The Future of Prediction Markets

Jul 30, 2025

From forecasting tools to infrastructure for intelligent societies

Prediction markets started as a niche concept: bet on an election, a sports match, maybe a crypto event. But in the last few years, they've evolved far beyond that. They've become not just tools for speculation—but components of something bigger.

In this closing post of the series, we explore where prediction markets are going next: what they could become, who might use them, and how they may shape governance, AI, and public knowledge itself.

1. Embedded in Everything

Prediction markets won’t just live on standalone websites. They’ll be embedded into apps, platforms, and governance systems.

Examples:

DAOs: Forecast the likelihood that a proposal will pass, succeed, or lead to regret before it’s even voted on
Discord/Twitter/Telegram: Run lightweight markets on community predictions (via bots or plugins)
Enterprise dashboards: Managers forecasting delivery timelines, launch delays, or customer churn
Newsrooms: Editors and readers use markets to track probabilities across geopolitical risks or tech developments

Markets become invisible backends that quietly help groups understand uncertainty better.

2. Powered by AI (and Used to Train AI)

AI is not just changing the world. It’s changing prediction markets too.

AI as trader: Large language models can browse, reason, and make structured forecasts at scale—especially for small or low-volume markets where humans don’t bother.
Markets as signal: Market prices become real-time feedback loops that help AIs recalibrate their beliefs.
Synthetic reasoning: AIs could propose markets, auto-resolve based on high-trust oracles, or run simulations to find critical unknowns.

This creates a hybrid ecosystem: humans and AIs co-predicting the world, and learning from each other.

3. Governance Beyond Voting

Voting is simple—but often shallow. People vote once, without incentives to be accurate or informed.

Prediction markets offer a richer alternative:

Continuous signal, not binary outcomes
Incentive-aligned accuracy, not popularity contests
Forecasting before deciding, not after

In a DAO, for example, a “pre-vote prediction market” can reveal whether a controversial proposal is likely to succeed—or cause unintended consequences. This helps surface dissent, identify risks, and enable more rational governance.

Some organizations may eventually evolve into market-steered governance systems, where key choices are forecasted, not just debated.

4. Reputation, Science, and Truth

Reputation is messy. Social media amplifies charisma and conflict, not track records. Prediction markets could reshape that.

Reputation markets: Forecast a person’s future impact, success, or reliability—based on public signals or community-defined criteria.
Science replication: Predict whether famous academic results will replicate, before allocating resources to re-testing them.
Media accountability: Track forecasters’ accuracy over time, and allow others to trade on their claims.

Over time, these systems could create merit-based visibility, where being consistently right matters more than being loud.

5. Public Goods and Collective Impact

Prediction markets may also help improve how we fund and coordinate public goods.

Imagine:

Forecasting the long-term benefits of infrastructure, open-source software, or climate mitigation programs.
Using conditional markets to estimate the difference made by each funding allocation.
Redirecting capital toward high-leverage outcomes, not just popular pitches.

Combined with tools like quadratic funding and governance DAOs, prediction markets can help communities get smarter about how they invest in the future.

6. Legal Evolution and Mainstream Legitimacy

The regulatory picture is still murky. But it’s evolving.

In the U.S., Kalshi has pushed forward a regulated path with CFTC oversight.
In Europe, some platforms are experimenting under gambling frameworks or sandbox regimes.
Elsewhere, play-money models and embedded “informational” markets are skirting legal risk altogether.

As prediction markets prove their civic value—as truth infrastructure, not just entertainment—they’re likely to gain broader acceptance.

The key: designing with purpose—not just for profit, but for insight, clarity, and collective intelligence.

Conclusion: From Niche Tool to Civic Infrastructure

Prediction markets are not a toy. They’re a social primitive—a protocol for transforming belief into probability, and probability into collective action.

In the next decade, they may: