much of the debt funding AI infrastructure also means "a relatively modest deterioration in demand, delays, or asset values can therefore produce much larger losses," he wrote in a conference paper, revised in October. AI AND 'THE RATE OF NEW WONDERS' The dizzying numbers have not stopped US AI bosses speaking with an otherworldly zeal about changes afoot.
Anthropic 的达里奥·阿莫代伊曾表示，AI 的未来可能成为“一件具有超凡之美的事物”；OpenAI 的山姆·奥尔特曼则表示，随着模型学会自我改进并加速突破，“创造新奇迹的速度将极其惊人”。
Anthropic's Dario Amodei has said an AI future could be "a thing of transcendent beauty", while OpenAI's Sam Altman has said "the rate of new wonders being achieved will be immense" as models learn to improve themselves and accelerate breakthroughs.
谷歌 DeepMind 首席战略官贾斯吉特·塞克洪在八月于加州大学伯克利分校举行的一场峰会上表示，这种被称为“递归式自我改进”的自我学习机制是“投资逻辑的关键组成部分”，一旦实现，可能带来前所未有的生产率提升。
Jasjeet Sekhon, chief strategy officer at Google DeepMind, told a summit at UC Berkeley in August that this self-teaching, known as recursive self-improvement, was a "key part of the investment thesis", and that it could, if achieved, deliver unprecedented productivity gains.
递归式自我改进有望带来指数级 AI 进展，但同时也引发了人们对人类生存风险的担忧。
Recursive self-improvement, while potentially delivering exponential AI advances, has also raised concerns about existential risks to humanity.
然而，生产率提升的速度最终可能仍无法满足企业会计部门的时间要求。
Yet the pace of change in productivity might still end up lagging the timelines needed by corporate accounts departments.
英国剑桥大学经济学家黛安·科伊尔表示，过去革命性技术对生产率产生全面影响，通常需要大约10至50年才会传导开来。
Diane Coyle, an economist at Britain's Cambridge University, said the productivity impact of past revolutionary technologies had usually taken about 10 to 50 years to feed through.
Anthropic 经济团队针对到2030年AI能带来多少额外增长建立了多种情景模型。假定没有AI时基线年增长率为2%，模型显示，在AI影响有限的情景下，增速为2.4%；在AI影响显著的情景下，增速为5.4%；在极端情景下，增速为15.4%。
Anthropic's economics team modeled a range of scenarios for how much extra growth AI would deliver at an annual rate in 2030. Assuming a baseline of 2 per cent in a non-AI environment, it suggested growth of 2.4 per cent in a scenario with modest AI impact, 5.4 per cent in a substantial scenario and 15.4 per cent in an extreme scenario.
报告称，增速越高，流失的岗位就越多，但并未为任何一种结果给出发生概率。
Higher growth would mean more jobs lost, it said, without assigning probabilities for any of the outcomes.
阿莫代伊去年预测，AI 可能在五年内消灭一半的入门级白领岗位。不过，目前一些研究人员表示，其影响似乎仅限于让求职办公室岗位的人更难找到工作。
Amodei forecast last year that AI could wipe out half of all entry-level white-collar jobs within five years. For now, however, some researchers say it appears to have been limited to making it harder for those seeking office work to find a job.
美国和英国的研究显示，尽管整体就业状况依然强劲，但面向职场新人的白领岗位招聘已经放缓，尤其是涉及AI擅长之任务的岗位。
Studies in the US and Britain have pointed to a slowdown in early career hiring for white-collar positions performing tasks at which AI is adept, even if overall employment remains strong.
斯坦福大学的研究人员在8月份指出：在那些依赖人工智能技术的行业中（如会计和法律助理等领域），22至25岁年轻人的就业率比在那些人工智能难以替代的工作岗位（如清洁工和建筑工人）低19%。
Researchers at Stanford University said in August that employment of workers aged 22 to 25 in AI-exposed industries, such as accountants and paralegals, was 19 per cent lower than for jobs that AI found hard to replicate, like janitors and builders.
然而，即使人工智能带来的变革所需的时间比人工智能企业所宣称的要长，实际的经济效益依然会存在——就像1873年的经济危机导致铁路巨头破产后，火车运输并未停止；同样，1990年代互联网泡沫破裂后，互联网服务也没有停止运行。
Yet even if the promised transformation takes longer than numbers surrounding AI companies imply, real economic benefits should stay - just as trains still ran after the Panic of 1873 that bankrupted railroad barons, while the internet didn't shut down after the 1990s dotcom bubble burst.
“历史是我们理解这一现象的重要工具，”科伊尔（Coyle）说：“只要我们仍然拥有支撑未来生产力发展的基础设施，那就没问题。”
"History is our friend in trying to understand this," said Coyle. "As long as one is left with the infrastructure that's needed to support all the productivity effects down the road, that's okay."