我们的经济未来会是什么样子？
我们还不知道 AI 将如何重塑经济。它会带来前所未有的增长吗？还是大规模失业？两者皆非，又或是其他情形？我们怎样才能判断？
Anthropic 的经济学团队构建了一个模型，用来研究 AI 在未来几年可能如何影响美国的工作岗位、增长和失业率。阅读关于可能的经济未来的内容，并对 AI 能力做出你自己的预测，看看它们所预示的经济图景。
阅读技术报告
我们研究 AI 如何重塑经济，是因为我们致力于确保这一转型对社会（包括劳动者）有益。通过更好地洞察我们可能的经济未来，我们可以采取措施，确保每个人都能从中受益。
我们的 Economic Index 衡量的是 AI 当前在整个经济中的使用情况，而这个情景探索工具则着眼于未来。基于我们的技术报告 《变革性 AI 的经济情景》（Korinek 等人，2026），这个探索工具让你有机会了解，随着 AI 能力不断增强，经济可能会呈现怎样的面貌。
从一切照常的情景，到AI将经济增长推高至约为正常水平两倍的经济情景，失业率都保持在历史区间内，工资则视行业不同而持平或上涨。但在增长速度快于经济史上任何时期的情景中，知识工作者的工资和就业前景会受到不利影响。在这些情景下，社会整体富裕得多，因此挑战在于确保收益被广泛分享。
向下滚动，你会看到AI如何影响经济的概览。然后，你可以输入你对AI能力有多强、以及未来AI在整个经济中被多广泛使用的预期。该模型会显示，如果你的预测成真，2030年的经济可能是什么样子——以及你的预测与他人相比如何。
经济由任务构成
该模型将人们在经济中从事的所有工作表示为任务的集合。AI可以帮助人们把某项任务做得更好或更快。它可以自动化这项任务。它也可能对这项任务毫无影响。它还可以催生新的任务。
想一想一名护士的一天
你可以把任何工作看作一个人所执行的一系列任务的集合。她要查房探视生病的患者。她要抽血。她要对新来的患者进行分诊，记录患者的生命体征，并为病房订购物资。这只是清单的开头。
上面列出的每项任务都基于美国劳工部的O*NET分类体系，该体系列出了每种职业的任务。
工作会随时间变化
任务会从任务包中消失（如今几乎没有人再手写纸质病历图表了），也会有新任务加入（30年前，没有人会远程监测患者）。任务包并非一成不变，工作也会随着任务的变化而改变。
有些任务只有人类才能完成
例如，AI 无法为患者洗澡。
有些任务将得到增强
AI 帮助人类把任务做得更好或更快。AI 帮助护士起草出院指导、远程监测患者，并规划当班护理安排。
有些任务可能会被完全自动化
例如，AI 可能会记录患者的生命体征，或订购病房的物资。
还会有新任务出现
从历史上看，新技术也为劳动者创造了新任务。对护士来说，新任务可能是检查 AI 对患者分诊的效果如何，或审阅 AI 提出的护理方案。
结果：护士的工作发生了变化
当护士将 AI 融入自己的工作后，她能够监管和处理更多事务。她可以花更多时间与患者交谈，帮助他们理解诊断结果。生产力随之提升。
每一项任务每天都在全国范围内被重复执行数百万次
护士们正在每一个病房、每一个班次中开展工作。随着越来越多的护士使用 AI，AI 在这些数百万次任务实例中所能支持的比例也在不断提高。
从任务到经济
今天，如果你把美国境内由人类及其所使用的机器和软件执行的每一项任务实例加总起来：过去一年创造了超过 30 万亿美元的价值。那么，AI 将如何塑造未来的经济？
答案取决于 AI 如何影响构成经济的各项任务、它所创造的新任务，以及 AI 承担这些工作的速度。AI 会更多地带来任务增强还是自动化？它能让我们的生产力提升多少？工人和企业采用它的速度会有多快？这些问题的答案将直接影响 GDP、劳动力市场，以及劳动者最终拿回家的那份收入占比。
未来有多种可能，但我们重点勾勒三种情景
未来将取决于 AI 能力如何演进，以及各行各业和劳动者如何采用这些能力。我们分享的三种情景，分别刻画了不同类型的影响。
在温和情景下，很难在宏观经济数据中看到AI的影响：其经济影响大致相当于互联网。在显著情景下，AI带来的影响超过互联网或铁路。而在极端情景下，AI将推动一个彻底转型、前所未有的经济形态，这很可能由递归式自我改进的AI系统以及更快的AI采用速度所驱动。
较小的经济收益
在温和情景下，AI的影响大致与互联网相当。它能带来实实在在的经济收益，但这些收益处于新技术的历史常态范围内，而且是逐步实现的。
知识工作的革命
在显著情景下，到2030年AI能够胜任一半的知识工作，其中大部分可以自主完成，但并非所有这类工作都会采用AI：大多数知识工作任务仍然在没有AI的情况下完成。经济增速达到正常水平的两倍。知识工作者的工资不会上涨，但其他劳动者会有所获益。
深刻的经济转型
在极端情景下，AI在绝大多数知识工作任务上的生产力都高于人类。它几乎能自主完成所有这些任务，而且基本上不会为人类创造新的知识工作任务。这种情景很可能需要递归式自我改进的AI，并被快速应用于知识工作领域。
