Conclusion

Prediction markets were created to surface collective intelligence and improve decision-making under uncertainty. Over time, however, most implementations drifted toward gambling-based designs that prioritize speculation, liquidity, and volume over accuracy, accessibility, and long-term value.

These design choices introduced structural limitations. Gambling incentives distorted signal, liquidity dependency constrained scalability, regulatory uncertainty limited adoption, and extractive business models aligned platform success with user losses. As a result, prediction markets struggled to evolve beyond niche use cases and headline events.

Hilo represents an attempt to rebuild prediction markets from first principles.

By removing gambling mechanics and financial risk, Hilo enables participation based on signal contribution rather than capital exposure. By eliminating liquidity dependency, the platform scales with users and data instead of money. By focusing on structured, validated data, Hilo transforms prediction markets from trading venues into reusable forecasting infrastructure.

This approach allows Hilo to support long-tail, local, and specialized markets across countries, cities, industries, businesses, and sectors. It enables the generation of data that is difficult to obtain through traditional markets, polling, or analytics, and that compounds in value over time.

Hilo is built on the belief that creating a healthier system and building a profitable business are not opposing goals. By aligning incentives around useful signal and high-quality data, the platform is designed to generate real value for users, institutions, and partners while sustaining itself through scalable, non-extractive revenue models.

In doing so, Hilo seeks to demonstrate that prediction markets do not need to rely on gambling, liquidity, or speculation to be effective. Instead, they can function as durable infrastructure for collective intelligence, capable of supporting better decisions in an increasingly uncertain world.