When evaluating AI startups, don't just consider the current product landscape. Instead, visualize the future state of giants like OpenAI as multi-trillion dollar companies. Their "sphere of influence" will be vast. The best opportunities are "second-order" companies operating in niches these giants are unlikely to touch.

During a major technology shift like AI, the most valuable initial opportunities are often the simplest. Founders should resist solving complex problems immediately and instead focus on the "low-hanging fruit." Defensibility can be built later, after capitalizing on the obvious, easy wins.

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.

In a gold rush like AI, the shared 'why now' forces many founders into a pure speed-based strategy. This is a dangerous game, as it often lacks long-term defensibility and requires an incredibly hard-charging approach that not all teams can sustain.

The AI investment case might be inverted. While tech firms spend trillions on infrastructure with uncertain returns, traditional sector companies (industrials, healthcare) can leverage powerful AI services for a fraction of the cost. They capture a massive 'value gap,' gaining productivity without the huge capital outlay.

Drawing a parallel to the early internet, where initial market-anointed winners like Ask Jeeves failed, the current AI boom presents a similar risk. A more prudent strategy is to invest in companies across various sectors that are effectively adopting AI to enhance productivity, as this is where widespread, long-term value will be created.

Avoid trendy, saturated markets. Instead, focus on stable, 'boring' industries that are slow to innovate and still rely on manual processes. These markets are ripe for disruption, have less competition, and typically offer higher margins for AI solutions.

For investors and builders, the key variable isn't the final market penetration of AI. It's the timeline. A 3-year adoption curve requires a vastly different strategy, team, and funding model than a 30-year one, making speed the most critical metric for strategic planning.

A VC offers an analogy for competing with AI giants like OpenAI: they are 'Godzilla.' Instead of direct confrontation, startups should 'find an alleyway to hide in.' This means focusing on niche applications or non-software domains where they won't be 'stomped' by inevitable foundation model improvements.