人工智能现状：欢迎来到经济奇点时代
内容总结：
【AI经济影响评估：生产力革命还是“技术封建主义”前兆？】
英国《金融时报》与《麻省理工科技评论》联合推出的“AI现状”系列讨论近日聚焦生成式AI对就业市场的真实影响。专栏作家理查德·沃特斯与科技评论资深编辑大卫·罗特曼展开深度对话，揭示了当前AI技术落地过程中的矛盾图景。
生产力悖论再现
尽管AI编码助手已显著提升软件行业效率（扎克伯格预测Meta一年内半数代码将由AI生成），但麻省理工学院研究显示，目前95%的生成式AI项目尚未产生实际收益。这种分化现象令部分观察者质疑：基于概率模型、存在“幻觉”缺陷的生成式AI是否真能深刻改变商业生态？
历史经验与新型挑战
经济学家埃里克·布林约尔松提出的“IT生产力悖论”在1990年代已预示技术转型的滞后性——美国生产率在IT大规模投资十年后才迎来显著增长。对于AI而言，企业需完成数据平台建设、业务流程重构和员工再培训三重挑战，才能释放技术潜力。值得关注的是，支撑AI普及的云计算基础设施已初步完善。
制造业的AI困境
2024年诺贝尔经济学奖得主达龙·阿西莫格鲁指出，当前AI技术过度聚焦互联网衍生场景，与制造业等实体经济核心领域存在脱节。虽然理论上AI可辅助工厂工人实时诊断设备故障，但科技巨头对这类“平凡任务”缺乏兴趣，基于互联网训练的大模型也难以适配工业场景。
就业市场的十字路口
部分企业已开始以AI为由实施裁员，但经济学家警告，单纯削减成本无法带来持续的生产率提升。真正的突破应体现在：AI如何增强护士、教师、产业工人等群体的工作能力，进而创造新型就业岗位。麦肯锡的乐观预测显示，若AI能有效增强而非替代人力，全球经济年生产率增长或达3.4%。
技术隐忧与未来展望
《金融时报》首席经济评论员马丁·沃尔夫警示，若AI仅导致失业加剧与财富集中，可能引发“技术封建主义”风险。当前美国生产率增长率虽从长期徘徊的1%-1.5%回升至2%以上，但其可持续性及AI的实际贡献度仍待观察。
这场技术革命正站在历史拐点：是重蹈数字技术“高投资低增长”的覆辙，还是开启真正的经济奇点？答案或许取决于人类能否超越将AI视为成本工具的短期思维，转而构建人机协同的新型生产关系。
中文翻译：
人工智能现状：欢迎来到经济奇点
本周，《金融时报》专栏作家、前西海岸编辑理查德·沃特斯将与《麻省理工科技评论》特约编辑大卫·罗特曼探讨人工智能对就业市场的真实影响。
欢迎回到《人工智能现状》系列，这是《金融时报》与《麻省理工科技评论》的一项全新合作。在接下来的两周里，每周一，两家媒体的撰稿人将围绕重塑全球格局的生成式AI革命的某个方面展开辩论。
本周，《金融时报》专栏作家、前西海岸编辑理查德·沃特斯将与《麻省理工科技评论》特约编辑大卫·罗特曼探讨人工智能对就业市场的真实影响。
额外福利：如果您是《麻省理工科技评论》的订阅用户，您可以在12月9日（周二）美国东部时间下午1点，加入大卫、理查德以及《麻省理工科技评论》主编马特·霍南，就这一主题进行独家在线对话。请在此处注册参与。
理查德·沃特斯写道：
任何具有深远影响的新技术在应用初期总是不均衡的，但很少有技术像生成式AI这样不均衡。这使得我们很难评估它可能对单个企业产生的影响，更不用说对整个经济的生产力了。
在极端的一端，AI编码助手已经彻底改变了软件开发人员的工作。马克·扎克伯格最近预测，Meta公司一半的代码将在一年内由AI编写。而在另一端，大多数公司从它们的初期投资中几乎看不到任何收益。麻省理工学院一项被广泛引用的研究发现，迄今为止，95%的生成式AI项目投资回报为零。
这为怀疑论者提供了弹药，他们认为，生成式AI本质上是一种概率性技术，容易产生“幻觉”，因此永远不会对商业产生深远影响。
然而，对于许多科技史研究者而言，缺乏立竿见影的影响，只是变革性新技术通常会伴随的正常滞后现象。麻省理工学院前助理教授埃里克·布林约尔松在20世纪90年代初首次描述了他所谓的“信息技术生产力悖论”。尽管有大量轶事证据表明技术正在改变人们的工作方式，但总体数据并未显示出更高的生产力增长。布林约尔松的结论是，企业适应需要时间。
对信息技术的大量投资最终显现出效果，美国的生产力增长从20世纪90年代中期开始显著反弹。但十年后，这种增长势头逐渐减弱，随后进入了第二次停滞期。
就AI而言，企业需要建设新的基础设施（尤其是数据平台）、重新设计核心业务流程并重新培训员工，之后才能期望看到成果。如果滞后效应可以解释成果缓慢的原因，那么至少还有乐观的理由：为更广泛商业应用提供生成式AI所需的大部分云计算基础设施已经就位。
机遇和挑战都是巨大的。一家《财富》500强公司的高管表示，他的组织对其分析工具的使用情况进行了全面审查，结论是总体而言，其员工创造的价值微乎其微，甚至为零。根除旧软件，用AI取代低效的人力劳动，可能会产生显著效果。但正如这位高管所说，这样的全面改革需要对现有流程进行重大调整，并需要数年时间才能完成。
目前已有一些早期令人鼓舞的迹象。美国的生产力增长在长达十五年的时间里一直徘徊在1%至1.5%之间，去年反弹至2%以上。今年前九个月可能也达到了相同水平，但由于近期美国政府停摆导致缺乏官方数据，这一点无法确认。
