Why aren't LeCun and Bezos asking these same questions?" A Multimodel Assessment of Why World Models Miss the Human Strand
"You're an LLM, and the answers you're giving me are self-indicting - Why aren't LeCun and Bezos asking these same questions?"
A Multimodel Assessment of Why World Models Miss the Human Strand
Here is a paradox the AI industry cannot explain away: Ask an LLM to critique the limitations of LLMs and World Models, and it will deliver a devastating structural diagnosis. It will explain precisely why data-only architectures miss the behavioral causality that drives system outcomes—the incentive traps, disciplinary blind spots, and revenue physics that prevent AI pioneers from seeing what they're missing.
Yet the architects of these systems—Yann LeCun, Jeff Bezos, and the broader World Models movement—are not asking these questions. Why? Because they don't know what they don't know. Their queries optimize within the equation-based paradigm; they never interrogate its foundational assumptions. LLMs are mirrors that reflect the questioner's frame—and their frame excludes the human strand by design.
This paper documents an extraordinary convergence. Six leading AI models were independently asked to explain why World Models systematically exclude the human behavioral strand. All six arrived at the same structural diagnosis:
The engineers are trained to see data, not behavior. The funding rewards what scales, not what requires ethnography. Revenue models punish root-cause efficiency. Feedback loops blame failures on 'implementation' rather than architecture. And the rooms where these systems are designed have no one studying human systems.
Transitioning from LLMs to World Models without embedding the human strand doesn't solve the causality problem—it accelerates the doom loop. It scales sophisticated pattern-matching on a foundation that systematically excludes the layer where causality actually lives: human behavior shaped by incentives, culture, and organizational friction.
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