Read it for me, write it for me, think it for me, do it for me. These are the essential commands of the AI era. By thinking we are freeing ourselves, even if we’re only freeing our time, we create more dependence and become further removed from problems. Instead of freedom, we get confinement, locked into doing things because of technique, not excellence, and inevitably dooming us to doing things because we can, not because we should.
We become almost giddy with excitement as we give up our skills and control, content with the illusion of competence, convinced we are the puppet master pulling all the strings, not realizing we’re the ones being pulled, shoved into tiny boxes and constrained in ways we don’t recognize. Novelty and ingenuity are replaced with mechanization and automation, creating conditions where we are far removed from the tasks and problems being solved, leading us to miss the point and build solutions not to the problems themselves, but to problems created by our previous solutions.
This is the world we accelerate into, our destiny. But we must recognize that we are not heading into a solved world. Far from it. We are headed into a world where problems persist, yet we lose the skills necessary to address them directly. Humans are shifting roles from operators to monitors and finally to spectators, creating a world not of abundance but dependence and leaving us vulnerable to a wide range of unintended consequences.
Note: Since people will get the wrong idea, this article isn’t telling people not to use AI. This article is about making purposeful choices, understanding the trade-offs, and deciding what’s best for you and your tasks. Use AI where it benefits you most and employ strategies to protect the skills you care about.
Table of Contents
So What?
As I returned from the annual pilgrimage to Las Vegas for Black Hat and DEF CON, I scribbled my thoughts in a notebook while I waited for my plane’s mechanical issue to be resolved. Needless to say, I didn’t make it home that day.
Fresh on my mind was the apparent nonchalance about the impending death of skills and the changing role of humans in processes. Various people I spoke with throughout the week expressed these views. If you are familiar with my writing, you’ll know this is a topic I spend an inordinate amount of time thinking about. So, before we begin, let’s address the inevitable response: “So what?”
Some may respond that AI and associated tooling let people do more with less, and do it even faster. AI also offers other advantages, like making some processes less error-prone or letting someone with little experience perform a complex task. So, who cares if people lose their skills, or never develop them in the first place? Although this perspective has some substance, it’s hardly universal or evenly distributed across industries and roles, but fair enough. Let’s roll with this.
The thing to consider is that we are not entering a solved world or even a world with a solved industry. This means the problems we face will persist long after we lose our skills. We may gain more tools and more abstraction, but not more insight or knowledge into the problems we need to solve. We are actually getting less. In essence, we create new vulnerabilities and dependencies, but we don’t give them a second thought as we trade them away.
Here are two points to consider about current-state and near-future AI solutions.
- AI doesn’t solve all of the problems of any given industry.
- AI creates new problems of its own.
Technology is always about trade-offs. Maybe a world with few skills is the world we want, but that’s not a consideration today. Nobody is asking if what we are getting is worth what we are losing. Or even if we can adjust our approach to offset what we are losing, either within the technology or through other methods and controls. Current AI technology is being treated as a total and complete solution without trade-offs, but that isn’t the case. Maybe this is because people think we are building a machine god. However, this thinking leads us to ignore the unintended consequences looming on the horizon.
This article isn’t meant to propose a universal solution to the issues emerging from trade-offs. In many cases, especially when transforming entire industries, there isn’t much we can do. In a way, it reminds me of Jacques Ellul writing an entire book on what he called “technique.” His book is filled with observations of decline and negative impacts on humans but not a single solution. He presented the information to surface the issues, making them visible to readers. Individuals were left to ponder and potentially mitigate how/if they pleased.
My hope is that at least some people will become more sensitive to what they may be losing so they can shore up gaps, either in their own knowledge or in the processes they employ. We as individuals need to protect what we care about, because other people won’t.
The Changing Role of the Human
What’s the human role in our newly developed technological systems? We’ve become so caught up in using tools that we become blind to their true impacts.
