Venice ($VVV): The Bubble's Mirror

Nikshep argues Venice's economic structure inverts the AI incumbents' cap-table trap. While OpenAI projects $85B in losses by 2028 and needs $200–280B annual revenue by 2030, open-source models (GLM-5.1, Kimi K2.6, DeepSeek V4-Pro) now match frontier performance at 5-15x cheaper pricing for the 80% of workloads already saturated to "good enough." Venice's zero training costs, token burn mechanics (42% of genesis supply destroyed), and agent-native architecture position it to capture inference demand that agents structurally cannot route through surveillance-based labs requiring KYC, with the agent economy projected at $3-5 trillion by 2030.