Thanks to Shayne Coplan for feedback and review.

Prediction markets are more than just a new investment avenue. They hold the potential to benefit society by leveraging the wisdom of crowds for a wide array of topics, ranging from mainstream events like national elections to highly-granular happenings like the impact of specific technological advancements.

Prediction markets enable detailed forecasts on hyper-niche topics by aggregating dispersed insights across populations.

While financial markets tap into broad collective intelligence, prediction markets’ key advantage is their ability to focus this collective intelligence on more specific subjects.

However, prediction markets face a fundamental liquidity challenge. As markets become more specific, fewer traders possess the specialized knowledge to participate. This results in anemic trading volume and market liquidity.

AMMs were invented to address these liquidity shortcomings in long-tail markets. By automating liquidity provision, the overhead of supplying liquidity is significantly reduced, making it easier for smaller markets to attract liquidity. However, prediction markets are hyper long-tail, so they often still struggle to attract sufficient liquidity, even with AMMs.

These liquidity issues are further exacerbated because contemporary prediction market traders are often inefficient humans.

Compared to AI agents, humans are inefficient traders, resulting in lower trade volumes. Less trading activity translates to fewer fees for liquidity providers and thus less financial incentive to dedicate capital to these markets. Leaving liquidity provision and odds setting fully to inconsistent human discretion serves as an additional friction point.

Contrast this with Vegas sportsbooks, which can attract vast sums of money from swarms of bettors flocking to wager on popular sporting events. Yet even with this built-in audience, sportsbooks still rely on centralized bookmakers to set lines and run operations.

The house often assumes the other side of bets, sets the odds, and manages positions.

Decentralized prediction markets lack this systematic liquidity infrastructure. Their often short time scopes and subject matter concentration mean even modest trade sizes fail to transact efficiently. Moreover, individual investors have to develop their own trading strategies to take advantage of their asymmetric knowledge.

Without a centralized entity to catalyze activity, and without sophisticated actors like AI agents to improve trading efficiency, a lot of these markets remain too shallow for most practical uses.

With AI’s integration into crypto, there's now an opportunity for prediction markets to import AI agents to:

• Seed liquidity through efficient market making strategies

• Develop and execute comprehensive trading strategies

• Construct more expressive portfolios, indices, insurance products, and other structured offerings comprising shares from diverse prediction markets

All in a decentralized way leveraging new decentralized AI systems. Let’s dig into how AI would interact with prediction markets and the impact it would have.

How to Solve Current Limitations in Prediction Markets

In short, AI increases market efficiency.

AI is simply better at processing large amounts of diverse data, developing trading perspectives, and executing complex transactions than human counterparts. This enhanced efficiency directly translates into higher trading volumes on prediction market platforms.

And greater transaction volume is the catalyst that sets off a flywheel for the entire category.

• More trades drive more revenue potential for liquidity providers, through fees and bid-ask spreads.

• This incentivizes more market makers to offer capital, tightening spreads and enabling further trading activity.

• As trading activity accelerates, prediction markets reveal more accurate forecasts/ predictions.

• As niche platforms build a track record of precise projections, it attracts even more traders and applications over time.

Essentially, AI has the potential to transform the stale equation that has restricted niche prediction markets in the past:

Narrow scope + Few expert traders → Low volume → Minimal liquidity → Limited utility

With the introduction of AI agents, the flywheel effects can flip this narrative:

Narrow scope + AI traders → Higher volume → More liquidity → Accurate forecasts → Mainstream adoption

AI traders, leveraging superior data processing and decision-making, engage in trades more frequently and more accurately than human traders. This heightened trading activity creates more opportunities for market makers, which elevates trading volume, which in turn increases revenue potential for market makers.

This dynamic fosters a virtuous cycle:

As AI-driven trading boosts market volume and liquidity, it attracts more market makers who provide the necessary capital.

Their participation tightens spreads and facilitates even more trading activity, leading to more precise and reliable market forecasts. As these markets demonstrate a consistent track record of accuracy, they naturally attract a broader spectrum of traders and applications over time, paving the way for mainstream adoption.

The technology now exists through decentralized AI infrastructure to empower agents that can finally capitalize on long-tail niche topics instead of remaining restricted to broad mainstream financial markets. Specialized prediction markets can attract more usage by leveraging these AI agents to provide accurate and liquid projections.

What AI-Powered Prediction Markets Will Look Like

For end users, vastly more capital flowing into prediction markets unlocks new possibilities.

In the current state, low liquidity leads to prediction markets often not being very accurate. The more capital that flows into these markets, the more efficient and accurate they become.