Instead of being operators, hands-on with the environment and problems we are trying to solve, we’re becoming monitors, hands-off, happy to observe, setting the process in motion but taking no part in it. Jacques Ellul noticed this shift back in the 1950s, and it’s gotten far worse since then.
This means that man participates less and less actively in technical creation, which, by the automatic combination of prior elements, becomes a kind of fate. Man is reduced to the level of a catalyst. Better still, he resembles a slug inserted into a slot machine: he starts the operation without participating in it. -Jacques Ellul, The Technological Society (1954)
Modern slot machines are a great example because they often give us the impression of winning even as we lose. In her excellent article on “dark flow,” Rachel Thomas said this:
On a traditional slot machine, you either win or lose. In contrast, multiline slot machines have 20 rows going at once and reward partial “credits” that create a false sense of winning even as you lose. For example, you can gamble 20 cents and receive a 15 cent “credit”. This is actually a 5 cent loss, yet the slot machine plays celebratory noises that trigger a positive dopamine reaction. -Rachel Thomas
We certainly don’t see ourselves as merely slugs in a machine. We see ourselves as winners, even as we lose, and don’t recognize the difference between AI doing something and us doing something. We see it as one and the same. However, even though AI is a tool, it’s also our competition, stealing opportunities and experience and robbing us of knowledge and understanding.
These issues are not new. In the “Monitoring” section of her 1983 paper called Ironies of Automation, Lisanne Bainbridge makes the following observation about the human capability to take over from an automated system:
“One complexity which it does raise of course is that the supervisor too will not be able to take-over if he has not been reviewing his relevant knowledge, or practising a crucial manual skill.” Lisanne Bainbridge, Ironies of Automation (1983)
Granted, it’s not always extreme, and we may not care about some skills. Fair enough. However, we aren’t recognizing the condition at all today and making conscious decisions about which skills to protect and which we are comfortable losing. We are setting the stage for the emergence of unintended consequences, including the death of skills and the persistence of problems.
The Death of Skills
So, we are confronted with a core question:
What does the death of skills and the changing role of humans mean as we face an unsolved world?
I’ve been warning about what I call cognitive atrophy, or what some call cognitive surrender, for years now. I’ve mapped out what I call the personas of personal AI. These are the personas users want AI to embody for them as they over-rely on the technology. I’ve also called out some impacts, which I refer to as the four D’s of personal AI risk. These are just a taste, but augmentation with technology has many consequences people don’t recognize.
Augmentation is Amputation
Marshall McLuhan observed that every augmentation is also an amputation, one that forbids self-recognition. That lack of self-recognition is why we often don’t notice the negative impacts of augmenting ourselves with technology. Although we will eventually notice muscle atrophy in our legs when we augment them with something like a robotic exoskeleton, atrophy in cognitive tasks can be much harder to confront.
Self-amputation forbids self-recognition. -Marshall McLuhan, Understanding Media (1964)
This lack of self-recognition means we won’t even recognize the condition as skills atrophy and wither.
The number of people who seem perfectly fine being replaced, outsourcing all of their tasks and reducing their relevance, is puzzling. As Marcus Hutchins recently put it, “Being promoted to the world’s saddest project manager.”
I believe people aren’t concerned for several reasons, one of which is that playing with tech can be fun. But I think this mostly comes from the impression of being an all-powerful puppet master, completely in control, commanding a fleet of agents doing your bidding. But that’s not how it works in practice.
Humans evolved to be cognitively lazy and, like water, we take the path of least resistance. The call of cognitive offloading is too powerful, and the allure of an oracle is too enticing for many to resist.
Take the case of Josh Autenrieth. In 2020, an explosion at Watson Grinding triggered a lawsuit that included the company 3M. 3M engaged Autenrieth, who works for Nighthawk Engineering, to prepare an expert report. When one of the plaintiff’s attorneys noticed that the report appeared to have been generated by ChatGPT, the attorney requested all the prompts, a precedent that will no doubt be utilized far more in the future.
