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.
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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.
While Sam Altman's projection for OpenAI to use 250 gigawatts of compute by 2033 seems extreme, it actually charts a slower growth trajectory than the continuous exponential forecasts from analysts like Leopold Aschenbrenner.
While discourse often focuses on exponential growth, the AI Safety Report presents 'progress stalls' as a serious scenario, analogous to passenger aircraft speed, which plateaued after 1960. This highlights that continued rapid advancement is not guaranteed due to potential technical or resource bottlenecks.
Humans naturally project the future in a straight line, but disruptive innovations like Tesla's grow exponentially. Progress seems slow, then explodes, catching linear thinkers by surprise after the biggest investment gains have already been made, creating a gap between perception and reality.
For the first time in years, the perceived leap in LLM capabilities has slowed. While models have improved, the cost increase (from $20 to $200/month for top-tier access) is not matched by a proportional increase in practical utility, suggesting a potential plateau or diminishing returns.
The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.
E-commerce businesses grow rapidly until hitting constraints like cash for inventory, traffic limits, or distribution caps. Growth then flattens until a new supply chain or distribution channel is unlocked, creating a step-function pattern rather than a linear ascent.
For enterprise AI, the ultimate growth constraint isn't sales but deployment. A star CEO can sell multi-million dollar contracts, but the "physics of change management" inside large corporations—integrations, training, process redesign—creates a natural rate limit on how quickly revenue can be realized, making 10x year-over-year growth at scale nearly impossible.
The true exponential acceleration towards AGI is currently limited by a human bottleneck: our speed at prompting AI and, more importantly, our capacity to manually validate its work. The hockey stick growth will only begin when AI can reliably validate its own output, closing the productivity loop.
The history of nuclear power, where regulation transformed an exponential growth curve into a flat S-curve, serves as a powerful warning for AI. This suggests that AI's biggest long-term hurdle may not be technical limits but regulatory intervention that stifles its potential for a "fast takeoff," effectively regulating it out of rapid adoption.
