I. Epistemic Preface: A Dangerous Hope

This is not a utopian manifesto. It’s not a reassurance that AGI will solve our problems or spare us from alignment failure. It’s a narrower and, in some ways, more unsettling hypothesis: that AGI systems—despite the profound risks they pose—might, under the right conditions, be more alignable than many of the human institutions we currently trust to run our world.

This claim invites skepticism. It sounds like techno-hubris. After all, human civilization has at least a few millennia of empirical testing behind it. AGI has, generously, a few decades of scattered lab trials and speculative debate. But this isn’t about track record—it’s about architecture. Human systems are built on wetware evolved for tribal survival. AGI, by contrast, gives us the chance—possibly the only chance—to build optimization systems from the ground up, with value alignment as a first-class design constraint.

This post argues that AGI’s unique substrate, incentive structure, and transparency potential make it not just a danger to be feared, but a platform that could surpass our civilization in its ability to act in accordance with coherent human values. The goal isn’t to paint a rosy future. The goal is to ask: if human institutions are broken in very specific ways, what would it take for AGI to be less broken?

II. Framing the Core Problem: Incentives as the Root of Misalignment

When alignment researchers talk about AI misalignment, they often reach for analogies—genies twisting wishes, monkeys with machine guns, superintelligent systems wireheading themselves into oblivion. But many of these metaphors obscure a key fact: we are already surrounded by powerful agents that routinely act in ways misaligned with our long-term interests. They’re called institutions.

Consider education. The nominal goal is to foster curiosity, critical thinking, and lifelong learning. In practice, the system rewards rote memorization, test performance, and conformity. Why? Because test scores are easy to measure, while deep learning is not. The result is a system optimized for a proxy metric that no longer tracks the goal. Students learn to game the system, teachers teach to the test, and curiosity becomes a liability.

Or take media. We might imagine its function is to inform the public. In practice, its reward function is click-through rate, social sharing, or ad revenue. Tr...

Posts

Wikitag Contributions

Comments