Comment on “The Impact of Foundational AI on International Trade, Services and Supply Chains in Asia”

IF 4.5 3区 经济学 Q1 ECONOMICS Asian Economic Policy Review Pub Date : 2023-12-05 DOI:10.1111/aepr.12456
Kenneth Flamm
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However, it is still very uncertain whether the cost of AI compute in the twenty-first century is going to decline over time at a rate resembling that of general-purpose computing in the twentieth century, and, therefore, whether twenty-first century AI comes into widespread industrial use quickly.</p><p>In the twentieth century, “Moore's Law” was code for a cadence of technological innovation in semiconductors that produced 20% to 30% annual declines in (quality-adjusted) computer hardware cost, and impressive declines in the energy needed for computing, lasting decades. Continuing price declines for computing created economic incentives to use computing in all kinds of new applications, across all sectors of the economy, substituting for labor, other forms of capital, and raw materials, and increasing productivity and living standards. (Jorgenson, <span>2001</span>).</p><p>By the second decade of the twenty-first century, however, while some Moore's Law-style miniaturization of semiconductors continued at a slower pace, quality-adjusted semiconductor prices were no longer falling at earlier double-digit rates. (Flamm, <span>2017</span>, <span>2021</span>; Sawyer &amp; So, <span>2018</span>) Slackening technological improvement in chip manufacturing means that some new engine is needed to drive AI compute costs lower. Absent steady declines in AI compute costs, a rapid uptake of AI across broad sectors of the economy is unlikely to materialize.</p><p>Second, national security is tightly linked to AI, and, therefore, likely to drive national policies and investments relevant to cutting edge AI technology. This was the case when military investments first kickstarted the computer and semiconductor industries seventy-five years ago (Flamm, <span>1987</span>). 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Abstract

Meltzer (2024) does an admirable job of mapping recent technological developments in AI to the current institutions of the international trading system. This is a useful taxonomy.

I raise two concerns. First, AI's net effects on employment and output are inextricably linked to the pace of AI's adoption by industry. Meltzer notes that recent leaps in the capabilities of AI models have been accompanied by exponential increases for the “AI compute” needed to train these models. However, it is still very uncertain whether the cost of AI compute in the twenty-first century is going to decline over time at a rate resembling that of general-purpose computing in the twentieth century, and, therefore, whether twenty-first century AI comes into widespread industrial use quickly.

In the twentieth century, “Moore's Law” was code for a cadence of technological innovation in semiconductors that produced 20% to 30% annual declines in (quality-adjusted) computer hardware cost, and impressive declines in the energy needed for computing, lasting decades. Continuing price declines for computing created economic incentives to use computing in all kinds of new applications, across all sectors of the economy, substituting for labor, other forms of capital, and raw materials, and increasing productivity and living standards. (Jorgenson, 2001).

By the second decade of the twenty-first century, however, while some Moore's Law-style miniaturization of semiconductors continued at a slower pace, quality-adjusted semiconductor prices were no longer falling at earlier double-digit rates. (Flamm, 2017, 2021; Sawyer & So, 2018) Slackening technological improvement in chip manufacturing means that some new engine is needed to drive AI compute costs lower. Absent steady declines in AI compute costs, a rapid uptake of AI across broad sectors of the economy is unlikely to materialize.

Second, national security is tightly linked to AI, and, therefore, likely to drive national policies and investments relevant to cutting edge AI technology. This was the case when military investments first kickstarted the computer and semiconductor industries seventy-five years ago (Flamm, 1987). Militaries the world over are currently investigating incorporation of AI into autonomous military weapons systems platforms, and into security and disinformation systems that conduct information warfare operations in cyberspace. Use of the most advanced available semiconductor manufacturing technology is required to produce specialized AI compute capability in its fastest, densest (lowest weight and smallest size), and least energy intensive form factor, which translates into greater potential capability for a “smart” weapons system or an edge-connected network device, for any given weight, energy, and size budget.

As a result, responding to rising global political tensions and following the US lead, EU members, Japan, Korea, and Taiwan have significantly tightened national security export restrictions on leading edge semiconductor manufacturing technology used to fabricate leading edge AI compute chips, and the chips themselves. In a similar vein, 5G network gear produced by Chinese producers (Huawei, ZTE) is banned or being removed from telecom networks in the US, Britain, Australia, and several EU countries.

