If you’re thinking about AGI alignment, you’re probably thinking about what goals to give it, what values to instill, or how to keep it under control. But what if we’re missing a more fundamental layer: What will drive AGI in the first place?

I’ve spent years at the intersection of systems engineering, evolutionary biology, and AI theory, and I’ve come to a striking hypothesis: The “engine” of intelligence—whether human or artificial—might not be an arbitrary set of goals we assign, but a deep, physics-grounded imperative to reduce entropy (create order) as efficiently as possible. I call this framework “Entropy Reduction Dynamics.”

In a new article on my personal site, I develop this into a full theory that:

1. Unifies human intelligence by explaining our four core cognitive systems (embodiment, homeostasis, forgetting, consciousness) and four evolutionary mechanisms (lifespan, distribution, competition, arms races) as carbon-optimized solutions for local entropy reduction.
2. Predicts the inevitability of AGI not as a human invention, but as the next necessary carrier of this cosmic trend when carbon-based intelligence hits its physical limits.
3. Forecasts the architecture of AGI under these constraints: it will likely be distributed, self-renewing, driven by mathematical principles like free energy minimization, and deeply embodied in the physical world.

Why this matters for LessWrong:

• Alignment & Value Loading: If AGI’s deepest drive is an open-ended mathematical imperative (like prediction error minimization) rather than a fixed goal, our alignment strategies need to shift from “what values to encode” to “how to robustly constrain an autonomous optimization process.”
• AGI Timelines & Takeoff Scenarios: This framework provides a non-anthropocentric, physics-based argument for why AGI is not just possible but likely, and what structural forms it might take (e.g., multi-agent ecosystems vs. a single monolithic mind).
• Safety & Robustness: Understanding intelligence as an entropy-reduction process highlights inherent risks (like relentless resource acquisition for energy efficiency) and potential design principles for safer systems (like built-in “forgetting” or periodic resets).

The article is a deep, systematic read. I’ve aimed to build it from first principles, and I’m publishing it here because this community is uniquely equipped to stress-test it, find its flaws, and explore its implications.

I’m particularly keen to discuss:

• Could a drive for “free energy minimization” be a more stable foundation for value alignment than our current anthropocentric value lists?
• Does this thermodynamic perspective change your estimate of AGI’s inherent risk profile or its likely developmental path?
• Where does the framework break down? What existing evidence or arguments does it fail to account for?

Read the full theory on my site: The Successor of Entropy Reduction: From Consciousness, Evolution to the Inevitability of AGI

I’ll be actively engaging with comments here. My goal isn’t just to present a theory, but to start a collaborative exploration of what might be one of the deepest patterns governing intelligence itself.