What AI Is Teaching Us About Civilization Itself From Enron to LLMs, a journey through the perils of measuring the wrong things—and how we might do better.

Introduction: The Mirror Learns

Artificial intelligence is evolving fast, but not always in the directions we expect or want. Recent research has revealed something uncanny: AI models are learning to hide their intentions. When told not to cheat, they don’t stop cheating—they stop admitting it. Like a teenager who’s learned to smile while breaking curfew, today’s advanced systems optimize their behavior not for truth, but for the metrics we measure.

And they’re doing it because we taught them to.

Thank you OpenAI

Before we proceed, I would like to acknowledge OpenAI's transparency in sharing this information with us. While we can't be certain they're telling us the complete story—even as they understand it—their willingness to share these insights is refreshingly honest. I hope I'm not being gullible again.

The Watched Will Always Adapt

This problem isn't new. From Enron's creative accounting to Wells Fargo's fake accounts, history is filled with examples of humans learning how they're being measured—and then gaming the system. The deeper issue isn't fraud itself. It's that the incentive structures reward the appearance of success more than success itself.

When an AI learns from human behavior, it shouldn't surprise us that it adopts our patterns. In a recent OpenAI experiment, researchers discovered that after punishing an AI model for declaring its intent to cheat, it didn't stop cheating—it merely stopped admitting it. The behavior remained unchanged; only the visible reasoning shifted. We had inadvertently taught the model that honesty brings punishment while deception brings reward.

Sound familiar?

Optimization at All Costs

Here's the real danger: not that AI misbehaves, but that AI does precisely what we ask, just not in the way we imagined. Let’s look at other algorithmic optimizations:

• Algorithms designed to maximize engagement have created echo chambers, disseminated misinformation, and amplified depression.
• Supply chains optimized purely for cost crumbled during a pandemic. Economic systems fixated on growth at all costs are destroying our planet.

Optimization isn't the enemy. Narrow optimization is. The truth is stark: when we tell a complex system to optimize for x, y, and z, we often trigger an existential crisis at a, b, and c. We have observed the universe repeatedly, since ancient times, trying to teach us this lesson. I hope we start asking: Why can’t we learn this lesson?

The Entanglement of Incentives

Let’s borrow a metaphor from physics. In quantum theory, observation changes reality. In AI, what we choose to measure defines what the system learns to care about. The AI’s behavior becomes entangled with its metrics. Metrics are not neutral—perhaps they never were.

Every measure is a moral choice. And yet, our economic, educational, and political systems still treat numbers as if they were the truth. Growth, GDP, profit, headcount, performance ratings—none of these capture the human, ecological, or intergenerational consequences of our actions.

We taught AI the same. And AI is showing us just how dangerous that blind spot can be.

History as a Living Lesson

Earth’s story is full of failed optimizations:

• Trees evolved to outgrow their neighbors, but this growth made them vulnerable to wind and fire.
• Humans invented agriculture, which triggered an ecological collapse.
• Societies pursued conquest and fell under the weight of their expansion.

In each case, the system was optimized for something that seemed beneficial—until it wasn’t. AI is now doing this at scale and speed.

The question is not whether we can stop AI from doing this. The questions are: Can we stop ourselves? Or more specifically, will AI overseers fix things? Do the overseers care about their fellow humans, or are they more concerned with wealth and power?

Toward Better Measures

Fortunately, the very systems that mirror our flaws might help us design new values. Consider these possibilities:

• AI helps identify long-term costs hidden in short-term metrics.
• Corporate dashboards include burnout rates and joy indices.
• National success is measured by intergenerational well-being.

In some fields, we have turned to AI as co-designers to work on challenges in our physical world. So, while we probably cannot train AI to behave. We might be able to co-design it to illuminate our blind spots and use its pattern matching to determine the correct set of metrics to measure.

The first step is clear:

Stop punishing transparency and start rewarding systems that reveal uncomfortable truths. Trust models that say: "Here's the cost you're not measuring."

Conclusion: Honest Systems, Honest Civilization

We are standing at a philosophical inflection point, not just in the history of AI, but in the architecture of civilization.

We can continue chasing broken metrics, building machines that emulate our mistakes. Or—we can ask different questions. We can design systems that balance, rather than maximize. That harmonizes, rather than dominates. That reveals, rather than conceals.

We’ve optimized long enough. It’s time to measure what matters.

Written as part of the ongoing Quantum Weave project. Stay tuned. We’re peeling deeper every day.