What is fluency in artificial intelligence, and why measure movement rather than position?

On July 10, 2025, METR published a controlled trial on productivity and AI. Sixteen experienced developers worked on 246 tasks in their own repositories, each randomly assigned to allow or prohibit AI use. With AI, they took 19% longer. At the end, they estimated that they had become 20% faster.

On February 24, 2026 the same authors redid the study. Among the ten from the original group who returned, the result was 18% faster, within an interval ranging from 38% faster to 9% slower. They themselves call the new data a weak signal, because a growing share of invitees refuse a study that requires them to work half the time without AI.

The result flipped sign between the two measurements, and the experiment's design stopped measuring what it used to measure, because AI use among participants changed. A scale that assigns a rung assumes the scale itself remains stable.

The term adopted by Giordano Cabral is fluency. He has used it since 2025 at FAROL, a research group he founded at CIn/UFPE and which in 2026 becomes the Instituto FAROL de Fluência Agêntica (FAROL Institute of Agentic Fluency); before that, without that name, the same approach appears in PsyFun's games and in classroom assessment.

The term comes from language learning: without practice, the level regresses. Fluency is measured by axis (an organization can be fluent in data and not in people) and depends on the surrounding ecosystem.

Adoption alone no longer differentiates organizations: the 2025 Stack Overflow survey records 84% of respondents using or planning to use AI in development, against 76% the year before, and 46% who distrust the accuracy of what it returns, against 33% who trust it.

AI maturity models inherit from 1990s software maturity models, built for stable processes. FAROL's critique, detailed in agentic AI, has three points: the organization ranked at the top tends to have more to lose in the next shift; these models reward standardization and penalize experimentation, while in AI fast discard has value; and position does not indicate speed.

There are six axes (knowledge, tools and integration, delegation and autonomy, processes, productivity and results, learning and culture), each with its own scale from N0 to N5, measured against a master bank of 264 items. The entry point is the open diagnostic: a leader answers on behalf of the organization in three to eight minutes, with no prior registration, and receives the radar immediately. What a company does next is described in transforming companies with AI.

Asked whether they would cooperate, children almost always say yes. In PsyFun's games, cooperating costs resources, the choice is made in about three seconds, and nothing on the screen is called a dilemma. In two experiments with 162 children aged 6 to 12, published in Behavior Research Methods in 2021, they cooperated more in one game than in the other, and conditionally, two things a questionnaire does not separate. Cabral coordinated the group at UFRPE in 2013 and 2014 and, until 2018, the CNPq project that originated it.

The distinction is between what a person reports about themselves and the record of what they actually did. The two measures have errors of a different nature and, for that reason, do not add up: self-report errs systematically and in the same direction, as in METR; the trace only sees the work that passes through a system that logs it, and records what was done, not what was understood.

In Tendências em Mídia e Interação, 2026.2 edition, each student sifts through five hundred tools item by item within the course's system, which records the time spent on each. The rule is in the syllabus: if the machine manufactures the product, what is assessed is the process of piloting the machine. The course does not use an AI detector; the criterion is evidence of process: repository, history, justification, testing. In the first submission, fourteen students brought 5,776 tools to the shared catalog and recorded 659 choices.

At FAROL, the portrait of observed use is a separate axis, outside the collection ladder, because it cannot be added to what people report about themselves. Meu Mapa de IA asks students what they have already done; the aggregate is published in the Observatório de adoção de IA (AI Adoption Observatory), with 2,943 responses in the extract of September 14, 2026. The survey is cross-sectional: the cycles do not follow the same people.

The measure does not assess the quality of what AI produces, no instrument sends conversation content to any server, and no individual result is disclosed: on public dashboards, cells with fewer than five responses are suppressed, which in the Observatory left 1,394,729 cells out of the totals. An unavailable combination does not mean zero.

Two objections are published alongside the index. The first is Goodhart's law: an indicator that becomes a target ceases to measure what it represented. A fluency index is vulnerable through three doors: rewarding volume of use, which is trivial to manipulate; being read as a seal of approval; and scoring delegation in a way that rewards those who delegate most outside their own competence. The second: an announced measure works as a public commitment and can be demanded later, including legally.

Competence distillation (delegating code generation exacts a price in skill formation, and a higher one from those who are just starting out) is a recurring hypothesis in Cabral's writings and has not yet been tested. Testing it requires following the same people over time, and FAROL's current instruments take cross-sectional measurements. Açude, a common-resource game opened in 2026, records the decision round by round.