Labor Automation Forecasting Hub
Real-time forecasts from our global forecasting community on the future of the US workforce as AI advances.
Overall Employment
Overall Employment
By Job Vulnerability
Activity Monitor
Jobs Monitor
AI is reshaping the job market, but not all fields are affected equally.
Median overall employment forecast:
(2027: +1%) (2030: -0.5%) (2035: -4%)
In the very short run, I expect lags in employment impacts because of limitations of the technology and slow AI adoption. For this reason my 2027 estimate takes the trend line of employment and slightly undershoots it. However, power users may be as important to monitor as laggards because they may exert competitive pressure on markets that lead to faster adjustment.
In the longer run (5-10 years), I am extremely uncertain. I expect the technology will be much more advanced and integrated into peoples' lives. My primary uncertainty is the policy response and rate of worker adjustment if AI leads to rapid change. I don't know how this will resolve itself. There may be scenarios where AI does less than it is capable of because of new regulation. The BLS may also measure activities as work that look more like leisure than what many people do today.
The economic changes we see from AI will be faster than almost anything seen before. As a general trend, each technological wave is adopted faster than the previous (e.g. mobile phone penetration vs landlines) and the nature of AI should accelerate its adoption even relative to this trend.
Despite this, adoption and job displacement may still be surprisingly slow in some important senses. My stereotype of how this might look is that new AI-first competitor companies have been (or will be) created in many industries and these new entrants will take some time - a period of several years - to displace the old ones. As an intuition, "Photographic Process Workers and Processing Machine Operators" took 5 years between 2010 and 2015 to decline 50% - and this is a job whose associated technology was ~obsoleted.
Relatedly, I expect many job roles, even some seen as relatively "low education" or "at risk of automation" will have a surprisingly large long tail of tasks that take some time for AI systems to be good at. I'm also quite skeptical that a majority of the job losses attributed to AI so far (e.g. tech layoffs) are truly proximately caused by AI.
Hours, Pay, and Financial Well-Being
Forecasts currently show that despite a predicted decline in overall employment, median wages are expected to grow. The workweek is also expected to become about three hours shorter by 2035 among all workers, while productivity grows.
Lower income households are expected to see their government benefits outpace their basic needs, while higher income households are expected to see much stronger growth in resources relative to needs.
As AI automates more routine tasks, the question is whether the time freed up will translate into genuine leisure for workers or simply be filled with new demands, making the average workweek a key barometer of whether AI's productivity gains are actually shared with labor.
Forecasters note the concept of “dark leisure” may confound reported hours worked, as people may remain at work but do something else as they often have no incentive to transfer the time gains to their company.
The wellbeing measure below reflects a family’s available resources (after taxes, government benefits, medical expenses, childcare, and more), relative to a poverty threshold to meet minimum needs such as food, clothing, and shelter. These predictions show how the 20th, 50th, and 80th percentile families are expected to fare in the coming decade under the potential impact of AI, with higher numbers indicating better financial well-being.
With only 13% of workers using AI daily as of early 2026, the workplace is still in the early stages of an adoption curve that could fundamentally change how most Americans do their jobs within a decade. But forecasters note that some people may only think about AI as LLM chatbots and not realize how many tools they use in their daily work involve AI, especially as integrations increase across productivity tools.
As AI automates more routine tasks, the question is whether the time freed up will translate into genuine leisure for workers or simply be filled with new demands, making the average workweek a key barometer of whether AI's productivity gains are actually shared with labor.
Forecasters note the concept of “dark leisure” may confound reported hours worked, as people may remain at work but do something else as they often have no incentive to transfer the time gains to their company.
The wellbeing measure below reflects a family’s available resources (after taxes, government benefits, medical expenses, childcare, and more), relative to a poverty threshold to meet minimum needs such as food, clothing, and shelter. These predictions show how the 20th, 50th, and 80th percentile families are expected to fare in the coming decade under the potential impact of AI, with higher numbers indicating better financial well-being.
With only 13% of workers using AI daily as of early 2026, the workplace is still in the early stages of an adoption curve that could fundamentally change how most Americans do their jobs within a decade. But forecasters note that some people may only think about AI as LLM chatbots and not realize how many tools they use in their daily work involve AI, especially as integrations increase across productivity tools.
Impact on the Next Generation of Workers
New college graduates are predicted to face difficult prospects in 2030 and beyond, as early-career tasks are more easily automated while experience and judgment remain harder to replace.