随着 AI 的扩散，年度 GDP 增长率达到每年 15%，经济规模每 4.5 年翻一番。作为一个社会，我们比以往任何时候都富裕得多，但从事知识工作的劳动者却少了很多，失业率已升至超过典型衰退期的水平。
强大的 AI 可能如何改变经济？
你认为未来几年 AI 发展会走向何方？这条路径对经济又意味着什么？我们邀请你思考这些问题，并通过我们的情景探索工具来探索可能的答案。
人们对未来 AI 能力的预期各不相同。
8 月，我们调查了超过 10,000 名美国人对当前及未来 AI 能力、采用情况，以及如果必须更换职业时找到新工作的难易程度的看法。
典型受访者的回答所隐含的结果接近“重大变革”情景：到 2030 年，GDP 比没有 AI 的情况下高出 10%，整体失业率升至 5% 左右。约有 10% 的受访者持有与极端情景一致的观点。
人们对这五个问题的回答情况（各选项的受访者占比）
能力
What tasks can AI do?采用
How much do people use AI?自主性
How much does AI do by itself?生产力
How much more productive does AI make people?调整
How long does it take people to find a new job?
- 普通公众 n = 10,980
你预测了经济的一种可能未来。以下是那个未来可能呈现的样子。
发现 1：GDP 增长——AI 在每一种情景下都会推动经济增长，但不同情景下的增幅差异显著
AI 在所有情景下都会推动 GDP 增长，不过增幅因情景不同而差异巨大。
2030 年美国 GDP，按情景划分（以万亿美元计）*
温和情景
+1.6%
GDP 34.1 万亿美元
显著情景
+8.3%
GDP 36.3 万亿美元
极端情景
+32.4%
GDP 44.4 万亿美元
任务类型被增强的任务被自动化的任务AI 创造的新任务生产力
*GDP 按 2025 年价格水平计算
但增长并不是我们唯一关心的经济动态。这些潜在未来对工人在工资中能获得多少增长份额，或者有多少人需要另寻新工作，又意味着什么？
这个模型并不是对现实的完整映射，但它向我们展示了一些有趣的发现。国家的 GDP 将会增长，但即便整个社会变得更加富裕，这些繁荣成果中可能有更大份额流向用于创造更多财富的资源和技术（资本），而非流向工人。
在大多数情景中，岗位重新配置和失业率都保持在历史曾见过的范围内，只有一个例外。在极端情景下，如果我们看到递归式自我改进和快速采用，失业率可能会飙升至历史性水平。
发现 2：岗位重新配置。在更具变革性的情景中，更多工人不得不转换职业。这可能意味着更高的失业率。
就业市场总是存在一定的流动——人们失去工作并找到新工作。在正常时期，这一过程可能令人痛苦，但从宏观经济角度来看运作相对良好。大多数求职者都能较快地找到新工作。
在我们的显著和极端情景中，知识工作者可能会面临大量自动化和岗位替代。在个人层面，这意味着程序员和呼叫中心客服人员可能不得不转向电工和护士等受 AI 影响较小的职业。
但彻底转换职业是困难的，而且很多人需要很长时间才能找到新工作。一个情景要求的职业转换越多，就会有越多的人处于失业待业状态。
2030 年各类工人的分布情况（占全体工人的百分比）
知识工作者｜所有其他工人｜被替代者
随着我们从 2026 年推进到 2030 年，AI 所影响的职业（知识工作）中可获得的岗位数量在减少，而 AI 不影响的职业中可获得的岗位数量在增加。
转换到新职业之所以困难，有几个原因：工人可能不想换职业。他们可能需要学习新技能。而且即使他们学了，找到新工作也并非易事。在极端情景下，随着大片知识工作被更快地自动化，受影响的工人可能会经历长期失业。
知识工作中的失业率上升；在其他职业中，失业率下降
温和情景
显著情景
极端情景
知识工作者｜所有其他工人｜总计
发现 3：工资水平。在三种情景下，平均工资都会上涨，但这一增长集中在知识工作之外的职业中。
这是因为劳动者需要时间才能转向需求上升的职业。如果对人类知识工作的需求减少，就会给工资带来下行压力。与此同时，随着 AI 提升知识工作内部的生产率，受益于这种生产率的体力劳动需求将会增加。例如，更快地完成实体基础设施的设计和审批，可能会增加建筑项目数量，从而带动建筑工人需求上升，推高他们的工资。在“显著”情景下，知识工作者的工资基本持平。在“极端”情景下，到 2030 年他们的工资将下降超过 10%。
各职业群体的薪酬，相对于无 AI 的同一经济体的百分比
温和情景
显著情景
极端情景
知识工作者所有其他工作者平均
发现 4：劳动与资本的份额——蛋糕会变大，但更大的一块可能流向资本
如今，经济每产出一美元，大约 60 美分归劳动者，40 美分归资本。如果经济增长，但 AI 自动化了更多任务，那么每美元中可能有更大份额归资本。即使所有劳动者的工资都大幅上涨，这种情况也可能发生。如果资本对更多事物变得更有用，它的需求就会更高，从而推高其价格。在那种世界里，经济增长带来的收益会有更大一部分流向资本所有者。