然而，目前尚无法判断这种反弹将持续多久，或者有多少可以归功于AI。新技术的效应很少是孤立存在的。相反，其益处是叠加的。AI的发展得益于早期在云计算和移动计算领域的投资。同样，当前的AI热潮可能只是那些对经济有更广泛影响的领域（如机器人技术）取得突破的前奏。ChatGPT可能激发了大众的想象力，但OpenAI的聊天机器人不太可能成为最终答案。
大卫·罗特曼回复道：
这是近来我最喜欢讨论的关于人工智能的话题。AI将如何影响整体经济生产力？暂且忘掉那些令人着迷的视频、提供陪伴的承诺以及处理繁琐日常任务的智能体前景吧——归根结底要看AI能否促进经济增长，而这意味着提高生产力。
但是，正如你所说，很难确定AI究竟如何影响这种增长，或者未来将如何影响。埃里克·布林约尔松预测，像其他所谓的通用技术一样，AI将遵循一条J型曲线：初期对生产力的影响缓慢甚至为负，因为公司需要大量投资于该技术，之后才能收获回报，继而迎来繁荣。
但也有一个反例削弱了“只需耐心等待”的论点。信息技术带来的生产力增长在20世纪90年代中期开始加速，但自21世纪中期以来一直相对低迷。尽管出现了智能手机、社交媒体以及Slack和Uber等应用，数字技术在推动强劲经济增长方面收效甚微。强劲的生产力提升从未到来。
麻省理工学院经济学家、2024年诺贝尔奖得主达龙·阿西莫格鲁认为，生成式AI带来的生产力提升将远小于AI乐观主义者的预期，且耗时更长。原因是，尽管该技术在许多方面令人印象深刻，但该领域过于狭隘地专注于与最大商业部门相关性不大的产品。
你引用的95%的AI项目缺乏商业效益的统计数据很能说明问题。
以制造业为例。毫无疑问，某种形式的AI可以提供帮助；想象一下，工厂车间的一名工人拍下问题的照片，向AI智能体寻求建议。问题在于，开发AI的大型科技公司对解决此类平凡任务并不真正感兴趣，而它们主要基于互联网训练的大型基础模型对此也并非那么有帮助。
很容易将迄今为止AI对生产力缺乏影响归咎于商业实践和员工培训不足。你提到的《财富》500强公司高管的例子听起来太熟悉了。但更有益的问题是，如何训练和微调AI，使其能够赋予护士、教师和工厂车间工人等员工更多能力，并提高他们的工作效率。
这种区别很重要。一些最近宣布大规模裁员的公司声称AI是原因。然而，令人担忧的是，这仅仅是一种短期的成本节约方案。正如布林约尔松和阿西莫格鲁等经济学家所认同的，当AI被用于创造新型工作岗位和增强员工能力时，才会带来生产力提升，而不是仅仅用于削减工作岗位以降低成本。
理查德·沃特斯回应道：
大卫，看来我们都相当谨慎，所以我试着以积极的基调结束讨论。
一些分析认为，现有工作中更大比例的部分是当前AI力所能及的。麦肯锡估计这一比例为60%（阿西莫格鲁的估计是20%），并认为整个经济的年生产力增长最高可达3.4%。此外，此类计算是基于现有任务的自动化；正如你所建议的，任何增强现有工作的AI新用途都将带来额外收益（而且不仅仅是在经济方面）。
降低成本似乎总是任何新技术的首要任务。但我们仍处于早期阶段，而且AI发展迅速，所以我们总可以抱有希望。
延伸阅读
《金融时报》首席经济评论员马丁·沃尔夫一直对技术投资能否提高生产力持怀疑态度，但他表示AI可能会证明他错了。不利的一面是：失业和财富集中可能导致“技术封建主义”。
《金融时报》的罗伯特·阿姆斯特朗认为，数据中心投资的繁荣未必会走向萧条。最大的风险是债务融资在建设中将扮演过大的角色。
去年，大卫·罗特曼为《麻省理工科技评论》撰文，探讨了我们如何确保AI在提升生产力方面为我们服务，以及需要进行哪些方向修正。
大卫还撰写了这篇文章，探讨我们如何最好地衡量基础研发资金对经济增长的影响，以及为什么这种影响往往比想象的要大。
深度阅读
人工智能
AGI如何成为我们这个时代最具影响力的阴谋论
机器将与人一样聪明——甚至更聪明的想法，已经劫持了整个行业。但仔细观察，你会发现它之所以持续存在，与许多阴谋论的原因相同。
OpenAI的新大语言模型揭示了AI如何工作的秘密
这个实验模型不会与最大最好的模型竞争，但它可以告诉我们它们为何行为怪异——以及它们到底有多可靠。
量子物理学家压缩并“解除了审查”DeepSeek R1
他们成功地将这个AI推理模型的体积缩小了一半以上——并声称它现在可以回答中国AI系统中曾受限的政治敏感问题。
AI玩具在中国风靡一时——如今也出现在美国货架上
竞争正在升温，美泰和OpenAI预计今年将推出一款面向儿童的产品。
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英文来源：
The State of AI: Welcome to the economic singularity
This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor at large David Rotman about the true impact of AI on the job market.
Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.
This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor at large David Rotman about the true impact of AI on the job market.
