At the beginning of the 20th century, the Wright brothers’ first flight barely took off, lasting just 12 seconds. But within a few decades, airplanes revolutionized global travel and connected the world in ways previously unimaginable. Today, a similar pattern is emerging with artificial intelligence, which is evolving from a niche innovation to a ubiquitous tool reshaping industries worldwide. Technological revolutions like these can be visualized and understood using a model called the “S-curve.”

The S-curve is a graphical representation of the evolution of a technology over time. It starts slowly, with early adopters, specialized use cases, and technocrats. As the technology proves its value, it enters a phase of rapid growth where adoption increases and it becomes more integrated across different industries and applications.

However, as technology advances, becoming cheaper, faster, and more efficient, it inevitably reaches a logical limit (often defined by a practical physical limitation based on the laws of physics) and reaches a natural “peak” of the S-curve. When a technology reaches its limits, progress is relatively slow and usually requires a significant increase in complexity. For example, consider the efficiency improvements in the internal combustion engine over the last 20 years. Over time, a new technology emerges, typically starting at a lower performance level than the original, but producing a new S-curve that has the potential to overtake the old one.