Episode 10 of The World Model Podcast confronts a problem more fundamental than algorithms, datasets, or training tricks a hardware crisis that threatens to cap the entire future of AI. This episode exposes the “Memory Wall,” the deep physical bottleneck that starves modern processors of data and renders even the most advanced GPUs powerless when confronted with the computational demands of true World Models.The episode opens by contrasting two paradigms of AI computation. Running an LLM like GPT-4 is a streamlined, feed-forward process massive, but predictable. GPUs excel at this kind of linear algebra–heavy workload. But World Models, especially those used for planning, require something radically different. An agent must imagine hundreds of futures, exploring a branching tree of possibilities. Each branch is its own simulated trajectory, each demanding full context, state, and temporal depth. The result is a combinatorial explosion of memory access patterns irregular, sparse, and bandwidth-intensive. Exactly the kind of workload GPUs handle worst.This is the Memory Wall: a hard limit where computation stalls, not because the processor is weak, but because the data can’t move fast enough to where it’s needed. The episode likens it to solving a chess puzzle while only being allowed to hold one piece in your mental workspace at a time you spend all your effort swapping context in and out, unable to think deeply.From here, the episode expands into the emerging hardware arms race. A new generation of chips aims to break the Memory Wall by co-locating computation and memory, just as the brain does. Neuromorphic architectures like Intel’s Loihi, BrainChip’s Akida, and experimental designs from startups such as Rain Neuromorphics take inspiration from event-driven neural activity rather than dense tensor multiplications. These chips excel at sparse, asynchronous, simulation-heavy workloads: precisely what World Models demand. Google’s forthcoming TPU designs and DARPA-backed research similarly signal a shift toward memory-centric computation.The episode’s controversial claim is blunt: NVIDIA’s dominance is tied to an AI paradigm that is already peaking. GPUs are perfect for perception and pattern recognition, but fundamentally mismatched to causal reasoning, counterfactual simulation, and internal planning the core ingredients of AGI. The company that commercialises the first efficient, scalable memory-centric architecture won’t just outperform GPUs; it will ignite the next revolution in artificial intelligence.The episode concludes by looking outward: the race for new hardware is not just a technical story—it is geopolitical. And some nations are already weaving World Models into their long-term strategic plans.Next episode: China’s exceptional, underreported bet on simulated reality and world-model-centric AI.If you want to understand why the path to AGI runs straight through the hardware stack, Episode 10 gives you the map.