The unemployment rate for new graduates is expected to have more than doubled in 2035 compared to 2025.
The number of degrees awarded for STEM is expected to see only minor change due to the long gestation time, while overall 4-year degrees are expected to see a modest decline and humanities degrees are expected to see a more substantial decline by 2035. Trade schools and community colleges are expected to see significant growth in degrees and certificates awarded by 2035.
The rise of AI is threatening to accelerate an already-looming decline in 4-year college enrollment, as fewer high school graduates and shrinking job prospects for degree-holders could combine to reshape the future of higher education. At the same time, enrollment in community colleges and trade schools is expected to increase.
The rise of AI is threatening to accelerate an already-looming decline in 4-year college enrollment, as fewer high school graduates and shrinking job prospects for degree-holders could combine to reshape the future of higher education. At the same time, enrollment in community colleges and trade schools is expected to increase.
Changing Economy
As AI capabilities continue to grow, the increase in productivity and automation of jobs are likely to lead to significant changes in the overall economy.
Companies will be able to make money with fewer employees, meaning both higher unemployment and less revenue sharing with the labor force.
While the forecasted changes may seem relatively modest, at the macroeconomic level these represent notable shifts that will likely have ripple effects throughout society.
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.
Even during periods when total unemployment rates spike significantly, the rate of long-term unemployment relative to the labor force stays relatively low. People do exit the unemployment statistics without finding employment. When workers become discouraged and stop looking for employment, they leave the labor force. Also when someone transitions from being unemployed to returning to school, retiring early, or focusing on family care, they disappear from unemployment statistics.
When discouraged workers fall off the unemployment rolls, the unemployment rate looks artificially lower. I’m forecasting a −2% change in overall employment by 2030 and −11% by 2035.
However, I don’t expect these declines to be fully reflected in the long-term unemployment rate.
If AI allows corporations to generate ever-greater output without proportionally growing their workforce, workers could claim a shrinking slice of the economic pie, marking a redistribution of income from labor to capital. Even a few percentage points marks a major shift in the context of historical trends.
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.
Even during periods when total unemployment rates spike significantly, the rate of long-term unemployment relative to the labor force stays relatively low. People do exit the unemployment statistics without finding employment. When workers become discouraged and stop looking for employment, they leave the labor force. Also when someone transitions from being unemployed to returning to school, retiring early, or focusing on family care, they disappear from unemployment statistics.
When discouraged workers fall off the unemployment rolls, the unemployment rate looks artificially lower. I’m forecasting a −2% change in overall employment by 2030 and −11% by 2035.
However, I don’t expect these declines to be fully reflected in the long-term unemployment rate.
If AI allows corporations to generate ever-greater output without proportionally growing their workforce, workers could claim a shrinking slice of the economic pie, marking a redistribution of income from labor to capital. Even a few percentage points marks a major shift in the context of historical trends.
Comparison to Existing Research
A number of recent research publications have identified occupations, tasks, and industries that are more exposed or vulnerable to automation, while other work has examined recent employment patterns for signs of AI’s labor market effects. Recent work from Stanford University argues that AI has already had an impact on early career work, while other sources such as research from the Budget Lab at Yale do not yet see strong signals. Exposure and vulnerability ratings typically are not intended to be predictive of the future, but instead are correlational measures of current AI usage and task patterns.
The forecasts Metaculus is presenting in the Hub fill a gap in our current understanding, directly providing wisdom of the crowd powered predictions on employment outcomes when taking into account the impact of AI. In many cases, the forecasts align with what the exposure and vulnerability literature would indicate, with some key differences.
In the AI exposure literature and research, teachers stand out as having high exposure ratings, but are predicted to see mild decline as forecasters expect human presence will be desired in classrooms by schools and parents, even if schools do increasingly adopt AI-powered educational tools. Conversely, warehouse workers are rated as low exposure due to the high physical nature of the work, but forecasters anticipate that robotic capabilities will begin to displace more of these roles by 2035. Forecasters do expect that the high exposure and vulnerability of lawyers, sales representatives, financial specialists, and software developers will translate to significant employment reductions in these fields over the next decade. These forecasts provide important context to our understanding of workforce prospects by quantifying the predicted impact of AI on employment levels.
- Occupational exposure figures from Felten et al. (2023), estimating how much the typical tasks and abilities in each occupation overlap with what generative AI systems are good at. Data was based on 2010 SOC codes, which Metaculus has crosswalked to the 2018 SOC. The paper presents both language modeling and image generation exposure scores, from which only language modeling scores are used here.
