Andreessen uses a simple heuristic to gauge a founder's intelligence, which he considers table stakes. If he finds himself opening his notebook and writing down a lot of notes because he is learning from the founder, it's a clear indicator that they possess the high IQ necessary for success.

The massive capital influx into AI means much of the discourse is marketing disguised as education. To find the signal, analyze the speaker's incentives. Are they trying to raise capital and justify valuations, or are they providing a grounded, factual perspective on the technology's actual capabilities?

Unlike software, where customer acquisition is the main risk, the primary diligence question for transformative hardware is technical feasibility. If a team can prove they can build the product (e.g., a cheaper missile system), the market demand is often a given, simplifying the investment thesis.

A leader in a highly technical field doesn't need to be the deepest scientific expert. Venture capitalist Jeanne Cunicelli, who is not a scientist, succeeds by mastering the skill of deconstructing complex topics through persistent questioning and listening, enabling her to make sound judgments.

To explain Confluent to public investors, the company didn't start from first principles. Instead, they anchored their complex "data in motion" concept to the well-understood category of "databases" (data at rest), making the opportunity size and strategic importance immediately graspable for a non-expert audience.

Bug Crowd's founder tested his pitch on Uber drivers. If he could explain his complex cybersecurity company in 30 seconds without jargon and get them to lean in, he knew the message was strong. This simplicity helps even when selling to technical experts who are time-poor and need to explain the product internally.

Unlike SaaS startups focused on finding product-market fit (market risk), deep tech ventures tackle immense technical challenges. If they succeed, they enter massive, pre-existing trillion-dollar markets like energy or shipping where demand is virtually guaranteed, eliminating market risk entirely.

To avoid being crushed by incumbents, AI startups must operate on ideas that are both non-obvious ("different") and difficult to execute ("hard"). If a startup's core idea becomes obvious to the world before it achieves significant scale, larger companies with more resources will inevitably co-opt the market.

Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.