Our brains evolved for a world of linear change, not exponential curves. This cognitive blind spot leads to underestimating threats like viruses and opportunities like compounding, as we tend to perceive exponential growth as linear in the short term.

The AI era is not an unprecedented bubble but the next phase in a recurring pattern where each new computing cycle (mainframe, PC, internet) is roughly 10 times larger than the last. This historical context suggests the current massive investment is proportional and we are still in the early innings.

While AI progress is marketed in revolutionary "step-changes" (e.g., GPT-3 to GPT-4), the underlying reality is more like compounding interest. A continuous stream of small, incremental improvements are accumulating, and their combined effect is what creates the feeling of an exponential leap in capability over time.

To grasp AI's potential impact, imagine compressing 100 years of progress (1925-2025)—from atomic bombs to the internet and major social movements—into ten years. Human institutions, which don't speed up, would face enormous challenges, making high-stakes decisions on compressed, crisis-level timelines.

Economists skeptical of explosive AI growth use a recent 'outside view,' noting that technologies like the internet didn't cause a productivity boom. Proponents of rapid growth use a much longer historical view, showing that growth rates have accelerated over millennia due to feedback loops—a pattern they believe AI will dramatically continue.

Karpathy pushes back against the idea of an AI-driven economic singularity. He argues that transformative technologies like computers and the internet were absorbed into the existing GDP exponential curve without creating a visible discontinuity. AI will act similarly, fueling the existing trend of recursive self-improvement rather than breaking it.

The vast disagreement on AI's future economic impact—from minor boosts to over 1000% annual growth—stems from conflicting reference points. Skeptics cite the last 150 years of steady 2% growth, while futurists point to the long-arc acceleration of human history since the agricultural revolution.

The current AI boom isn't a sudden, dangerous phenomenon. It's the culmination of 80 years of research since the first neural network paper in 1943. This long, steady progress counters the recent media-fueled hysteria about AI's immediate dangers.

Both COVID's spread and technological progress, like AI, appear exponential but are constrained by real-world limits, turning them into logistic or S-curves. Pandemics cap out at population size, while tech hits bottlenecks before the next innovation creates a new growth curve.