Author: eric
Translation: Deep Tide TechFlow
In the prediction market, I rarely hear anyone use the term "betting"; instead, "trading" is preferred.
The distinction is crucial.
Once the stigma of gambling is removed, the Total Addressable Market (TAM) expands to nearly everyone: macro investment managers, Pokémon card unpackers, your classmates, and colleagues.
Mild speculative behavior has become mainstream among various groups. Office March Madness basketball pools and fantasy leagues, video game card draw mechanics, zero-commission day trading and 0DTE (zero days to expiration) options, crypto perpetual contracts, and meme coins, etc. As long as it is referred to as "trading" or "asset allocation" rather than "gambling," people are always drawn to it rather than shying away. @j0hnwang wrote a brilliant article on soft gambling, which, while not explicitly mentioning InfoFi, has clear relevance. Prediction markets will soon be viewed as no longer taboo, akin to the stock market. Coupled with the potential to profit from culture (a perfect example provided by @dkposts), prediction markets will become a mainstream entry point.
Two Reasons for the Separation of Prediction Markets from Gambling
(1) No fear of the "house." Regardless of the facts, users always feel they have an edge, thus viewing trading in prediction markets as a "normal" activity akin to portfolio management.
(2) Clear and understandable themes. Contracts settle based on observable data and real events: the Consumer Price Index (CPI) on a certain date, ETF approval deadlines, election vote counts, issuance windows, etc. You can profit by gathering information, simulating outcomes, and timing your actions. This applies intelligence to risk, significantly diminishing the stigma of "gambling."
Additional Reasons for Optimism about Prediction Markets
(1) Event pricing is more direct, and feedback is quicker
In day trading, stocks (as well as commodities/forex) often act as proxies for world events. For example, when a war breaks out, Lockheed Martin and Brent crude oil prices rise; when important shipping routes are blocked, Maersk's stock price increases; when the Federal Reserve unexpectedly raises interest rates, the dollar strengthens, and gold prices fall. Prediction markets price events directly, with clearer attribution and faster feedback.
(2) Prediction markets are excellent hedging tools
Consumer case: If you plan an outdoor wedding and rain would require renting a $2,000 tent, you can buy a contract for "rain in a certain city on a certain date" at $0.20 per share. If the outcome is "yes," this profit can offset most of the tent rental cost.
Institutional case: A portfolio focused on growth tech companies may face the risk of unexpected inflation. They can buy a contract for "U.S. CPI year-on-year growth ≥ 4.0% (September)" at $0.18 per share. If CPI reaches ≥ 4.0%, the portfolio may drop about 3% (around $6 million). Each "yes" contract nets $0.82, and about 7.3 million shares would cost around $1.3 million, but if the event occurs, they would gain about $6 million; otherwise, the premium is the cost of protection.
(3) The market and mechanisms are simple and easy to understand, with low barriers to financial engineering
Prediction markets may be the most accessible form of cutting-edge financial engineering. For example:
Commodity futures: "Will orange juice prices reach X dollars in June?"
Credit default swaps: "Will Argentina default before 2026?"
Interest rate swaps: "Will the average policy rate be above 4.75% next year?"
Volatility swaps: "Will the S&P 500's volatility exceed 20% in March?"
Traders can see probability changes in real-time, rather than the confusing numbers in sports betting (like from -140 to +50, or +500 to +200), but rather simple percentages.
Similar to stocks, traders can sell their positions before contracts settle, rather than the "black or white" (0 or 100) outcomes in traditional sports betting.
(4) Prediction markets have historically performed well in forecasting major events
Historically, prediction markets have been significant participants in forecasting major world events. For example, during the 2024 U.S. election, Polymarket set Trump's winning probability at 99% at 1:30 AM ET, while Fox News announced the result at 1:47 AM, with other media outlets delaying even longer. Continuous arbitrage and rising marginal costs of price deviations from fair value make prediction market errors brief and easily correctable.
(5) The association with intelligence and skill drives acceptance among high-end groups
Whether true or not, young people want to appear smarter or be perceived as such. For instance, I've seen Harvard students mock DraftKings (considered a low impulse control activity) while proudly discussing arbitrage in prediction markets or successfully predicting the outbreak of the Russia-Ukraine war. This association with skill and intelligence drives adoption among highly educated university groups and investment professionals.
(6) Prediction markets will gradually replace the need for tokenization of private companies
Tokenization of private companies is challenging due to founder resistance, legal risks, false "governance" rights, and lack of liquidity, while prediction markets can more easily meet similar needs.
(7) The market is still in its early stages
We got in too early; it's almost absurd. Polymarket's total locked value (TVL) is only $148 million (data source: DeFiLlama), which is minuscule compared to the previously discussed total market value (TAM) or the peak TVL of about $400 million during the 2024 election. Kalshi only began to delve into the cryptocurrency space last week.
