The firm's thesis focuses on a rare founder type: a technical expert who also deeply understands how new technologies shift human behavior. This avoids the common pitfall of building technology in search of a problem, leading to products with innate market pull.

Before building, founders in complex industries must deeply understand the operational rigor and nuances of their target vertical. This 'operator market fit' ensures the solution addresses real-world workflows, as a one-size-fits-all approach is doomed to fail.

Instead of pitching an idea upfront, the founders first conducted broad interviews, asking security leaders for their top 5 problems. Only after identifying a recurring pain that matched their thesis did they switch to phase two: presenting a specific solution to validate its acuity and demand.

The primary advantage of orbital data centers isn't cost, but speed to market. Building on Earth involves years of real estate, permitting, and power grid challenges. The space-based model can turn manufactured chips into operational compute within weeks by treating deployment as an industrial manufacturing and launch problem.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

For deep tech startups aiming for commercialization, validating market pull isn't a downstream activity—it's a prerequisite. Spending years in a lab without first identifying a specific customer group and the critical goal they are blocked from achieving is an enormous, avoidable risk.

Don't jump straight to building an MVP. The founders of unicorn Ada spent a full year working as customer support agents for other companies. This deep, immersive research allowed them to gain unique insights that competitors, who only had a surface-level idea, could never discover.

Robinhood co-founder Baiju Bhatt, a self-described "finance bro" and novice in aerospace, leveraged AI to quickly get up to speed. He found AI dramatically lowered the barrier to entry by allowing him to learn complex physics concepts conversationally, without getting stuck on the formal language and mathematics of textbooks.

Instead of asking for general feedback, Decagon's founder systematized ideation by pressing potential customers on exactly how much they would pay, who approves the budget, and how they would justify ROI. This filters out weak ideas and provides strong commercial signals.