Predicting the market doesn't predict the truth; it only rewards those who guessed correctly.

Title: Truth Comes Later

Author: Thejaswini M A

Source:

Repost: Mars Finance

Every time prediction markets become controversial, we tend to circle around the same question repeatedly, but never truly face it:

Are prediction markets really about the truth?

Not accuracy, not practicality, nor whether they outperform polls, journalists, or social media trends. It’s about the truth itself.

Prediction markets price events that have not yet occurred. They are not reporting facts but allocating probabilities to a future that remains open, uncertain, and unknowable. At some point, we began treating these probabilities as a form of truth.

For most of the past year, prediction markets have been immersed in their victory parade.

They have beaten polls, cable news, and experts holding PhDs and PowerPoint presentations. During the 2024 US election cycle, platforms like Polymarket reflect reality at a speed almost faster than all mainstream prediction tools. This success has gradually solidified into a narrative: prediction markets are not only accurate but superior—a purer way to aggregate truth, a more authentic signal reflecting people’s beliefs.

Then, January arrived.

A new account appeared on Polymarket, betting about $30,000 that Venezuelan President Nicolás Maduro would be ousted before the end of the month. At the time, the market considered this outcome highly unlikely—single-digit probability. It looked like a bad trade.

A few hours later, U.S. forces arrested Maduro and charged him in New York. The account closed, making over $400,000 in profit.

The market was right.

And that is precisely the problem.

People often tell a comforting story about prediction markets:

Markets aggregate dispersed information. People with different viewpoints support their beliefs with money. As evidence accumulates, prices change. The crowd gradually approaches the truth.

This story assumes an important premise: that the information entering the market is public, noisy, and probabilistic—such as tightening polls, candidate missteps, storm shifts, or company earnings falling short.

But Maduro’s trade was not like that. It was less like reasoning and more like precise timing.

At this moment, prediction markets no longer seem like clever forecasting tools but appear as something else: a place where proximity matters more than insight, channels matter more than interpretation.

If markets are accurate because someone holds information about the world that others do not and cannot know, then markets are not discovering the truth but monetizing asymmetric information.

This distinction is far more significant than the industry is willing to admit.

Accuracy might serve as a warning. When critics challenge prediction markets, supporters often repeat the same line: if insiders trade, markets will react earlier, helping others. Insider trading accelerates the revelation of truth.

This argument sounds clear in theory, but in practice, its logic often collapses.

If a market becomes accurate because it contains leaks of military operations, classified intelligence, or government internal schedules, then it is no longer an information market at any publicly meaningful level. It becomes a shadow arena for secret trading. There is an essential difference between rewarding better analysis and rewarding proximity to power. Markets that blur this line will inevitably attract regulatory scrutiny—not because they are inaccurate, but because they are excessively precise in the wrong way.

“They make over $1 million daily on the Maduro event. I’ve seen this pattern too many times—undeniably: insiders always win. Polymarket just makes it easier, faster, and more conspicuous. Wallet 0x31a5 turned $34,000 into $410,000 in three hours.”

What makes the Maduro incident unsettling is not just the scale of returns but the context of these market eruptions.

Prediction markets have evolved from fringe novelties into an independent financial ecosystem that Wall Street takes seriously. According to a Bloomberg Markets survey last December, traditional traders and financial institutions see prediction markets as durable financial products, even as they acknowledge these platforms expose the blurred line between gambling and investing.

Trading volume has surged. Platforms like Kalshi and Polymarket now handle tens of billions of dollars in nominal annual trading—Kalshi alone processed nearly $24 billion in 2025. With political and sports contracts attracting unprecedented liquidity, daily trading records are continually broken.

Despite regulatory scrutiny, daily trading activity in prediction markets still hits record highs—around $700 million. Regulated platforms like Kalshi dominate trading volume, while native crypto platforms maintain cultural centrality. New terminals, aggregators, and analytical tools emerge weekly.

This growth has also attracted the interest of heavyweight financial capital. The New York Stock Exchange’s owner has pledged up to $2 billion in strategic trading with Polymarket, valuing it at about $9 billion, signaling Wall Street’s belief that these markets can compete with traditional trading venues.

However, this boom is colliding with regulatory and ethical ambiguities. After Polymarket was banned early on for operating without registration and paid a $1.4 million CFTC fine, it only recently regained conditional approval in the US. Meanwhile, legislators like Ritchie Torres have introduced bills to prohibit insiders from trading after the Maduro event, arguing that the timing of these bets looks more like front-running than informed speculation.