- A vulnerability score calculated from Manning and Aguirre (2026) from measures of AI exposure and adaptive capacity (a measure of a worker’s ability to navigate job transitions if displaced). These scores were combined into a vulnerability score using the same approach as used in Figure 1 of the paper.
- Occupational exposure measured by Anthropic using usage data from their AI system, Claude, as reported in the data for the Anthropic Economic Index. More details about Anthropic’s findings are reported in Massenkoff and McCrory (2026).
State-Level View
To complement the national forecasts, we also look at the state of Washington to see whether short-term expectations at the state level track the broader national pattern. Washington is especially useful as a test case because of its concentration in dynamic industries like technology and aerospace, which could potentially see more dynamic short term changes.
The healthcare sector (employing 13% of Washington residents) is forecasted to grow through 2027, largely unaffected by AI in this timeframe. Aerospace (employing 2%) is expected to see minor growth in the short-term, while technology (employing 10%) is expected to see marginal growth, largely consistent with historical trends. These forecasts are short-term, leading to minimal predicted change.
Methodology
The forecasts presented on this page were designed to address key uncertainties about the future impact of artificial intelligence on labor in the United States. They are produced by aggregating many individual forecasts into a prediction that research has shown to be more accurate on average than individuals typically produce. The sections below provide more details about how the information above was produced.
We thank the following individuals for their thoughtful input on the Labor Automation Forecasting Hub:
- Jeremy Avins (Arnold Ventures)
- Frank Britt (Valor Equity Partners and Schultz Family Foundation)
- Brennan Brown (Charles Koch Foundation)
- Bharat Chandar (Stanford Digital Economy Lab)
- Jared Chung (Career Village)
- Tom Cunningham (METR)
- David Daigler (Maine Community College System)
- Christian Edlagan (Washington Center for Equitable Growth)
- Stuart Elliott (OECD)
- John Garcia III (StriveTogether)
- Daniel Gavin (Benson High School)
- Andrea Glorioso (European Commission)
- Dan Goldenberg (Call of Duty Endowment)
- Steve Lee (SkillUp)
- Adam Leonard (Data for Prosperity)
- Chauncy Lennon (Lumina Foundation)
- Gad Levanon (Burning Glass Institute)
- Cass Madison (Center for Civic Futures / Renaissance Philanthropy)
- Sam Manning (GovAI)
- Kerry McKittrick (The Project on Workforce at Harvard)
- Michael Meotti (Washington Student Achievement Council)
- Cheryl Oldham (Bipartisan Policy Center)
- Brent Orrell (American Enterprise Institute)
- Sneha Revanur (Encode AI)
- Philipp Schmitt (Axim Collaborative)
- Dane Stangler (Bipartisan Policy Center)
- Shayna Strom (Washington Center for Equitable Growth)
- Elizabeth Texiera (Britebound)
- Julia Trujillo (Maine Community College System)
- Matt Tully (Gates Ventures)
- Teresa Kroeger (Urban Institute)
- Matt Zieger (GitLab Foundation)
This acknowledgement does not imply agreement with or endorsement of the predictions and content presented.
How often will these forecasts be updated?
These forecasts are updated in real-time. As AI developments occur and more information comes to light, forecasters update their predictions and the Hub tracks how views shift over time. Each time a forecaster makes a new prediction, the aggregate is recalculated and potential updates are reflected on this page. The narrative descriptions are reviewed and refreshed when significant changes appear in the forecast data.
For more about how the forecasts are made, see the Making the forecasts section.
How do I stay up to date with new forecasts?
You can click the “Bell” icon at the top to get notified when there are substantial updates to the forecasts. This Hub aims to provide a resource to track developments over time, and provide the latest information each time you return. If you find it useful, please share it with others who could benefit from seeing these forecasts.
You can also view and contribute to the forecasts on the Labor Automation Tournament page.
What if I don’t see a forecast that I think would be important to have, or have other feedback?
Please reach out and let us know! We can’t guarantee that your question or feedback will be included, but we want to hear how we can make this resource as useful as possible. You can get in touch with us by emailing labor-hub@metaculus.com.
Can this be expanded to additional focus areas, and how can partners get involved?
Yes, this forecasting approach is flexible and can be applied to many different related or unrelated topics. If you would like to explore ways this Hub could be expanded, including by featuring your work or your thinking, or other areas where forecasts could be valuable, please reach out to us at labor-hub@metaculus.com.
