This is a fantastic breakdown really gets at the core tension of prediction markets: extracting meaningful information vs. sustaining the platform. The low friction revenue ideas like real time market closures, rounding bias, and arbitrage optimization are elegant because they preserve user trust and signal integrity. I especially like the point about late stage trades markets get most informative when stakes are highest.

Sponsorships feel like a huge untapped lever let people fund questions they want answered, without corrupting outcomes. It’s a subtle but powerful way to align incentives. Also, charging for market creation could work similarly to Ordinals inscriptions: a small cost to deter spam while allowing serious actors to surface the questions that matter most.

The Lightning-native angles are smart too routing fees and treasury strategy turn infrastructure overhead into upside. If volume and node connectivity are optimized, those sat trickles could add up fast.

Overall, this is exactly the kind of thinking we need to make Lightning native prediction markets viable long-term without degrading their epistemic value. Curious what do you think the biggest risk to this model is: low liquidity, regulatory pressure, or something else?