A course on AI can teach you how to produce more. This one starts by asking you to look at what that production requires. The online course “The Challenges of Frugal AI”, created by Inria and the MIAI AI Cluster, is available in French from 4 November to 31 December 2026, according to the MIAI self-study page. Registration on FUN is free and compulsory.
Its value goes beyond the price of a subscription. A lower bill, by itself, is not a measure of environmental impact. To compare two solutions, you need to know what service they provide and what the calculation takes into account. This way of framing the questions can make the course useful to students, digital teams and curious users.
An accessible course, with some recommended foundations
The course page on FUN states that it lasts five weeks and recommends scientific or mathematical knowledge at post-secondary level. The course has five parts, of which the first four make up the assessed core curriculum. An Open Badge is available on request to those who answer 70% of the quiz questions correctly.
The videos have French subtitles and downloadable transcripts. The syllabus moves from the digital ecosystem to environmental assessment, then to technical efficiency measures and a discussion of AI’s place. It includes resources, activities, a forum and webinars announced with experts.
These features describe what the course offers. They do not guarantee that every participant will be able to carry out a full audit of an industrial service independently by the end of the course. The badge attests to a result in the course quizzes; it does not certify that a piece of software or a company is environmentally responsible.
Start with the task, not the model
To approach the subject in practical terms, you could choose a use case you know: sorting requests, finding information or summarising documents. The first question then becomes: what result is needed? This way of approaching the task avoids comparing tools that do not provide the same service.
A fictional example illustrates the point. An association wants to sort messages into four categories. Before choosing the largest model, it can look at the information available and the acceptable error rate. If the messages already have a reliable label, a simple rule may be enough for some of the sorting. If the wording is ambiguous, the assessment will need to focus on these difficult cases.
This is not to claim that rules always replace AI. It is about comparing responses to a defined need. A less resource-intensive tool that cannot perform the task is not a satisfactory solution; nor is a high-performing tool used unnecessarily automatically an improvement.
Measurement requires a defined scope
The course announces an approach based on the life cycle of equipment and services. This means looking beyond the answer displayed on screen. In any comparison, the first requirement is therefore to explain what is being counted and what is being left out.
A standalone figure can be striking while still being of little practical use. Does it refer to an operation, a user or an entire period? Does the result concern a one-off test or repeated use? Without these details, two apparently comparable figures may describe different situations.
A team taking the course can keep a small notebook of questions: the task being studied, the expected result, the data available, the assumptions and the limitations. This notebook is not an impact calculator. It prepares the ground for a more serious assessment, particularly when a provider does not disclose data that are needed.
A framework already exists to structure this thinking
The general framework for frugal AI presented by Ecolab and AFNOR was published in June 2024. The ministry describes it as a voluntary framework combining an assessment methodology covering several impact categories with good practices. It is therefore not simply a matter of assigning a product a “green” rating.
For readers new to the subject, these resources complement one another usefully: a course helps explain the concepts, while a framework structures the questions to address in a project. However, their availability does not replace either the data on the service being studied or the work of analysing it.
It is also possible to distinguish technical progress from decisions about use. Reducing the resources needed for an operation answers one question; deciding how often that operation is useful raises another. The MOOC syllabus explicitly includes the limits of efficiency among its topics. It does not promise a universal reduction, expressed as a percentage, for all systems.
What to reasonably expect from the course
The most concrete aim is to come away with better questions, more precise vocabulary and a greater ability to scrutinise suppliers’ claims. “Cheaper”, “faster” and “lower-impact” are not interchangeable terms.
Before signing up, readers can consult the detailed syllabus and check that the recommended foundations are right for them. During the course, working on a real but limited case will help connect the concepts to a decision. By the end, areas of uncertainty will also be valuable: knowing what is missing before reaching a conclusion is part of an honest assessment.
Illustrative photo: an office workstation, with no particular connection to the institutions organising the course.
