Column: AI Minds

Chatbots ask new users to imagine what artificial intelligence (AI) can do, while code editors invite them inside a workflow they already understand. This structural difference also represents a trust difference.

Even two AI products that use similar underlying models can create very different first-session outcomes. The difference often has less to do with model capability than with how the user interface introduces the model to a new user.

Reach and retention are no longer the same problem. In a16z’s March 2026 analysis of consumer AI apps, ChatGPT had reached roughly 900 million weekly active users. Yet Amplitude’s 2025 Product Benchmark Report found that more than 98% of new users abandon a product within two weeks if they have not experienced value. ChartMogul’s 2025 SaaS Retention Report adds a sharper AI-specific signal: AI-native products at a price of under $50 per month retained just 23% of their gross revenue—that is, 20 percentage points below comparable business-to-business (B2B) and business-to-consumer (B2C) software-as-a-service (SaaS) products at the same price tier. Read More

Welcome to my new UXmatters column AI Minds. The design conversation around artificial intelligence (AI) often divides into two camps: technical capabilities and user-interface (UI) layouts. AI Minds will fill that gap. While UX designers typically focus on model parameters and prompt boxes, our most critical design work revolves around the people on the receiving end of an AI’s outputs. Now, more than ever, UX professionals must prioritize understanding users and how they navigate AI technology that is inextricably linked with high expectations, fears, and confusion. In my monthly column, I’ll explore the psychology of human-AI interactions, examining how cognitive biases, behavioral patterns, and psychological concepts shape the way people adopt, trust, resist, or oversee autonomous systems.

I’ll ground these discussions in established research—drawing from behavioral economics, cognitive psychology, and human factors—and apply these learnings directly to the design friction that UX professionals observe every day. Whether an incident manager is reviewing a database alert or a consumer is evaluating a suggested price, human judgment remains the primary safety check on the decisions software makes. In this installment of AI Minds, I’ll examine how anchoring operates in AI-assisted workflows, when it helps and when it harms, and what UX researchers and designers can do to preserve the independent, human judgment on which AI oversight depends. Read More