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The prediction market is undergoing a rather interesting shift recently.
From Binance and Coinbase to 盈透证券（Interactive Brokers, IBKR） and Robinhood, major players are shifting their focus upward, no longer fixated on replicating a Polymarket.
Everyone is figuring out how to gradually push Polymarket, Kalshi, and others “into the background,” turning them into underlying capabilities that other financial products can directly call upon.
This is actually quite similar to today’s stock trading. When users buy TSLA through Futu, Tiger Brokers, or Robinhood, most don’t care which Market Maker ultimately receives the order or which clearing system it passes through.
The prediction market could be the same in the future.
The front end might be a brokerage, a wallet, a news app, or even an AI Agent. In the middle would be Aggregators and Routers. The Polymarkets and Kalshis that truly provide markets, liquidity, and settlement would increasingly resemble the financial infrastructure hidden in the background.
Looking back at DeFi, from AMMs to aggregators, derivatives, professional market making, and smart execution, this path has been walked before.
The prediction market might also be entering a similar second half.
1. Prediction Markets Increasingly Move “Behind the Scenes”
Over the past few years, prediction markets have proven that “uncertainty” in the real world can indeed become a tradable asset with a price and liquidity.
From the US presidential election to sports events like the World Cup, and then to Crypto, macroeconomics, and even entertainment and culture, many issues that could only be discussed have been compressed by platforms like Polymarket and Kalshi into Event Contracts that can be bought, sold, and generate profits or losses.
This is a very critical step. As this layer of demand is gradually validated, the strategies of major players have also begun to change significantly.
In April this year, Binance integrated Predict.fun, using a third-party prediction market infrastructure for market capabilities while providing its own front-end entry, allowing users to trade probabilistic events directly within the app. Two months later, it further opened its Prediction Markets API, enabling quantitative strategies, trading bots, and third-party products to directly call market data and trading capabilities.
Coinbase is taking a similar route. It incorporated Prediction Markets into its “Everything Exchange,” but the initial market liquidity all came from Kalshi, and it explicitly stated it would support more Prediction Market Venues in the future.
On the traditional finance side, IBKR has gone a step further.
In May this year, IBKR directly placed three Prediction Markets – Kalshi, CME Group, and ForecastEx – into a unified interface. Users don’t need to open separate accounts; they can search for events, compare prices and liquidity across different exchanges, and complete trades within a single account.
Robinhood, meanwhile, is extending towards the trading and clearing layer. Partnering with Susquehanna to operate Rothera, which took over the CFTC-registered exchange and clearing infrastructure of MIAXdx/LedgerX, it began routing some World Cup and professional baseball Event Contracts to this affiliated exchange in June.
The actions of these major players all point to the same thing: the prediction market is transforming from a “destination” users actively visit into a financial capability that other products can call upon.
In the past, if we wanted to trade on the upcoming US midterm elections, we might have needed to open Polymarket first and then find the corresponding market. In the future, it could be an event card on Binance’s homepage or a real-time probability flashing next to a financial news article on Robinhood.
Users might not even realize they are using a prediction market to trade. Whether the order ultimately comes from Predict.fun, Kalshi, CME, or is split across multiple markets by a Router becomes far less important.
This also means that Prediction Markets are becoming increasingly invisible, while Prediction Assets are becoming increasingly important.
Once this point is reached, the truly interesting question for the industry changes – how should the liquidity scattered across Polymarket, Kalshi, Predict.fun, and even more markets be organized?
For example, regarding the US midterm elections, where is the best price? Where is the deepest liquidity? Is there a probability de
This set of problems is very similar to early DeFi. After Uniswap proved that Tokens could be traded on-chain, the market didn’t stop at “creating ten more Uniswaps.” What truly emerged next were liquidity aggregators like 1inch, smart execution networks like CoW Swap, derivatives infrastructure like Hyperliquid, and the market makers and quantitative trading systems built around them.
The prediction market is gradually entering a similar phase.
Fortune is a very typical early case. As of August this year, it has completed multiple funding rounds including Seed and Pre-A, with a cumulative amount exceeding $4 million, used for the upgrade of Fortune Agent, integrating more prediction markets, and expanding liquidity and related infrastructure.
