2025 in review and what 2026 has in store

The year-end roundup. The five models that shaped 2025, where vibe coding is heading inside real businesses, and the trends to watch in 2026.

Welcome to 2026. This first edition is a look back at the models, tools and shifts that shaped 2025, and the trends I’ll be watching closely over the next twelve months.

The five models that defined 2025

My take: Claude (especially Opus 4.5) ran away with coding. OpenAI's reasoning models reshaped how people use AI for planning and strategy. Google's image work raised the bar for visual outputs. Chinese open-weight models like DeepSeek and Kimi came out of nowhere. And GPT-5's underwhelming launch is partly why everyone briefly worried AI had plateaued. If you only learn one lesson, it's that no single vendor wins everything. Pick the right model for the job.

Vibe coding is moving from prototypes to production

My take: Building software by describing what you want, in plain English, has gone mainstream. Product managers prototype with it. Non-technical staff are spinning up event sites and internal tools. The real shift in 2026 will be HR, marketing and legal teams building load-bearing tools themselves, not waiting on IT. If you have a backlog of small internal tools that never get built, this is your year.

Enterprise focus shifts from experiments to ROI

My take: 2026 is being called the year of the dashboard. Companies are done with proof-of-concepts and want measurable business value. Two things matter most: clean, accessible data, and a willingness to redesign processes around what agents can do, not just bolt AI onto existing workflows. Boards will start asking for numbers.

The AI divide will widen significantly

My take: The gap between companies that effectively integrate AI and those that don't is going to grow fast. Leaders will compound advantages through better products, faster cycles, and lower costs. Laggards will get squeezed. Where you sit in that gap by Q4 2026 is largely being decided right now.

A 10-weekend plan to actually get fluent in AI

My take: If you want a structured way to build personal AI fluency this year, the suggested path is genuinely useful: model mapping, deep research sprints, data analysis on your own data, building one automation, creating an information pipeline, ending with a simple AI app of your own. Two hours a weekend for ten weekends gets you further than most paid courses.