A year of building an AI business has given Lewis Liu a lot to think about. He gives us his 12 AI assumptions to live by in 2026

‘Tis the season for 2026 predictions, a ritual I usually hate. Every December, pundits confidently forecast the future, only to forget their words 12 months later. I’m hardly innocent: I’ve made my share of half-formed predictions across podcasts and blog posts, promptly buried by the next news cycle.

This year feels different. Building a new company has shifted my thinking from forecasts to assumptions; it’s the kind I’m organising my company and bets around. So here’s my version of the 12 Days of Christmas: 12 AI assumptions for 2026 that I’m actively betting on.

These aren’t abstract thought experiments. They’re shaped by a year of building a venture from scratch, investing in early-stage companies, talking to 300 executives across a variety of industries, advising policymakers and spending time with people who are both deeply technical and unusually plugged into how AI is actually being deployed.

Context and privacy will merge as the integrated hot topic in AI. Privacy and context are two sides of the same coin: you can’t have truly effective AI without deep context, and you can’t share deep context without robust, granular privacy controls woven in. This remains under-developed as the industry focuses on foundational LLMs and basic GPT-wrapper applications, but if you want “live AI” in your workflow, context and privacy as twin-concepts will need to be borne together.

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Vibe-coding will enter a trough of disillusionment, then mature. We’ve seen precipitous drops in AI-coding agents like Lovable and Cursor; AI coding platforms saw

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