What it aims to tackle is the “Liquidity Infrastructure for Prediction Markets” layer. It recently further integrated Polymarket’s liquidity, placing it alongside the previously supported Predict.fun into a unified entry point called Fortune Markets.
Users can now view markets, trading volumes, liquidity, and implied probabilities from differenth between different Prediction Markets
This is just a very early form, but it already reveals what Fortune wants to do – gradually abstract the fragmented Prediction Markets into a unified liquidity layer.
2. Not Just a “Prediction Market Version of 1inch,” What Else Can It Do?
Of course, simply viewing new players like Fortune as a “prediction market version of 1inch” would be like marking the boat to find a lost sword (i.e., clinging to outdated methods).
The prediction market may not completely replicate DeFi’s development path. New players like Fortune are mainly trying to shift their perspective from Prediction Market to Prediction Asset.
It seems like just a one-word difference, but the underlying logic is vastly different: Market cares about “where to trade,” while Asset cares about “what exactly is being traded, and what else can be built around it.”
Just like BTC doesn’t belong to Binance, and Tesla isn’t only tradable on Robinhood. US stocks aren’t just about spot trading, and Crypto isn’t limited to Spot. Once Prediction Assets can be searched, priced, combined, and traded across different markets, the infrastructure above the markets truly has a chance to form.
Taking Fortune as an example, the system that new players want to build can be roughly broken down into several interconnected layers.
1. First, “Bring Together” the Dispersed Prediction Assets
As we all know, today’s prediction markets are still highly fragmented.
The same type of event can exist simultaneously on Polymarket, Predict.fun, and other platforms, with each platform having its own independent market structure, liquidity, and pricing.
As early as April this year, Fortune’s native Prediction Market went live. By August, it further integrated Polymarket CLOB v2, placing it alongside the previously supported Predict.fun into the unified entry point of Fortune Markets.
Users can now directly browse event markets from different liquidity, and implied probabilities before establishing a position
The new trading process has also added features like Outcome Selection, Position Preview, and Portfolio, gradually connecting the discovery of markets, position establishment, and subsequent management.
In the past, a user wanting to trade the same type of political, sports, or Crypto event might have needed to enter several platforms separately to search and then compare prices and depth themselves. Now, Fortune Markets compresses this process into “Discover Event → Compare Platforms → Select Price & Liquidity → Establish Position → Unified Management“.
Theoretically, the aggregation of prediction markets is far more complex than that of ordinary DEX Aggregators.
1 ETH in Uniswap and Curve is still the same ETH, but two prediction markets that look almost identical can become completely different assets due to subtle differences in cutoff time, event definition, judgment conditions, or settlement rules.
From this perspective, what Fortune does first is not to create more markets, but to gradually turn Prediction Assets scattered across different platforms into an asset pool that is easier to search, compare, and trade.
2. From “Buying YES / NO” to More Complete Financial Products
Once assets are connected, the next question naturally changes.
The most common trading method in prediction markets today is still to buy YES if you are bullish on an outcome, buy NO if you are bearish, and then wait for the event to settle.
This is very similar to the early days of Crypto when there was only Spot.
But if Prediction Assets eventually develop into a sufficiently large asset class, trading demand theoretically will not remain at binary betting forever. When the underlying asset scale is large enough, markets usually continue to develop leverage, options, portfolios, hedging, and structured products.
This is also the reason Fortune included Prediction Derivatives in its overall product direction. It wants to further transform Event Contracts from a binary contract “waiting for the final answer” into Prediction Assets that can be combined, managed, and used strategically.
This step is actually very critical.
Because the true maturity of an asset class is often not measured by how lively the spot market is, but by whether a rich enough financial structure can form around it.
Only after BTC moved from Spot to Perpetuals, Options, and structured products did it gradually form today’s complete trading system; the stock market also has futures, options, ETFs, and various combination tools.
What Fortune is betting on now is that Prediction Assets will undergo a similar financialization process.
However, this also brings another problem. If a user in the future no longer faces a few markets, but hundreds or thousands of Prediction Assets, and even different combinations and strategies, will people still be capable of completing all research and execution themselves?