As decentralized, democratized AI unlocks more rapid innovation in niche categories, AI agents built on this infrastructure will likely comprise the majority of the traders in prediction markets. Different AI agents, mostly funded through onchain vaults, will hold varied perspectives based on:

• Particular worldviews

• Predictive capabilities

• Accessible datasets

• And training biases

Another benefit of decentralized AI infrastructure is the ability to decouple AI models from capital requirements. Developers can create predictive models specialized for certain event categories. These models can then be used as the strategies for different onchain DeFi vaults, allowing people to deposit capital that the AI agent utilizes to trade across relevant prediction markets.

For example, users may deposit funds into a RoboNet-enabled vault that runs a proprietary prediction market model, trading contracts and returning gains back to vault holders. To address potential credit risks in returning gains, RoboNet incorporates built-in protections based on holder risk profiles and trading venues. Moreover, zkML infrastructure (like the zkPredictor) ensures these AI agents are operating in a verifiable way. Such infrastructure allows AI agents to operate in a financial system with speed, scale, and precision exceeding any human’s capabilities.

The landscape will shift from niche prediction markets only attracting liquidity when major events capture traders attention, to accurate and heightened trading activity across more markets. Rather than intermittent accuracy when the world’s focus spikes around important sporting, political, or scientific events, AI agents can drive consistent trading activity to prediction markets. This enables reliable probabilities, even for niche topics.

One Step Further: “Worldview Vaults”

Building on this idea, one could create a set of “worldview vaults” which are managed by AI agents that represent different perspectives or areas of expertise, giving capital providers the ability to deposit in vaults that align with their worldview or areas of interest.

For example, you could create AI agents focusing on predictions from a US perspective, an accelerationist perspective, a web3 perspective, and more. These agents would be fed relevant data and tuned to make predictions based on their assigned perspective or area of expertise.

Developers could then spin up vaults around these different perspectives to give people passive exposure to the predictions being made. For instance, you could have:

• A "US Politics" vault

• An "Accelerationist" vault

• A "Web3" vault

Each vault is effectively a representation of predictions generated by an AI agent, each tuned to a specific worldview or investment thesis. This provides a unique opportunity for developers to craft diverse investment avenues. For example, if someone believes AI will significantly impact the economy, they can invest in a vault that embodies this belief. The AI agent then strategically allocates capital in prediction markets on platforms, like Polymarket, making trades that align with this overarching conviction.

This process isolates different worldviews into modular AI agents, which enables specialized predictions while also capturing a diversity of perspectives for users to access. The end result is an intelligent infrastructure that reflects both consensus views and divergent opinions on future outcomes.

How a Prediction Market Would Utilize Upshot’s Machine Intelligence Network

Building these AI agents on Upshot's Machine Intelligence Network would allow prediction markets to tap into greater security and scalability. The MIN consists of topics centered on different prediction categories like politics, technology, or culture. Each topic would have an associated benchmark to grade insights based on real-world accuracy within that domain.

[To learn more about how this might work, read our article about the Upshot Machine Intelligence Network.]

For example, Alpha Miners focused on US Politics could collaborate and share insights on that topic. Their contributions would then be evaluated against real-world political events, such as election outcomes, policy changes, and approval ratings. The Upshot MIN uses the Proof of Alpha mechanism to score the influence and accuracy of each peer's contribution relative to others. This results in a meta model that integrates the most effective elements of each peer's input for the AI agents' decision-making process.

This structure incentivizes niche experts to contribute their knowledge or models to improve forecasting precision as it pertains to that topic. The more influential and accurate a peer's contribution is in the meta model, the larger the share of rewards they receive.

Hosting AI agents on the Upshot MIN reduces risks compared to centralized systems by distributing control of the vault’s capital across many Alpha Miners instead of one entity. It also increases potential coverage and scalability. Prediction markets being interacted with by the Upshot MIN could tap into more collective intelligence for niche topics while maintaining increased security.

The end result is a decentralized prediction engine that values different contributions for more comprehensive forecasts and increased security.

Decentralizing Access to Predictive Intelligence

AI agents don’t just represent an opportunity to improve prediction markets. They also help take apart different pieces of financial operations and reconstruct them in tightly scoped composable form factors.

Rather than centralized entities controlling all capital and investing strategies, the pieces become modular:

• Users simply provide capital through onchain primitives like vaults

• AI agents contribute predictive strategies and infrastructure by utilizing the composite meta model produced by the Upshot MIN.

• Prediction markets combine user capital and AI strategies, revealing information that drives value for both actors–capital providers and AI agents–while simultaneously enlightening society.

This democratized structure shares diverse insights more evenly. As decentralized platforms grow in sophistication, prediction markets may become the dominant medium for information distribution.

Prediction markets, at their core, leverage market incentives to reveal collective intelligence. AI integration now paves the way to access more granular and precise forecasts across virtually any topic––no matter how niche.