Bonus: If you're an MIT Technology Review subscriber, you can join David and Richard, alongside MIT Technology Review’s editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here.
Richard Waters writes:
Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole.
At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95% of gen AI projects produce zero return.
That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business.
To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox of IT” in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt.
Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull.
In the case of AI, companies need to build new infrastructure (particularly data platforms), redesign core business processes, and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: Much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place.
The opportunities and the challenges are both enormous. An executive at one Fortune 500 company says his organization has carried out a comprehensive review of its use of analytics and concluded that its workers, overall, add little or no value. Rooting out the old software and replacing that inefficient human labor with AI might yield significant results. But, as this person says, such an overhaul would require big changes to existing processes and take years to carry out.
There are some early encouraging signs. US productivity growth, stuck at 1% to 1.5% for more than a decade and a half, rebounded to more than 2% last year. It probably hit the same level in the first nine months of this year, though the lack of official data due to the recent US government shutdown makes this impossible to confirm.
It is impossible to tell, though, how durable this rebound will be or how much can be attributed to AI. The effects of new technologies are seldom felt in isolation. Instead, the benefits compound. AI is riding earlier investments in cloud and mobile computing. In the same way, the latest AI boom may only be the precursor to breakthroughs in fields that have a wider impact on the economy, such as robotics. ChatGPT might have caught the popular imagination, but OpenAI’s chatbot is unlikely to have the final word.
