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Meet the world’s top AI-pilled economists

全球顶尖的人工智能经济学家

Most of them are not found in ivory towers

他们中的大多数并非身处象牙塔

Artificial intelligence has already created trillions of dollars of market value and turned a handful of tech nerds into celebrities. The public is excited but also terrified. Many brainy people, from Bill Gates to Elon Musk, say that the technology is just getting started. Talk to academic economists, though, and most seem oddly uninterested in studying the impact of the potentially world-changing technology. The centre of gravity in AI economics is instead shifting away from universities—and a gang of “AI-pilled economists” is leading the charge.

人工智能已经创造了数万亿美元的市场价值，并将少数科技极客捧成了名人。公众既兴奋又恐惧。从比尔·盖茨到埃隆·马斯克，许多智者都认为这项技术才刚刚起步。然而，如果你与学院派经济学家交谈，你会发现他们中的大多数人似乎对研究这项可能改变世界的技术的影响漠不关心。人工智能经济学的重心正在从大学转移——而一群“人工智能经济学家”正引领着这一潮流。

University researchers can move fast when they want to. After Lehman Brothers, an investment bank, collapsed in 2008 and sparked the global financial crisis, economists turned the study of bank runs and credit crunches from a niche pursuit into a mainstream interest. Two months into covid-19, in 2020, close to a third of working papers on economics published by America’s National Bureau of Economic Research (NBER), a prestigious repository of economic thought, focused on the pandemic’s effects in some way.

大学研究人员如果想的话，可以迅速行动。2008年，投资银行雷曼兄弟倒闭，引发了全球金融危机，此后，经济学家们将银行挤兑和信贷紧缩的研究从一个小众领域发展成了主流研究方向。 2020年，新冠疫情爆发两个月后，美国国家经济研究局（NBER）——一个享有盛誉的经济思想库——发表的经济学工作论文中，近三分之一都以某种方式关注了疫情的影响。

Some of this work burst into the mainstream, including that by Nicholas Bloom of Stanford University, an expert in working from home, and Emily Oster of Brown University, who studied school closures.

其中一些研究成果迅速成为主流，例如斯坦福大学的尼古拉斯·布鲁姆（Nicholas Bloom）的研究（他是居家办公领域的专家），以及布朗大学的艾米丽·奥斯特（Emily Oster）的研究（她研究了学校停课的影响）。

Three and a half years after the launch of ChatGPT ushered in the AI age, by contrast, economic analysis of the technology remains comparatively scarce. The share of NBER papers which focus on AI is rising, but not especially fast. Even in 2024, after the covid emergency was over and the AI era had truly begun, the number of covid-related papers exceeded the number of AI-related ones. This year the NBER is likely to host more conferences on health care than on AI.

相比之下，在ChatGPT发布三年半，人工智能时代真正到来之后，对这项技术的经济分析仍然相对匮乏。NBER论文中关注人工智能的比例正在上升，但速度并不快。即使到了2024年，新冠疫情危机结束，人工智能时代真正开启之后，与新冠疫情相关的论文数量仍然超过了与人工智能相关的论文数量。今年，NBER举办的医疗保健会议数量可能会超过人工智能会议。

Some academic economists have seized the AI opportunity. Susan Athey of Stanford is exploring what happens if AI puts people out of work. Basil Halperin of the University of Virginia has written lucidly about how financial markets price AI developments. Yet none is nearly as recognisable as Mr Bloom or Ms Oster. And few economists seem to recognise the research potential. “I’ve been shocked by how few of my colleagues have even tried to speak with Anthropic or OpenAI,” says one superstar academic economist who insists he talks to AI labs all the time.

