Wisdom and the Refusal to Simplify

In The You On AI Field Guide

The AI discourse is full of clean cuts that sever arteries. 'AI will replace fifty percent of jobs within ten years' is a clean cut. It is memorable, citable, actionable — and it is a reduction of a phenomenon with dozens of dimensions to a single variable, concealing everything that matters: which jobs, replaced by what, at what cost to whom, with what structures in place to manage transition. The clean cut severs the arteries connecting the prediction to the reality it claims to predict, and the prediction bleeds out while looking impressively precise.

Even the formulation 'AI is an amplifier' — the core claim of Edo Segal's You On AI — requires Midgley's kind of care. The formulation is better than most because it preserves the moral agency of the human who does the amplifying. But even it simplifies: an amplifier does not merely increase volume. It changes the signal. Distortion is a property of amplification, not a failure of it. The question is not just whether you are worth amplifying but what the amplification does to you in the process — whether the version of you that emerges from collaboration with the machine is the version you intended, or whether the machine's preferences (for fluency over struggle, for completion over uncertainty) have reshaped the signal in ways you did not choose.

Midgley's distinction between earned and premature simplification is the critical move. An earned simplification has been through the complexity first — surveyed the full landscape, identified what can be left out without distortion, produced a summary that preserves essential features while omitting inessential ones. A premature simplification has not done this work — it has jumped to the formula before understanding what the formula leaves out. The two look identical on the surface. The earned simplification knows what it has omitted. The premature simplification does not.

Most of what passes for public understanding of AI is premature simplification. The public has been given formulas: AI will create or destroy jobs. AI is or is not conscious. AI will or will not surpass human intelligence. Each formula captures a fragment of a phenomenon that resists fragmentation, and the fragments have been assembled into a mosaic that looks like understanding but is actually a collection of clean cuts that have severed connections between fragments and the reality they claim to represent.

Wisdom, in Midgley's framework, is not the possession of correct answers. It is the ability to navigate a situation in which correct answers are not available, using judgment, experience, care, and the stubborn refusal to accept a formula as a substitute for understanding. The formulas are everywhere. The understanding is rare. And the rarity is not because understanding is difficult — though it is — but because understanding is slow, and the culture has decided that slow is a synonym for obsolete. It is not. Slow is a synonym for careful.

Origin

The distinction is developed across Midgley's later work, particularly in What Is Philosophy For? (2018), her final book, and in essays collected in Are You an Illusion? (2014). It draws on the Aristotelian tradition's conception of phronesis as practical wisdom exercised in specific, unrepeatable situations.

Key Ideas

Ockham's razor cuts both ways. Reducing below necessity is as much a methodological error as multiplying beyond it.

Earned vs. premature simplification. Both produce clean formulas; only the earned one knows what it has omitted.

Wisdom is slow. The culture has equated slowness with obsolescence; Midgley insists that carefulness takes time and its product cannot be optimized.

Inclusion vs. exclusion. Formulas exclude; complexity includes. In a moment whose decisions will affect every person, inclusion is a moral necessity.