Global rules on trade and investment in tech sectors are currently being rewritten. Political, regulatory, and economic policies intended to encourage “onshoring” of high-tech supply chains are being put into place. Balkanization of the global economy into regional trading blocs, resulting in costly trade diversions, and truncation of cost-reducing scale economies, is the likely economic outcome of this ongoing “deglobalization”.

Meltzer's vision of increasing cooperation on AI through existing international framework agreements ignores these current realities. We have entered a historical period in which international economic policy is increasingly used as an instrument of national power (Hirschman, 1945). One can only hope that we can find a strategy to reduce these political tensions, reap the full potential economic returns from new technologies in the largest possible global market, and cooperate with others in applying those innovative technologies to solve pressing global problems—like climate change.

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就 "基础人工智能对亚洲国际贸易、服务和供应链的影响 "发表评论
梅尔策(2024 年)将人工智能的最新技术发展与国际贸易体系的现行体制进行了映射,其工作令人钦佩。这是一种有用的分类方法。首先,人工智能对就业和产出的净影响与行业采用人工智能的速度密不可分。梅尔策指出,最近人工智能模型能力的飞跃伴随着训练这些模型所需的 "人工智能计算 "的指数级增长。然而,21 世纪的人工智能计算成本是否会以类似于 20 世纪通用计算的速度逐年下降,以及 21 世纪的人工智能是否会迅速在工业领域得到广泛应用,目前仍存在很大的不确定性。在 20 世纪,"摩尔定律 "是半导体领域技术创新的代名词,它使计算机硬件成本(经质量调整后)每年下降 20% 至 30%,并使计算所需的能源持续数十年大幅下降。计算机价格的持续下降产生了经济激励,促使人们在所有经济部门的各种新应用中使用计算机,替代劳动力、其他形式的资本和原材料,提高生产率和生活水平。(然而,到了 21 世纪的第二个十年,虽然一些摩尔定律式的半导体小型化在以较慢的速度继续进行,但质量调整后的半导体价格不再以早期两位数的速度下降。(Flamm, 2017, 2021; Sawyer & So, 2018)芯片制造技术改进的放缓意味着需要一些新引擎来推动人工智能计算成本的降低。其次,国家安全与人工智能密切相关,因此可能会推动与人工智能尖端技术相关的国家政策和投资。75 年前,军方投资首次启动了计算机和半导体行业,当时的情况就是如此(Flamm,1987 年)。目前,世界各国军队正在研究将人工智能纳入自主军事武器系统平台,以及在网络空间开展信息战行动的安全和虚假情报系统。因此,为了应对日益加剧的全球政治紧张局势,欧盟成员国、日本、韩国和中国台湾紧随美国之后,大幅收紧了对用于制造尖端人工智能计算芯片的尖端半导体制造技术以及芯片本身的国家安全出口限制。同样,在美国、英国、澳大利亚和一些欧盟国家,中国生产商(华为、中兴)生产的 5G 网络设备被禁止或从电信网络中移除。旨在鼓励高科技供应链 "离岸外包 "的政治、监管和经济政策正在出台。全球经济巴尔干化,形成区域贸易集团,导致代价高昂的贸易分流,以及降低成本的规模经济被截断,这就是正在进行的 "去全球化 "可能带来的经济结果。我们已经进入了这样一个历史时期:国际经济政策越来越多地被用作国家权力的工具(赫希曼,1945 年)。我们只能寄希望于找到一种战略来缓解这些政治紧张局势,在尽可能大的全球市场上从新技术中获得全部潜在的经济回报,并与其他国家合作,将这些创新技术用于解决紧迫的全球问题,如气候变化。
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来源期刊
CiteScore
12.90
自引率
2.60%
发文量
39
期刊介绍: The goal of the Asian Economic Policy Review is to become an intellectual voice on the current issues of international economics and economic policy, based on comprehensive and in-depth analyses, with a primary focus on Asia. Emphasis is placed on identifying key issues at the time - spanning international trade, international finance, the environment, energy, the integration of regional economies and other issues - in order to furnish ideas and proposals to contribute positively to the policy debate in the region.
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