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 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.
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?
Almost noneSome of themAbout halfMost of themAlmost all of them
Productivity
How much more productive does AI make people?
Adjustment
How long does it take people to find a new job?
1–2 months~3 months~6 months~9 monthsA year2 years3+ years or never
- General publicn = 10,980
- Site visitorsn = 8,983
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
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
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.
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
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.