predictionmarkets

Prediction markets are slowly shedding their image as speculative betting platforms. According to Gianluca P., founder of the prediction market platform Predik, the real direction of the industry isn’t gambling at all—it’s becoming core infrastructure for information and automated trading.

Instead of competing on flashy features or rapid geographic expansion, newer platforms are focusing on fundamentals: liquidity design, market standardization, and machine-driven participation.

Gianluca argues that the early surge of crypto-native prediction markets came down to one simple factor: access.

Wallet-based onboarding removed nearly every friction point found in traditional betting and financial platforms. No long approval processes, no geographic restrictions—users could participate instantly. That structural simplicity is why crypto-first platforms scaled globally far faster than compliance-heavy alternatives like Kalshi.

“Polymarket proved that if you go crypto-first, you can reach almost the entire planet,” Gianluca said, pointing to wallets as a major advantage over identity-gated systems.

Liquidity Fragmentation Is Still the Biggest Structural Problem

Despite growing interest, Gianluca is clear about the industry’s main weakness: fragmented liquidity.

Each major platform operates its own isolated pools of capital. The result is shallow markets, inefficient price discovery, and probabilities that fail to converge into a single, reliable signal.

While regulation tends to dominate public discussion, Gianluca believes liquidity design matters far more in practice. Without depth and recurring flow, even well-designed markets only come alive during major events like elections or high-profile political outcomes.

AI Agents Could Fundamentally Change How These Markets Work

Looking ahead, Gianluca doesn’t expect the next growth phase to come from retail users, but from automation.

He sees AI agents becoming active participants—trading continuously, arbitraging price differences, and providing liquidity across platforms.

“If you let agents interact directly with markets through APIs, the scale of volume changes completely,” he said.

At that point, prediction markets stop being consumer products and start functioning as programmable probability layers—inputs that other systems can consume, similar to price feeds or risk signals in traditional finance.

Standards and Resolution Rules Will Decide Long-Term Trust

As volume and automation increase, vague market questions and unclear settlement rules become unacceptable.

Gianluca emphasized that poorly defined outcomes and inconsistent resolution processes are one of the fastest ways to destroy trust, especially when disputes arise.

For prediction markets to mature into credible forecasting tools—rather than event-driven trading venues—they will need shared standards: clear resolution criteria, consistent time definitions, and transparent dispute mechanisms.

Without that foundation, higher volume won’t improve accuracy. It will only amplify conflict.
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