The details are egregious, and the full story, detailed by 404 Media, can be read here. Let’s just say this wasn’t someone using ChatGPT to write a report for them. Autenrieth also used ChatGPT to do the analysis and thinking for him, basically doing the job he was hired to do. Let’s keep in mind that he’s supposed to be an expert, but the 404 Media article had examples like this:
Although it’s easy to roll your eyes and call this an exception, it’s not the exception people think it is. We are creating an entire society of Autenrieths. They are out there, doing work every day. Not at some distant point on the horizon, but today. The only difference is they don’t have a court of law to scrutinize their work product. They may be in different industries with different stakes, but they are out there nonetheless.
We all know someone, or work with someone, who was barely competent at their job in the first place, but now, with ChatGPT, Copilot, or whatever, they project themselves as rock stars, flexing the epitome of competence but creating a slop-filled swamp, leaving more competent people to take on the role of slop janitor.
As a critic, I find the stories people tell me both comical and terrifying. For example, the developers who are told that they care too much and that their job isn’t to write code anymore, but to be QA. Meanwhile, their co-workers hurriedly push code into production without understanding it. Others don’t realize their builds are failing and struggle with foundational technologies like Terraform. Today’s foundational skills will confound tomorrow’s workers.
Is it any wonder we have headlines like this?
The sad part is that this is exactly the situation AI companies want. A society of Autenrieths with degraded skills and high tool dependence. Ah, I can smell the utopia now.
Losing skills has a blast radius we often don’t recognize. Most people don’t give using GPS a second thought compared with using maps or landmarks, but it’s been linked to a loss of spatial memory. What else might we be giving up that we don’t realize until it’s too late?
Trading Strong Skills for Weak Skills
People who lose skills can struggle to recognize the loss. This is especially true when a role or industry has already shifted, and new people enter the workforce. You are unlikely to identify missing skills you never developed. Even worse, when shown these foundational skills, newcomers to a field may find practicing them painful.
However, during the shift, this lack of identification is further obscured when exchanging strong skills for weak ones, because weak skills can masquerade as strong skills. Basically, the person with the traded skills still feels like they are accomplishing the same tasks, even though they’re not exercising the same skills. In many cases, this may even be true, but only up to a point.
For example, monitoring a dashboard or configuring a UI is a weak skill because it completely obscures the underlying activities and their complexity. It’s both a bug and a feature. In many cases, the user knows there’s a configuration option or checkbox, but has no idea what actually happens when they select it. This is what I mean when I say we are transitioning to being more “hands-off” with processes.
Imagine an analyst who used to reverse engineer malware to understand how it works, now merely uploading it to an AI tool and getting a high-level report. Or a penetration tester who previously used a variety of techniques and tools to identify weaknesses in a system, now merely pointing an AI-powered scanner at an environment and reading the results.
We are replacing discovery through exploration and the direct application of skills and knowledge with automated responses curated through dashboards and user interfaces. This is further fueled by people’s preference for being told things rather than discovering them, which also negatively impacts satisfaction. Of course, only one of these scenarios allows technical knowledge to grow and new discoveries to emerge.
“One result of skill is that the operator knows he can take-over adequately if required. Otherwise the job is one of the worst types, it is very boring but very responsible, yet there is no opportunity to aquire or maintain the qualities required to handle the responsibility.” -Lisanne Bainbridge, Ironies of Automation (1983)
In many ways, this aligns with our innate human need for shortcuts and an organization’s desire for optimization. We’d rather be told answers than discover them. But nobody thinks about the current situation this way. This skill trading is a byproduct of a role change or a new tool that reshapes the landscape, so it can “feel” as though we’ve gained something even though we’ve lost it.
Death of Future Skills
Humans aren’t being replaced en masse by AI in the workforce yet, although they are being replaced by high hopes. Actually, this isn’t entirely true. AI is affecting some industries more than others, mainly jobs in media and arts. However, where AI is deployed and how it’s used can have a cascading effect.
