We Can Predict the Future
Aemula Writer Spotlight - 2.19.26
Yesterday, Substack announced a partnership with Polymarket, the popular prediction market. The partnership enables Substack creators to embed Polymarket data into posts and notes, while Polymarket joins the Substack partnership pilot program. The nature of the partnership deal is unclear, as Substack CEO, Chris Best, avoided answering questions on the financials of the agreement in his live discussion with tech journalist Alex Heath.
The timing of this announcement is fortuitous, as we have also just released new prediction market features on Aemula, aligning with our roadmap to outline how prediction markets will improve our information environments. This week, we discuss prediction markets, their benefits, their downsides, and how Aemula addresses common concerns in our implementation of the technology.
Prediction Markets
Prediction markets, in their common form, are binary (yes/no) option markets for real-world events. Participants are able to place yes or no bets on whether or not they think a specific future event will occur, with the price of placing those bets reflecting the market-implied probability of the outcome. After the event, the market resolves, with the correct side receiving a payout.
To use specific numbers (and highlight the new Substack feature), see the below prediction market on whether or not Prince Andrew will be sentenced to prison after his arrest on suspicion of misconduct in public office in relation to his connections with Jeffrey Epstein.
At the time of this writing, the market indicates a 10% chance of Prince Andrew being sentenced to prison. Using traditional market terminology, this means you can purchase a “yes” bet for $0.10/share. If you believe the odds of a prison sentence are greater than 10%, then a “yes” bet would be underpriced at $0.10/share, creating an opportunity for you to profit by purchasing a “yes” share.
You could either sell your “yes” share once the price exceeds a probability you think is accurate, or you could hold onto the “yes” until the market resolves. In the event Prince Andrew is sentenced to prison, the value of your bet would resolve to $1/share (100% probability since it already occurred).
For example, if you bought 100 “yes” shares for $10 and correctly held them until the prison sentencing, you would receive back $100. However, if he were not sentenced, your shares would resolve to $0/share (0% probability) and you would lose your $10.
So the inverse of the market is also true, where if you think 10% odds of a prison sentence are too high, you can purchase a “no” bet for $0.90/share (90% chance of no, 10% chance of yes). With a $10 bet, you could purchase 11.11 shares, which would receive $11.11 if you held onto the bet and Prince Andrew was not sentenced to prison.
When you believe the probability of an outcome is misstated, then you can profit by placing a bet on the side of the market you think is underpriced. People will continuously place bets until the market price moves to a point where they believe it accurately represents the real probability of the event occurring, meaning there is no perceived opportunity to profit. Of course, there are additional frictions to this process in practice, but this is the basic premise.
Fundamentally, the purpose of prediction markets is to use price signals and market dynamics to determine the general consensus probability of a specific outcome. Everyone has their own information and their own opinions on the probability of the outcome, which they record by placing bets until all of the relevant information is reflected in the market price.
In practice, prediction markets have proven their ability to accurately predict the outcome of important events. Notably, in the 2024 US Presidential Election, Polymarket and other prediction markets were more accurate in forecasting the election results than traditional polling.
For this reason, Polymarket included in their announcement of the Substack partnership that "journalism is better when it’s backed by live markets.”
Admittedly, this relationship might not be immediately clear, but in ideal conditions, as journalists work diligently to investigate sources, provide credible information, and uncover the truth, prediction markets can be a powerful complement to their process, informing them on public sentiment and consensus expectations. Importantly, prediction markets should be viewed as a tool for discovery rather than a replacement for the journalistic process. They can signal what stories are capturing public attention and which uncertain events would benefit from additional reporting, but market participants are only able to make accurate predictions if they have access to high-quality information.
Journalism and prediction markets create an iterative feedback loop that improves overall information quality, so it comes as no surprise that newsrooms are working closely with prediction markets to provide these tools to their journalists. Kalshi, the primary competitor to Polymarket in the prediction market arms race, recently announced their partnership with CNN. Similarly, Polymarket struck an exclusive partnership with Dow Jones, a division of News Corp and parent to The Wall Street Journal, Barron’s, and MarketWatch.