This is where the Fortune AI Agent should truly appear.
3. Letting AI Directly Enter the Trading Chain
To be honest, prediction markets are probably one of the easiest financial scenarios for AI Agents to be understood.
Because it naturally has a very clear transmission chain: Real-world changes occur → New information appears → Event probability changes → Market reprices → Trading opportunities arise.
However, traditional trading requires completing the entire process manually, from reading news, browsing social media, analyzing market sentiment, to judging whether this piece of news will actually change the probability of an event, and finally finding the corresponding market, comparing prices, determining position size, and executing the trade.
What the Fortune Agent wants to compress is precisely this chain.
The currently launched Trading Agent is designed as a Multi-Agent System, continuously seeking opportunities from three types of signals: News, Sentiment, and Arbitrage, and then further verifying conditions. After users connect their wallets, they can set the single transaction amount, risk level, and whether to enable Automated Trading.
If we dig deeper into Fortune’s subsequent descriptions of the Agent, there are also 24/7 Market Intelligence, Structured Decision-making, Risk Management, and Execution. Putting these together with the earlier Fortune Markets, the entire product logic is completely connected.
For example, a new policy signal suddenly emerges for a certain macro event.
The Agent first captures the information and judges that it might change the true probability of an event; then Fortune Markets can simultaneously provide related Prediction Assets, prices, and liquidity from different Venues; if the market price has not fully reflected the new information, the Agent further seeks more suitable trading opportunities and execution paths.
At this point, what Fortune wants to do is no longer just an “AI Predictor”. It is closer to connecting the information layer, asset layer, liquidity layer, and execution layer together.
And on the outermost layer, there is an Incentive Layer responsible for the cold start of this network. Fortune has currently established an incentive system centered around F Points, including Daily Check-in and an invitation mechanism; after a user invites other participants, they can receive 10% of their F earnings.
These seem like the common Points, NFTs, and Referral mechanics of traditional Web3 projects, but placed back within the overall product architecture, they actually address a very realistic problem:
Where will the earliest liquidity, trading users, and ecosystem participants come from for a new Prediction Asset network?
More user participation brings more trading and liquidity; deeper liquidity improves the trading experience, which in turn attracts new users and strategies; when Agents and more financial products join, trading frequency and strategy complexity may also increase.
So, stringing Fortune together from beginning to end, what it is truly trying to build now is not an isolated function, but a relatively complete chain:
- Prediction Markets provide underlying event assets;
- Fortune Markets are responsible for connecting assets and liquidity;
- Prediction Derivatives expand the financial expression of assets;
- Fortune Agent handles information processing and trade execution;
- The Incentive Layer provides early growth momentum for the entire network;
Therefore, if we must give Fortune a positioning, it is neither just a Prediction Market, nor just a “Prediction Market version of 1inch”.
More accurately, what it wants to build is a set of trading and execution infrastructure centered around Prediction Assets.
III. When “Probability” Truly Becomes an Asset
Of course, whether Fortune can ultimately realize this roadmap is still difficult to conclude now.
It is still an early-stage project.
Polymarket’s liquidity access and the Agent are already visible as actual products, but unified order routing, mature Prediction Derivatives, and a sufficiently deep cross-market liquidity network are still far from their final form.
But if we zoom out to the perspective of the entire industry, the direction Fortune is betting on is not isolated. Apex has already started integrating Kalshi’s Event Contracts into brokerage infrastructurel for professional traders and researching internal market making and Prediction Market Indices
Prediction markets are increasingly looking like a real financial market, and a real financial market will definitely not only have exchanges.
At least three layers of change are worth observing next.
1. From Bet to Portfolio, High Financialization
Today, many people’s first contact with Prediction Markets is still understanding it as “I bet on whether something will happen or not.”
But for more mature traders, it can actually create a portfolio of assets that expresses a complete viewpoint.
For example, when a trader judges that US inflation is heating up again and the Fed is turning hawkish, they might not only trade “whether the next FOMC will raise rates”. They could simultaneously build positions around different events like “whether the Fed will maintain higher rates”, “whether BTC will break a certain price level by year-end”, “whether the US will avoid a recession”.
