AI tax debate misses the threat that’s already here
This article was revised on September 12, 2026, to provide additional detail on the forces driving the projected changes in federal debt.
本文于 2026 年 9 月 12 日进行了修订，以提供有关推动联邦债务预计变化的力量的更多详细信息。
On Thursday, Senator Wyden (D-Ore.) proposed limiting tax benefits for new data centers and levying a new excise tax “to help the communities and workers most impacted as data center construction continues.”
周四，参议员怀登（俄勒冈州民主党人）提议限制新数据中心的税收优惠，并征收新的消费税，“以帮助随着数据中心建设的继续而受影响最大的社区和工人”。
He is one of many lawmakers who want to use taxes to address worries that AI could eliminate jobs, inflict environmental harms, and have broader negative consequences for society and global security.
他是众多希望利用税收来解决人工智能可能消除就业、造成环境危害以及对社会和全球安全产生更广泛负面影响的担忧的立法者之一。
Proposals so far range from taxing the computing power to build or use AI models, the energy they consume, or the windfall profits accruing to AI companies themselves.
迄今为止的提议范围广泛，从对构建或使用人工智能模型的计算能力、它们消耗的能源，或人工智能公司本身获得的意外利润征税。
But the debate has jumped to “which tax?” before answering two fundamental questions: What are lawmakers trying to accomplish?
但争论已经转向“哪种税？”在回答两个基本问题之前：立法者想要实现什么目标？
And what can a tax actually do about it?
税收实际上能起到什么作用呢？
For many who want to tax AI, the answer to the first question is straightforward and sympathetic: raise money to help workers displaced by AI.
对于许多想要对人工智能征税的人来说，第一个问题的答案是简单而富有同情心的：筹集资金来帮助因人工智能而失业的工人。
The second question is harder.
第二个问题比较难。
A specific tax on AI would likely do far less than its supporters hope.
对人工智能征收特定税的效果可能远远低于其支持者的希望。
And the danger lawmakers are most focused on, widespread job loss , is less certain than one they rarely mention: the nation’s perilous fiscal path.
立法者最关注的危险——普遍失业——比他们很少提及的危险更不确定：国家危险的财政道路。
Whether the goal is to protect workers or improve the fiscal outlook, shoring up the taxation of capital income, where much of AI’s gains are likely to accrue, would likely be more effective than a new tax that singles out AI.
无论目标是保护工人还是改善财政前景，加强对资本收入的征税（人工智能的大部分收益可能来自资本收入）可能比针对人工智能的新税收更有效。
A new National Bureau of Economic Research (NBER) working paper from economists Karen Dynan, Douglas Elmendorf, and Louise Sheiner examines how AI could affect the U.S. fiscal trajectory under different assumptions about productivity, jobs, wages, inequality, and returns to capital.
美国国家经济研究局 (NBER) 经济学家 Karen Dynan、Douglas Elmendorf 和 Louise Sheiner 发表了一份新的工作论文，研究了在生产力、就业、工资、不平等和资本回报的不同假设下，人工智能如何影响美国的财政轨迹。
The scenarios range from a rising economic tide that lifts everyone to a wave of automation that displaces millions of workers as financial gains flow to a wealthy few.
这些情景的范围很广，从经济浪潮的上涨让每个人都提振，到自动化浪潮取代数以百万计的工人，因为经济收益流向了少数富人。
At first, the news is encouraging.
起初，这个消息令人鼓舞。
Faster growth would mean more income to tax and a bigger economy to carry the debt the country already owes.
更快的增长意味着更多的税收收入和更大的经济体来承担该国已经欠下的债务。
In every scenario, AI improves the budget outlook, trimming projected federal debt by 39 to 49 percentage points of gross domestic product (GDP) three decades from now.
在每种情况下，人工智能都会改善预算前景，将 30 年后预计的联邦债务占国内生产总值 (GDP) 的比例削减 39 至 49 个百分点。
But even those reductions would leave federal debt rising sharply: the Congressional Budget Office (CBO) currently projects federal debt will climb from 101% of GDP today to 175% by 2056.
但即使是这些削减也会导致联邦债务急剧上升：国会预算办公室 (CBO) 目前预计联邦债务占 GDP 的比例将从目前的 101% 攀升至 2056 年的 175%。
Even the paper’s most optimistic AI scenario would leave debt near 126% of GDP, higher than at any point in American history, including the peak after World War II.
即使是该论文最乐观的人工智能情景，债务也将占 GDP 的 126% 左右，高于美国历史上的任何时候，包括二战后的峰值。
AI, in other words, might slow the climb but would not reverse it.
换句话说，人工智能可能会减缓攀登速度，但不会逆转它。
In every scenario the authors model, federal debt would “continue to rise significantly relative to GDP.”
在作者建模的每种情况下，联邦债务“相对于国内生产总值都会继续大幅上升”。
The deepest potential for debt reduction comes not when AI’s gains are broadly shared, but when AI costs workers their jobs and sends resulting gains from cost savings to the top.
债务削减的最大潜力不是当人工智能的收益被广泛分享时，而是当人工智能让工人失去工作，并将成本节约带来的收益传送给高层时。
But the driver there is the faster growth AI adoption enables .