我们发现，在显著和极端情景下，劳动收入占比明显下降，而资本收入占比上升。平均工资上涨——非知识型劳动者的薪酬大幅提高——但知识型劳动者的工资停滞或下降，同时失业状况恶化。
在极端情景下，经济快速增长带来的收益分配不均。大多数知识型劳动者面临工资下降或失业，而劳动者整体从更大的经济蛋糕中获得的份额反而更小。到 2030 年，劳动总收入几乎没有什么变化。
在这种情景下，主要挑战不在于实现经济增长，而在于确保收益被广泛分享、成本不会不均地分散。
GDP 在劳动者与资本之间的分配方式
温和情景
GDP 34.1 万亿美元
59.4%归劳动者40.6%归资本（上升 0.6 个百分点）
显著情景
GDP 36.3 万亿美元
56.1%归劳动者43.9%归资本（上升 3.9 个百分点）
极端情景
44.4 万亿美元 GDP
45.2%归劳动54.8%归资本（上升 14.8 个百分点）
任务类型被增强的任务被自动化的任务AI 创造的新任务生产力
在每一种情景下，整体经济都会增长（以 GDP 衡量）。但与人们获得的工资相比，增长中有更大一部分流向了资本。某些职业的工资会显著上涨，但总体而言，工资在国民经济总增长中所占的份额会变小。
未来并非注定。
归根结底，2030 年的经济面貌取决于许多因素，比如 AI 能做什么，以及企业和劳动者如何选择采用它。它还取决于这项技术的经济收益如何分配。
与任何经济模型一样，这个模型也有其局限。例如，我们没有纳入人类发展出超强能力机器人的情景。该模型借鉴了我们的研究和外部评审，随着证据不断积累，我们会持续补充完善。
该模型连同我们的全部研究组合，将为 Anthropic 资助的研究提供参考，以识别应对劳动力市场冲击的有效干预措施。它还将为 我们提出的政策构想提供参考，目标是确保 AI 的经济效益在美国及全球范围内得到广泛共享。
免责声明与致谢
经济情景探索器 v1.0，2026 年 9 月
经济情景探索器目前为 1.0 版本。与所有模型一样，它是对复杂现实的极大简化：它隔离了少数几个关键力量，而忽略了未来几年可能变得相关且重要的许多其他因素。例如，它没有纳入政策应对、商业周期、潜在的总需求或金融市场冲击，以及可能存在的灾难性风险。情景探索器是一项持续进行的工作，我们预计它将随着我们投入更多时间以及经济研究本身的发展而不断演进。
我们感谢多位经济学家阅读了阐述该探索工具背后框架的技术报告初稿——《变革性 AI 的经济情景》——并为我们提供了详细意见：Daron Acemoglu、Lukas Althoff、David Autor、Tom Cunningham、Lukas Freund、Joe Hazell、Ben Jones、Pete Klenow、Danial Lashkari、Kurt Mitman、Ben Moll、Emi Nakamura、Pascual Restrepo、David Romer、Jón Steinsson、Chris Tonetti、Ludo Visschers 和 David Wiczer。他们的反馈既慷慨又坦诚，已经改进了我们的模型。举两个例子：几位评审人指出，AI 的进步可能会提高资本回报率，从而使更多收益流向资本所有者；还有几位指出，受 AI 影响最大的职业中工人的工资，可能与几乎不受影响的职业中的工资出现分化。这两条路径现在都已纳入情景之中。外部评审人并未被要求认可我们的结论，任何遗留错误均由我们自行承担。
其他批评意见仍然悬而未决，我们计划在未来的版本中解决其中许多问题。评审人指出，该模型不追踪单个工人，因此只能描绘出岗位流失成本非常粗略的图景。有人质疑，暴露于 AI 的职业是否会萎缩，而非增长。有人觉得最极端的情景更适合被理解为思想实验而非情景，也有人认为最温和的情景低估了数据中已经可见的趋势。有几位要求我们更明确地说明，该模型不包含由数据中心建设拉动的总需求效应。而且不止一位评审人认为，我们可能低估了 AI 加速技术进步本身的程度。我们同意，其中许多都是当前模型的局限。情景探索工具应被视为一种思考工具，用来思考不同的技术发展对经济意味着什么——实际结果可能会有重大差异。
Anton Korinek、Chad Jones、Szymon Sacher、Tess Cotter 和 Peter McCrory 开发了经济模型，并共同撰写了配套技术报告。Santi Ruiz 与他们合作撰写了本文，Sarah Pollack 和 Adam Farina 提供了编辑支持。Kelsey Nanan 设计并构建了交互式体验，视觉设计和艺术指导由 Nikki Makagiansar 和 Monika Tuchowska 负责；Kyle Turman 和 Szymon Sacher 构建了情景探索器，并主导将模型转化为交互形式；Fayaz Ashraf 和 Ryan Heller 贡献了工程支持，Kim Withee 和 Maria Gonzalez 支持了制作工作。Szymon Sacher 和 Tess Cotter 与 Morning Consult 合作设计并实施了配套调查，Ben Fowler 提供了支持。Peter McCrory、Anton Korinek 和 Charles Yang 协调了该项目，Jack Clark 全程提供了指导。Miriam Chaum、Jack Clark、Saffron Huang、Maxim Massenkoff 和 Peter McCrory 帮助发起了这项工作。Jim Baker、Shan Carter、Johannes Hermle、Zoë Hitzig 和 Eva Lyubich 提供了反馈。
