How they’re reshaping governance, AI, and online decision-making

Most people encounter prediction markets through headlines about elections, sports, or crypto prices. And it’s true — these are popular and engaging use cases. But stopping there is like assuming the internet is just for email.

Prediction markets can do much more. In fact, they’re already being used as governance tools, AI feedback mechanisms, and even surrogates for human judgment in complex systems.

This article is about those deeper, less obvious use cases — and why they matter.

From Game to Governance

Let’s say you run a decentralized organization, or even just a large online community. Decisions pile up constantly:

• Should we ship Feature A or Feature B?

• Will this proposal pass if we put it to a vote?

• Is Project X going to finish on time?

In most systems, these questions are either debated endlessly or decided by a small group of power users.

But there’s a third option: ask the market.

By setting up prediction markets on expected outcomes—e.g. “Will Feature A increase user retention by 10% within 30 days?”—you can turn idle opinions into active forecasting. Participants bet based on their knowledge, expectations, or inside context. And the resulting price becomes a credible signal to inform real-world decisions.

Predictive Governance: The “Futarchy” Vision

Economist Robin Hanson proposed a model called futarchy in the early 2000s: “Vote on values, but bet on beliefs.”

Here’s the idea:

• Citizens vote on broad objectives (e.g. “maximize GDP per capita” or “improve life satisfaction”)

• Then, markets are used to forecast which proposed policies will best achieve those goals

• The policy with the highest predicted impact gets implemented

This may sound radical, but it’s essentially a structured way to let markets test policy options—not based on ideology, but on expected results.

While no country has adopted full futarchy, elements of it are already being tested in DAOs, research communities, and blockchain ecosystems.

AI Meets Prediction Markets

As AI systems become more autonomous, we face a new problem: how do we verify what AI models tell us?

One emerging solution: prediction markets as a truth layer for AI.

Here's how it might work:

1. An AI model makes a claim:
   “Company X will meet its earnings target next quarter.”

2. A market is created around that claim.

3. Human (and even AI) participants can bet for or against the claim.

4. If the AI is right, its forecast looks good. If it’s wrong, people profit by correcting it.

This system creates a feedback loop that helps identify weak or overconfident model behavior — especially on factual or time-bound questions.

Over time, models could even learn from market prices, using them to update their internal confidence scores.

Distilled Judgment: Scaling Human Wisdom

Sometimes, you have a trusted judgment system—maybe a committee, an expert board, or a DAO vote—but it’s slow and expensive to run.

Can you get a quick approximation of what that system would decide, without calling it into action?

Vitalik Buterin describes a clever mechanism for this:

• Every time a decision is needed, you create a prediction market on what the costly mechanism would decide.

• Most of the time, you just use the market result.

• Occasionally, you run the actual mechanism to check accuracy—and reward participants who aligned with it.

This creates a cheap, fast, and credibly neutral approximation of a trusted decision-making process.

Think of it as a "distilled version" of collective judgment—similar in spirit to how smaller AI models are trained to mimic larger ones.

Use Cases You Might Not Expect

Prediction markets are expanding into places you wouldn’t expect:

• Scientific peer review
  Forecast whether new results will replicate before wasting time on re-checking

• Public goods funding
  Predict long-term impact of projects to guide grant allocations more rationally

• Social media
  Use conditional markets to predict if a post will be flagged, or if a “community note” will be attached

• Reputation systems
  Forecast a person’s long-term impact or trustworthiness, with designs that resist manipulation

These aren't just theoretical ideas. Many of them are being piloted by researchers, DAOs, and startups today.

What Makes All This Possible Now?

Three trends are converging:

1. Blockchains are cheap enough to host markets
   On-chain gas fees used to make frequent forecasting impractical. That’s changed.

2. AIs can participate in markets
   On small, low-volume questions, humans often don’t have enough incentive. But AIs can process and trade efficiently.

3. Trust is broken — and markets can help fix it
   In a world where institutions are doubted and headlines feel performative, markets provide a new, incentive-aligned way to seek consensus.

Conclusion: Beyond the Bet

Yes, prediction markets began with betting. But their real power is in decision support, governance augmentation, and collective reasoning at scale.

They don’t just answer “What’s going to happen?”
They can help answer:

• “What should we do?”

• “What will work better?”

• “Who is most likely right?”

As these systems evolve, they’ll become less like games—and more like foundational tools for how online societies think and act together.

Coming up next: How to design prediction markets that actually work — incentives, liquidity, and truthfulness.