Cognitive outsourcing and human skill atrophy
Background
The core idea behind this blog post is to explore a profound and often overlooked risk of our increasing reliance on AI: cognitive outsourcing and the subsequent atrophy of human skill.
My aim is to go beyond the usual discussions of job displacement and ethical alignment to focus on a more subtle and arguably a more dangerous long-term consequence. As AI agents become our default assistants for thinking, reasoning, and recommending, our own cognitive abilities might begin to wane. This isn’t a fear of robots taking over, but a concern that we might voluntarily give away the very skills that allow us to innovate, solve complex problems, and ultimately, maintain meaningful control over our own future. In this blog, I will try exploring a few key questions:
Which specific cognitive skills are most vulnerable to this erosion?
How does the loss of these skills impact our societal resilience during times of AI failure?
When does the convenience of AI cross a critical threshold, moving from a progressive tool to a source of strategic fragility?
And finally, how might our own degraded expertise shape the development of future agents, potentially creating a dangerous feedback loop?
I invite you to think about these questions with me. This is not a post about Luddite fears, but a candid look at the long-term, second-order effects of a technology that is reshaping our minds as much as it is our world.
Vulnerable Cognitive Skills
The skills most at risk are not our rote memory or our ability to follow a formula; although these are the tasks AI excels at. The most vulnerable are the meta-skills that constitute true mastery and innovation.
Problem Formulation and Abductive Reasoning: AI is adept at answering a well-posed question. But the difficult, often unacknowledged, work of a true expert is in defining the right question to ask in the first place. This requires a form of abductive reasoning—the ability to infer the most likely explanation from a set of incomplete observations. For instance, a software architect doesn’t ask, “How do I make this function faster?”, they ask, “Why is this system’s latency increasing, and could it be a symptom of a deeper design flaw or an unexpected interaction between microservices?” Relying on AI to suggest the problem can lead to a kind of solutioning bias, where we only address challenges the AI can easily frame.
Systemic Synthesis and Pattern Recognition: Expertise involves the ability to connect disparate, seemingly unrelated pieces of information into a coherent, causal model. It’s the doctor who connects a patient’s diet and stress levels to a seemingly random symptom or the engineer who sees a bug in the front-end as a symptom of a deeper database problem. AI can perform powerful associative linking, but its “reasoning” is often an emergent property of statistical correlation, not true causal understanding. As we outsource this synthesis, our own ability to see the “big picture” and spot a looming systemic crisis might atrophy.
Critical Skepticism and Intuitive Failure Spotting: An expert doesn’t just verify an answer; they instinctively look for the edge cases and logical inconsistencies that would break it. They have a “failure library” built from years of experience. When an AI provides a seemingly perfect answer that works well for the visible test set, the human’s role can devolve into passive verification. This erodes the cognitive muscle for spotting subtle yet critical errors, creating a collective blindness to what a trained eye would instantly recognize as “wrong.”
Impact of loss of our cognitive skills on societal resilience during times of AI failure
Skill erosion will fundamentally change a society’s resilience from robustness to brittleness in my opinion.
Loss of Redundancy: Human expertise serves as a critical redundancy layer in complex systems. If the primary system (the AI) fails due to a bug, a malicious attack, or a new, unforeseen problem—the human experts can step in and take over. As human skills atrophy, this redundancy is lost. A society that relies on AI to manage its power grids, financial markets, or supply chains is inherently fragile if the human operators lack the embodied skills to manage a crisis without the AI’s assistance.
Systemic Failure: When multiple interconnected systems fail simultaneously, a brittle system collapses completely. Our reliance on AI could create a situation where a single AI failure triggers a domino effect of cascading failures across interdependent sectors, and with no human experts capable of intervention, the system becomes non-recoverable.
Convenience as a Catalyst for Strategic Fragility
The transition from progress to strategic fragility occurs when a tool shifts from being augmentative to substitutive. There are readings out there that predict when certain tasks will be solved end to end by agentic AI.
Stage 1: Augmentation: AI is a fantastic tool that offloads tedious tasks, making us faster and more efficient while we retain the core cognitive load. This is a clear gain.
