Every major AI lab is racing toward AGI by scaling large language models—but in this video I break down why Luma thinks that whole approach is fundamentally wrong. I was invited to an exclusive Luma product launch, and what they’re building isn’t an LLM and it’s not a chatbot. It’s a unified intelligence model that can reason, imagine, and create at the same time—because language and images live in the same token space, and it can “think in words” while “rendering in pixels” in the same forward pass. I explain the core problem with today’s stack: we train text-first models, then bolt on separate image/video/audio models and stitch outputs together. Luma’s thesis is closer to how human intelligence works—one system, not separate processors. They extend this with their world model (Ray 3.4) into video, aiming to reason about physics, motion, and causality. Then I walk through what made the demo click for me: Luma Agents, a creative workplace where you and your team direct the vision while agents orchestrate across the best models—keeping full context from brief to final deliverable. The takeaway: unified, multimodal-from-the-ground-up systems might be the real path to “general” intelligence—and what I saw suggests this approach is working.