CPAI Issue Brief · Cognition & wellbeing

Cognition & Digital Wellbeing

AI use reshapes thinking in measurable ways — and the best-replicated finding is that people cannot feel it happening.

What’s happening

As AI takes over more cognitive work — drafting, analyzing, remembering — researchers are measuring effects on the underlying human capacities. The findings are real but modest, immediate-term, and easy to misjudge from the inside.

What the evidence shows

In a randomized trial, experienced developers were about 19% slower using AI on their own mature codebases — while believing they were 20% faster. That perception gap is the best-replicated part of this literature. Preregistered work with thousands of participants finds people systematically overestimate what AI saves them and reach for it even when it saves nothing.

Learning research adds the mechanism: the practice conditions that feel most fluent tend to produce the least durable learning. AI is very good at removing exactly the friction the learning was made of. Effects are small-to-moderate and short-term — stated honestly, this is a call for measurement, not alarm.

19% slower

experienced developers with AI — while feeling 20% faster

METR 2025 — RCT

N=2,691

preregistered: people overestimate AI's time savings and over-rely

Efficiency-gain illusion, 2026

Where it reaches constituents

Every worker, student, and agency now relies on AI for cognitive tasks. Because the effects are invisible to self-perception, individuals and institutions cannot course-correct on feel alone — which makes measurement a public question, not just a personal one.

The current legal & regulatory landscape

This is largely unregulated territory. The relevant policy surface today is research funding and measurement standards rather than restriction; several major studies in this area are publicly and privately funded.

Considerations policymakers are weighing

·Support for longitudinal research on AI's cognitive effects, which barely exists.
·Measurement standards for "cognitive impact" claims, so products and programs can be compared honestly.
·Procurement questions schools and agencies can ask about how a tool is designed to be used.

Listed as live debates, not recommendations. CPAI does not take a position on how these should be resolved.

This brief condenses a full, sourced public guide. The complete evidence and citations:

The Perception Gap →

Key sources

Randomized controlled trialMETR (2025) — Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity ↗Randomized controlled trial: 16 experienced developers, 246 tasks on their own mature open-source repositories (avg. 5 years' prior experience). They took 19% longer with AI assistance — despite forecasting a 24% speedup beforehand, and still estimating a 20% speedup afterward. Small sample; the authors are explicit about that.

Preprint · not yet peer-reviewedYu, Cheng, Jabbar, Sucholutsky, Collins, Jurafsky & Hawkins (2026) — The Efficiency-Gain Illusion ↗Three preregistered studies, N=2,691, on AI reliance for cognitively simple tasks. People frequently chose AI even when it saved no meaningful time or effort, with two systematic miscalibrations: believing they use AI less than they do, and overestimating what it saves. Prior AI use also begat further AI use within a session — a self-reinforcing loop. Preprint.

Review of prior researchSoderstrom & Bjork (2015) — Learning Versus Performance: An Integrative Review ↗Perspectives on Psychological Science. Conditions that make practice feel fluent and productive often produce the least durable learning — and vice versa. You feel most effective precisely when you are learning least.

A nonpartisan resource

The Center for Practical AI is a nonpartisan 501(c)(3) nonprofit. We provide education, research, and analysis, and we offer briefings and testimony on request. We do not endorse candidates or lobby for or against specific legislation. Everything here describes the evidence and the current landscape — the policy choices are yours.

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