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.

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, you get the US economy: 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.

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.

Finding 1: GDP growth

AI drives GDP growth in all scenarios, although the scale varies enormously depending on the scenario.

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 reallocation

In 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.

Finding 3: Wages

Across 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.

Finding 4: Labor vs. capital share

The 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.

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.