21/6/2025 ☼ meaningmaking ☼ education
tl;dr: AI tools are increasingly accessible, cheap, and seem potentially able to produce any output a human can produce — they’ll certainly reconfigure what work looks like. I agree with this read on AI, except for one important difference: Humans can and must do what I call “meaning-making” because AI can’t do it yet. Meaning-making consists of making subjective judgments about the relative value of things. Education at all levels, but especially higher education, has largely abandoned teaching students how to make and justify subjective value judgments. To remain relevant, education must reorient around helping students learn what meaning-making is, and how to do it well.
The coming ubiquity of seemingly totipotent AI tools pushes education to confront an existential question: When fast, cheap AI tools seem potentially capable of producing any human output, what should education be trying to do?
My position is that the only reasonable and useful way to answer this question is to ask what humans uniquely can do — and make education about helping students learn how to do that.
The one thing humans do which AI can’t do (at least not yet; maybe not ever) is what I call meaning-making. This is the work of making inherently subjective decisions about the relative value of things.
Meaning-making work is what drives Ben declaring that he prefers grapefruit over pineapple, or Supreme Court justices writing their decision overturning Roe v. Wade, or suffragiste movements forming back when women couldn’t vote, or Apple’s leadership deciding to build and launch the iPhone despite widespread industry belief that consumers wouldn’t want it.
Meaning-making is how we decide what outputs to produce — outputs like purchases of fruit, written legal judgments that create or overturn precedent, protest signs and proposed legislative changes, and smartphones — but the act of meaning-making is not the same as the output artifact that results from meaning-making. Meaning-making is a crucial form of reasoning because it is fundamental to human action, social change, and innovation — and because only humans can do it. What we call AI today can’t make meaning.
The most important function of education in the future is to help students avoid the fallacy of using output-indistinguishability as a criterion of quality. Students will need to learn what meaning-making is, and how to distinguish between meaning-making work (that humans must do) and non-meaning-making work (that can be left to the machines).
The future of education at all levels, particularly higher education, must reorient away from teaching students how to make outputs (our current implicit approach) and toward explicitly helping them learn to decide for themselves what outputs to make.
For this to happen, students must learn how to rethink what it means to use AI tools to amplify human meaning-making abilities. This requires them to develop three critical skills often missing in current education:
More concisely: Education must reorient toward helping students learn how to develop taste with rigour, and defend their taste to others. (I’m working on building an AI “thought mirror” tool now that is intended to build users’ meaning-making capacity.)
Ever-cheaper, more competent AI tools are already increasing the value of sophisticated meaning-making and the humans who can do it. As AI handles more routine, non-meaning-making work at scale, sophisticated human meaning-making capability will become the main rate-limiting resource — and we all know what happens to those.
For the last few years, I’ve been wrestling with the practical challenges of meaning-making in our increasingly AI-saturated world, developing frameworks for how humans can work effectively alongside these powerful tools while preserving the meaning-making work that is the irreplaceably human part of the reasoning we do. I’ve published this as a short series of essays on meaning-making as a valuable but overlooked lens for understanding and using AI tools
I’ve also been working on turning discomfort into something productive. idk is the first of these tools for productive discomfort.
And I’ve spent the last 15 years investigating how organisations can succeed in uncertain times. The Uncertainty Mindset is my book about how to design organisations that thrive in uncertainty and can clearly distinguish it from risk.