What will our economic future look like?
We don’t know yet how AI will reshape the economy. Will it lead to unprecedented growth? Widespread unemployment? Neither, or something else? How can we tell?
Anthropic’s Economics team built a model of how AI might affect jobs, growth, and unemployment in the US in coming years. Read about possible economic futures and make your own predictions about AI capabilities to see the economy they imply.
Read technical report
We study how AI is reshaping the economy because we’re committed to ensuring that this transition is beneficial for society, including workers. By providing better visibility into our possible economic future, we can take steps to make sure that everyone benefits from it.
While our Economic Index measures how AI is being used across the economy right now, this scenario explorer is about looking ahead. Based on our technical report, Economic Scenarios for Transformative AI (Korinek et al., 2026), this explorer gives you a chance to find out what the economy might look like as AI continues to get more capable.
In scenarios ranging from business as usual to an economy where AI increases growth to about twice the normal rate, unemployment stays within the historical range and wages remain flat or rise depending on the industry. But in scenarios where growth is faster than anything in economic history, there are adverse impacts on wages and job prospects for knowledge workers. In those scenarios, society is far wealthier, so the challenge is making sure that the gains are broadly shared.
As you scroll down, you’ll see an overview of how AI affects the economy. Then, you can plug in your expectations for how capable AI will be, and how extensively it will be used across the economy in the future. The model will show you what the economy in 2030 might look like if your predictions come true—and how your predictions compare to others.
