Prediction markets are beginning to move beyond their roots as speculative betting platforms and toward becoming core infrastructure for information and automated trading, according to Gianluca P., founder of prediction market platform Predik.
Rather than competing on novelty or regional expansion, newer platforms are increasingly focused on liquidity design, standardization, and machine-driven participation.
Gianluca argued that the early growth of crypto-native prediction markets was driven largely by accessibility.
Wallet-based onboarding removed many of the barriers found in traditional betting or financial platforms, allowing users to participate immediately without lengthy approval processes.
That ease of access, he said, explains why crypto-based models scaled globally faster than regulated alternatives, even as platforms like Kalshi pursued a compliance-first approach.
“Polymarket showed that if you go crypto first, you can tap almost the entire planet,” Gianluca said, pointing to wallet access as a structural advantage over identity-gated platforms.
Liquidity Fragmentation Remains The Core Constraint
Despite rising interest, Gianluca identified liquidity fragmentation as the industry’s most persistent structural weakness.
Major platforms operate with isolated pools of capital, limiting the efficiency of price discovery and preventing markets from converging on a single, reliable probability signal.
He noted that while regulation often dominates public debate, liquidity design matters more in practice. Without sufficient depth and recurring participation, even well-designed markets struggle to maintain relevance outside of major events such as elections or headline political outcomes.
AI Agents May Redefine Market Participation
The most forward-looking shift, Gianluca suggested, will come from automation rather than retail growth.
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He expects AI agents to become increasingly active participants in prediction markets, executing trades, arbitraging price discrepancies, and providing continuous liquidity across platforms.
“If you allow agents to interact directly with markets through APIs, you unlock an entirely different scale of volume,” he said.
This transition would change the role of prediction markets from consumer-facing products into programmable probability layers that other systems can consume, similar to price feeds or risk signals in traditional finance.
Standards And Resolution Will Determine Long-Term Trust
As markets scale and automation increases, ambiguity around market questions and resolution criteria becomes harder to tolerate.
Gianluca emphasized that poorly defined outcomes undermine confidence, particularly when disputes arise over settlement.
Clear resolution rules, consistent time standards, and transparent dispute mechanisms, he argued, will be necessary for prediction markets to mature into credible forecasting tools rather than episodic trading venues.
Without that foundation, increased volume risks amplifying disputes rather than improving accuracy.
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