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Big AI companies are doing 10 trillion tokens a day of inference according to Sam Altman. We expose the blitz scaling playbook they're all following to grab maximum market share. OpenAI has roughly 100 million paying customers and 600 million free users because they want to be a verb, the AI on everyone's tongue. They're willing to lose tons of money just to get that mind share. College students and professors now say "chat GPT" when they mean AI, and it's working. Sam Altman admitted they don't give you the highest intelligence available. They give you the least intelligence they can get away with that will keep you subscribed without quitting. The GPT-5 autorouter was marketed as simplifying consumer experience, but it's actually a way to give you the dumbest model possible at any given point. Maybe it's based on your query complexity or maybe just their available capacity at that moment. They're not transparent and reserve the right to give you different experiences based on their internal state. If you're getting legal advice and they swap in a compressed or completely different architecture like nano or mini, that advice might not hold up in court. When you're making business decisions or assessing new markets with a trusted AI advisor, are you actually getting the intelligence you paid for? -------------------------------------------------------------------------------------------------------------------------- About Ambient.xyz: Ambient is an SVM-compatible Proof of Work Layer 1 that pairs Solana-style execution with a new consensus mechanism called Proof of Logits (PoL). The goal is simple but non-negotiable: deliver fast, cheap, verified AI inference at scale, and do it in a way developers can integrate into on-chain, cross-chain, and web2 applications. Ambient’s design centers on one large network model (600B+ parameters) and its fine-tunes, so miners optimize for utilization instead of wasting cycles across fragmented “model marketplaces.” The project positions verified inference as infrastructure: low overhead verification, high throughput, and a path toward open, transparent training over time. —------------------------------------------------------------------------------------ About Travis Good: Travis Godwin Good, PhD is the CEO and Co-founder of Ambient.xyz. His public writing and research focuses on trustworthy AI infrastructure, verification vs. “truth,” incentives that degrade model quality, and why agentic systems need auditability down to execution rather than vibes and benchmark screenshots. —------------------------------------------------------------------------------------ Key words: verified inference, Proof of Logits, useful PoW, SVM, Solana-style dApps, agentic economy, on-chain AI, open weights, audit trails, model provenance, censorship resistance, privacy-preserving compute, miner economics, decentralized training, Travis Good Ambient, verifiable inference