The economy is made out of tasks
This model represents all the jobs people do in the economy as bundles of tasks. AI can help people do a given task better or faster. It can automate the task. It might not affect the task at all. And it can lead to new tasks.
Think of a day in the life of a nurse
You can think of any job as a bundle of tasks that someone does. She does rounds to check on a sick patient. She draws blood. She triages incoming patients, charts patients’ vitals, and orders supplies for the ward. That’s just the start of the list.
Each of the tasks listed are based on the US Department of Labor’s O*NET taxonomy, listing the tasks for each occupation.
Jobs change over time
Tasks leave the bundle (hardly anyone hand-writes paper charts anymore) and new tasks arrive (30 years ago, no one monitored patients remotely). The bundle of tasks isn’t static, and the job changes as tasks change.
Only humans can do some tasks
For instance, AI can’t bathe a patient.
Some tasks will get augmented
AI helps a human do them better, or faster. AI helps the nurse draft discharge instructions, monitor patients remotely, and plan the shift’s care schedule.
Some tasks may get fully automated
For instance, AI may chart a patient’s vitals, or order the ward’s supplies.
And new tasks will appear
Historically, new technologies have also created new tasks for workers. For a nurse, that might be checking how well an AI triages patients, or reviewing an AI-proposed care plan.
The result: the nurse’s job changes
As the nurse incorporates AI into her job, the nurse is able to oversee and accomplish more. She can spend more time talking with patients and helping them understand diagnoses. Productivity increases.
Every task happens millions of times every day, across the country
Nurses are doing their work on every ward and on every shift. As more nurses use AI, AI supports a higher percentage of these millions of instances of each task.
From tasks to the economy
Today, if you add up every single instance of tasks performed in the US, by people and by the machines and software they work with: over $30 trillion of value created over the past year. So how will AI shape the economy of the future?
The answer depends on how AI affects all the tasks that make up the economy, the new tasks it creates, and how fast AI takes on this work. Will AI lead to more task augmentation or automation? How much more productive will it make us? How quickly will it be adopted by workers and companies? The answers to these questions have direct effects on GDP, the labor market, and the share of the pie taken home by workers.
There are many possible futures, but we’re highlighting three scenarios
The future will depend on how AI’s capabilities advance, and how industries and workers adopt those capabilities. The three scenarios we share capture distinct kinds of impact.
In the modest scenario, it’s hard to see the effect of AI in macroeconomic data: its economic impact is something like the internet’s. In the substantial scenario, AI makes a bigger impact than the internet, or the railroad. And in the extreme scenario, AI drives a completely transformed, unprecedented economy, likely driven by recursively self-improving AI systems and a faster rate of AI adoption.
Small economic gains
In the modest scenario, AI has roughly the same kind of impact as the internet did. It drives real economic gains, but they’re within the historical norm for new technologies, and they arrive gradually.
A revolution in knowledge work
In the substantial scenario, AI is capable of doing half of all knowledge work by 2030, the majority of it autonomously, but it’s not adopted for all of that work: most knowledge work tasks are still done without AI. The economy grows at twice its normal rate. Wages for knowledge workers don’t rise, but other workers see gains.
A profound economic transformation
In the extreme scenario, AI is more productive than humans at the vast majority of knowledge-work tasks. It does nearly all of them autonomously, and it creates essentially no new knowledge tasks for people. This scenario would likely require recursively self-improving AI, adopted quickly for knowledge work.
As AI diffuses, annual GDP growth rates reach 15% a year, leading the economy to double in size every 4.5 years. As a society, we’re far richer than we’ve ever been, but many fewer workers have jobs in knowledge work, and unemployment has risen beyond typical recessionary levels.
How might powerful AI change the economy?
How do you think AI development will go over the next few years? And what would that path mean for the economy? We invite you to consider these questions, and explore potential answers with our scenario explorer.
People’s expectations about future AI capabilities vary.
In August, we surveyed more than 10,000 Americans about their views on present and future AI capabilities, adoption, and the ease of finding new work if they have to change occupations.
The typical respondent’s answers imply outcomes close to the “substantial change” scenario: GDP is 10% higher by 2030 than it would be without AI, and the overall unemployment rate has risen to around 5%. Around 10% of respondents have views in line with the extreme scenario.
How people answered the five questions (share of respondents at each answers)
Capabilities
What tasks can AI do?Adoption
How much do people use AI?Autonomy
How much does AI do by itself?Productivity
How much more productive does AI make people?Adjustment
How long does it take people to find a new job?
