How AI “skills” are reused and other usability issues with skills | User research job listings for junior staff dry up, and senior staff need AI experience to be hired | Data comics generated with AI increase student learning | Quick view as a shortcut for seeing product info | The AI economy has $175 billion in revenue | AI use spreads from peers | Automatically creating advertisements based on a website

In 1993 and 1996, I predicted that user interfaces would abandon commands: computers would infer and execute user intent instead of obeying point-and-click orders. Modern AI delivered that paradigm, 30 years later. Scoring the papers’ 23 specific predictions against 2026 reality produces a decent grade: 71% correct. The biggest hit: language as the primary interface. The biggest miss: I predicted expert users, and AI instead became the great equalizer.

AI agents expand user tasks | Agentic AI makes expertise more valuable, not less | Users need control over the complexity of AI results | Constraints help users | Meta launched a new image model, Muse Image | ByteDance upgrades its image model to Seedream 5 Pro | Comparing the leading image models | GPT receives a major upgrade to v. 5.6 Sol

Progressive disclosure puts the few features that serve most tasks on the first screen and defers the rest to a clearly labeled second level. The pattern has 4 decades of evidence behind it: novices learn faster and err less, while experts pay 1 click. It’s about to matter more than ever, because AI answers and long-running agents need layered revealing even more than settings screens do.

UX benchmark for AI | Being mentioned in AI answers drives subsequent user behavior | Cheaper character design workflow for new video | AI maturity model | My hero: Dr. Stuart K. Card of Xerox PARC | Microsoft FDE

Halfway through 2026, AI is evolving faster than expected, but usability is struggling to keep pace. I graded my 18 predictions on autonomous agents, compute shortages, and interface design to separate the hype from reality.