AI can become a self-fulfilling prophecy for the extinction of skills, creating conditions for skill degradation in the present and guaranteeing their extinction in the future. This happens as junior roles are eliminated, change completely, or lose chances to flex real skills. This condition isn’t obvious or a concern for a CEO focused on quarterly numbers, but it should matter to us and society at large. A lack of skills creates vulnerabilities.
There is some concern that the present generation of automated systems, which are monitored by former manual operators, are riding on their skills, which later generations of operators cannot be expected to have. -Lisanne Bainbridge, Ironies of Automation (1983)
Fragility
Our technologically advanced world seems all-powerful, as though we can accomplish anything, but this perception masks a dark truth. Our modern, advanced world is incredibly fragile. We see it every time a natural disaster, power outage, war, or other unexpected event occurs. Although unexpected events on this scale seem rare, they are far more common in everyday life and business.
When people lose or fail to develop skills, the world becomes even more fragile because dependency skyrockets. Small issues can balloon into major problems. People fail to understand not only how the systems they interact with work, but also how the whole world works. We are left with a world where technology-created problems can only be solved by piling on even more technology because we lack insight into the true nature of problems. Human ingenuity is no longer a factor because we’re too far removed.
AI Is Not Just Any Tool
Part of the nonchalance about AI stems from the perception that AI is just a tool like any other. Although I agree that (today) AI is just a tool, it’s not just any tool. It’s a cognitive tool that can plan and take action. A tool that abstracts us from the problems we are trying to solve, adding complexity in the process. Only the complexity is completely obscured behind a simple-looking response.
Many now believe what we are going through is like other technological revolutions, and that we’ll just adapt to this new way of doing things. One example is the shift from programming in assembly language to a higher-level language like C or Python, and now we are programming in “natural language,” but this isn’t remotely the same.
With a high-level programming language, you’re still confronted with a representation of the problem space. You subsequently need to perform actions such as setting and reading variables, performing iterations, and debugging and troubleshooting. What changes is syntax. Sure, the developer becomes abstracted from opcodes, registers, and other platform specifics, but the shape of the problem remains.
Sure, the generated code can still be reviewed and debugged by a human. This is true. But those skills degrade when you don’t write code manually at least some of the time. The human simply becomes a meat sack button pusher, approving every change and often not realizing something isn’t working as expected.
In addition, when performing the task yourself, you’re forced to reason about the situation and problems you face. There’s less cognitive laziness in writing code by hand. Well, for those who don’t simply copy/paste everything from Stack Overflow anyway. In many cases, you need to reason about what you are doing and why. This is not the case with AI.
Developers are now told their job isn’t to write and understand code or to understand architecture, but to push more code. This is like telling a carpenter their job isn’t to understand construction or architectural plans, but to use more lumber. The negative consequences of both scenarios should be obvious.
As I mentioned, AI use doesn’t represent a problem space. It’s more like an amorphous blob giving no indication of shape or form. Requests are issued, output is spit out, and decisions are made. We are no longer hands-on with problems. We are miles away munching on a cheeseburger, waiting for a notification of a result.
Each AI advancement is dehumanizing and removes agency. This isn’t always bad. For example, we may decide that self-driving cars are preferable to human-driven cars, so we give up some agency and the human aspects of driving. But we should recognize that this dehumanization and removal of agency happens to whatever activity AI is applied to.
Losing the shape and form of problems creates many unintended consequences. In the self-driving car example, humans are asked to take control in the most challenging of situations. The very situations they are now unprepared for due to the dehumanization of driving.
Ultimately, we lose our understanding of both the domain the problem exists in and our ability to recognize true problems. The human is abstracted from both, further distancing us from the problems we are trying to solve.
Problem Proximity
Proximity to a problem is essential to understanding it. Being close and understanding the mechanics involved is essential for creating solutions. A layperson isn’t going to point ChatGPT at cancer and command it to provide a cure. This may seem like a silly example, but it is exactly how many people try to solve problems with AI today.
Removing yourself from a problem obscures all the surrounding complexity. It’s one of the reasons people thought the vulnpocalypse would happen. With AI identifying so many vulnerabilities, how could it not? But real-world attacks and scenarios require far more than vulnerabilities, and attackers have monetary constraints too.
