Just last year alone, tech companies laid off more than 125K workers, part of a broader wave that has claimed nearly 600K jobs over the preceding three years.
Experts can debate the causes: pandemic-era over hiring, rising interest rates, AI-driven efficiency gains. But the cumulative cost is not in dispute. The job cuts have resulted in billions in severance, immeasurable talent loss, and countless careers interrupted or derailed.
The public conversation about AI and jobs is too often shaped by anecdote, media hype, and anxiety rather than transparent evidence and real-time data. That gap between narrative and fact carries its own price tag in misallocated capital, strategic paralysis, and a growing sense among executives and policymakers that they are making high-stakes decisions in the dark.
Longer-term workforce planning needs a reset.
So far, the response has come primarily from philanthropic organizations, concerned that AI-driven disruption will deepen economic inequality and further stifle mobility. The GitLab Foundation, for example, is funding the Credential Value Index, which draws on data from the Burning Glass Institute (BGI) to evaluate training programs’ actual employment outcomes. And the Schultz Family Foundation is supporting the Where You Work Matters dataset, also developed with BGI, which focuses on how effectively employers invest in and advance talent.
These are promising examples of the next generation of workforce intelligence. Yet even so, these initiatives don’t provide the full picture that companies need to make hiring decisions. We need greater coordination.
And for that, we should look to prediction markets.
Could Prediction Markets Be a Solution?
Prediction markets allow participants to buy and sell contracts tied to future events. If a contract trades at $0.60, the market estimates a 60% probability of that outcome. This harnesses the “wisdom of crowds” by aggregating dispersed information from thousands of participants, each of whom brings unique knowledge. In talent and workforce planning, the probability could accurately reflect the combined wisdom found in datasets like those created by data.org and the Burning Glass Institute. Probabilities could be further shaped, for example, by a combination of engineers who understand AI capabilities, recruiters who identify early hiring trends, executives who know future-state productivity automation budgets, and economists who track labor dynamics.
Prediction markets excel because they don’t require participants to have more information than any single company possesses; instead, they offer a sum-of-the-parts perspective on the future. In a labor market as volatile and fast-moving as todays, that distributed intelligence is a superpower.
One example could be a consortium model where large employers co-fund a labor prediction market in exchange for early access to signals, similar to how industry groups fund pre-competitive R&D. Or the platform could be a venture-backed play where the prediction market itself becomes a commercial product with a freemium data tier, or it could pursue a blended finance structure where philanthropic capital de-risks the first phase and private capital scales it once the data proves out.
Philanthropy is already laying the groundwork. Metaculus’ Labor Automation Forecasting Hub (a project of Renaissance Philanthropy and the Schultz Family Foundation) is a forecasting pilot examining AI’s projected impact on employment, wages, skills, and productivity across the U.S. over the next three to ten years. The platform combines aggregated probabilistic forecasts with expert analysis, delivering in practice what prediction markets promise: continuously updated probabilistic signals grounded in real incentives. The prize pool rewards forecast accuracy and insightful commentary, creating the incentive structure that gives these signals credibility.
Imagine a prediction market contract tied to the question: “By Q4 2028, will the top-10 investment banks collectively post fewer entry-level analyst job openings compared to 2024?” The contract trades at $0.55, meaning the market collectively assigns a 55% probability to that outcome.
What makes that number meaningful is who’s behind it. AI researchers tracking capability benchmarks, seeing models that already outperform junior analysts on earnings summaries and financial modeling, bid the contract up. Corporate CFOs who understand the glacial pace of enterprise software procurement, change management, and compliance review bid it down. Recruiters noticing that entry-level analyst job postings have declined 18% year-over-year add a different signal entirely. No single participant sees the full picture, but the market price synthesizes all of their knowledge into a continuously updated probability.
That signal, grounded in real incentives and diverse expertise, is precisely what workforce planners, educators, policymakers, and companies currently lack.
Like any frontier technology, the promise comes with real risks. Realizing prediction market’s potential will require careful safeguards: robust governance frameworks, diverse participant pools, transparency requirements, and meaningful regulatory oversight, to ensure these tools deliver genuine foresight rather than new vectors for exploitation. Those are steps worth taking.
More accurate workforce signals would help us avoid the extreme cycles of hiring and firing that inflict so much damage on careers and communities.
Need for Private Sector Engagement
Making this shift will require private investment. Philanthropy alone cannot achieve the scale required to meaningfully transform employment data and hiring strategy.
For private investors, funding a workforce prediction market is the kind of rising-tide investment that lifts an entire portfolio, improving efficiency and supporting stronger business outcomes across the board.
The urgency is real. AI is compressing decade-long skill cycles into just a few years, which means the chronic inability of workforce and education systems to adapt in real time is no longer merely inefficient. It is dangerous. We risk a future where workers are displaced faster than retraining pipelines can absorb them, and where students graduate into jobs that may not exist by the time they finish their degrees.
When the pipeline between education and employment breaks down, private sector companies feel it first: widening skills gaps, rising recruiting costs, and a shrinking pool of workforce-ready candidates.
What’s needed is agility: the ability to sense and respond to labor market shifts in something closer to real time. Philanthropic and private sector collaborations are uniquely positioned to build that agility where government and traditional institutions simply cannot move fast enough. The infrastructure exists for prediction markets, and the incentive models are proven.
It’s time for philanthropy and the private sector to work together to scale a better approach to workforce planning.
Arrun Kapoor is managing director for SJF Ventures’ New York City office. Marie Groark is managing director at Shultz Family Foundation, where she leads grant making. Frank Britt is a senior partner at Schultz and senior advisor at Valor Equity Partners.