À quoi ressemblera notre avenir économique ?
Nous ignorons encore comment l'IA va remodeler l'économie. Entraînera-t-elle une croissance sans précédent ? Un chômage massif ? Ni l'un ni l'autre, ou autre chose ? Comment le savoir ?
L'équipe d'économistes d'Anthropic a élaboré un modèle simulant l'impact potentiel de l'IA sur l'emploi, la croissance et le chômage aux États-Unis dans les années à venir. Découvrez différents scénarios économiques et formulez vos propres prédictions sur les capacités de l'IA pour en comprendre les implications économiques.
WNous étudions comment l'IA remodèle l'économie car nous sommes déterminés à faire en sorte que cette transition soit bénéfique pour la société, y compris pour les travailleurs. En offrant une meilleure visibilité sur notre avenir économique possible, nous pouvons prendre des mesures pour que chacun puisse en profiter.
Alors que notre Indice économique mesure l'utilisation actuelle de l'IA dans l'économie, cet outil d'exploration de scénarios vise à anticiper l'avenir. S'appuyant sur notre rapport technique « Scénarios économiques pour une IA transformatrice » (Korinek et al., 2026), il vous permet de découvrir à quoi pourrait ressembler l'économie à mesure que l'IA continue de gagner en puissance.
Dans des scénarios allant du statu quo à une économie où l'IA double la croissance habituelle, le chômage reste dans sa fourchette historique et les salaires stagnent ou augmentent selon le secteur. En revanche, dans des scénarios de croissance sans précédent, les salaires et les perspectives d'emploi des travailleurs du savoir sont affectés négativement. Dans ces cas-là, la société est bien plus prospère ; le défi consiste donc à garantir une répartition équitable des bénéfices.
En faisant défiler la page, vous découvrirez un aperçu de l'impact de l'IA sur l'économie. Vous pourrez ensuite indiquer vos prévisions quant aux capacités de l'IA et à son utilisation future dans l'économie. Le modèle vous montrera à quoi pourrait ressembler l'économie en 2030 si vos prédictions se réalisent, et vous permettra de comparer vos prévisions à celles d'autres personnes.
L'économie est constituée de tâches
Ce modèle représente l'ensemble des emplois exercés dans l'économie comme des ensembles de tâches. L'IA peut aider à accomplir une tâche donnée plus efficacement ou plus rapidement. Elle peut automatiser la tâche. Elle peut aussi ne pas l'affecter du tout. Enfin, elle peut engendrer de nouvelles tâches.
Imaginez une journée dans la vie d'une infirmière
On peut considérer chaque emploi comme un ensemble de tâches accomplies par une personne. Elle fait sa tournée pour examiner un patient malade. Elle effectue des prises de sang. Elle trie les patients à leur arrivée, note leurs constantes vitales et commande les fournitures pour le service. Et ce n'est que le début de la liste.
Chacune des tâches énumérées est basée sur la taxonomie O*NET du département du Travail américain, qui répertorie les tâches pour chaque profession.
Les emplois évoluent avec le temps
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.
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
- Site visitorsn = 32,533
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
US GDP in 2030, by scenario (measured in trillions of dollars)*
Modest scenario
Substantial scenario
Extreme scenario
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.
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
Substantial scenario
Extreme scenario
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 et Peter McCrory ont développé le modèle économique et co-rédigé le rapport technique associé. Santi Ruiz a co-écrit cet article avec eux, avec le soutien éditorial de Sarah Pollack et Adam Farina. Kelsey Nanan a conçu et réalisé l'expérience interactive, sous la direction artistique de Nikki Makagiansar et Monika Tuchowska ; Kyle Turman et Szymon Sacher ont développé l'explorateur de scénarios et piloté la conversion du modèle en format interactif ; Fayaz Ashraf et Ryan Heller ont contribué à l'ingénierie, et Kim Withee et Maria Gonzalez ont assuré la production. Szymon Sacher et Tess Cotter ont conçu et mené les enquêtes d'accompagnement avec Morning Consult, avec le soutien de Ben Fowler. Peter McCrory, Anton Korinek et Charles Yang ont coordonné le projet, et Jack Clark en a assuré la direction. Miriam Chaum, Jack Clark, Saffron Huang, Maxim Massenkoff et Peter McCrory ont été à l'origine de ce projet. Jim Baker, Shan Carter, Johannes Hermle, Zoë Hitzig et Eva Lyubich ont donné leur avis.