但驱动因素是人工智能采用带来的更快增长。
That those gains accrue to capital mutes the debt reduction, however, because capital income is lightly taxed and the tax system reaches only a fraction of it.
然而，这些收益归于资本，却削弱了债务削减的作用，因为资本收入的征税很轻，而且税收制度只涉及到其中的一小部分。
Were those gains taxed at ordinary rates, federal debt would fall below its current share of GDP.
如果这些收益按普通税率征税，联邦债务将降至目前占国内生产总值的比例以下。
What moves debt is less about whether and how to tax AI and more about how we tax capital.
债务的转移不在于是否以及如何对人工智能征税，而更多在于我们如何对资本征税。
There are plenty of options for strengthening capital income taxation.
加强资本所得税有很多选择。
They range from raising corporate and capital gains tax rates and ending step-up in basis to more far-reaching approaches, such as taxing wealth .
它们的范围从提高企业税和资本利得税、结束基础税率的提高到更深远的方法，例如对财富征税。
Then there is the perennial favorite of many public finance economists: a broad based consumption tax .
此外，还有许多公共财政经济学家长期青睐的政策：基础广泛的消费税。
It would tax income, including income derived from capital, when it is ultimately spent.
它将在最终支出时对收入征税，包括来自资本的收入。
A specific tax on AI does two things at once.
对人工智能征收特定税可以同时起到两件事作用。
It slows the adoption of AI, since taxing anything gives you less of it, and it raises revenue.
它减缓了人工智能的采用，因为对任何东西征税都会减少你的税收，而且会增加收入。
But these two objectives pull in opposite directions: A tax that sharply reduces AI use shrinks the tax base and forgoes gains from growth and productivity.
但这两个目标背道而驰：大幅减少人工智能使用的税收会缩小税基，并放弃增长和生产力带来的收益。
A tax that raises real money, on the other hand, can likely do little to slow AI down.
另一方面，筹集实际资金的税收可能对减缓人工智能的发展几乎无济于事。
Consider the leading proposals.
考虑主要提案。
That conflict is embedded in their designs.
这种冲突已经融入到他们的设计中。
A compute tax, levied on the computing power used to train models, discourages the very training that makes models better, and its base shrinks as that training grows more efficient.
对用于训练模型的计算能力征收的计算税阻碍了使模型变得更好的训练，而且随着训练效率的提高，其基础也会缩小。
A token tax, on each use of a model, falls on all users, both productive and wasteful.
对模型的每次使用都会征收象征性的税，无论是生产性的还是浪费性的，都落在所有用户身上。
To the extent that AI is an input in the production of goods and services, the costs of a token tax can spread across the economy.
就人工智能作为商品和服务生产的投入而言，象征性税收的成本可以分散到整个经济中。
Other proposals would target profits instead.
其他提案则以利润为目标。
A windfall profit tax would not discourage AI use, since taxing a true windfall leaves the investment behind it intact.
暴利税不会阻碍人工智能的使用，因为对真正的暴利征税会使其背后的投资完好无损。
But such windfalls are hard to capture in today’s tax system, and any real revenue may be years away.
但在当今的税收体系中，这样的意外之财很难获得，而且任何真正的收入可能还需要数年的时间。
Even working as designed, none of these levers are likely to raise enough revenue, or with enough certainty, to meaningfully change the debt trajectory, risking the “fiscal illusion” that a narrow tax on AI has solved what only broader capital income taxation can fix.
即使按设计发挥作用，这些杠杆都不太可能筹集足够的收入，或者有足够的确定性来有意义地改变债务轨迹，从而冒着“财政幻觉”的风险，即对人工智能的狭隘税收已经解决了只有更广泛的资本所得税才能解决的问题。
The worry driving the debate over taxing AI is real.
引发关于对人工智能征税的争论的担忧是真实存在的。
AI may displace workers on a large scale as well as the labor income on which our existing tax system relies.
人工智能可能会大规模取代工人以及我们现有税收制度所依赖的劳动收入。
But the danger of the national debt—a financial hole that an AI tax cannot fill—is already here.
但国家债务的危险——人工智能税无法填补的财务漏洞——已经存在。
Rising interest costs consume a growing share of the federal budget.
不断上升的利息成本消耗了越来越多的联邦预算。
That leaves less for everything else the government does, or can do—like respond to the next recession, pandemic, or the very wave of displacement that AI taxes are meant to address.
这使得政府所做的或可以做的其他事情变得更少——比如应对下一次经济衰退、流行病，或者人工智能税旨在解决的流离失所浪潮。
Lawmakers can prepare for both dangers.
立法者可以为这两种危险做好准备。
AI’s gains are likely to keep flowing disproportionately to capital, income that’s already taxed more lightly than labor.
人工智能的收益可能会继续不成比例地流向资本，而这些收入的税收已经比劳动力的税收更轻。
Closing that gap would likely do more for the fiscal outlook than a narrow tax on AI and help fund responses to its costs.
缩小这一差距可能比对人工智能征收小额税收更能改善财政前景，并有助于为应对其成本提供资金。