- General publicn = 10,980
You predicted one possible future for the economy. Here’s what that future could look like.
Finding 1: GDP growthAI grows the economy in every scenario, but some more than others
AI drives GDP growth in all scenarios, although the scale varies enormously depending on the scenario.
US GDP in 2030, by scenario (measured in trillions of dollars)*
Modest scenario
+1.6%
$34.1T GDP
Substantial scenario
+8.3%
$36.3T GDP
Extreme scenario
+32.4%
$44.4T GDP
Task typesTasks augmentedTasks automatedNew tasks created by AIProductivity
*GDP is calculated at 2025 price levels
But growth isn’t the only economic dynamic we care about. What would these potential futures mean for how much of this growth workers receive in their paychecks, or how many people have to find new jobs?
This model isn’t a complete map of reality, but it shows us some interesting findings. The country’s GDP will grow, but a larger share of that prosperity might go to the resources and technology used to create more wealth (capital) compared to workers, even if society as a whole is much wealthier.
And in most scenarios, job reallocation and unemployment both stay within ranges history has seen before, with one exception. In the extreme scenario, if we see recursive self-improvement and rapid adoption, unemployment could spike to historic levels.
Finding 2: Job reallocationIn more transformative scenarios, more workers have to change occupations. That may mean higher unemployment.
There is always some churn in the job market—people losing jobs and finding new ones. In normal times, this process can be painful, but works relatively well from a macroeconomic perspective. Most job seekers find new jobs fairly quickly.
In our substantial and extreme scenarios, knowledge workers may see a lot of automation and displacement. At the individual level, it means coders and call service center agents may have to switch to jobs like electrician and nurse, which are less exposed to AI.
But changing occupations entirely is hard, and it takes many people a long time to land a new job. The more of this switching a scenario requires, the more people will be between jobs.
Where workers are in 2030 (percent of all workers)
Knowledge workersAll other workersDisplaced
As we progress from 2026 to 2030, the number of jobs available in occupations AI affects (knowledge work) decreases, while the jobs available in occupations AI doesn’t affect increase.
Switching to a new occupation is difficult for a few reasons: workers may not want to change occupations. They may need to learn new skills. And even when they do, it’s not easy to get a new job. In the extreme scenario, as large swathes of knowledge work are automated more quickly, affected workers may be unemployed for a prolonged period.
Unemployment in knowledge work rises; in other occupations, it falls
modest scenario
substantial scenario
extreme scenario
Knowledge workersAll other workersTotal
Finding 3: WagesAcross the three scenarios, average wages rise, but this increase is concentrated in occupations outside of knowledge work.
That’s because it takes time for workers to switch to occupations where demand is rising. If there’s less demand for human knowledge work, that puts downward pressure on wages. Meanwhile, as AI increases productivity within knowledge work, the demand for manual work that benefits from that productivity will increase. For example, more quickly producing designs and permitting for physical infrastructure could increase the number of construction projects, resulting in rising demand for construction workers, which pushes those wages higher. In the substantial scenario, wages for knowledge workers are essentially flat. In the extreme scenario, they fall by more than 10% by 2030.
Pay by occupation group, percent above the same economy without AI
modest scenario
substantial scenario
extreme scenario
Knowledge workersAll other workersAverage
Finding 4: Labor vs. capital shareThe pie will grow, but a larger share might go to capital
Today, of each dollar the economy produces, about 60¢ goes to workers and 40¢ go to capital. If the economy grows, but AI automates more tasks, more of each dollar might go to capital. This can happen even when wages for all workers rise substantially. If capital becomes more useful for more things, it will be in higher demand, which raises its price. In that world, more of the gains from a growing economy flow to owners of capital.
We find that the labor share falls noticeably in the substantial and extreme scenarios, and the capital share rises. Average wages rise—non-knowledge workers are paid much more—but wages for knowledge workers stagnate or decline alongside worsening unemployment.
In the extreme scenario, the gains from a rapidly expanding economy are unevenly distributed. Most knowledge workers face either lower wages or unemployment, and workers overall get a smaller fraction of the larger pie. Total labor income is barely changed by 2030.
In this scenario, the main challenge is not achieving economic growth, but making sure the benefits are broadly shared and the costs aren’t unequally dispersed.
