Scenarios for our Economic Future
Anthropic (AI lab research) · 23 Oct 2026
Read original sourceOpen accessNot peer reviewed
Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
- Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
- How fast will the impact arrive? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Source excerpt
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. 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. 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 t
About this publication
Lab or institute research, not peer reviewed. A report or blog post published by an AI lab or research institute on its own site. It has not been through independent peer review, and the publisher may have a stake in the findings.
- Published
- 2026 (recently)
- Citations
- Not recorded for this source
Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations.
Related by theme, not necessarily by method or conclusion.
- The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers
SSRN · 2024 - GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
arXiv · 2023 - Experimental evidence on the productivity effects of generative artificial intelligence
Science · 2023 - The simple macroeconomics of AI
Economic Policy · 2024
Overview assembled automatically from collected metadata. How this site is built.