You can also visit the Metaculus Services page to learn more about our forecasting services.
How do I join the conversation or share my own forecasts?
If you want to share your own forecasts, or ask a question or share a thought with participants and Metaculus staff, you can do so by creating a Metaculus account and forecasting in the Labor Automation Tournament, or jumping in with a comment on the Hub Forum.
If you have established experience in AI, economics, or a related field, and are interested in having your thinking or work highlighted on the Hub, please contact us at labor-hub@metaculus.com so we can discuss further.
Why did you pick these occupations?
The occupations were picked based on the categorizations and data available from the Bureau of Labor Statistics (BLS)’ Occupational Employment and Wage Statistics (OEWS) dataset. OEWS annually compiles a comprehensive occupational makeup of the US workforce, detailing employment data for 830 occupations. Metaculus selected sets of occupational groupings based on the following heuristics:
- Capturing occupations identified by the literature at high and low levels of AI vulnerability
- Capturing occupations that are of public interest and have been discussed in the media as at risk of being impacted by AI
- Selecting occupations that each represent a sizeable portion of the workforce
- Choosing a wide range of types of occupations
- Selecting occupations so that a visitor who does not see a particular occupation they’re interested in will ideally find a related or comparable occupation
How are the listed occupations defined?
The OEWS data used to measure occupational employment defines the occupations using the Standard Occupational Classification (SOC) system. The most recent edition of this system is the 2018 SOC. The detailed definitions for each occupation can be found in the 2018 SOC Definitions documentation. We have renamed some of these occupations for brevity in the Hub; for each of these you can find its full formal name and SOC code, and any occupation categories it contains, at the Metaculus Employment Forecasting Tool (or at this direct link to the definitions page available at the tool). Note that, as shown in the 2018 SOC Structure documentation, the SOC structure consists of four levels which from largest to smallest are: Major Group, Minor Group, Broad Group, and Detailed Occupation.
What previous literature is there and how did you use it?
We’ve learned a lot from prior literature and from talking to experts for their feedback while developing this project. We referred to research on occupational exposure to AI when selecting which occupations to focus on, ensuring that some of the selected occupations are rated as highly exposed or vulnerable to AI automation.1, 2, 3, 4, 5, 6 We made sure that recent college graduates and entry-level workers were a key focus, based in part on research suggesting that group may be one of the first impacted by AI.7 We structured some questions around metrics developed or identified by the literature, such as the change in occupational mix.8 Prior research helped inform us about what government data and projections might capture and not capture. 9 And we considered other available estimates, reports, and data sources as we honed the Hub focus and presentation.10, 11, 12, 13, 14, 15, 16 All kinds of resources, perspectives, and discussions not mentioned here have informed our thinking for the Labor Automation Forecasting Hub, and we’re grateful to everyone who has been thinking carefully about this topic and sharing their reasoning and findings publicly.
For more information and comparisons, see the Hub section Comparison to Existing Research
- Felten, E. W., Raj, M., & Seamans, R. (2023, April 10). Occupational heterogeneity in exposure to generative AI.
- Tomlinson, K., et al. (2025). Working with AI: Measuring the applicability of generative AI to occupations.
- U.S. Department of the Treasury. (2024, December). Artificial intelligence in the financial services sector: Report on the uses, opportunities, and risks of artificial intelligence in the financial services sector.
- International Labour Organization. (2025, May 20). Generative AI and jobs: A refined global index of occupational exposure.
- Anthropic. Economic Index.
- Manning, S. J., & Aguirre, T. (2026, January). How adaptable are American workers to AI-induced job displacement?
- Brynjolfsson, E., Chandar, B., & Chen, R. (2025, August 26). Canaries in the coal mine? Six facts about the recent employment effects of artificial intelligence.
- Gimbel, M., et al. (2025). Evaluating the impact of AI on the labor market: Current state of affairs.
- Massenkoff, M. (2025, October 16). How predictable is job destruction? Evidence from the Occupational Outlook.
- McKinsey Global Institute. Generative AI and the future of work in America.
- Encode AI. Game Plan.
- Kokotajlo, D., et al. (2025, April 3). AI 2027.
- Karpathy, A. US Job Market Visualizer.
- Zieger, M. jobsdata.ai: Early Signals of AI Impact.
- Forecasting Research Institute. Longitudinal Expert AI Panel (LEAP).
