In 1999, we learned a simple mistake could break the world. For decades, programmers saved memory by storing years as two digits—"79" for 1979. Nobody imagined their code would still be running when the year 2000 arrived. But it did, and suddenly "00" could mean 1900. Banks, airlines, and governments scrambled to fix it. The code wasn’t broken; it simply followed flawed assumptions.

When writing software, we often imagine a tidy, predictable world where inputs match expectations. In reality, the world is messy. Consider a restaurant’s point-of-sale system. On paper, it’s straightforward: a customer orders, pays, and leaves. But in practice, one table splits the bill three ways, another pays half in cash and half with gift cards, while yet another tries redeeming a coupon from 2016. Suddenly, you're not just processing payments—you’re managing chaos.

And it’s not just restaurants. Airline booking systems run smoothly until storms cancel flights, triggering cascading rebookings governed by obscure policies. Financial trading platforms operate efficiently—until a sudden market plunge transforms them into chokepoints flooded by panicked sell orders. What we dismiss as “edge cases” aren’t rare anomalies—they’re the default state of reality.

Accepting this doesn't mean anticipating every possible scenario. Trying to cover all eventualities with more rules and validations only increases complexity—and complexity breeds its own failures. Instead, build software resilient enough to handle the unexpected. Assume things will go wrong. Design for failure—not by eliminating errors, but by gracefully containing them.

The key insight is straightforward: reality doesn't conform neatly to our expectations. The sooner we embrace uncertainty as the norm, the better our software will become at surviving it.