However, as prediction markets continue to become integrated as a tool for information discovery, people are voicing their valid concerns over the potential for these tool to introduce misaligned incentives.
History of Prediction Markets
To address these concerns, it is worth understanding how prediction markets rose to prominence in their modern form. The concept of collective wisdom through independent decision-making has existed for hundreds of years (the history of which is unfortunately outside of the scope of this newsletter), but modern prediction markets first started to take form when three University of Iowa professors ideated the Iowa Political Stock Market (later referred to as the Iowa Electronic Markets) as a financial market for betting on the outcomes of political elections based on their belief that market dynamics could outperform traditional polling. The accuracy of this early implementation began to gain traction, leading to the advent of corporate prediction markets, first implemented as part of Project Xanadu, the first hypertext protocol and competitor to the World Wide Web.
Robin Hanson, creator of the prediction market project at Xanadu Inc and now a professor of economics at George Mason University, began to formalize the concepts of prediction markets into the idea for Futarchy, a form of democratic governance where market speculators would bet on the predicted effectiveness of potential public policies, informing elected officials’ decisions on the best options to implement. He first proposed the idea in 2000 and continued to refine it through official publication of the proposed governance method in the Journal of Political Philosophy in 2013.
Around the same time, the release of new decentralized technologies were beginning to show promise in their capability of providing the infrastructure necessary to implement these decentralized governance ideas in our modern digital landscape. Notably, Vitalik Buterin, the founder of Ethereum, wrote how Futarchy and prediction markets could be used to facilitate collective decision making in decentralized organizations. With the launch of Ethereum in 2015, Augur began development to become the first truly decentralized prediction market, allowing us to test how these tools would work in practice.
After Augur’s launch in 2018, it did not take long for market speculators to begin creating death pools and assassination markets, highlighting how financial incentives for determining the outcome of real-world events can introduce perverse incentives. This raises the question of whether prediction markets are a “thermometer or a thermostat”. Do they allow us to gain insight into the world around us, or do they empower us to incentivize actions and outcomes that would otherwise not have occurred?
The Dark Side of Prediction Markets
For instance, take the prediction market bets around this year’s Super Bowl (to use a more lighthearted example compared to assassination markets). Ahead of the game, there were social media posts circulating about how prediction markets were offering bets on the probability of a streaker running onto the field during the event. After doing some quick math, fans realized they could place a bet that a streaker would halt play, then attend the game and streak the field themselves, earning a profit after accounting for tickets and travel. A streaker did inevitably crash Super Bowl LX, and while it was rumored that he placed a prediction market bet or was paid an endorsement to promote a company, it appears that he was doing it for his own thrills and viral fame, indicating that prediction markets might not be necessary to incentivize such behavior.
More recently though, prediction markets made headlines when social media users called attention to the existence of a Polymarket bet on the odds that the Artemis II crewed mission to the moon would explode. The market has since been pulled offline, but it opened the door to the possibility of a saboteur being incentivized to disrupt the launch with fatal consequences.
However, perverse incentive structures always exist even without prediction markets. In the case of a Super Bowl streaker, companies could avoid shelling out for an expensive broadcast advertising slot by simply paying for someone to attend the game and capture more attention by streaking the field waving a banner. Or, as seen with the 2021 streaker, someone could have acquaintances place prop bets with offshore sports book, Bovada, to earn a payout from the act (though the bets were voided after the streaker announced this is what they were doing). It also doesn’t take much of a conspiracy to imagine how a foreign adversary would attempt to incentivize the sabotage of a US space mission. Fortunately, we have a rule of law in place to punish illegal behavior, which typically serves as the deterrent against these negative incentives.
After the moral false starts of decentralized prediction markets, regulators were quick to limit their use until they had a better understanding of their effects and mechanics. As the regulatory landscape began to incorporate the new technologies, and decentralized platforms improved their user experience and addressed the technical limitations stunting their adoption, prediction markets began to make their return. Notably, Polymarket and Kalshi, now regulated by the CFTC, were allowed to grow their US-based audience in recent months.
