As AI infrastructure giants become government-backed utilities, their investment appeal diminishes like banks after 2008. The next wave of value creation will come from stagnant, existing businesses that adopt AI to unlock new margins, leveraging their established brands and distribution channels rather than building new rails from scratch.

Benchmark's successful AI investments (e.g., Sierra, Langchain) weren't the result of a top-down thematic strategy. Instead, their founder-centric approach led them to back exceptional individuals, which organically resulted in a diverse portfolio across the AI stack before it was obvious.

When a new technology stack like AI emerges, the infrastructure layer (chips, networking) inflects first and has the most identifiable winners. Sacerdote argues the application and model layers are riskier and less predictable, similar to the early, chaotic days of internet search engines before Google's dominance.

With so much flux from AI, betting on undervalued "bargains" is a losing game. The smarter play is to be a momentum investor, buying stocks that are already winning. Their success creates a flywheel of talent and opportunity that is more predictive of future success than traditional valuation metrics.

Benchmark's diverse AI portfolio (data centers, agents, dev tools) is not the result of a top-down, thematic strategy. Their "entrepreneur out" model focuses on backing exceptional founders first, which often leads them to invest in nascent categories before they become widely recognized.

An alternative to chasing hyper-growth AI is to invest in categories where AI adoption is slower. This provides founders with a crucial time advantage to build durable businesses, but it necessitates a more capital-efficient model that can't sustain a hyper-frequent fundraising pace.

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.

Instead of betting on unknowable AI winners, a better strategy is to find quality companies the market has written off as "losers" due to AI fears. Similar to the unloved "old economy" stocks during the dot-com bubble, these perceived victims could offer significant upside if the disruption threat is overblown.

Analysis shows that the themes venture capitalists and media hype in any given year are significantly delayed. Breakout companies like OpenAI were founded years before their sector became a dominant trend, suggesting that investing in the current "hot" theme is a strategy for being late.