Wiz's product team, trained at Microsoft, avoids building features that only solve for today's customer but break with tomorrow's enterprise giant. This 'infinite scale' mindset isn't about slowing down; it's about making conscious architectural choices that prevent time-consuming and costly refactoring later on.
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In the fast-paced world of AI, focusing only on the limitations of current models is a failing strategy. GitHub's CPO advises product teams to design for the future capabilities they anticipate. This ensures that when a more powerful model drops, the product experience can be rapidly upgraded to its full potential.
Avoid the trap of building features for a single customer, which grinds products to a halt. When a high-stakes customer makes a specific request, the goal is to reframe and build it in a way that benefits the entire customer base, turning a one-off demand into a strategic win-win.
Saying yes to numerous individual client features creates a 'complexity tax'. This hidden cost manifests as a bloated codebase, increased bugs, and high maintenance overhead, consuming engineering capacity and crippling the ability to innovate on the core product.
To avoid the customization vs. scalability trap, SaaS companies should build a flexible, standard product that users never outgrow, like Lego or Notion. The only areas for customization should be at the edges: building any data source connector (ingestion) or data destination (egress) a client needs.
The misconception that discovery slows down delivery is dangerous. Like stretching before a race prevents injury, proper, time-boxed discovery prevents building the wrong thing. This avoids costly code rewrites and iterative launches that miss the mark, ultimately speeding up the delivery of a successful product.
