The Labor Automation Forecasting Hub
The Labor Automation Forecasting Hub, developed in collaboration with Metaculus and with support from the Schultz Family Foundation, will pilot whether forecasting can deliver accurate insights into AI’s impact on the labor market. This approach will help decision-makers navigate workforce automation.
Challenge & Opportunity
Rapid AI advancement is already disrupting employment, especially in white-collar roles involving routine cognitive tasks. However, the true scope and pace of this shift remain uncertain. As new AI capabilities reshape the job market for graduates and seasoned professionals alike, the need for better and accurate labor market intelligence has never been more urgent.
The Labor Automation Forecasting Hub addresses this by moving beyond guesswork to test whether forecast tournaments can provide decision-makers in government, education, and industry with timely, useful intelligence to help them navigate the unknowns of workforce automation. These forecasts will change over time and should be considered alongside other data points, reports, and predictions.
Why Competitive Forecasting
Competitive forecasting is a rigorous, structured method of aggregating diverse viewpoints into consensus predictions. With this type of forecasting and the tournaments that accompany it, participants are rewarded on the accuracy of their predictions over a defined time horizon, incentivizing deep research and objective reasoning.
For policymakers, this project will assess whether competitive forecasting can provide them with viewpoints beyond conventional wisdom and the noise of individual opinions. By synthesizing thousands of data points into clear probabilities, we want to learn if forecasters can move beyond general sentiment to identify the specific sectors and job types where AI-driven disruption is most imminent, allowing for more precise resource allocation and workforce planning.
Critically, forecasting differs from betting-oriented prediction markets. While those platforms rely on financial stakes to crowdsource predictive outcomes, competitive forecasting focuses on intellectual rigor and research done by their forecasters. Forecasting platforms like Metaculus are not markets; forecasters do not buy shares or stake any money on their predictions. Instead, they are incentivized by scoring rules designed to reward those who predict real-world activity, repeatedly and over time.
On the Labor Automation Forecasting Hub, forecasters must provide the qualitative reasoning behind their numbers – the “why” behind their “what” – creating transparent, evidence-based projections that can be useful for decision-makers.
Findings
Employment Drops: Overall employment is projected to fall 1.9% by 2030 and fall 3.4% by 2035 due to AI-driven displacement. This sharply contrasts with government baselines projecting +3.1% growth over the decade when accounting for aging-adjusted population trends.
The Leaner Fortune 500: AI is expected to enable companies to generate more revenue with far fewer employees, and over the next decade a growing share of Fortune 500 giants could operate with workforces once associated with small businesses rather than corporate behemoths.
New Realities for the Next Generation: For decades, the default advice to young people was to get a four-year degree. These forecasts suggest a more complicated picture. Unemployment for new 4-year college graduates is projected to more than double to 12% by 2035. At the same time, degrees awarded by trade schools and community colleges are forecast to increase 26% by 2035.
Background
The forecasts found at the Labor Automation Forecasting Hub are the result of a public tournament. Led by predictions from five of Metaculus’ leading forecasters, the tournament is open to anyone with an interest in labor market economics, AI, or the integration of AI into industry. The Hub is a public resource that will be updated in real time as forecasts to come in over the coming months.
The tournament’s $35,000 in total prizes will be awarded across three pools:
Commenting Prize ($5,000) for comments posted before September 1, 2026, distributed across the top eight contributors based on quality and quantity (awarded by November 2026).
Near-Term Forecasting Prize ($10,000) for questions closing by the end of 2030 (to be awarded in 2031).
Long-Term Forecasting Prize ($20,000) for questions resolving after 2031 (to be awarded in 2036).