AI adoption isn't linear. A small, 1% improvement in model capability can be the critical step that clears a usability hurdle, transforming a "toy" into a production-ready tool. This creates sudden, discontinuous leaps in market adoption that are hard to predict from capability trend lines alone.

Unlike past tech cycles with a single constraint, the AI boom is constrained by numerous interdependent bottlenecks at once: power, transmission, memory, optical components, and skilled labor. Solving one piece (e.g., memory supply) doesn't fix the overall systems-level challenge, making the problem uniquely complex.

The sudden arrival of powerful AI like GPT-3 was a non-repeatable event: training on the entire internet and all existing books. With this data now fully "eaten," future advancements will feel more incremental, relying on the slower process of generating new, high-quality expert data.

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.

Cresta's CEO argues that while the internet's evolution from 1995-2001 was somewhat foreseeable, the advancements in AI since 2019 would have been unimaginable even to the experts who wrote the foundational papers. This highlights the unprecedented nature of the current technological shift.

Unlike COVID's growth, which had a hard population limit, AI's potential is tied to energy and computation, which have vast room to expand. However, its real-world application will manifest as a series of S-curves, as different technologies and industries hit temporary plateaus before the next breakthrough occurs.

With past shifts like the internet or mobile, we understood the physical constraints (e.g., modem speeds, battery life). With generative AI, we lack a theoretical understanding of its scaling potential, making it impossible to forecast its ultimate capabilities beyond "vibes-based" guesses from experts.

Unlike electricity or the internet itself, which required massive physical infrastructure build-outs over decades, AI can be "downloaded" instantly by 5+ billion people. The internet acts as a pre-built carrier wave, enabling a rate of adoption never before seen in technological history.

While GenAI continues the "learn by example" paradigm of machine learning, its ability to create novel content like images and language is a fundamental step-change. It moves beyond simply predicting patterns to generating entirely new outputs, representing a significant evolution in computing.