Trust isn't just about good intentions. It's built on a foundation of competence (the product works) and care (the product has the user's best interests at heart). This framework translates a soft concept into actionable product principles, especially for AI systems.
A breakdown in trust, such as Starbucks' AI marketing mishap, can lead to immediate and massive financial consequences. The company saw a 26% revenue drop in one week, a $580 million annualized mistake, proving trust is a critical, high-stakes business metric, not just an ethical ideal.
A key risk of AI is the atrophy of human skills like judgment and taste. Instead of optimizing solely for efficiency, product teams should design AI-powered workflows with intentional friction that encourages users to practice and develop their core competencies, preventing them from becoming mere QA checkers for an algorithm.
To operationalize trust, embed it directly into your company's strategic framework, like Martin Erickson's decision stack. By establishing a principle like "trust over short-term profit," you create a clear guideline that shapes all subsequent product and business decisions, making ethics a tangible part of the process.
To ensure long-term ethical decision-making, founders can embed it into their corporate governance from day one. Following Anthropic's model, they can create an ethics committee and mandate that some board seats are filled by people who hold no shares, creating an incorruptible check on purely profit-driven decisions.
You don't need to be in leadership to influence a company's approach to ethics and trust. Individual contributors can be powerful catalysts by starting conversations, asking curious questions, and sharing relevant articles and books to highlight the risks and opportunities.