Stage 2: Substitution: The AI becomes so good that we no longer feel the need to learn or practice the underlying skills at all. We become “query-monkeys” or “provers” who simply check the AI’s work without understanding how it arrived at the answer. This is the point where convenience has transitioned into strategic fragility. We will eventually lose the ability to perform such tasks independently.
A Feedback Loop of Degraded Expertise
This is perhaps the most insidious risk: the degradation of human expertise could create a negative feedback loop that shapes the development of future AI in harmful ways.
Lack of Oversight and Control: As human expertise wanes, so does our ability to set meaningful constraints, define appropriate safety protocols, and audit an AI’s behavior. We risk creating agents that we cannot meaningfully supervise or contest, leading to a loss of oversight by design and potentially resulting in “runaway systems” that operate on principles we no longer fully understand.
The Problem of “Dumb Feedback”: AI models are improved through human feedback. If the humans providing that feedback have atrophied skills, they may not be able to provide the nuanced, expert corrections needed to improve the AI. They may only be able to correct simple, surface-level mistakes, leading to the creation of agents that are superficially correct but fundamentally flawed in their underlying logic.
The core problem I envision here is that instead of using humans to judge/rate the capabilities of a model, we would be using intelligent models to judge/rate the capabilities of a human.
This would involve having two people (say A and B) having to label some model responses, train two models separately on their rated responses and then see which human is more capable.
This leads to tertiary risks of AI models subverting RL and making the less capable human victorious and hence the one overseeing and auditing its behavior. This makes it easier for the model to then scheme against the less capable human and gain control.
This would also lead us to believe the smartest humans on the planet were unable to develop measure for AI Control and hence make deals earlier than we would want to with the agents.
I value and warmly welcome any feedback regarding the blog or my writing style. I also welcome any opinionated questions about my thoughts, it would be great to hear from you!
The risk is not overlooked at all. Many people are complaining about exactly this issue: students using AI instead of learning, professors using AI instead of teaching and grading, “vibe coders” writing crap code, and so on.
That’s a great point, and I agree that the general concern about over-reliance on AI is being widespread now. Also when I say overlooked, I mean it in the way that there are no constraints being put in place for the use of AI wrt limits for a particular task. For example if staring a screen is hurting your eyes, the doctor would recommend you to set an upper cap on your mobile phone usage (or wear blue-ray glasses). Unlike the above example, I don’t see much efforts in the field to limit AI usage (this comes from my original view that everyone company wants their employers to be super productive at all times, but if this comes at the cost of a potential human creativity cap in the future, it should be taken seriously and governed).
What I’m trying to highlight, though, isn’t the simple act of ‘using AI instead of learning’ which is a very valid and common complaint but the more insidious, long-term effect on our fundamental cognitive skills.
Everything the LLMs do is an “emergent property of statistical correlation”, including scoring gold at the IMO. I tested Claude to see how it does on causal questions::
Claude’s reply:
Another example. My question:
Claude’s reply:
I’d say Claude did pretty well.
Claude does better and better! My question:
Claude’s answer:
Thanks for these well-researched examples. I agree completely that Claude’s response is impressive and demonstrates a sophisticated ability to reason through causal relationships based on the vast amount of text it’s trained on, which includes explanations of physics and logical principles.
However, through the blog I am trying to argue that this still operates on a different plane than what a human does. The AI’s ‘causal understanding’ is an emergent property of it having read countless examples of causal reasoning (from textbooks, scientific papers, etc.). It’s essentially a brilliant pattern-matcher for the linguistic representation of causation. A human’s understanding, on the other hand, is built from embodied experience, hands-on experimentation, and the abductive reasoning I mentioned in the post.
So, while the AI can describe the causal relationship flawlessly, could it, for instance, infer a novel, unknown causal relationship from incomplete data in a real-world, messy scenario? That’s where I believe that humans still have a critical edge, and hence it’s the skill at risk of atrophy.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid, and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that an AI wouldn’t detect.