- Karger, E., et al. (2026, March). Forecasting the economic effects of AI
What makes an occupation vulnerable or not vulnerable?
Occupations were initially selected to include high, medium, and low exposure or vulnerability occupations based on previously published literature. In the literature, these exposure or vulnerability ratings were generally created by judgmental assessments of the automatability of various tasks and mapping those tasks onto occupational classifications.
In the work presented on this dashboard, the vulnerability assessments are based on the predictions made by forecasters. The most and least vulnerable series in the By Job Vulnerability chart represents a grouping of the three occupational categories that are forecasted to have the highest and lowest changes in employment in 2035. The forecasted percent changes across these occupations present the simple average of the median forecasts, but more details about each occupation can be seen in the Jobs Monitor.
What underlying occupation data is being used?
The resolution source for the occupation-based questions is the Bureau of Labor Statistics (BLS)’ Occupational Employment and Wage Statistics (OEWS). Each year BLS updates its occupation-level employment data, providing employment and wage levels. While BLS advises caution when working with OEWS data for trend assessments, the resolution criteria to the forecasting questions specify that in the event of reclassifications or changes the forecasting questions will be resolved according to the 2025 classifications, using crosswalks to map the data back to the 2025 classifications if necessary.
The forecasting questions in the Hub aren’t explicitly posed as being dependent on AI, so why are we asserting that AI is what’s driving the changes being predicted? How do we know these forecasts aren’t driven by recession or some other rare outcomes?
While the forecasts presented above cannot be claimed to be solely driven by AI, we have used a variety of supporting evidence to strengthen and support our claims that these changes are primarily attributable to AI.
Projections from other sources not accounting for AI
BLS employment projections show that despite an aging population, employment levels are expected to grow by approximately 3% over the next decade. By treating this as a baseline in a no-AI or limited-AI scenario, it significantly reduces the likelihood that the effects presented here are driven by factors such as an aging population or other employment or demographic trends.
Reasoning and assessments from Pro Forecasters
We can use more qualitative reasoning to help confirm our hypothesis. See below for a selection of quotes from the Metaculus Pro Forecasters participating in this project which demonstrate that the core driver of the changes they’re forecasting is due to the development of advanced AI:
All else somehow being equal (never mind how), what happens to employment if AI just stops getting much better?
I still expect economic impacts from AI, but I expect them to mostly be wide and shallow, other than in a few already-impacted occupations. Elicitation (especially via scaffolding) will continue to unlock capabilities in current AI models. AI will continue to diffuse through the economy, and will even eliminate the need for some roles. But in any stagnation scenario, AI should behave much more like a normal technology, and the labor market should be able to absorb those changes without too much loss of employment.
My forecast for 2027 is based on a “mostly normal economy” scenario. For 2030 and 2035, I used a weighted average of all Topline employment questions from this tournament. These projections are grounded in [the] following set of core ideas and assumptions:
- AI is transforming society faster than [the] industrial revolution, computers, or internet. AI will not fizzle out.
- AI is capital intensive, high-skill biased, and labor saving. It will disproportionately benefit owners of capital at the expense of workers.
- . . .
- Rising technological unemployment: AI advances outrunning the pace at which we find new uses of labor.
Conditional forecasts on recession and AI stagnation
To test how much AI is driving these forecasts we’ve also asked a few questions to our forecasters aimed at measuring how much of the prediction is being driven by AI. To do this, we ask conditional questions of the form:
- What will the percent change in the US employment levels be in these years vs 2025, conditional on positive real GDP growth?
- This shows us what the overall employment forecast looks like if GDP growth remains strong; employment if the economy is not in an output downturn
- What will the percent change in the US employment levels be in these years vs 2025, conditional on AI benchmark stagnation?
- This shows us what the overall employment forecast looks like if AI progress were to halt; employment if AI were to remain at roughly 2026-level capabilities
In a subsequent update we’ll present results comparing these forecasts, likely from the cohort of Pro Forecasters to ensure we’re comparing results from the same set of forecasters.
In the page above, we only report the median forecast from each question, but forecasters who predicted in the Labor Automation Tournament have actually expressed their full probability distributions over these outcomes. The aggregate median also masks if there is disagreement among the forecasters.
We've published a tool that provides data on disagreement and uncertainty, as well as other statistics and detailed information, here.
Why make predictions with probabilities?
The forecasting Metaculus employs involves making explicit predictions about the future. Instead of vague assertions, forecasters share specific probabilities (like the weather forecast that helps you decide if you should take an umbrella), concrete dates, and measurable outcomes.