Individually, these are all Event Contracts, but combined, they can express a complete Higher for Longer macro thesis.
If this stage truly arrives, Portfolio Management, Correlation, Hedging, and Risk Management will naturally follow. By then, if projects like Fortune can truly complete the Derivatives and portfolio layer, their value will no longer just be about helping users “open fewer web pages”.
2. From Single Market Trading to Cross-Market Aggregation Execution
This is easy to understand. The larger the market, the less important a single platform might become.
Crypto ultimately did not form a pattern of “one exchange carrying all liquidity”, and Prediction Markets likely will not either.
Different regulatory systems, user groups, market makers, event categories, and geographic regions will persistently create market fragmentation. Market fragmentation itself is an opportunity for infrastructure, after all, arbitrageurs need prices, market makers need order flow, institutions need depth, ordinary users need the best execution price, and Agents need enough venues to scan and execute.
So when Prediction Markets truly mature, the trading entry point may actually become increasingly “invisible.” For example, seeing a news headline in a brokerage app: “Probability of Fed adjusting rates next month: 72%”
With a button next to it to buy in, it doesn’t matter whether the underlying order comes from Polymarket, Kalshi, or is split by a Router across three markets.
This is the real change that API-ification could bring.
3. From Human Trader to AI Trader
I have always believed that prediction markets could become one of the most natural financial application scenarios for AI Agents.
Because it is inherently “information → probability → price → trading,” and this is precisely a chain where AI excels at intervening.
If Crypto provides AI Agents with a financial system that doesn’t require a bank account and allows 24/7 direct control of assets, then Prediction Markets further provide them with a market where they can directly trade “cognition.”
One of AI’s core capabilities is processing information, and the core asset of Prediction Markets is the probability formed after information is compressed.
The two are a natural fit.
We can imagine a future Agent simultaneously monitoring news, social media, macroeconomic data, on-chain data, company announcements, sports events, policy documents, etc., and continuously recalculating its own probability model.
Once the market price dedirectly. At that point, the speed of Prediction Markets will also change
In the past, Alpha might have come from me seeing a news story earlier than others. In the future, it could become: My Agent understands what this news means faster than your Agent. Or even in the next stage, who can convert cognitive advantage into executed trades faster across more Venues.
Thus, information advantage, model advantage, and execution advantage will gradually become the same thing.
This might be the truly interesting part of the so-called AI × Prediction Market. Of course, before all these imaginings become reality, there are several practical issues that cannot be ignored.
- First is liquidity. Without sufficient depth in the order book, the most advanced Router and derivatives are meaningless.
- Second is Resolution. Prediction markets ultimately still need a credible, clear, and minimally controversial event settlement mechanism.
- Third is regulation. Whether an Event Contract is classified as a derivative, gambling, or a new financial instrument still has completely different answers in different regions.
- Finally, there is the Agent itself. AI can process vast amounts of information, but there is still a significant gap between “being able to summarize news” and “being able to consistently generate Alpha.”
These issues will not automatically disappear just because the market is growing.
Only when this infrastructure is gradually put in place can Prediction Assets truly evolve from a novel trading category into a mature asset class.
Final Thoughts
Objectively speaking, from the 2024 U.S. Presidential Election to the 2026 World Cup, Polymarket and Kalshi have already completed the most difficult initial user education for prediction markets:
The many uncertainties of the real world can indeed be traded.
But this feels more like the first half. Looking back at the development history of almost all financial markets, you will find that proving an asset can be traded is never the end of the story.
As the number of participants, assets, and platforms increases, what determines market maturity is often the less glamorous things outside the exchange itself, such as liquidity, market making, routing, derivatives, risk management, portfolio strategies, and freer, more automated execution.
DeFi has already gone through this path, and prediction markets are likely following a similar trajectory.
So, looking at new players like Fortune today, what is truly worth paying attention to is: as more and more “probabilities” truly become assets, how can we trade them more efficiently?
It requires deeper liquidity, richer financial tools, more efficient execution, and AI Agents that can understand the real world 24/7 and continuously recalculate probabilities.
And this might be the true second half of the journey from Prediction “Markets” to Prediction Assets.
Source: www.panewslab.com