@prlnet is interesting because it flips the usual mining logic. In Bitcoin, mining cost helps determine price. In Pearl, price determines whether useful compute even bothers to join the network. The long-term thesis is simple: A GPU already running inference earns real revenue. If Pearl adds PRL rewards on top with minimal overhead, the same GPU cycle can generate two income streams: - Inference fees - PRL emissions At that point, miners are no longer competing with AI workloads. They are the AI workloads. This creates a different flywheel: Higher PRL price → more inference providers integrate → more useful compute joins the network → stronger organic demand for PRL. Lower PRL price → providers leave → the network falls back to ordinary GPU mining. That’s why the key metric isn’t raw hashrate. It’s the percentage of hashrate coming from real AI workloads rather than dedicated miners. The entire Pearl thesis comes down to one question: Can useful compute grow faster than mining sell pressure?