Five days after Digg rolled out the biggest redesign in its history, users filled its front page with links to Reddit. Features they depended on had disappeared, the site was unstable, and the community that made Digg powerful was directing people to a competitor. Years of loyalty unraveled in weeks.
Most products lose trust more slowly. Customers learn to work around a feature, double-check its answers, or go back to the tool it was supposed to replace. They rarely file a bug report when they quietly give up. Product Quality means setting a clear bar and continually checking whether the experience still meets it, across customers, contexts, and the full life of the product.
What this essay explores
- Learn from Digg, Internet Explorer, and Skype about what happens when quality loses its place in product decisions.
- Set a bar for technical, functional, and business quality that holds across devices, markets, and use cases.
- Evaluate the experience after launch, including the workarounds customers may never report.
- Test AI features across varied inputs and customers, and watch for quality drifting without a code release.
- Judge short-term gains against the customer trust a poor experience can cost.
- Build the team practices and company standards that keep quality from depending on one person’s vigilance.
With AI, the quality bar has to hold as models, information, and customer behavior change.
Key takeaway
Quality is not a launch checklist. It is a commitment to the people who rely on what we’ve built.
