Prediction markets have moved from niche tools to mainstream attention, but their rise has brought a new level of regulatory and ethical pressure. After earning recognition for accurately forecasting major public events, the sector is now facing criticism over alleged insider trading, manipulation risks, and the moral hazards created by certain categories of contracts. Amit Mahensaria, co-founder and CEO of peer-to-peer sports prediction exchange Pred, argues that the long-term viability of the industry will depend on whether platforms build what he calls strong “integrity infrastructure.”
According to Mahensaria, any platform aiming for longevity should not wait for regulators to force change. Instead, it should proactively establish surveillance systems, clear settlement standards, manipulation detection tools, and transparent reporting practices. In his view, these measures are no longer optional for operators that want to be taken seriously as durable financial-information platforms rather than speculative novelties.
From Forecasting Accuracy to Political and Ethical Scrutiny
The latest debate comes after prediction markets gained broader recognition following their near-spot-on forecasting of Donald Trump’s 2024 U.S. presidential victory. That public success strengthened the argument that these platforms can aggregate dispersed information more effectively than many traditional polling or forecasting methods. Supporters say prediction markets transform beliefs, incentives, and information into prices that can offer useful signals about future events.
Yet mainstream visibility has also amplified criticism. Regulators and governments in multiple jurisdictions have moved toward enforcement, including action against operators and restrictions on specific high-stakes contracts. In the United States, lawmakers recently introduced legislation that would bar contracts tied to death and war, reflecting growing discomfort with markets built around deeply sensitive real-world outcomes.
This has created a familiar tension seen in other emerging technologies: how to preserve room for innovation while preventing abuse. Proponents of prediction markets warn that heavy-handed regulation at an early stage could choke off a potentially valuable mechanism for information discovery. Critics, by contrast, argue that without stronger oversight, the sector risks normalizing harmful incentives and opening the door to misconduct that could damage both participants and the public interest.
Why Self-Regulation Matters — and Why It Is Not Enough
Mahensaria agrees with the industry camp that self-regulation is essential. He described it as a “must-have,” not a luxury. In practical terms, self-regulation means creating internal controls before crises emerge: monitoring trading behavior, setting transparent rules for how markets resolve, detecting attempts at manipulation, and disclosing processes in ways users can evaluate.
At the same time, he does not believe self-regulation alone can carry the full burden. His warning is rooted in history. Industries that are left entirely to police themselves often do not discover or enforce strong principles until after a scandal forces the issue. Mahensaria pointed to recurring patterns seen in financial markets, aviation, and pharmaceuticals, where major failures often became the catalyst for more serious governance.
For that reason, he advocates what he calls “proportionate regulation”. Rather than imposing blanket restrictions that erase the structural benefits of prediction markets, regulators should focus on baseline standards that address the most critical sources of risk. In his framework, the priority areas are settlement integrity, transparency regarding counterparties, and anti-manipulation safeguards. The goal is not to replace market innovation with bureaucracy, but to create a minimum trust layer that allows the sector to mature responsibly.
Not All Markets Carry the Same Level of Risk
A central part of Mahensaria’s argument is that prediction markets should not be treated as a single undifferentiated category. Some contracts, he suggests, are structurally easier to govern than others. Blockchain-based markets tied to verifiable outcomes with natural timelines — such as many sports-related events — may face fewer disputes over resolution and fewer ethical objections than markets based on politics, geopolitical crises, or events where outcomes are subjective or vulnerable to influence.
That distinction matters because some categories raise deeper concerns than whether they can technically be settled. Mahensaria said markets on assassinations, wars, or political crises pose real ethical questions that the industry should not dismiss as mere squeamishness. The issue is not only whether these contracts can be resolved accurately. It is also whether they create perverse incentives and whether the information they produce is worth the moral cost of the mechanism generating it.
On the question of who should decide what gets listed, he favors a hybrid approach. Platforms should exercise judgment and explain their reasoning publicly, while regulators should define clear boundaries around categories that are obviously harmful. That combination, in his view, would preserve operational flexibility without leaving curation entirely to private incentives.
AI Surveillance Is Emerging as a Key Tool
As scrutiny intensifies, industry participants are increasingly looking to artificial intelligence to strengthen market monitoring. The article notes that Polymarket recently partnered with Palantir Technologies and TWG AI to develop an AI-driven monitoring system for sports prediction trading. The partnership reflects a broader belief that advanced analytics can help the sector catch suspicious activity earlier and more systematically than manual review alone.
Mahensaria believes AI has a legitimate role to play here, especially in pattern recognition across large datasets. Its core utility, he said, is the ability to identify trading behavior that deviates from expected models in ways that may correlate with insider knowledge or coordinated manipulation. In a market environment where suspicious signals can be subtle and distributed across many accounts or transactions, that analytical capacity could become increasingly important.
Still, he emphasized that surveillance systems must be designed carefully. One of the major risks of aggressive automated monitoring is the possibility of false positives — cases where highly skilled or unusually successful traders are flagged simply because their performance stands out. Mahensaria argued that prediction markets should avoid replicating what he sees as a negative pattern in traditional sports trading, where winners are often punished through account restrictions or reduced limits.
In his view, AI alerts should never trigger automatic penalties on their own. They must be followed by human review and contextual analysis. That distinction is crucial because the point of prediction markets is to reward sharp analysis, not to penalize it. If surveillance becomes too blunt, it could discourage exactly the kind of informed participation that gives these markets their forecasting power.
Why On-Chain Records May Offer an Advantage
Mahensaria also highlighted what he sees as a structural benefit of blockchain-based prediction markets: transparency. On-chain systems produce a public, immutable record of every trade, creating a richer and more auditable dataset than many traditional off-chain environments. For AI-driven monitoring, that matters a great deal. The better the data, the better the ability to identify unusual conduct, trace patterns, and review contested activity after the fact.
He argued that the combination of on-chain transparency and AI-powered analysis could yield a stronger integrity framework than what exists in much of the conventional sports trading world today. Rather than treating blockchain merely as a settlement rail, this view positions the ledger itself as part of the compliance and trust architecture.
That said, even stronger monitoring tools do not eliminate the need for thoughtful market design. Mahensaria suggested that one of the best defenses against insider trading may be refusing to list markets that are especially vulnerable to manipulation in the first place. In other words, surveillance should not be the only solution; product design, listing standards, and category selection all form part of the same integrity system.
The Real Debate Is About Balance
The broader message from the discussion is that the future of prediction markets will likely depend on balance rather than ideological extremes. A purely laissez-faire model risks scandal, public backlash, and eventual overcorrection. An overly restrictive regulatory model, on the other hand, could suppress a tool that many believe offers genuine value in aggregating information and improving forecasts.
Mahensaria’s position attempts to bridge those poles. Self-regulation, in his telling, is necessary because platforms understand their own mechanics best and can move faster than lawmakers. Government oversight is also necessary because history shows that markets do not always correct themselves before harm occurs. The answer, then, is not regulation versus innovation, but a calibrated system in which baseline external rules support internally built integrity measures.
For the prediction market industry, the stakes are now larger than platform growth alone. If operators cannot convince users, lawmakers, and the broader public that they can manage manipulation risks and ethical boundaries responsibly, their reputational progress may prove fragile. But if they can combine transparent infrastructure, disciplined listing policies, effective monitoring, and proportionate oversight, they may strengthen the case that prediction markets deserve a lasting place in the digital financial landscape.