At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold, a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote, “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor. With the election cycle heating up, their trading volume increased to over $100 million per month. Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry. At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold , a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. Manifold One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote , “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” On X, Paul Graham wrote This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor . With the election cycle heating up, their trading volume increased to over $100 million per month . Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. Polymarket investment of $70 million Nate Silver even coming on board as an advisor $100 million per month On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Kalshi suing the CFTC partnership with SIG Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. sweepstakes model As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. Manifold’s data What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Manifold sent an email 20 minutes before news outlets Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry.