Expected Trends in the Next Six Months
In the next six months, I expect the following trends to emerge:
(1) Prediction market data will become standard in news reporting
Polymarket and Kalshi's market predictions will continue to appear on major news media screens. Every journalist making predictions must reference prediction market probability data to maintain credibility.
(2) Significant clarity in regulation = driving a viable federal framework
Polymarket's confirmation of entry into the U.S. market and Kalshi's recent legal victory with the U.S. Commodity Futures Trading Commission (CFTC) means that prediction markets will have a clearer path for listing and settling event contracts and attracting institutional capital.
(3) Influx of specialized funds and trading teams
From the launch of a pure prediction market trading fund managing tens of millions of dollars (as I understand, @curiouscamilo is preparing this project) → to major trading firms (especially quantitative firms) establishing dedicated prediction market trading departments (directional micro-teams, not just market making, like the services SIG provides for Kalshi).
(4) Prediction market data becoming standard at terminal levels
Prediction market probabilities will appear on terminals like Bloomberg and Refinitiv, just like other price data, including real-time quotes, historical records, alert functions, chart displays, and native API bindings supporting Excel/Python and content management systems (CMS). In fact, editing and trading teams will handle these probability data like other data streams.
Most Likely Successful Prediction Market-Related Projects
Which prediction market-related projects are most likely to succeed?
(1) Limiting choice overload
The smartest approach is to use artificial intelligence (AI) to automatically recommend three customized markets to users daily. A general rule of thumb is that AI capable of executing predictive functions (without user input, directly making decisions for users) tends to succeed more easily.
(2) Projects focusing on social and gamification aspects
Prediction markets are inherently very social and consumer-oriented products. Therefore, they must have some form of native gamified social graph features, such as: live streaming, teams and leagues, copy trading, creator-led rooms, quarterly leaderboards, "club" wallet pools, chat functions linked to market trends, recommendation tiers, shareable trade tickets, and one-click auto-reconstruction of trades.
I particularly like the concept of "shareable trade tickets," which is similar to a link that pre-fills specific betting content for others to use. You can create a betting ticket: choose which market, yes/no, betting amount, and any restrictions, such as "only bet when odds are below 65%." Click "share" to generate a link. When friends click the link, the app automatically opens and fills in all the details. They just need to review and confirm to complete the trade. Imagine:
Like a Spotify playlist, clicking loads the same set of songs.
Like an Amazon shopping cart, after you send it to a friend, all items are already in the cart.
(3) Infrastructure projects: the "pick and shovel" of prediction markets
Utilizing the underlying architecture of prediction markets to provide distribution channels, a streamlined user experience (UX), and data processing capabilities to meet mainstream needs. For example, Kalshi's employee @kxchefsteve actively encourages developers to build related packaging tools ("ready-to-use control waiting for deployment").
(4) Projects addressing market fragmentation issues
As pointed out by @ryohhno, the core issue of market fragmentation is structural: the same event exists on multiple platforms but lacks a shared ID, leading to a natural split in liquidity and rules across different platforms. The solution (though difficult to implement) is to build a National Best Bid and Offer (NBBO) and router for events, standardizing issues, routing orders to the best prices and depths, and compensating for different resolution conditions and collateral currency differences. Similar to a CUSIP (Committee on Uniform Securities Identification Procedures) event ID, equipped with a depth-aware smart order router that can reconstruct the same trade from any interface (including social networks).
(5) Infrastructure Projects Related to "Multiverse Finance"
Earlier this year, @dave__white wrote a brilliant article on "Multiverse Finance." Assets exist in a universe where certain conditions are met, and users can only borrow and trade within the same universe; when oracles resolve, balances return to the base world. The related opportunity is to build infrastructure that supports universe awareness:
ERC-20 wrappers and credit tools that price split/pull operations
A lending layer that allows ‘notFiredUSD’ to safely support ‘notFiredETH’, as both values would drop to zero if the event resolves as "Fired."
The Potential and Momentum of Prediction Markets Surpassing Other Sub-industries
I feel that the product-market fit (PMF) and development momentum of prediction markets are stronger than any other sub-industry. Just look at these headlines from the past few weeks:
"Robinhood partners with Kalshi to launch a prediction market."
"The U.S. Commodity Futures Trading Commission (CFTC) withdraws its appeal in the Kalshi election contract case, paving the way for regulated political markets."
"Polymarket's monthly trading volume reaches $1.16 billion."
"Polymarket acquires CFTC-licensed QCEX for $112 million to re-enter the U.S. market."
"Donald Trump Jr. joins Polymarket as an advisor, while also advising Kalshi."
I disagree with the notion that prediction markets are an evolution of Memecoins. I believe they are the next evolution of the stock market. The potential of this market is limitless, and I am very confident about its prospects.
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