Large Language Models (LLMs) lack the fundamental ability to predict the physical consequences of actions or perform autonomous planning, rendering them insufficient for achieving human-level intelligence. True progress requires a shift toward "world models," specifically architectures like the Joint Embedding Predictive Architecture (JEPA), which learn abstract representations of reality rather than generating pixels or tokens. While LLMs excel at language-based reasoning, they remain intrinsically unsafe for agentic tasks due to their inability to verify outcomes or adhere to hardwired safety constraints. The "Tapestry" initiative addresses the need for global AI sovereignty, proposing a federated platform where international contributors can collaboratively train models while maintaining data control. Moving beyond the current industry focus on short-term LLM scaling is essential to developing reliable, objective-driven systems capable of navigating the complexities of the real world.
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