Yet, these prediction market platforms still face an existential threat since the public still views them as “gambling on everything”. And for the most part, this is not wrong. As with any nascent technology, the volatility of initial adoption and lack of oversight introduces the ability for speculation and manipulation by early risk-takers. In the case of decentralized technology, it is important that we look past this phase in order to discern the computer from the casino.
Still, for now, the vast majority of recent trading volume on both platforms is sports betting, and the remaining minority of bets still raise the question of whether people should be allowed to profit from gambling on overthrowing foreign regimes, exploding rockets, and the Second Coming of Jesus Christ. However, it is specifically in these scenarios, the ones involving life or death decisions, that we should leverage the most accurate information possible. Of the tools at our disposal, markets do a phenomenal job (not a perfect job) at predicting the future, drawing our attention where it matters most.
For example, after the explosion of the Space Shuttle Challenger, financial markets determined the contractor responsible for the faulty O-rings within minutes, signaling this information five months before the government investigation concluded the cause of the disaster. Within journalism specifically, financial markets predicted the declining profitability of newsrooms due to competition with internet platforms for more than five years before revenues actually started to decline. If we have the ability to access accurate forecasting for the outcomes of critical events, we should leverage it to the best of our abilities.
Prediction Markets on Aemula
Fortunately, there are better ways to benefit from the insights offered by prediction markets while avoiding many of the negative edge cases. In November of 2024, Vitalik Buterin outlined the vision for “info finance”, which utilizes prediction markets to improve informational quality and “create better implementations of social media, science, news, governance, and other fields.”
We largely agree with Vitalik’s vision of improving our information environments. However, we differentiate in our belief that “finance” is not explicitly necessary to achieve the benefits of info finance. Instead, we align more with the implementation of the Good Judgment Open, a reputational-based forecasting competition as part of the Good Judgment Project, the IARPA-funded superforecasting research initiative (we recommend the book Superforecasting, which discusses this in more detail). With Good Judgment, forecasters stake their reputations rather than their finances when casting their predictions. While the forecasters participating in the Good Judgment Open challenges do it mostly out of their love for the game, the concept of betting reputations in prediction markets solves some core issues in their implementation.
Importantly, financial markets use prices as their signal because there is a correlation between the amount of money someone can wager and their ability to make a judgement on an outcome. People who have a financial stake in a company’s financial performance are most incentivized to ensure the price of their shares are accurately reflected. People who are best at discerning the credibility of share prices in financial markets typically earn more money, and are able to place larger bets, weighting the decision making power that drives price signals into the hands of the experts. The key mechanic is that the more conviction someone has that they are right, the bigger a bet they are willing to place. Additionally, the size of the bet they consider to be big is relative to the amount of financial wealth they control, which should theoretically be proportional to their historical financial success.
However, in information markets, the amount of capital you have access to is not necessarily correlated with your ability to make a judgement on a specific outcome. The type of participant we want to incentivize into the market is someone who has unique insight into a specific event. Prediction markets, when adopted at scale, should be wide and shallow, meaning there will be a large number of events to wager on, with each event having relatively low trading volumes. You do not want deep-pocketed generalists coming in to manipulate low-volume markets, but you want individuals holding niche expertise providing high-signal bets on potential outcomes. Currently, in this type of prediction market, financial capital is an external resource that is not correlated to one’s success in the prediction market as a whole.
If we wish to reconcile this misalignment, we must rely on internal resources to inform the weights we assign to people’s influence in the market. With Aemula, we rely on reputation. Journalists and readers earn their reputation as they build a track-record of providing high-signal insight into the quality of information circulating on the platform.
If you consistently publish credible reporting that is supported by diverse user groups, you will have a high reputation as a journalist. As a reader, if you consistently support high-quality articles, disagree with poorly reported articles, and report harmful content, you will build a strong reputation as a reader. These interactions do not require anyone to make additional financial decisions or place bets, but they continuously refine and update user reputations, which the community can then use to determine what information is worth paying attention to on any given topic.