One powerful example is diagnosing a complex, multi-system failure in a large-scale industrial setting, such as a chemical plant or a power grid. A human operator might notice a seemingly unrelated set of events like a subtle temperature fluctuation in one part of the system, a delayed sensor reading in another, and a slight change in the viscosity of a fluid and, based on years of embodied experience and intuitive pattern recognition, infer a novel, underlying causal chain that the current AI models won’t detect. An AI would struggle because:
Incomplete and Messy Data: The data from these systems is often noisy, incomplete, or not logged with the explicit labels an AI would need. A human can interpret a flickering gauge or an unusual sound as a critical data point, something an AI might not be able to easily do.
Abductive, Not Deductive, Reasoning: The operator isn’t working with a clear set of facts to deduce a single answer. They are performing abductive reasoning inferring the most likely explanation from a set of incomplete, messy observations, which requires intuition and experience.
No Existing “Textbook” Answer: The causal relationship might be unique to that specific plant’s design, age, or a recent maintenance change, so there is no existing causal model in its training data to draw from. The human expert is essentially creating a new causal model in real-time.
Each type of disruptive innovation devalues some human skills while making other skills become more important. To know which skills atrophy and which get trained with AI you need to look at how the relevant workflows look like.
At the current tech level, it seems to me like I’m practicing more intuitive failure spotting when I’m interacting with AI. It makes a bunch of errors and the way to use AI well is about spotting the failures AI makes and working around them.
This seems an interesting way to look at things. While some of your arguments suggest that AI use can lead to the atrophy of critical skepticism, you’re pointing out a valid counter-argument: that the current unreliability of AI actually trains a new form of “intuitive failure spotting.”
You’re right. In its current state, an AI doesn’t consistently provide perfect answers. It might confidently give a wrong fact, a hallucination, or a subtly flawed logical sequence. As a user, you learn to develop a new “Spidey-sense” for these errors. The skill you practice is no longer just deep problem-solving, but an agile, real-time validation and error correction skill. You become a “proofer” or “auditor” of the AI’s output, looking for subtle inconsistencies and illogical leaps that a novice might miss. This is a very different kind of skill from the deep expertise of an experienced programmer or doctor.
This dynamic, however, is likely temporary. As AI models become more reliable and error-free, this new form of failure-spotting will diminish in importance. The danger is that as the AI’s accuracy approaches perfection, humans may become less vigilant, leading to a loss of the very skills you’re currently practicing. The real threat to skill atrophy isn’t the AI’s current fallibility but its eventual, perceived infallibility.
This isn’t inevitable. It’s possible to have regimes where humans are trained to take.over from.AI’s..staging outages and so on. My go-to example is airline pilots, who are trained to fly manually in the case of equipment failures.
The airline pilot example is a perfect illustration of how a human-in-the-loop system can be designed to preserve critical skills. The key here is the distinction between passive oversight and active training.
In the context of my blog post, the airline industry’s approach to pilot training serves as a model for a “regime” that actively combats cognitive atrophy. Pilots don’t just sit back and watch the autopilot; they undergo rigorous, recurrent training in flight simulators where they are forced to handle rare, complex failures and fly the plane manually. This deliberate practice prevents the atrophy of their core flying skills, even when they rely on automation for most of their flights.
However, how much of the code being written in 4-5 year’s time will be actually written by us, without any AI inputs, my guess is that it will be barely a couple of lines per 10k lines of code.
I don’t understand this. If the AI becomes truly general, then the AI, and not the humans, will have the skills necessary to avert the crisis. Did you mean something like titotal’s Slopworld? Or that a society dependent on the AGIs will have less value than a pure human society? If the latter, then I would fully agree.
I also don’t understand how degradation of human expertise could lead to subverting RL. There are domains where one can use RL with no human feedback at all, like math, coding or technical sciences.
I somewhat do agree with “a society dependent on the AGIs will have less value than a pure human society”.
With respect to subverting RL, I meant subverting RL to prevent us from aligning them without us even realizing. This is plausible since it is us who we can ultimately trust (rather than another model) to have the maximum coverage (since we are more creative), and more alignment oriented (since might be the ones at risk) to label safety-alignment data.