Focusing only on trendy sectors leads to intense competition where the vast majority of startups fail. True opportunity lies in contrarian ideas that others overlook or dismiss, as these markets have fewer competitors.

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.

While a strong business model is necessary, it doesn't generate outsized returns. The key to successful growth investing is identifying a Total Addressable Market (TAM) that consensus views as small but which you believe will be massive. This contrarian take on market size is where the real alpha is found.

Jensen Huang's core strategy is to be a market creator, not a competitor. He actively avoids "red ocean" battles for existing market share, focusing instead on developing entirely new technologies and applications, like parallel processing for gaming and then AI, which established entirely new industries.

Nvidia dominates AI because its GPU architecture was perfect for the new, highly parallel workload of AI training. Market leadership isn't just about having the best chip, but about having the right architecture at the moment a new dominant computing task emerges.

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.

Traditional market sizing, which analyzes existing demand, is useless for true technological breakthroughs. A fundamental change on the supply side (e.g., GPUs for AI, cloud for software) unlocks markets that are orders of magnitude larger than their predecessors (e.g., gaming, on-prem software).

In semiconductors, missing a key innovation cycle (like mobile or EUV manufacturing) is catastrophic. Leaders like TSMC attract top customers, which helps them improve their tech, creating a flywheel that makes it incredibly difficult for laggards like Intel to ever recover.

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.