Prediction Markets Bet on Scaling Resolutions With Blockchain and AI

In today’s era of uncertainty, geopolitical instability, rapid technological change and other destabilizing macro forces, the ability to aggregate and price collective belief is being positioned as one of providing tangible value in the real-world by purpose-built platforms like Polymarket and Kalshi, as well as by traditional iGaming, sports betting and even retail investing and crypto platforms like Robinhood, DraftKings, Coinbase, Kraken and more.

Even global financial institutions are eyeing the market, with Goldman Sachs Chairman and CEO David Solomon noting last week (Jan. 15) that the bank is looking into how it might get involved in prediction markets.

At its core, the argument underpinning the scalability of prediction markets is a bet on information as an economic primitive. Of course, information is not always evenly distributed, and PYMNTS covered Wednesday (Jan. 21) how efforts to uncover insider trading within prediction markets is reportedly gaining steam.

Still, prediction markets can fail in unpredictable ways, particularly with the growth of the number of questions exploding faster than the system’s ability to resolve them. Straightforward markets can be settled automatically, but edge cases like ambiguous outcomes, contested facts, or politically sensitive events can require judgment.

The result is a ceiling on complexity. Markets work best when the answer is obvious and uncontested. But those questions involving interpretation, causality or partial outcomes are becoming the hardest issues to resolve.

The tech and venture capital space, however, has shown signs of believing that its two favorite innovations, blockchain and artificial intelligence (AI), have an answer to this dilemma.

See also: Prediction Market Boom Blurs Line Between Trading and Gambling

Decentralized Resolution as an Economic Process

In a 2026 prediction post, the VC firm a16z posited that the year ahead could see crypto evolving beyond blockchains as a financial substrate and into a general-purpose toolkit for coordination, verification and incentive alignment across industries. Prediction markets, per the post, may be one of the clearest beneficiaries of that shift.

The argument is that verifiable computation, decentralized resolution mechanisms and on-chain commitments can address the structural bottlenecks that have historically kept prediction markets small. If successful, these systems could push prediction markets beyond betting and toward a broader role in how information is validated, challenged and trusted.

Underlying this approach is a relatively consequential technical shift: the maturation of cryptographic proofs that make computation itself verifiable. For years, technologies like SNARKs (Succinct Non-Interactive Argument of Knowledge) and zero-knowledge (zk) proofs were largely confined to blockchain validation, where high overhead could be justified by network security. That constraint is now loosening, and could shift debates around information from “Who do we trust?” to “What do we verify?”

Among the most ambitious extensions of this logic is what a16z calls “staked media.” The idea is that public claims such as journalism, analysis, forecasts and more can be paired with on-chain commitments that put economic weight behind credibility.

Other applications of technology also include the potential use of AI systems as judges for prediction markets. Rather than serving merely as data sources or trading agents, large language models could be embedded directly into market resolution, evaluating evidence, interpreting criteria and issuing judgments on disputed outcomes.

The appeal is straightforward. AI systems can process vast amounts of text, data and context far more quickly than human moderators. They can be applied uniformly across thousands of markets, reducing bottlenecks and operational overhead.

The obvious objection is trust. AI systems can be probabilistic, opaque and prone to error.

See also: Prediction Markets Eye US Growth While Watching Out for Crypto Whales 

Can Prediction Markets’ Regulatory Elephant Defy Gravity?

The proposed scalability engines of blockchain and AI are being presented against a backdrop where regulatory and cultural challenges continue to face prediction markets.

Though many event-based contract platforms are regulated as federally licensed exchanges designated by the Commodity Futures Trading Commission (CFTC), as they grow in popularity and potentially impede on other gaming services, their oversight is becoming a flash point between federal and state regulators.

News broke Tuesday (Jan. 20), for example, that Kalshi is facing a preliminary injunction that may block it from including sports and related event contracts on its prediction market in Massachusetts. A hearing scheduled for Friday (Jan. 23) will determine how the ban will be implemented and whether it will be paused if the company appeals the injunction.

Still, despite regulatory tremors, the events contract market is enjoying a relative period of growth and attention. Robinhood Chairman and CEO Vlad Tenev said in November that since launching prediction markets on its platform in 2024, the company had doubled its volume of contracts each quarter, reaching north of 2 billion.

Coinbase expanded its prediction markets business in December by acquiring The Clearing Company; and during the same month, DraftKings entered prediction markets with the launch of DraftKings Predictions, a standalone app and web product that it said would offer event contracts across 38 states.

That outcome of prediction markets in the U.S. is far from guaranteed, but the wager being placed is increasingly on the idea that, in an uncertain world, better tools for pricing belief can prove their value.