The meaningmaking lens on AI (updated)

2/8/2026 ☼ not-knowing ☼ risk ☼ uncertainty ☼ meaningmaking ☼ AI ☼ tools

Meaningmaking is a simple concept but one that is counterintuitively powerful, concrete, and applicable to understanding how we think about work and technology in a world where AI exists.

What is meaningmaking? the tl;dr is that meaningmaking is any decision we make about the subjective value of a thing.

I’ve been working on meaningmaking for a while. It’s a natural extension of thinking about how we construct and deal with the many types of not-knowing that can’t be precisely and accurately estimated. Meaningmaking is connected to one type of not-knowing in particular: not-knowing about relative value.

Why is meaningmaking a powerful and vital lens for understanding work and AI? Because every single one of the important and difficult decisions we make in business — including “judgment calls” and “moral decisions” — requires meaningmaking. And only humans can do meaningmaking work, while machines cannot do meaningmaking work at all (for now).

So here are 16 short essays I’ve written over the last 30 months. They unpack the concept of meaningmaking and how it applies to thinking strategically about AI, AI products, and the future of business, work, content, and learning.

1. AI is a mirror, not our rival.
2. Meaningmaking as a uniquely human capacity.
3. Why AI has a meaningmaking problem.
4. Why AI wins where it doesn’t have to do meaningmaking.
5. The seductive mirage of AI.
6. Meaningmaking and the future of work.
7. AI’s missing middle.
8. The future of education is meaning-making.
9. Building AI tools for better meaning-making.
10. Business is a meaning-making act.
11. The AI expertise conundrum.
12. Designing AI tools that support critical thinking.
13. An AI tool for learning critical thinking.
14. Prototyping a reasoning scaffold based on meaning-making.
15. A better conceptual model for the AI agent
16. Content, representation, re-presenting (in a time of LLMs)