雅思口语9分带你阅读《经济学人》：The AI boom lessons ...

Finance&Economics

February 2nd 2023|947words

How powerful new technologies transform economies

IT CAN TAKE a little imagination to see how some innovations might change an economy. Not so with the latest AI tools. It is easy—from a writer’s perspective, uncomfortably so—to think of contexts in which something like ChatGPT, a clever chatbot which has taken the web by storm since its release in November, could either dramatically boost a human worker’s productivity or replace them outright. The GPT in its name stands for “generative pre-trained transformer”, which is a particular kind of language model. It might well stand for general-purpose technology: an earth-shaking sort of innovation which stands to boost productivity across a wide-range of industries and occupations, in the manner of steam engines, electricity and computing. The economic revolutions powered by those earlier GPTs can give us some idea how powerful AI might transform economies in the years ahead.

ears ahead.

想要看到一些创新如何改变经济，可能需要一些想象力。但最新的人工智能工具却并非如此。从作家的角度来看，想象一个像ChatGPT这样的智能聊天机器人（自去年11月发布以来就席卷网络）可以显著提高人类工作者的工作效率，或完全取代人类工作者的情景，是很容易的，但这却令人不安。其名称中的GPT表示“生成式预训练转换器”，这是一种特殊的语言模型。它很可能代表通用技术的：一种翻天覆地的创新，这将提高广泛行业和职业的生产力，就像蒸汽机、电力和计算机一样。由这些早期“GPTS”推动的经济革命，可以让我们了解未来几年人工智能将如何强大地改变经济。

In a paper published in 1995, Timothy Bresnahan of Stanford University and Manuel Trajtenberg of Tel Aviv University set out what they saw as the characteristics of a general-purpose technology. It must be used in many industries, have an inherent potential for continued improvement and give rise to “innovational complementarities”—that is, induce knock-on innovation in the industries which use it. AI is being adopted widely, seems to get better by the day and is being deployed in ever more R&D contexts. So when does the economic revolution begin?

在1995年发表的一篇论文中，斯坦福大学的Timothy Bresnahan和特拉维夫大学的Manuel Trajtenberg，阐述了他们所认为的通用技术的特征。这种技术必须在许多行业中使用，具有持续改进的内在潜力，并产生“创新互补性”——也就是说，在使用这种技术的行业中诱导连锁创新。我们现在正广泛采用人工智能，似乎一天比一天好，并把这种技术部署在越来越多的研发环境中。那么经济革命何时开始呢？

The first lesson from history is that even the most powerful new tech takes time to change an economy. James Watt patented his steam engine in 1769, but steam power did not overtake water as a source of industrial horsepower until the 1830s in Britain and 1860s in America. In Britain the contribution of steam to productivity growth peaked post-1850, nearly a century after Watt’s patent, according to Nicholas Crafts of the University of Sussex. In the case of electrification, the key technical advances had all been accomplished before 1880, but American productivity growth actually slowed from 1888 to 1907. Nearly three decades after the first silicon integrated circuits Robert Solow, a Nobel-prizewinning economist, was still observing that the computer age could be seen everywhere but in the productivity statistics. It was not until the mid-1990s that a computer-powered productivity boom eventually emerged in America.

历史给我们的第一个经验是，就算是最强大的新技术，也需要时间来改变经济。詹姆斯·瓦特在1769年为他的蒸汽机申请了专利，但是直到19世纪30年代的英国和19世纪60年代的美国，蒸汽动力才取代了水成为工业动力的来源。据苏塞克斯大学的尼古拉斯·克拉夫特称，在英国，蒸汽机对生产力增长的贡献，在1850年后达到顶峰，也就是瓦特获得专利的近一个世纪之后。以电气化为例，关键的技术进步都在1880年之前完成，但实际上在1888年到1907年，美国的生产率增长有所放缓。在第一个硅集成电路问世近30年后，诺贝尔经济学奖得主罗伯特•索洛仍在观察，计算机时代这些集成电路无处不在，但在生产率统计数据中却看不到。直到20世纪90年代中期，计算机驱动的生产力繁荣才最终在美国出现。

The gap between innovation and economic impact is in part because of fine-tuning. Early steam engines were wildly inefficient and consumed prohibitively expensive piles of coal. Similarly, the stunning performance of recent AI tools represents a big improvement over those which sparked a boomlet of AI enthusiasm roughly a decade ago. (Siri, Apple’s virtual assistant, was released in 2011, for example.) Capital constraints can also slow deployment. Robert Allen of New York University Abu Dhabi argues that the languid rise in productivity growth in industrialising Britain reflected a lack of capital to build plants and machines, which was gradually overcome as capitalists reinvested their fat profits.

