Anthropic 发布经济情景模型，推演 AI 对 2030 年美国就业与工资的影响

我们还不知道人工智能将如何重塑经济。它会带来前所未有的增长吗？会导致广泛的失业吗？都不是，还是会发生其他情况？我们如何判断？

Anthropic 的经济学团队建立了一个模型，预测人工智能在未来几年可能对美国就业、经济增长和失业率产生的影响。阅读可能的经济未来，并根据人工智能能力自行预测，以了解它们暗示的经济状况。

我们研究人工智能如何重塑经济，因为我们致力于确保这一转型对社会有益，包括对工人有益。通过提供对可能经济未来的更好可见性，我们可以采取措施：https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf 确保每个人都能从中受益。

虽然我们的经济指数：https://www.anthropic.com/economic-index 衡量了人工智能当前在整个经济中的使用情况，但这个情景探索器是为了展望未来。基于我们的技术报告，《变革性人工智能的经济情景》：https://www-cdn.anthropic.com/files/4zrzovbb/website/cf58f84d46a4a76bf5a5b039ac695fba6b80041c.pdf (Korinek 等，2026)，该探索器让您有机会了解随着人工智能能力不断提升，经济可能会是什么样子。

在情景范围从按部就班到人工智能将增长提升至约正常速率两倍的经济中，失业率保持在历史范围内，且工资根据行业情况保持平稳或上升。但在增速超过经济史上任何水平的情景中，知识工人的工资和就业前景会受到负面影响。在这些情景下，社会总体财富更高，因此挑战是确保收益被广泛共享。

向下滚动时，您将看到人工智能如何影响经济的概览。然后，您可以输入自己对未来人工智能能力及其在经济中使用广度的预期。模型将显示如果您的预测成真，2030 年的经济可能是什么样子——以及您的预测与他人的比较情况。

这个模型将人们在经济中所做的所有工作表示为一捆任务。人工智能可以帮助人们更好或更快地完成某项任务。它可以自动化该任务。它可能对任务没有任何影响。同时，它也可能产生新的任务。

想象一下护士的一天

你可以把任何工作都视为某人完成的一捆任务。她进行查房以查看病人的情况。她抽血。她对新来的病人进行分诊，记录病人的生命体征，以及为病房订购物资。这只是任务清单的开始。

列出的每个任务都基于美国劳工部的O*NET分类法，列出了每个职业的任务。

任务会离开这捆任务（几乎没人再手写纸质病历了），也会有新任务出现（30年前，没人远程监测病人）。任务捆不是静态的，随着任务的变化，工作的内容也会发生变化。

例如，人工智能无法为病人洗澡。

人工智能帮助人类更好或更快地完成任务。人工智能帮助护士起草出院指导，远程监测病人，并规划班次的护理计划。

例如，人工智能可能会记录病人的生命体征，或订购病房的物资。

从历史上看，新技术也为工人创造了新的任务。对于护士来说，这可能是检查人工智能分诊患者的效果，或审核人工智能提出的护理计划。

结果：护士的工作发生了变化

随着护士将人工智能融入工作，她能够监督并完成更多任务。她可以花更多时间与病人交谈，帮助他们理解诊断。生产力提高了。

每项任务每天在全国范围内发生数百万次

护士们在每个病房和每个班次都在完成工作。随着越来越多的护士使用人工智能，人工智能支持了这些每项任务数百万次中的更高比例。

今天，如果将美国人及其使用的机器和软件所执行的每一个任务实例加起来：在过去的一年里创造了超过30万亿美元的价值。那么人工智能将如何塑造未来的经济呢？

答案取决于人工智能如何影响构成经济的所有任务，它创造的新任务，以及人工智能承担这些工作的速度。人工智能会导致更多的任务增强还是自动化？它会让我们提高多少生产力？工人和公司会多快采用它？这些问题的答案会直接影响国内生产总值、劳动市场，以及工人所获得的收益份额。

未来将取决于人工智能能力的进展，以及各产业和工人如何采用这些能力。我们分享的三种情景捕捉了不同类型的影响。

在温和情景下，很难在宏观经济数据中看到人工智能的影响：其经济影响类似于互联网。在显著情景下，人工智能的影响比互联网或铁路更大。在极端情景下，人工智能推动了完全转型、前所未有的经济，这很可能由递归自我改进的人工智能系统推动：https://www.anthropic.com/institute/recursive-self-improvement，并伴随更快的人工智能采用速度。

在温和情景下，人工智能的影响大致与互联网相同。它带来实际的经济收益，但这些收益在新技术的历史标准内，并且是逐渐产生的。

在显著情景下，到2030年，人工智能能够完成一半的知识工作，其中大部分是自主完成的，但并非所有工作都采用人工智能：大多数知识工作任务仍由人工执行。经济增长速度是正常水平的两倍。知识工作者的工资没有上涨，但其他工人的收入增加。

在极端情景下，人工智能在绝大多数知识工作任务上比人类更高效。它几乎自主动完成所有这些任务，并且实际上不会为人类创造新的知识任务。此情景很可能需要递归自我改进的人工智能，并快速在知识工作中采用。

随着人工智能的扩散，年GDP增长率达到每年15%，使经济规模每4.5年翻一番。作为一个社会，我们比以往任何时候都更富有，但知识工作岗位的工人数量大幅减少，失业率已超过典型的衰退水平。

