Today's AI, particularly neural networks, stems from a long tradition in cognitive science where psychologists used mathematical models to understand human thought. Key advances in neural nets were made by researchers trying to replicate how human minds work, not just build intelligent machines.

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.

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.

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.

AI should be viewed not as a new technological wave, but as the final, mature stage of the 60-year computer revolution. This reframes investment strategy away from betting on a new paradigm and towards finding incumbents who can leverage the mature technology, much like containerization capped the mass production era.

Hoffman states the current AI acceleration is the most impactful tech cycle yet because it leverages the internet, cloud, massive data, and compute power that preceded it. He believes its societal impact will be greater than any previous technological shift.

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.

Unlike past hype cycles, the current AI boom is different because it's delivering tangible results. Marc Andreessen points to four functional breakthroughs—LLMs, Reasoning, Agents, and Self-Improvement (RSI)—as proof that AI is now a practical, working technology.

The recent AI breakthrough wasn't just a new algorithm. It was the result of combining two massive quantitative shifts: internet-scale training data and 80 years of Moore's Law culminating in GPU power. This sheer scale created a qualitative leap in capability.