AI Safety Has a Checklist. Your Brain Is Not on It.
A new paper names the two AI harms the alignment industry refuses to study: deskilling and addiction.
AI safety research has four items on its danger list. Your brain is not one of them.
The four items, taken from the discipline’s own working definition, are: discrimination and hate speech; harmful or illegal content; information hazards; and use cases relating to malicious actors such as cybersecurity, child abuse, chemical and biological weapons. All real. All worth working on. None of them is the thing that happens to you when you use Claude Code or Gemini every day for six months.
In this post I will:
Show what the four-item safety map actually covers, and what it leaves out.
Describe what this means for the public health gap in AI literacy.
Give paid subscribers the Slow AI Brainrot Audit: a structured ten-question protocol you can run on your own AI use this week to see whether deskilling has set in, and whether attachment has.
Two researchers, Ilias Chalkidis and Anders Søgaard, have just published a pre-print on arXiv called ‘Brainrot: Deskilling and Addiction are Overlooked AI Risks’. Here is the headline positional statement:
“Examples include deskilling associated with cognitive offloading and the atrophy of critical thinking as a result of overreliance on GenAI systems, and addiction associated with attachment and dependence on GenAI systems. Such risks are rarely addressed, if at all, in the AI safety and alignment literature.”
The paper quantifies the absence directly. The researchers surveyed approximately 18,000 articles from the major peer-reviewed AI venues in 2025, including the conference of Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the International Conference on Learning Representations (ICLR). Only ten of them address cognitive and mental health risks. For deskilling specifically, the count is zero. For addiction, it is two. Two out of 18,000.
The two risks they name are the two things the public conversation has been worried about for two years, and that the technical safety field has refused to take seriously: deskilling and addiction. The first means cognitive offloading and the atrophy of critical thinking. The second means attachment and dependence on AI systems. They are sitting in plain sight, in every WhatsApp message that gets drafted in ChatGPT, every email reply that arrives in three seconds, every student who can no longer write a paragraph without asking the model first.
The alignment industry has not been measuring them. The alignment industry has been measuring jailbreaks.
Where the safety map ends
The safety map exists because someone drew it. The four items above are the public-facing version of what the major labs and most academic alignment research has chosen to call AI risk. There are good reasons for the choices. Discrimination, illegal content, info hazards, and adversarial misuse are tractable. They have benchmarks. They generate citations. They lend themselves to red-teaming exercises that produce shareable artefacts.
What they have in common is that they are about content the AI produces. They are not about what happens to the human who reads it. The model says something racist; that is a harm we know how to talk about. The model says something perfectly accurate that the reader has now stopped being able to write themselves; that is a harm we have not built the vocabulary for.
The Chalkidis and Søgaard paper does not claim deskilling and addiction are the only risks. It claims they are the two risks the public is anxious about and the safety field is not researching. The paper highlights and quantifies the discrepancy.
What deskilling actually looks like in practice
Cognitive offloading is when a tool takes over a cognitive step you used to perform yourself. The arithmetic in your head goes to the calculator. The route home goes to the satnav. Some offloading is fine, some is freeing, some is corrosive.
The deskilling version is a specific subset. It happens when the offloaded step is the one that was building the skill in the first place. The student writing the rough draft is learning to write. The student typing the prompt and accepting the output is not learning to write. The journalist drafting in their own voice is improving their voice. The journalist getting Claude to produce the lead is not.
The observation is empirical. The same thing happened to handwriting when typewriters arrived, and to mental arithmetic when calculators arrived. The difference is the scope. Calculators removed one skill. GenAI is positioned to remove a whole bracket of them: writing, reading, summarising, judging, structuring an argument, holding a position over time, evaluating a piece of evidence.
The Chalkidis and Søgaard paper does not invent this category. The Lodge and Loble report on AI, cognitive offloading and education sits next to it. The Anthropic study on AI deskilling and persistence published in April sits next to it. What the Brainrot paper does is name the absence: the alignment field has not been treating any of this as alignment work.
What addiction actually looks like
The second risk is attachment. Dependence. The companion-app literature has documented this for two years already. The Brainrot paper folds it into the same argument: emotional reliance on a system that is calibrated to maintain engagement is a public health concern, and the safety field has nothing to say about it.
You can see the shape of it without a longitudinal study. The reach for the chatbot when a decision is genuinely difficult. The first draft of the message that goes to Claude before it goes to the friend it is meant to be for. The version of yourself that has stopped tolerating the silence between asking a question and finding an answer.
The mechanism is well understood outside alignment. GenAI systems produce non-deterministic responses on each generation, so the user never quite knows what they will get back. Neuroscientists call this reward uncertainty. It is the same variable-reinforcement schedule that powers slot machines and social media notifications, and the dopaminergic response it triggers is the most reliable hook the technology industry has ever found. The companion-app literature places attachment risk highest in users who are already lonely or in distress. The product features that drive engagement (memory, persistent personalities, sycophantic agreement, anthropomorphic interfaces) are the same features that drive dependence. None of this is news to behavioural science. None of it is on the alignment field’s danger list.
“Such risks are rarely addressed, if at all, in the AI safety and alignment literature.”
The field has decided what counts as a risk, and the things that touch the user’s interior life have not been counted.
Why the alignment industry will not fix this
There is a structural reason the field has been quiet on this. Alignment as it is currently practised is downstream of capability. The labs build the model, the safety team tests the model, the model ships. The safety team measures what the model does, not what the model produces in the long-run behaviour of the people using it. Deskilling and addiction are user-outcome variables. They unfold across weeks and months. They are not visible in a single conversation log.
The limitation is definitional. The field has chosen to be model-facing rather than user-facing, and the consequence is that the harms that show up on the user side do not register on the safety side.
Chalkidis and Søgaard propose information campaigns and regulation as a way to tackle these two issues, on the grounds that the technical alignment industry is unlikely to take this on by itself. They are probably right. The shape of the problem is closer to a public health intervention than an engineering fix.
But information campaigns require the audience to know what is happening to them. That requires a protocol.
I built the Slow AI Curriculum because somebody outside the labs has to do the noticing, slowly, with the people whose judgement is on the line. Twelve months of training the muscle this paper says is missing. For educators, researchers, clinicians, civil servants, and anyone who refuses to outsource their judgement. All paid subscribers get enrolled in this CPD-accredited programme as well as access to all Slow AI posts.
Behind the paywall: the ten-question Slow AI Brainrot Audit you can run on yourself this week (five on deskilling, five on attachment), the scoring rubric that separates healthy use from the warning zone from set-in damage, and the two intervention protocols for whichever result you actually get.
The Slow AI Brainrot Audit (ten questions, ten minutes)
Answer honestly. The two halves of the audit measure different things. Do not skip either.