你认为未来几年人工智能的发展会如何？这条发展路径对经济意味着什么？我们邀请你思考这些问题，并通过我们的情景探索器探索潜在答案。

今年八月，我们对超过10,000名美国人进行了调查，了解他们对当前和未来人工智能能力、采纳情况以及如果需要更换职业，找到新工作的难易程度的看法。

典型受访者的回答暗示了接近“重大变化”情景的结果：到2030年，GDP比没有人工智能时高出10%，总体失业率上升至约5%。大约10%的受访者的观点与极端情景一致。

人们对五个问题的回答（各答案的受访者比例）

按情景划分的2030年美国GDP（以万亿美元计）*

任务类型 增强的任务 自动化的任务 人工智能创造的新任务 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。这些潜在未来会对工人从工资中获得多少收益或有多少人需要寻找新工作意味着什么？

该模型并不完全反映现实，但它向我们展示了一些有趣的发现。虽然国家的GDP会增长，但相比工人，创造更多财富所使用的资源和技术（资本）可能会获得更大份额的繁荣，即使整个社会整体更为富裕。

在大多数情景下，工作重新分配和失业率都保持在历史曾见的范围内，但有一个例外。在极端情景下，如果出现递归自我改进和快速采纳，失业率可能会飙升至历史高点。

劳动力市场总是存在一些流动——有人失业，同时有人找到新工作。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。大多数求职者能相对快速地找到新工作。

在我们的重大变化和极端情景中，知识型工作者可能会经历大量自动化和职位替换。在个人层面，这意味着程序员和呼叫中心客服可能必须转向电工和护士等不太容易被人工智能取代的工作。

但完全更换职业是困难的，许多人需要很长时间才能找到新工作。情景中这种转换越多，越多的人会处于失业状态。

2030年工人的分布（占所有工人的百分比）

知识工作者 其他所有工人 被替代者

随着我们从2026年推进到2030年，AI影响的职业（知识工作）的可用职位数量减少，而AI未影响的职业的可用职位数量增加。

转换到新职业困难有几个原因：工人可能不想换职业。他们可能需要学习新技能。即便如此，找到新工作也并不容易。在极端情景下，随着大量知识工作更快被自动化，受影响的工人可能会长期失业。

知识工作中的失业上升；其他职业中的失业下降

知识型工作者 其他所有工人 总计

按职业群体的薪酬，高于无AI经济的百分比

知识型工作者 其他所有工人 平均

今天，经济每产生一美元，其中约60美分属于工人，40美分属于资本。如果经济增长，但AI自动化更多任务，每美元中流向资本的比例可能会增加。即使所有工人的工资大幅上涨，也可能发生这种情况。如果资本在更多事情上变得更有用，它的需求会增加，这会推高其价格。在这种情况下，经济增长带来的更多收益会流向资本所有者。

我们发现，在“显著”和“极端”情景下，劳动份额明显下降，而资本份额上升。平均工资上升——非知识工人的工资涨幅更大——但知识工作者的工资停滞或下降，同时失业情况恶化。

在极端情景下，快速扩张的经济收益分配不均。大多数知识工作者面临工资下降或失业的情况，而整体工人占更大蛋糕的份额减少。到2030年，总劳动收入几乎没有变化。

在这种情景下，主要挑战不是实现经济增长，而是确保收益广泛分享，并避免成本分布不均。

GDP在工人和资本之间的分配情况

劳动力占59.4%，资本占40.6%（上升0.6个百分点）

劳动力占56.1%，资本占43.9%（上升3.9个百分点）

劳动力占45.2%，资本占54.8%（上升14.8个百分点）

在每种情境下，总体经济都会增长（以GDP衡量）。但与人们在工资中获得的金额相比，更多的增长流向了资本。一些职业的工资会显著增加，但总体上，工资在国家经济增长中的占比更小。

最终，2030年的经济状况取决于许多因素，比如人工智能能够实现的能力，以及公司和员工选择如何采用它。还取决于这项技术的经济利益如何分配。

像任何经济模型一样，这个模型也有其局限性。例如，我们没有包括人类开发超能力机器人的情景。该模型基于我们的研究和外部评审，并将在证据发展时不断补充。

该模型以及我们的完整研究项目将为Anthropic资助的研究提供信息：https://www.anthropic.com/economic-futures，以识别应对劳动力市场中断的有效干预措施。它还将为我们提出的政策建议提供参考：https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf，目标是确保人工智能的经济利益在社会中得到广泛分享，无论在美国还是全球范围内。

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.

W e 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：https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf to make sure that everyone benefits from it.

While our Economic Index：https://www.anthropic.com/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 ：https://www-cdn.anthropic.com/files/4zrzovbb/website/cf58f84d46a4a76bf5a5b039ac695fba6b80041c.pdf (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.

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.

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.

For instance, AI can’t bathe a patient.

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.

For instance, AI may chart a patient’s vitals, or order the ward’s supplies.

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.

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.

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：https://www.anthropic.com/institute/recursive-self-improvement and a faster rate of AI adoption.

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.

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.

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 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.

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)

US GDP in 2030, by scenario (measured in trillions of dollars)*

Task types Tasks augmented Tasks automated New tasks created by AI Productivity

*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.

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 workers All other workers Displaced

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

Knowledge workers All other workers Total

Pay by occupation group, percent above the same economy without AI

Knowledge workers All other workers Average

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

59.4 % to labor 40.6 % to capital (up 0.6 points)

56.1 % to labor 43.9 % to capital (up 3.9 points)

45.2 % to labor 54.8 % to capital (up 14.8 points)

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.

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：https://www.anthropic.com/economic-futures to identify effective interventions for labor market disruptions. It’ll also inform the policy ideas we propose：https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf, with the goal of ensuring that the economic benefits of AI are broadly shared across society, both in the US and around the world.