When you start regularly using Metaculus, it becomes more than a forecasting platform—it becomes a whole new way of thinking, one that generates more productive disagreements and conversations that are grounded in what will actually happen, and in which pieces of evidence point toward which future.
Additionally, forecasting with probabilities allows the predictions to be scored in a manner that incentivizes forecasters to enter their true beliefs. You can find more on the scoring and incentive mechanisms in our Scores FAQ.
How are forecasts made on Metaculus?
On Metaculus, anyone can sign up and make a prediction, and each forecaster can submit how likely they think an outcome is on each forecasting question. For example, a user can predict on the question Will there be a US-China war before 2035? by submitting a probability, say 10%, and can share their reasoning in the comments if they wish. All questions will have a clear outcome backed by specific resolution criteria defined on each question, and all forecasters will receive a score. Individual forecasts are aggregated together to produce the Community Prediction, which outperforms nearly all forecasters across many questions.
For the Labor Automation Forecasting Hub questions, forecasts assume broad stability in global population size.
Who made these forecasts?
Anyone can register and forecast on Metaculus, and there are also five Metaculus Pro Forecasters predicting on the questions who are required to share their reasoning, enabling better interpretation of the forecasts and providing more information that can be used by the broader community of forecasters. Metaculus has always allowed anyone to join and forecast, which ensures a diverse range of views and information are represented in the aggregate forecast. Currently, the aggregate forecast, or Community Prediction, represents the recency-weighted median of the forecasters, and this approach has displayed good accuracy and calibration as reflected in our track record.
If you would like to participate in the Labor Automation Tournament that feeds into this Hub, you can do so here.
What is Metaculus?
Metaculus is an online forecasting platform and aggregation engine working to improve human reasoning and coordination on topics of global importance. As a Public Benefit Corporation, Metaculus provides decision support based on these forecasts to a variety of institutions.
Metaculus features questions on a wide range of topics, including artificial intelligence, biosecurity, geopolitics, climate change, and nuclear risk.
Is Metaculus a market?
No, Metaculus is not a market. Unlike platforms like Kalshi or Polymarket, our forecasters do not buy shares or stake any money.
Instead, Metaculus forecasters are motivated by yearly accuracy leaderboards, winning tournaments that award cash prizes or medals, and, most importantly, a desire to be right, learn more about the world, contribute to a valuable public resource, and have fun. Much like Wikipedia editors, our forecasters are often driven by the joy of contributing to shared knowledge and helping others access valuable information.
Is Metaculus accurate?
The wisdom of the crowd is surprisingly accurate—often startlingly so. That's not to say that it's always right, but it is extremely difficult to beat consistently over time. Metaculus' publicly available track record demonstrates that the Community Prediction is also well-calibrated, meaning that it correctly estimates its own uncertainty—when Metaculus predicts things are 70% likely to happen, those things happen roughly 70% of the time.
The questions on the Labor Automation Forecasting Hub are longer term than the average Metaculus question, past performance doesn’t guarantee future results, and we don’t claim to be an oracle. But we think that leveraging the wisdom of the crowd with our carefully designed incentive structures is one of the best, if not the best, methods available to anticipate what the future holds. The Hub fills an information gap where there is currently little information available and lots of speculation and punditry from individuals. By aggregating views we create a resource that aggregates many different views into a view likely more accurate than most individual perceptions, and which will update in real-time as news breaks.
Read more: Less Noise, More Truth: Metaculus’ method for clear decisions in a complex world
What are Metaculus Pro Forecasters?
Five Metaculus Pro Forecasters have submitted predictions and reasoning on these forecasting questions and some of this reasoning has been highlighted on the Hub. Metaculus employs Pro Forecasters who have demonstrated excellent forecasting ability and who have a history of clearly describing their rationales. Pros forecast on private and public sets of questions to produce well-calibrated forecasts and descriptive rationales for our partners.
Pros are selected for having the top scores from among the community, based on robust track records of at least 75 scored questions (questions where the outcome has become known). Most Pros have hundreds or thousands of resolved questions under their belt. Additionally, Pro Forecasters must have a history of making clear and insightful comments. Recruiting based on these factors allows Metaculus to deploy Pro Forecasters on projects for partners and clients to provide both calibrated forecasts and clear reasoning to help observers understand the factors behind the prediction.
Read more: Metaculus Pro Forecasters
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