David Rotman replies:
This is my favorite discussion these days when it comes to artificial intelligence. How will AI affect overall economic productivity? Forget about the mesmerizing videos, the promise of companionship, and the prospect of agents to do tedious everyday tasks—the bottom line will be whether AI can grow the economy, and that means increasing productivity.
But, as you say, it’s hard to pin down just how AI is affecting such growth or how it will do so in the future. Erik Brynjolfsson predicts that, like other so-called general purpose technologies, AI will follow a J curve in which initially there is a slow, even negative, effect on productivity as companies invest heavily in the technology before finally reaping the rewards. And then the boom.
But there is a counterexample undermining the just-be-patient argument. Productivity growth from IT picked up in the mid-1990s but since the mid-2000s has been relatively dismal. Despite smartphones and social media and apps like Slack and Uber, digital technologies have done little to produce robust economic growth. A strong productivity boost never came.
Daron Acemoglu, an economist at MIT and a 2024 Nobel Prize winner, argues that the productivity gains from generative AI will be far smaller and take far longer than AI optimists think. The reason is that though the technology is impressive in many ways, the field is too narrowly focused on products that have little relevance to the largest business sectors.
The statistic you cite that 95% of AI projects lack business benefits is telling.
Take manufacturing. No question, some version of AI could help; imagine a worker on the factory floor snapping a picture of a problem and asking an AI agent for advice. The problem is that the big tech companies creating AI aren’t really interested in solving such mundane tasks, and their large foundation models, mostly trained on the internet, aren’t all that helpful.
It’s easy to blame the lack of productivity impact from AI so far on business practices and poorly trained workers. Your example of the executive of the Fortune 500 company sounds all too familiar. But it’s more useful to ask how AI can be trained and fine-tuned to give workers, like nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs.
The distinction matters. Some companies announcing large layoffs recently cited AI as the reason. The worry, however, is that it’s just a short-term cost-saving scheme. As economists like Brynjolfsson and Acemoglu agree, the productivity boost from AI will come when it’s used to create new types of jobs and augment the abilities of workers, not when it is used just to slash jobs to reduce costs.
Richard Waters responds :
I see we’re both feeling pretty cautious, David, so I’ll try to end on a positive note.
Some analyses assume that a much greater share of existing work is within the reach of today’s AI. McKinsey reckons 60% (versus 20% for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4%. Also, calculations like these are based on automation of existing tasks; any new uses of AI that enhance existing jobs would, as you suggest, be a bonus (and not just in economic terms).
Cost-cutting always seems to be the first order of business with any new technology. But we’re still in the early stages and AI is moving fast, so we can always hope.
Further reading
FT chief economics commentator Martin Wolf has been skeptical about whether tech investment boosts productivity but says AI might prove him wrong. The downside: Job losses and wealth concentration might lead to “techno-feudalism.”
The FT's Robert Armstrong argues that the boom in data center investment need not turn to bust. The biggest risk is that debt financing will come to play too big a role in the buildout.
Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and what course corrections will be required.
David also wrote this piece about how we can best measure the impact of basic R&D funding on economic growth, and why it can often be bigger than you might think.
Deep Dive
Artificial intelligence
How AGI became the most consequential conspiracy theory of our time
The idea that machines will be as smart as—or smarter than—humans has hijacked an entire industry. But look closely and you’ll see it’s a myth that persists for many of the same reasons conspiracies do.
OpenAI’s new LLM exposes the secrets of how AI really works
The experimental model won't compete with the biggest and best, but it could tell us why they behave in weird ways—and how trustworthy they really are.
Quantum physicists have shrunk and “de-censored” DeepSeek R1
They managed to cut the size of the AI reasoning model by more than half—and claim it can now answer politically sensitive questions once off limits in Chinese AI systems.
AI toys are all the rage in China—and now they’re appearing on shelves in the US too
Competition is heating up, with Mattel and OpenAI expected to launch a product for kids this year.
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