Institutions cannot expose their trading strategies or customer data on public blockchains. They view privacy not as a feature but as a 'non-negotiable' prerequisite. Until scalable, compliant privacy technologies are widely available, deep institutional engagement with DeFi will remain limited.

The CFTC views informational advantages in prediction markets, like knowing about a secret Super Bowl ad, as a form of insider trading. The agency confirms it has legal authority under its anti-fraud rule, similar to the SEC's, to surveil markets and prosecute such cases, extending the doctrine beyond traditional corporate securities.

The case of a trader profiting from advance knowledge of an event highlights a core dilemma in prediction markets. While insider trading undermines fairness for most participants, it also improves the market's primary function—to accurately forecast the future—by pricing in privileged information.

Prediction markets like Polymarket operate in a regulatory gray area where traditional insider trading laws don't apply. This creates a loophole for employees to monetize confidential information (e.g., product release dates) through bets, effectively leaking corporate secrets and creating a new espionage risk for companies.

Prediction markets thrive on information asymmetry, mirroring the stock market before 2000's Regulation FD, when selective disclosure was common. This structure means 'sharps' with privileged information will consistently profit from 'squares' (the public), making it difficult for casual participants.

A more significant danger than insider trading is that individuals in power could actively manipulate real-world outcomes to ensure their bets on a prediction market pay out. This moves beyond leveraging information to actively corrupting decision-making for financial gain, akin to throwing a game in sports.

Unlike securities, there's a debate where some argue insider trading enhances prediction market accuracy, fulfilling their core purpose. This philosophical schism complicates regulation, as the "harm" is unclear, leaving platforms to self-police a practice some users actively defend as beneficial.

New legislation aims to ban government insiders from trading on prediction markets. However, the true edge isn't direct insider knowledge but "adjacent information"—piecing together public signals and cocktail party chatter. This mosaic-theory approach remains legal and is the core mechanism that makes these markets predictive.

Extreme conviction in prediction markets may not be just speculation. It could signal bets being placed by insiders with proprietary knowledge, such as developers working on AI models or administrators of the leaderboards themselves. This makes these markets a potential source of leaked alpha on who is truly ahead.