AI is good at coding open-ended survey answers | AI agents shop without Google Gullibility | Half of AI conversations include a breakdown, so the recovery experience deserves more attention | AI needs context to suit the task | Field reports from projects that implement AI agents | People develop feelings for their AI | Respect users’ time: every extra step carries a cost for them and for you | AI can make users feel they’re creating for an audience | The ends of names and la

Graph-based UI for AI is better than linear chat for design tasks | Storytelling improves with a keyframing UI | With a better history UI, clients reused a designer’s past alternatives 2.6× as often as they generated new material | Fast wins make users stay | Mental models: the map in the user’s head always wins | Users hold only 2 mental models of AI: tool or coworker | Sticky navigation gives users a firm foundation but consumes pixels

Show a progress indicator when response time exceeds 10 seconds, preferably as a percent-done bar. It tells users two things: the system heard you, and here’s how long the ordeal will last. But never let the bar stall, lie, or reset. AI agents that work for hours have raised the stakes: progress indicators must now serve users who walked away. Follow the 12 design guidelines in this article.

Design guidelines held steady for 30 years but are shifting with AI | Users who are anxious about AI use it more, not less | A toggle is a light switch, not a form field | What are the Erdős problems and why are they relevant for AI? | An ambient monitoring interface with glanceable feedback on agent progress cut errors in hal

Almost every screen you design is assembled from 10 basic interaction elements: buttons, forms, menus, links, dialog boxes, alerts, icons, checkboxes and radio buttons, tabs, and search. Each is decades old but still misused daily. This article defines the GUI design alphabet, traces its history, and distills 86 evidence-based design guidelines, plus bonus coverage of windows and the pointer.

AI is saving time at scale, reshaping enterprise behavior, and exposing a new design problem: products must be built for uneven capabilities, uneven adoption, and an uneasy public. While macroeconomic data shows immense productivity gains and US enterprise dominance, micro-level metrics reveal usability flaws, job losses among juniors, and severe algorithmic trust issues.

AI needs much faster response time to meet human demands, which remain unaltered from previous UI paradigms.