A recurring response to the cognitive-offloading concern goes like this: the new reasoning models think harder. They walk through problems step by step. They show their work. Doesn't that change the analysis? It's a reasonable question and it deserves a careful answer.
OpenAI's o-series — the reasoning-model family announced across 2024–2025, including o1 and o3 — works differently from earlier ChatGPT models. Instead of producing an answer in one pass, these models spend more compute on chain-of-thought-style processing before responding. The architecture is built around "thinking time." OpenAI's marketing language for this is, roughly, think before answering.
The honest read is that the reasoning-model upgrade is real, and the cognitive-offloading concern is not addressed by it. Here is why.
The offloading question isn't about the model
The Risko and Gilbert framework — published in Trends in Cognitive Sciences in 2016 — defines cognitive offloading as the use of external tools to reduce internal cognitive demand. The definition is about the user's side of the loop, not the tool's. When you type a question into a search engine instead of trying to retrieve a fact from memory, you've offloaded the retrieval. The search engine could be a 1998-era list of ten blue links or a 2026-era reasoning model walking through a multi-step proof. The behavioural variable — the user skipping the cognitive act — is the same.
The 2025 MDPI paper on AI and cognitive offloading and the EDUCAUSE Review 2025 piece, The Paradox of AI Assistance: Better Results, Worse Thinking, both make this point in different ways. The paradox the EDUCAUSE piece names is the gap between artefact quality and thinking quality. Better AI produces better artefacts. That same better AI can produce worse thinking in the human counterpart — because the artefact arrives without the human doing the thinking that would normally produce one.
A reasoning model that thinks longer is, from this angle, a worse offloading partner than a non-reasoning model. The non-reasoning model gives you a guess you might second-guess. The reasoning model gives you a confident multi-step answer that looks like the work of someone who reasoned about the problem. The temptation to skip your own reasoning is higher, not lower.
What the o-series actually changes
To be fair to OpenAI's family of reasoning models, they do change real things. Three are worth naming.
Multi-step problems improve. Math, code with constraints, formal logic — domains that benefit from chained inference improve substantially with the o-series. The benchmark gains across 2024–2025 are documented.
Hallucination on reasoning-heavy queries drops. When the model walks through a problem, the wrong-shaped guess fails earlier in the chain, which means fewer confidently-wrong answers on questions where the structure matters.
The "show your work" surface is bigger. The models expose more of the reasoning trace, which means a user who wants to engage can follow along. That's a real affordance.
All three are improvements for the model. None of the three is an improvement for the user who doesn't read the reasoning, doesn't verify the steps, and doesn't independently arrive at the answer.
ChatGPT adoption is the load-bearing context
Reuters' reporting on ChatGPT adoption — 700 million weekly active users by mid-2025, on track to higher numbers since — is the scale variable that makes this conversation matter. Even if the cognitive-offloading effect were small per user, the per-user count is now large enough that aggregate effects on workplace cognition are documented in the BCG and HBR-aligned AI brain fry research and in the ChatGPT user research pages we keep updated.
The reasoning-model upgrade affects the same population. If 700 million weekly users are now reaching for a reasoning model instead of a non-reasoning model, the question of how that interaction shapes the user's own thinking practice is more important, not less.
What the published evidence actually says about model quality and user thinking
The relevant studies all hold the model variable roughly constant and look at the user's cognitive engagement.
The 2025 EDUCAUSE paradox piece argues that the gap between artefact quality and thinking quality widens as the model gets better. Better model, worse human reasoning, when the human takes the artefact at face value.
The 2025 MDPI paper on AI and cognitive offloading reports the same direction: students with access to higher-capability AI systems do less independent reasoning work and recall less of their own conclusions, even when the produced artefacts are higher quality.
The 2016 Risko and Gilbert framework predicted this pattern before AI was the medium. The mechanism is general: when a tool lowers the cost of a cognitive act, the human does less of that act. Better tools lower that cost more efficiently. The behavioural drift is in the same direction.
None of these studies condition on whether the model used chain-of-thought processing internally. The studies are about what the user does. The user's behaviour is the variable.
What this is not
A few hedges, because we always hedge here.
This is not an argument against using o-series models. They are better at hard problems. They produce fewer confident hallucinations on reasoning-heavy queries. Using them is reasonable. The argument is narrower — the upgrade doesn't solve the offloading concern.
This is not a claim that reasoning models cause cognitive harm. No study has measured that. The published evidence is about the offloading pattern, not about durable cognitive effects of any specific model family.
This is not a prediction. AI is moving fast. The o-series of 2025 is not the reasoning architecture of 2027. Whatever comes next will be evaluated on its own evidence. The point of the current analysis is the published 2025–2026 state of the literature.
How to use a reasoning model without losing the thinking practice
The same calibration that works for ChatGPT works for the o-series. Three rules from the no-edge-loss playbook:
Bound the use. Reasoning models for the hard problems. Don't open one to ask what time it is in Paris.
Think first. Even when you plan to use the reasoning model, write down what you think the answer is — or at least the shape of the answer — before you read what the model says. This is the version of the Kosmyna et al. generation-after-thinking finding adapted to reasoning queries.
Verify against your own logic. Read the reasoning trace. Find one step you wouldn't have made. Find one step you would have made differently. That's the cognitive engagement that keeps the practice surface alive.
These rules are not novel. They show up across the published guidance on AI use and they don't change when the model gets better at multi-step inference. The user's habits are the variable.
Where Senwitt fits
Senwitt does not weigh in on which AI model is best, and we don't track the OpenAI release calendar. What we maintain is the Daily Set — seven minutes of mixed unmediated reps across writing, math, code, memory, reading, and reasoning. The point isn't to compete with reasoning models. It's to keep the practice surface alive so that on the queries where you decide to think for yourself, you're in shape to do it.
