$500B AI Bill Is Getting Challenged 🔥 $TAO rewired how the market values decentralized AI intelligence by proving distributed networks can compete with centralized compute at scale. $0G is extending that argument into a specific cost claim that's becoming harder to ignore. Centralized AI hyperscalers are tracking toward over $500 billion in combined infrastructure CAPEX this year. That capital builds compute, power capacity, and data centers, all to run AI systems that users cannot inspect or verify. You send a prompt, get a response, and trust the proxy entirely. For most consumer applications, that tradeoff is manageable. For AI agents handling financial workflows, sensitive decisions, or persistent memory operations, centralized opacity becomes a structural risk as these systems scale. 0G founder Michael Heinrich spoke on the NYSE floor this week about where AI infrastructure spending is headed. His argument was direct. Centralized AI is the most expensive way humanity has ever built intelligence, and it charges that cost to the power grid. 0G runs on a different model. Inference is encrypted and verifiable onchain. Every output can be cryptographically attested at the hardware level. And 0G claims the cost of running inference on its network comes in at roughly 95% lower than centralized providers on comparable models. That cost gap matters more than most people realize. Once AI agents start choosing their own compute providers, they will route toward the cheapest verifiable execution layer. The cost advantage doesn't need a narrative push to drive adoption at that point. Aristotle Mainnet has been live since September 2025. The 0G App is shipping with sealed hardware inference today. At 300+ ecosystem partners and a $100M annualized revenue target, the pipeline is already building toward scale. That's the thesis I've been watching build all year. #AI Agents 🤖# #AI