AI narrows skill gaps by helping weak performers the most. Employers are reacting: hiring in AI-exposed job categories now weights human capital 7.8% less and price 1.1% more than before ChatGPT and the demand premium for highly skilled workers is shrinking. Competence has become commodity, so sell judgment. Agentic AI will reverse the equalization and widen skill gaps again.

AI use becoming more localized, according to Gemini data from Southeast Asia | Heavy AI use in a company increases hiring, not layoffs | Winners of the AI Film Festival awards | The inventor of hypertext, Ted Nelson | Helping users recover from errors | AI drones help replant the rainforests | Annotations seem a simple feature, but are often designed wrong | Token use is going through the roof with agentic AI

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.