Yet, despite legal, political, and reputational pressures, participation remains high. In fact, prediction markets are expanding from sports betting into more areas like corporate earnings indicators, with traditional gambling firms and hedge fund sectors now deploying experts for arbitrage and low-efficiency pricing.

All these developments suggest prediction markets are no longer fringe. They are deepening ties with financial infrastructure, attracting professional capital, and prompting new legislation, while their core mechanism remains fundamentally about betting on an uncertain future.

A warning often overlooked: the Zelensky Suit Incident

If the Maduro event exposed issues with insiders, the Zelensky suit market reveals deeper problems.

In mid-2025, Polymarket launched a market betting whether Ukrainian President Volodymyr Zelensky would wear a suit before July. It attracted huge trading volume—hundreds of millions of dollars. It seemed like a joke market but evolved into a governance crisis.

Zelensky appeared in a black jacket and trousers designed by a well-known men’s fashion designer. Media called it a suit; fashion experts called it a suit. Anyone with eyes could see what was happening.

But the oracle vote determined: not a suit.

Why?

Because a few large token holders bet huge sums on the opposite outcome, and they held enough voting power to push through a resolution favorable to themselves. The cost of bribing the oracle was even lower than the potential payout they could receive.

This is not a failure of decentralization but a failure of incentive design. The system operates strictly according to preset rules—human-led oracles whose honesty depends entirely on the “cost of lying.” In this case, lying is clearly more profitable.

It’s easy to see these incidents as extreme cases, growing pains, or temporary glitches on the way to a better prediction system. But I believe that’s a mistake. These are not accidents but the inevitable result of three combined factors: financial incentives, ambiguous rule statements, and imperfect governance mechanisms.

Prediction markets do not discover the truth; they merely reach a settlement.

What matters is not what most people believe but what the system ultimately recognizes as valid results. This recognition process often occurs at the intersection of semantic interpretation, power struggles, and capital battles. When huge interests are involved, this intersection quickly fills with competing forces.

Once you understand this, such controversies no longer seem surprising.

Regulation does not come out of nowhere

Legislative responses to the Maduro trading are foreseeable. A bill currently advancing in Congress would prohibit federal officials and employees from trading in political prediction markets when they possess material nonpublic information. It’s not radical—just basic rules.

Stock markets understood this decades ago. Government officials should not profit from privileged access to state power—this is uncontroversial. Prediction markets are only now realizing this because they’ve been pretending to be something else.

I think we’ve overcomplicated this.

Prediction markets are places where people bet on outcomes that have not yet happened. If events develop in the direction they bet on, they make money; if not, they lose. Everything else is commentary afterward.

They won’t become something else just because the interface is more streamlined or odds are expressed as probabilities. They won’t become more serious just because they run on blockchain or economists find the data interesting.

What matters is incentives. You are rewarded not because you have insight but because you correctly predict what will happen next.

I believe it’s unnecessary to elevate this activity to a higher moral plane. Calling it prediction or information discovery doesn’t change the risks you take or why you take them.

In some ways, we are reluctant to admit: people just want to bet on the future.

Yes, they do. That’s fine.

But we shouldn’t pretend it’s something else.

The growth of prediction markets fundamentally stems from people’s demand to bet on “narratives”—whether about elections, wars, cultural events, or reality itself. This demand is real and persistent.

Institutions use them to hedge uncertainty, retail traders to express beliefs or entertain, media to gauge trends. None of this needs to be dressed up.

In fact, this veneer creates friction.

When platforms present themselves as “truth machines” and claim moral high ground, every controversy becomes a life-or-death crisis. When markets settle in unsettling ways, events are elevated into philosophical dilemmas rather than their core—high-risk betting products with disputes over settlement methods.

Displaced expectations stem from the dishonesty of the narrative itself.

I am not opposed to prediction markets.

They are one of the more honest ways humans express beliefs amid uncertainty, often surfacing unsettling signals faster than polls. They will continue to grow.

But to romanticize them as something nobler is irresponsible. They are not epistemological engines but financial instruments linked to future events. Recognizing this distinction can make them healthier—more transparent regulation, clearer ethics, and better design will follow.

Once you admit you are operating a betting product, you will no longer be surprised by betting behaviors emerging within it.