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.

Some companies execute a 3-5 year plan and then revert to average returns. Others 'win by winning'—their success creates new opportunities and network effects, turning them into decade-long compounders that investors often sell too early.

Investment philosophy often aligns with psychological disposition. Growth investing demands an optimistic view of the future, betting on innovation and expansion. In contrast, value investing is inherently more pessimistic, focusing on buying assets below their current worth with the hope of mean reversion.

To vet ambitious ideas like self-sailing cargo ships, first ask if they are an inevitable part of the world in 100 years. This filters for true long-term value. If the answer is yes, the next strategic challenge is to compress that timeline and build it within a 10-year venture cycle.

Departing from typical risk-averse advice, Jim Cramer insists that investors dedicate one of their five individual stock positions to speculation. This is designed to capture the potential of the 'next NVIDIA or Tesla.' He advises that the younger the investor, the more speculation is warranted, as they have a longer time horizon to recover from potential losses.

For 20 years, Netflix's identity was built on 'no ads, no live sports, and no big acquisitions.' Its recent reversal on all these fronts to maintain market dominance shows that adapting to new realities is more critical for long-term success than rigidly adhering to foundational principles.

For venture capitalists investing in AI, the primary success indicator is massive Total Addressable Market (TAM) expansion. Traditional concerns like entry price become secondary when a company is fundamentally redefining its market size. Without this expansion, the investment is not worthwhile in the current AI landscape.

Financial models struggle to project sustained high growth rates (>30% YoY). Analysts naturally revert to the mean, causing them to undervalue companies that defy this and maintain high growth for years, creating an opportunity for investors who spot this persistence.

This provides a simple but powerful framework for venture investing. For companies in markets with demonstrably huge TAMs (e.g., AI coding), valuation is secondary to backing the winner. For markets with a more uncertain or constrained TAM (e.g., vertical SaaS), traditional valuation discipline and entry price matter significantly.