一些经济学家已经抓住了人工智能带来的机遇。斯坦福大学的苏珊·阿西正在探索人工智能导致人们失业后会发生什么。弗吉尼亚大学的巴兹尔·哈尔珀林则清晰地阐述了金融市场如何为人工智能的发展定价。然而，他们远不如布鲁姆先生或奥斯特女士那样家喻户晓。而且，似乎很少有经济学家意识到人工智能领域的研究潜力。“令我震惊的是，我的同事中竟然很少有人尝试与Anthropic或OpenAI公司沟通，”一位自称经常与人工智能实验室交流的明星经济学家说道。

A lot of the research that exists is highly abstract. According to IDEAS/RePEC, a bibliographic database dedicated to economics, Daron Acemoglu of the Massachusetts Institute of Technology is the highest-ranked wonk in AI economics. His paper from 2024 featuring a complex model of economic growth under AI implies modest aggregate productivity gains. It has received more than 1,000 citations. But the model underestimates the potentially transformative effect of new AI products coming to market, argues Tyler Cowen of George Mason University. “The gains from AI measure as small because it is assumed AI will not be doing new things.”

现有的许多研究都高度抽象。根据专注于经济学的文献数据库IDEAS/RePEC的数据，麻省理工学院的达龙·阿西莫格鲁是人工智能经济学领域排名最高的专家。他于2024年发表的一篇论文，构建了一个复杂的人工智能经济增长模型，结果表明总体生产率的提升幅度不大。该论文已被引用超过1000次。但乔治梅森大学的泰勒·科文认为，该模型低估了即将上市的新型人工智能产品可能带来的变革性影响。“人工智能带来的收益之所以被低估，是因为人们假设人工智能不会创造新事物。”

Many AI-linked empirical studies also appear to rest on flawed assumptions. A paper by Erik Brynjolfsson of Stanford (ranked fifth by IDEAS/RePEC) and colleagues suggests that young people’s employment in AI-exposed occupations has sharply dropped, implying that the technology is already transforming the labour market. Yet attributing the trend to AI means believing that firms started shedding young workers on the very first release of ChatGPT—a product that was nowhere close to being good enough to replace humans.

许多与人工智能相关的实证研究似乎也基于有缺陷的假设。斯坦福大学的埃里克·布林约尔松（在IDEAS/RePEC排名中位列第五）及其同事的一篇论文指出，年轻人从事与人工智能相关的职业的比例急剧下降，这意味着这项技术已经开始改变劳动力市场。然而，将这一趋势归因于人工智能，就意味着企业在ChatGPT（一款远未达到足以取代人类水平的产品）首次发布时就开始裁员。

Academic economists may be slow and sloppy for two reasons. The first relates to the type of shock that AI represents. In 2020 covid changed the world almost overnight, and the effects were visible almost instantly in the data. By contrast, AI is changing the economy under its bonnet. The average unemployment rate across the OECD, a club of rich countries, is about the same as when ChatGPT was first released. GDP numbers also contain virtually no AI-specific data—investment in AI data centres, for instance, can only be guessed at. With no clear macroeconomic impact and no microeconomic data, there is little for wonks to analyse.

学术经济学家反应迟钝、思维粗糙的原因可能有两点。首先，人工智能带来的冲击类型不同。2020年，新冠疫情几乎在一夜之间改变了世界，其影响几乎立即在数据中显现出来。相比之下，人工智能正在潜移默化地改变经济。经合组织（一个富裕国家联盟）的平均失业率与 ChatGPT 首次发布时基本持平。GDP 数据几乎不包含任何人工智能相关的具体数据——例如，对人工智能数据中心的投资只能靠猜测。由于缺乏清晰的宏观经济影响和微观经济数据，专家们几乎没有什么可以分析的。

The second factor is that economists, as a rule, are a fairly techno-sceptical bunch. Historical research shows that technology raises incomes, but only slowly, with all sorts of non-technological factors (including financial frictions and cultural resistance) holding it back. In Britain, it was decades before the technological breakthroughs of the industrial revolution translated into faster growth.

第二个因素是，经济学家通常对技术持相当怀疑的态度。历史研究表明，技术能够提高收入，但速度缓慢，各种非技术因素（包括金融摩擦和文化阻力）都会阻碍其发展。在英国，工业革命的技术突破花了数十年才转化为更快的经济增长。

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