创新和经济影响之间的差距部分是由于微调。早期的蒸汽机效率极低，并且消耗大量昂贵的煤。同样，最近人工智能工具的惊人表现，与大约10年前引发人工智能热潮的工具相比，已经有了很大的进步。（比如，苹果的虚拟助手Siri于2011年发布）。资本的限制也会减缓技术部署速度。纽约大学阿布扎比分校的罗伯特·艾伦认为，英国工业化过程中生产率增长的缓慢，反映了建造工厂和机器所需资本的缺乏，而这一问题随着资本家将他们丰厚的利润进行再投资而逐渐被克服。

More recent work emphasises the time required to accumulate what is known as intangible capital, or the basic know-how needed to make effective use of new tech. Indeed, Erik Brynjolfsson of Stanford University, Daniel Rock of the Massachusetts Institute of Technology and Chad Syverson of the University of Chicago suggest a disruptive new technology may be associated with a “productivity J-curve”. Measured productivity growth may actually decline in the years or decades after a new technology appears, as firms and workers divert time and resources to studying the tech and designing business processes around it. Only later as these investments bear fruit does the J surge upward. The authors reckon that AI-related investments in intangible capital may already be depressing productivity growth, albeit not yet by very much.

最近的研究强调了，积累所谓的无形资本或有效利用新技术所需的基本知识所需的时间。的确，斯坦福大学的Erik Brynjolfsson、麻省理工学院的Daniel Rock和芝加哥大学的Chad Syverson认为，颠覆性的新技术可能与“生产力J形曲线”有关。在一项新技术出现后的几年或几十年里，由于企业和员工将时间和资源用于研究这项技术，并围绕该新技术设计业务流程，衡量的生产率增长实际上可能会下降。只有在这些投资取得成果之后，J曲线上才会大幅上升。作者认为，与人工智能相关的无形资本投资可能已经在抑制生产率增长，尽管还不是很大。

Of course for many people, questions about the effects of AI on growth take a back seat to concerns about consequences for workers. Here, history’s messages are mixed. There is good news: despite epochal technological and economic change, fears of mass technological unemployment have never before been realised. Tech can and does take a toll on individual occupations, however, in ways that can prove socially disruptive. Early in the Industrial Revolution, mechanisation dramatically increased demand for relatively unskilled workers, but crushed the earnings of craftsmen who had done much of the work before, which is why some chose to join machine-smashing Luddite movements. And in the 1980s and 1990s, automation of routine work on factory floors and in offices displaced many workers of modest means, while boosting employment for both high- and low-skilled workers.

当然，对许多人来说，人工智能对经济增长的影响问题，被置于对工人影响的担忧之后。在这里，历史给我们的信息是好坏参半的。好消息是：尽管发生了划时代的技术和经济变革，但对大规模技术性失业的担忧从未成为现实。然而，科技可以也确实会对个人职业造成影响，其影响方式可能会对社会造成破坏。在工业革命早期，机械化极大地增加了对相对无技能工人的需求，但减少了以前做过大部分工作的工匠的收入，这就是为什么一些人选择加入粉碎机器的勒德运动。在20世纪80年代和90年代，工厂和办公室日常工作的自动化，取代了许多中等收入的工人，同时促进了高技能和低技能工人的就业。

Gee, Pretty Terrific

AI might well augment the productivity of workers of all different skill levels, even writers. Yet what that means for an occupation as a whole depends on whether improved productivity and lower costs lead to a big jump in demand or only a minor one. When the assembly line—a process innovation with GPT-like characteristics—allowed Henry Ford to cut the cost of making cars, demand surged and workers benefited. If AI boosts productivity and lowers costs in medicine, for example, that might lead to much higher demand for medical services and professionals.

人工智能很可能提高所有不同技能水平的工人的生产力，甚至是作家。然而，这对一个职业整体来说意味着什么，这取决于生产率的提高和成本的降低，是否会导致需求的大幅增长，还是说只是小幅增长。当流水线——一种具有gpt特征的工艺创新——可以让亨利·福特削减汽车制造成本时，需求激增，工人受益。例如，如果人工智能提高了生产力并降低了医疗成本，这可能会导致对医疗服务和专业人员的需求大幅增加。

AI might well augment the productivity of workers of all different skill levels, even writers. Yet what that means for an occupation as a whole depends on whether improved productivity and lower costs lead to a big jump in demand or only a minor one. When the assembly line—a process innovation with GPT-like characteristics—allowed Henry Ford to cut the cost of making cars, demand surged and workers benefited. If AI boosts productivity and lowers costs in medicine, for example, that might lead to much higher demand for medical services and professionals.

强大的人工智能有可能打破历史模式。一项几乎可以处理我们普通人能做的任何任务的技术，将把人类带入未知的经济领域。然而，即使在这种情况下，过去也有一些经验可循。伴随着蒸汽革命而来的持续经济增长，以及伴随着电气化和其他后来的创新而来的进一步加速增长，本身就是前所未有的。这些创新促使人们争相发明新思想和新制度，以确保重大的经济变革转化为广泛的繁荣，而不是混乱。也许很快就会是再次变革的时候。