How GDP is shared between workers and capital
Modest scenario
$34.1T GDP
59.4%to labor40.6%to capital (up 0.6 points)
Substantial scenario
$36.3T GDP
56.1%to labor43.9%to capital (up 3.9 points)
Extreme scenario
$44.4T GDP
45.2%to labor54.8%to capital (up 14.8 points)
Task typesTasks augmentedTasks automatedNew tasks created by AIProductivity
With each scenario, the total economy grows (measured in GDP). But more of the growth goes to capital, compared to the amount people receive in their wages. Some professions see their wages increase significantly, but overall, wages make up a smaller share of the country’s economic growth.
The future is not predetermined.
Ultimately, what the economy looks like in 2030 depends on many factors, like what AI can do, and how companies and workers choose to adopt it. It also depends on how the financial benefit of this technology is shared.
Like any economic model, this one has limits. For example, we did not include scenarios where humanity develops hyper-capable robots. The model draws on our research and external review, and we’ll keep adding to it as the evidence develops.
This model, alongside our full research portfolio, will inform the research Anthropic funds to identify effective interventions for labor market disruptions. It’ll also inform the policy ideas we propose, with the goal of ensuring that the economic benefits of AI are broadly shared across society, both in the US and around the world.
Disclaimer and thanks to reviewers
v1.0 of the Econ Scenario Explorer, September 2026
The economic scenario explorer is currently Version 1.0. Like every model, it is a stark simplification of a complex reality: it isolates a few key forces and omits many others that may become relevant and important in the coming years. For example, it leaves out policy responses, business cycles, potential aggregate demand or financial market disruptions, and possible catastrophic risks. The scenario explorer is a work in progress, and we expect it to evolve both as we invest more time and as economic research itself develops.
We are grateful to the economists who read an early draft of the technical report that lays out the framework behind the explorer, Economic Scenarios for Transformative AI, and gave us detailed comments: Daron Acemoglu, Lukas Althoff, David Autor, Tom Cunningham, Lukas Freund, Joe Hazell, Ben Jones, Pete Klenow, Danial Lashkari, Kurt Mitman, Ben Moll, Emi Nakamura, Pascual Restrepo, David Romer, Jón Steinsson, Chris Tonetti, Ludo Visschers, and David Wiczer. Their feedback was generous and candid, and it has already improved our model. Two examples: several reviewers noted that advances in AI may raise the returns to capital, so that more of the gains flow to the owners of capital; and several noted that the wages of workers in the occupations AI affects most may diverge from those in occupations it barely affects. Both channels are now part of the scenarios. External reviewers were not asked to endorse our conclusions, and any remaining errors are ours.
Other criticisms are still open, and we plan to address many of them in future versions. Reviewers pointed out that the model does not follow individual workers, so it can only paint a very coarse picture of the costs of job displacement. Some questioned whether occupations exposed to AI will shrink at all rather than grow. Some felt the most extreme scenario is better read as a thought experiment than a scenario, while others felt the most modest one understates what is already visible in the data. Several asked us to be clearer that the model does not include the aggregate demand effects driven by the data center buildout. And more than one reviewer argued that we may be underestimating how much AI could accelerate technological progress itself. We agree that many of these are limitations of the current model. The scenario explorer should be viewed as a tool for thinking about what different technological developments would imply for the economy—actual outcomes may differ materially.
Anton Korinek, Chad Jones, Szymon Sacher, Tess Cotter, and Peter McCrory developed the economic model and co-authored the companion technical report. Santi Ruiz wrote this piece with them, with editorial support from Sarah Pollack and Adam Farina. Kelsey Nanan designed and built the interactive experience, with visual design and art direction by Nikki Makagiansar and Monika Tuchowska; Kyle Turman and Szymon Sacher built the scenario explorer and led the translation of the model into interactive form; Fayaz Ashraf and Ryan Heller contributed engineering, and Kim Withee and Maria Gonzalez supported production. Szymon Sacher and Tess Cotter designed and fielded the accompanying surveys with Morning Consult, with support from Ben Fowler. Peter McCrory, Anton Korinek, and Charles Yang coordinated the project, and Jack Clark provided direction throughout. Miriam Chaum, Jack Clark, Saffron Huang, Maxim Massenkoff, and Peter McCrory helped originate this effort. Jim Baker, Shan Carter, Johannes Hermle, Zoë Hitzig, and Eva Lyubich provided feedback.