The Illusion of Building

14 hours ago

AI makes it cheaper to produce software that appears to work, but there's a difference between 'building an app' and 'engineering a system'.
Many viral posts celebrate the creation of apps but ignore the ongoing operational challenges, likening them to 'clay Bugattis'—looks real but lacks engineering.
Google employs thousands of engineers not to build its simple search interface but to handle relevance, latency, scale, reliability, abuse, security, privacy, cost, and change.
Software engineering is about fighting entropy—maintaining order as code rots, dependencies change, and user expectations evolve.
Stages of software maturity: Code → Prototype → Product → Service → Institution. AI accelerates the first two but struggles with the latter stages.
AI reduces the cost of boilerplate and prototyping but doesn't address distribution, trust, reliability, or compliance, which require real-world experience.
Cloning an interface doesn't replicate the accumulated advantages (data, integrations, reliability, trust) of established products.
AI commoditizes implementation, making it cheaper to produce software, but the premium shifts to harder, less visible work like system design and operational reliability.
The gap between 'looks like a product' and 'is a product' becomes harder to see as AI improves, but the underlying challenges remain.

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