Who Gets to Participate in the Prediction Economy? The Access Gap That No-Wager Platforms Can Close

The prediction economy has a participation problem that is rarely discussed in the policy literature focused on its harms. Almost all of the regulatory attention to date, and most of the journalism, has concentrated on who is harmed by prediction markets. Very little attention has been paid to who is systematically excluded from them, and why that exclusion itself constitutes a policy concern worth taking seriously.

The exclusions are structural, not incidental, and they affect precisely the people whose participation would be most valuable.

Begin with professional exclusion. A large proportion of the people best equipped to forecast financial markets accurately, trained economists, equity analysts, derivatives traders, central bank economists, commodities specialists, and macro strategists, are barred by their employers' compliance rules from participating personally in the markets they analyze for a living. These rules exist for good reasons, primarily to prevent conflicts of interest and information misuse, and they are not going away. For these professionals, wager-based prediction markets are simply off-limits. Their employer's policies say no, and the compliance infrastructure that enforces those policies does not distinguish between a Kalshi contract and a stock bet.

SafeBets falls into a different category. Because its users make no deposit and place no wager, the platform does not involve financial speculation in the sense that compliance rules typically target.

A professional economist who cannot hold a personal equity position may have no compliance barrier to demonstrating their market judgment on a no-risk prediction platform. The result is that SafeBets can access a pool of analytical talent that the wager-based industry has never been able to reach, not because those people lack interest in prediction, but because they lack the legal ability to participate in the existing products.

The second exclusion is geographic. Polymarket and Kalshi are unavailable to users in France, Germany, Spain, Portugal, Hungary, Belgium, the Netherlands, Romania, Switzerland, Poland, and an expanding list of other jurisdictions where wager-based prediction markets have been blocked. The combined population of those jurisdictions runs to hundreds of millions of people. Many of them are highly educated, financially literate, and interested in global markets. They have simply been locked out of the fastest-growing forecasting platform category in history because of how those platforms are designed.

This exclusion is not a regulatory error. It reflects a coherent policy judgment that consumer wagering products should be regulated, and that unlicensed wagering platforms should not operate freely in markets with established consumer protection regimes. The policy judgment is defensible. What it produces is a large population of would-be forecasters with no legal outlet for their market views.

SafeBets was designed for exactly this population. Because it requires no wager, it does not trigger the gambling classifications that have closed European markets to wager-based competitors. Users in Paris, Berlin, Madrid, and Amsterdam can participate on equal terms with users in New York and Chicago, judged only on the accuracy of their calls.

The third exclusion is economic. Wager-based prediction markets require capital. To participate meaningfully, a user must deposit funds they are prepared to lose. This requirement has the effect of making prediction market participation a wealthy person's activity, or at least a person-with-disposable-capital's activity. The people who have the sharpest analytical views on specific markets often have the least capital to stake: junior researchers, graduate students, economists in emerging market countries, independent analysts who have spent years developing expertise in a niche that does not pay Wall Street salaries.

The no-wager design eliminates this barrier entirely. The 100 free unicoins every SafeBets user receives at signup are the same starting capital whether the user is a hedge fund manager in Greenwich or a commodity analyst in Nairobi. The platform's ranking system then does what markets are supposed to do: reveal who is actually right, regardless of their ability to back that view with money.

This last point has specific implications for emerging market forecasters that deserve more attention than they typically receive. Some of the most valuable market intelligence in the global economy is locally embedded knowledge that no centralized research team can replicate: an oil analyst in Lagos tracking West African infrastructure, a currency economist in Nairobi observing East African monetary dynamics, a trade specialist in Jakarta monitoring Southeast Asian supply chain shifts. This knowledge exists. It is genuinely valuable. It is economically inaccessible to the institutions that would benefit most from it, because the people who possess it are not in the cities where those institutions operate, do not hold the credentials those institutions require, and cannot stake the capital those institutions' platforms demand.

SafeBets creates the infrastructure to identify and compensate that distributed intelligence for the first time. The platform's Collective Intelligence algorithm does not care where a forecaster is located, what credentials they hold, or how much capital they can stake. It cares whether their predictions are accurate. That is a genuinely different premise from anything currently operating at scale in financial services, and it is a premise with significant implications for who gets to participate in the global prediction economy and who benefits from it.

The policy question the prediction industry has almost entirely neglected is not just how to protect consumers from its harms, though that question is important and deserves the attention it has received. It is also how to structure the market so that its benefits, the earnings potential, the reputational rewards, and the economic value of accurate forecasting, are accessible to the broadest possible range of people rather than confined to those with capital, credentials, and geography on their side.

No-wager prediction platforms are not a perfect answer to that question. But they are the first answer the industry has produced that takes the access gap seriously at the level of product architecture, not just policy rhetoric.

This article is provided for informational and educational purposes and does not constitute legal advice or regulatory guidance.