We map the violent collision between two converging trends: embodied AI — robots, factory automation, robotaxis and humanoids — and the astronomical economics of foundational models that power them. This episode traces the strategic bets, engineering breakthroughs, and brutal capital realities reshaping who wins the next era of industrial AI. First, the factory floor is becoming a product. Rivian’s Mine Robotics spinout pulled a startling $115 million seed round to turn assembly-line telemetry into a commercial data flywheel — a play that pits it against legacy automakers and Tesla’s manufacturing AI ambitions. In China, Xpeng doubles down on a cost-first strategy: vision-only robotaxis, four in-house Turing chips per vehicle, and a single VLA 2.0 brain to unify robotaxis, humanoids and flying cars — with robo-taxi trials next year and humanoid mass production promised by late 2026. Then the capital contradiction hits hard. US hardware startups aiming for $10k humanoids can’t raise the tens of millions they need — KScale Labs folded, returned preorders, and open-sourced its tech even as its core team relaunched as Gradient Robots. At the opposite extreme, industry leaders are asking for state-scale support: OpenAI publicly seeking government-backed guarantees and citing the need for near-trillion-dollar infrastructure to stay competitive, while Google accelerates Gemini releases and experiments with deeply personalized workspace-integrated AI (raising fresh privacy trade-offs). It’s not all doom: engineering fixes are moving fast. MIT’s new smartphone-based 3D mapping dramatically lowers costs for mapping and rescue robotics, and Perplexity’s code lets trillion-parameter mixture-of-experts models run across standard AWS servers — unlocking existing data center capacity and earning big commercial deals like Snap’s $400M arrangement. Those advances reinforce a two-tier economy: giant, infrastructure-hungry closed systems vying for national-scale support, alongside practical, cheaper open-source stacks already delivering business ROI. For marketers and AI practitioners the playbook is clear: treat operational data as a product, design partnerships that bridge software and hardware economics, and be blunt about timelines. The promise of mass-market $10k humanoids by 2026 now runs up against real capital limits — so prioritize defensible data flywheels, privacy-first integration strategies, and alliances that spread hardware risk. The big question for brands and builders: will you monetize the factory brain, or get left selling yesterday’s sensors?