Under pressure, organizations tend to shut down external feedback loops for self-protection. This creates a "self-referencing" system that can't adapt. Effective leadership maintains permeable boundaries, allowing feedback to flow in and out for recalibration, which enables smarter, systems-aware decisions.

Brené Brown notes a decline in systems thinking among leaders. This skill, which involves understanding interconnected parts and maintaining permeable boundaries for feedback, is essential. Without it, organizations become dangerously self-referencing and fail to adapt, as seen in many failed AI investments.

The ambition to fully reverse-engineer AI models into simple, understandable components is proving unrealistic as their internal workings are messy and complex. Its practical value is less about achieving guarantees and more about coarse-grained analysis, such as identifying when specific high-level capabilities are being used.

Government programs often persist despite failure because their complexity is a feature, not a bug. This system prevents average citizens, who are too busy with their lives, from deciphering the waste and holding the "political industrial complex" accountable, thereby benefiting those in power.

Similar to technical debt, "narrative debt" accrues when teams celebrate speed and output while neglecting shared understanding. This gap registers as momentum, not risk, making the system fragile while metrics still look healthy.

Seemingly sudden crashes in tech and markets are not abrupt events but the result of "interpretation debt"—when a system's output capability grows faster than the collective ability to understand, review, and trust it, leading to a quiet erosion of trust.

Focusing solely on making communication faster or shorter is a mistake. Communication ultimately fails if the recipient doesn't interpret the message as the sender intended. The true goal is creating shared understanding, which accounts for the recipient's personal context and perspective, not just transmitting data efficiently.

AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.

Historian Joseph Tainter argues societies collapse when maintaining their complexity consumes all available resources. This applies to organizations, which become fragile by constantly adding complex solutions without a mechanism for simplification. This leaves no buffer to handle the next major, inevitable crisis.