History shows that major technological shifts like the internet and AI require a fundamental re-architecting of everything from silicon and networking up to software. The industry repeatedly forgets this lesson, mistakenly declaring parts of the stack, like hardware, as commoditized right before the next wave hits.

The S&P 500's heavy concentration in a few tech giants is not unprecedented. Historically, stock market returns have always clustered around the dominant technology transformation of the time. Before 1980, leaders were spinoffs of Standard Oil, car companies like GM, and General Electric, reflecting the industrial and automotive revolutions.

Contrary to the belief that new form factors like phones replace laptops, the reality is more nuanced. New devices cause specific tasks to move to the most appropriate platform. Laptops didn't die; they became better at complex tasks, while simpler jobs moved to phones. The same will happen with wearables and AI.

Significant disruption often comes from applying mature technologies in novel contexts, not just from new inventions. Gaonkar points to 1970s lithium-ion batteries revolutionizing EVs and old gaming GPUs now powering the AI boom as prime examples of this powerful investment thesis.

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.

The most significant companies are often founded long before their sector becomes a "hot" investment theme. For example, OpenAI was founded in 2015, years before AI became a dominant VC trend. Early-stage investors should actively resist popular memes and cycles, as they are typically trailing indicators of innovation.

GPUs were designed for graphics, not AI. It was a "twist of fate" that their massively parallel architecture suited AI workloads. Chips designed from scratch for AI would be much more efficient, opening the door for new startups to build better, more specialized hardware and challenge incumbents.

Luckey's invention method involves researching historical concepts discarded because enabling technology was inadequate. With modern advancements, these old ideas become powerful breakthroughs. The Oculus Rift's success stemmed from applying modern GPUs to a 1980s NASA technique that was previously too computationally expensive.

Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.