Artificial intelligence is often discussed through extremes: either as an existential threat or as a miraculous productivity booster. Much less attention is given to a quieter, more structural question: what does systematic reliance on AI do to our capacity to learn?
The video La Fabrique à Idiots (Micode) explores precisely this blind spot. Beyond the polemics, it raises a fundamental issue for education systems, organisations and professional development: are we using AI to strengthen learning processes, or to bypass them?
The answer matters far more than short-term efficiency gains.
From Tool to Cognitive Shortcut
Most generative AI tools are designed to reduce effort. They draft, summarise, code, rephrase and decide faster than humans. From a performance perspective, this is undeniably attractive. From a learning perspective, it is ambiguous.
As the video highlights, many users progressively delegate:
- writing,
- reasoning,
- searching,
- structuring ideas,
- and even decision-making.
What starts as assistance becomes substitution. The risk is not dependence on technology as such – humans have always externalised effort – but systematic avoidance of the mental operations that enable learning.
This concern is not theoretical. It echoes what cognitive psychology has already observed with other tools.
What Happens in the Brain When We Learn
Learning is not the accumulation of information. It is a biological process, involving distinct but interdependent brain systems.
Research in neuroscience broadly identifies three essential phases:
- Encoding (Theory)
New information is initially processed through the prefrontal cortex (attention, reasoning) and encoded by the hippocampus. This phase is cognitively demanding but necessary. - Practice (Retrieval and Application)
Repeated effort to retrieve and apply knowledge strengthens neural connections. This is where learning becomes durable. - Metacognition (Error Processing and Adjustment)
Feedback, correction and reflection generate what neuroscientists call prediction error signals, often associated with dopaminergic activity. These signals guide the brain in reinforcing effective strategies and abandoning ineffective ones.
Together, these stages allow learning to move from conscious effort to automatisation via the basal ganglia.
Short-circuiting any of these phases weakens the entire process.
AI and the Bypass of Practice
The video provides multiple examples – students, junior developers, professionals – where AI is used primarily during the practice phase.
When an essay, a solution or a block of code is generated externally:
- retrieval effort disappears,
- error detection is reduced,
- metacognitive adjustment is minimal.
The result is output without internalisation.
This explains a recurring observation: individuals who produce sophisticated work with AI support may struggle to explain the concepts they use. The knowledge has not been integrated; it has been outsourced.
The “Google Effect”, Amplified
This phenomenon is not new. Studies on digital memory have shown that when information is easily retrievable online, individuals are less likely to store it internally – a mechanism known as the Google Effect (Sparrow, Liu & Wegner, 2011).
Generative AI intensifies this effect:
- it does not only retrieve information,
- it structures reasoning,
- it resolves uncertainty,
- it removes cognitive friction.
From a neurocognitive perspective, this risks atrophying the very systems needed for complex thinking, because the hippocampus and prefrontal cortex are under-stimulated.
Empirical Evidence: Cognitive Load and AI Use
The video refers to a 2025 experimental study examining brain activity during writing tasks under different conditions: AI-assisted, internet-assisted, and unassisted.
Using EEG measurements, researchers observed:
- lower neural activity in AI-assisted participants,
- intermediate activity with internet use,
- highest activity in unassisted writing.
The authors describe this phenomenon as cognitive debt: short-term efficiency gained at the cost of long-term learning capacity.
While this line of research is still emerging, it aligns with established findings in educational psychology: learning requires effortful processing (Bjork & Bjork, 2011).
Why Experts Benefit and Novices Struggle
An important nuance emerges in the video: experienced professionals tend to use AI differently.
Senior developers, for example:
- verify,
- debug,
- reinterpret,
- and integrate AI output into existing mental models.
Their learning cycle was completed before AI entered the picture. As a result, AI extends their capabilities rather than replacing them.
Novices, by contrast, are at risk of skipping foundational stages. They are placed in a position of “orchestration” without having learned the instruments.
This distinction is crucial for training, onboarding and education policies.
AI as Tutor, Not as Substitute
The most constructive part of the video is not the warning, but the alternative.
AI can support learning if it is deliberately constrained to:
- propose exercises,
- guide reflection,
- delay answers,
- adapt difficulty,
- encourage error analysis.
Research on intelligent tutoring systems supports this approach. When AI is designed to promote active engagement, manage cognitive load and reinforce a growth mindset, learning outcomes can improve significantly.
However, these results depend on pedagogical design, not on the tool alone.
Left unstructured, learners tend to exploit AI for immediate answers. Learning requires friction, and AI does not impose it naturally.
A Structural Challenge for Education and Organisations
This creates a paradox.
AI is, in theory, an egalitarian tool. In practice, it may increase inequality:
- between those who understand how learning works,
- and those who prioritise immediate output.
The same applies to organisations. Those that optimise short-term productivity at the expense of employee learning may erode their long-term competence base.
Learning is not a by-product of performance. It is a condition for sustainable performance.
Choosing Effort Over Ease
The central message of the video is not anti-technology. It is a reminder of a basic principle:
It is effort that shapes the brain, not results.
For the first time in history, we have tools capable of removing almost all cognitive friction. Whether this becomes an opportunity or a liability depends on how consciously we choose to use them.
AI will not make us less intelligent.
But delegating thinking systematically might.
The question is no longer whether AI is intelligent.
It is whether we are willing to remain so.
References (non-exhaustive)
- Bjork, R. A., & Bjork, E. L. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning.
- Kandel, E. R. et al. (2021). Principles of Neural Science.
- Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. Science.
- Sweller, J. (2011). Cognitive Load Theory.
- Dweck, C. (2006). Mindset: The New Psychology of Success.
Video reference
Micode – La Fabrique à Idiots (see annex)