The AI frontier is shifting from words to worlds — and that change rewrites product roadmaps, budgets, and ethics. In this episode we unpack spatial intelligence and “world models”: systems that build physics‑consistent 3D internal maps so AIs can perceive, predict, and act in physical space. We trace the evidence (GPT‑5’s 33% solve rate on a 9x9 Sudoku benchmark, GPT‑5 Pro solving a physics problem in under 30 minutes), explain why meta‑reasoning still limits real‑world adaptability, and highlight the new sensory datasets (egocentric10K) that are the raw fuel for embodied AI. We then flip to the money fight driving the race: Anthropic’s efficiency‑first bet (smaller, diversified hardware + fast path to cashflow) versus OpenAI’s scale land‑grab (huge multi‑year compute projections), with Nvidia sitting squarely at the center of access and power. Practical impacts are already arriving — Microsoft Copilot’s vision/voice workflows turn spreadsheets into hands‑free analytics, omnilingual ASR aims for 1,600+ languages, and enterprise agents are creeping into commerce and operations — even as public anxiety and infrastructure gaps threaten adoption (half of people in many Western countries report worry about AI). For marketing leaders and AI practitioners this episode delivers three takeaways: spatial models will open new product categories (robotics, AR, simulation) that demand different data, UX and testing strategies; vendor bets now hinge on compute access and hardware relationships as much as model quality; and ethical/governance planning must be baked into go‑to‑market timelines as automation moves from niche to systemic. We close with a provocation: when one player is willing to burn four times the cash of its rival to accelerate development, who should you be designing your product and workforce transitions for — the fastest innovator, or the society that has to live with the consequences?