While wagering reputations is similar to the Good Judgment Open, Aemula does provide financial incentive to increase your reputation. Having a higher reputation as a journalist means that your articles are more likely to be widely circulated and supported, earning you bigger payouts from our community subscription pool for your work. Similarly, high-reputation readers earn a higher weighting in moderation decisions on the platform, providing the opportunity to earn larger payouts from moderation bounties. The key is that the weight of the financial incentives is not tied to your preexisting access to financial capital, but is instead weighted based on the reputation you have built from consistently upholding the values the Aemula community is aiming to promote on the platform.
We are intensely focused on preserving freedom of speech and individual liberties. We believe all journalism should be conducted independently. And we believe that independent journalists should have access to the best tools possible to conduct high-quality reporting. Decentralized prediction markets are a powerful tool to support this mission, as they provide accurate insights derived from individual decision making.
To begin implementing prediction market features into Aemula, we have released a trending topic monitor in the Aemula Newsroom, integrating live market data from Kalshi to show which topics are currently capturing the most attention and which topics are experiencing the most volatility as they develop. These metrics signal a market’s need for more information through better reporting. Note that we are simply using public data and have no partnership with Kalshi, but we aim to make this information easily accessible to journalists within their workflow on Aemula so they can best determine where to focus their efforts.
We are committed to aligning incentives across our media environment, and we will leverage powerful tools to support high-quality independent journalism to the full extent we are able to do so without compromising that alignment. We are realists in our understanding that prediction markets are speculative betting platforms that are still working out the issues of their early form. However, with the correct implementation, we believe they are capable of powering our collective mission to rebuild trust and reverse polarization in media by providing accurate insights into the state of our consensus shared reality.
This week, we highlight writers discussing prediction markets and their impact on social platforms and world events. We encourage you to explore their work and consider subscribing directly.
Feed Me
Written by Emily Sundberg, a New York–based writer and director who draws on her experience across media, brand strategy, and cultural reporting for outlets including New York Magazine, GQ, ELLE, and The New York Times, previously featured in our spotlight, “Forecasting”.
“Chris Best, the CEO of Substack, is ‘hyped’ about the platform’s new partnership with Polymarket. It includes updates to Polymarket’s embedding feature, which has been available since 2024. Polymarket is also part of Substack’s sponsorships pilot, so you’ll see ads from them in more newsletters this year. Substack’s announcement letter demoed the feature via shit you should care about and a Polymarket wager about Harry Styles, which tells you this isn’t just about new revenue streams for Substack; it’s about Polymarket positioning itself alongside cultural conversations. I’m guessing we’ll see more female-focused marketing from Polymarket in 2026. I get concerned when people speak about Substack as if it’s a sorority or a community — it is a venture-backed tech company. Here’s how some writers have responded to this news”
Pantsuit Politics
Written by Sarah Stewart Holland and Beth Silvers, cohosts of the independent podcast Pantsuit Politics, whose decade-long conversations and bestselling books focus on making political news more manageable, thoughtful, and grounded in healthier civic dialogue.
“‘The prediction market is partnering with an AI engine that tracks social media data to create markets about cultural relevance’ is a sentence that makes me want to join a nice off-the-grid commune.
As much as I don’t like any of this, and I very much do not, it’s important to understand it. So, here’s a short overview of prediction markets and what you need to know as they gobble up ever more money and attention.”
Astral Codex Ten
Written by Scott Alexander, whose writing first came to prominence through the blog Slate Star Codex, where Meditations on Moloch became an often referenced piece in Ethereum subculture, previously featured in our spotlight, “Information Diets”.
“Prediction markets have two good qualities: in ideal situations, they are accurate and canonical.
By accurate, I mean that that over the long run, they will be at least as accurate as any other source of information.
By canonical, I mean that they short-circuit discussion of ‘which expert should we trust?’ or ‘how do we know which sources are biased?’ All prediction markets speak with a single unified voice, that voice will always be at least as trustworthy as any individual expert, and it cannot be biased. If you’re not sure which of many competing experts (or supposed experts) to trust, you should always trust a prediction market instead of any of them. And the same is true of people on the opposite side of the political spectrum who doubt all the sources you trust and vice versa.”
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