TurboPuffer achieved its massive cost savings by building on slow S3 storage. While this increased write latency by 1000x—unacceptable for transactional systems—it was a perfectly acceptable trade-off for search and AI workloads, which prioritize fast reads over fast writes.

Data businesses have high fixed costs to create an asset, not variable per-customer costs. This model shows poor initial gross margins but scales exceptionally well as revenue grows against fixed COGS. Investors often misunderstand this, penalizing data companies for a fundamentally powerful economic model.

Investors don't need deep domain expertise to vet opportunities in complex industries. By breaking a problem down to its fundamentals—such as worker safety, project costs, and labor shortages in construction—the value of a solution becomes self-evident, enabling confident investment decisions.

Startups often fail by making a slightly better version of an incumbent's product. This is a losing strategy because the incumbent can easily adapt. The key is to build something so fundamentally different in structure that competitors have a very hard time copying it, ensuring a durable advantage.

To build a multi-billion dollar database company, you need two things: a new, widespread workload (like AI needing data) and a fundamentally new storage architecture that incumbents can't easily adopt. This framework helps identify truly disruptive infrastructure opportunities.

By taking apart an IBM PC as a teenager, Dell realized it was merely assembled from third-party parts. Calculating the component costs revealed IBM's massive markup, creating the market opening for a lower-cost, direct-to-consumer competitor. This highlights the power of first-principles analysis.