Cybersecurity vendors are now working to completely remove analysts from the security operations center (SOC) and replace them with AI agents. What happens when attackers keep their skills but defenders either lose theirs or their jobs disappear completely? That’s not a good scenario. Companies will be constantly surprised by successful attacks.
What happens when attackers keep their skills but defenders either lose theirs or their jobs disappear completely?
Sometimes people are too close to problems, and a bit of distance helps them find solutions. This is true. However, in this scenario, people still need the perspective gained from being close to the problem in the first place.
We are moving to a distance-only future. We will no longer have close proximity to techniques and problems. We are approaching the world as though we already have AGI and are headed for a life of leisure, but we doom ourselves to a future of constant problems and unknowns.
Domain Expertise
Domain expertise requires depth and experience, two things that run counter to what AI promotes. Thankfully, we still have a wealth of domain expertise today. However, this domain expertise creates a blind spot when it comes to AI.
When a domain expert experiments with AI, they tend to overlook the issues. They can spot the problems and make corrections. They end up “filling in the blanks,” both physically and mentally. This is partly how smart people can get captured by AI hype.
Although AI seems to reward domain expertise today, it also atrophies it, as we have seen in the previous sections. As people are further abstracted away from performing direct tasks, domain expertise weakens.
Technique
We are searching for the one best way, the ultimate optimization, the final optimization, if you will. Previously, I mentioned Jacques Ellul and his concept of “technique.” In the foreword section of The Technological Society, Robert K. Merton summarized Ellul’s term “technique” as “Far more than machine technology. Technique refers to any complex of standardized means for attaining a predetermined result.”
AI represents the ultimate attempt at technique. It’s the attempt to build a universal technique machine, to step aside, and let the machine continue the path itself, with humans no longer even catalysts but passive observers, reduced to cheering fans in front of television screens. This represents the ultimate transformation of the human role in processes: from operator to monitor to spectator. Proponents believe that this technique will conquer space and time. It’s easy to lose sight of the fact that this is all purely speculative.
We forget that the world is complex, often confounding our assumptions, and the implementation of “technique” has an odd way of opening us up to new attacks and failures. These are not unlike the Air France flight 447 incident, in which deskilled pilots couldn’t manually fly the plane after the autopilot disengaged and an unexpected situation arose.
ARM Yourself
All of these conditions beg the question: what can we do? In many cases, individuals can’t do much to drive broader change, especially when industry roles are changing, or your boss tells you your job is simply to commit more code. Society and industries seem to accept these changes as destiny. After all, AI and related AI-powered tooling are a form of “technique,” as Ellul elucidated. Still, that doesn’t mean we are completely helpless.
As individuals, we should enact mitigations to protect what we care about. What we care about shouldn’t be limited to applied skills or technical applications. We should also consider scenarios involving human communication and interaction, given their dehumanizing effects.
Look for conditions where a tool performs the analysis or “thinking” for you, performs a critical skill necessary for your job, or even communicates with a human on your behalf. These are ripe areas for protection.
To support this analysis, I suggest a three-step process I call ARM.
- Awareness
- Recognition
- Mitigation
The first step is simply to be aware that conditions of cognitive degradation and decay exist. Hopefully, my work and the work of others help with this.
Second, recognize when these conditions show up in your daily life. This isn’t as easy as it seems. Remember McLuhan’s quote about self-augmentation forbidding self-recognition. This requires purposeful analysis. Periodically perform a skills audit to examine your tasks and how you accomplish them. Identify conditions that may be affecting the skills you care about.
To support the recognition step, you can also build a habit of occasionally forming a hypothesis and then checking your work against AI output. Basically, you perform the task, then have the AI perform it. If the outputs match, that’s a positive sign that you maintain at least some of the core skills you’re trying to protect. If they don’t, then you need to implement some mitigations.
Finally, implement mitigations to protect the skill or scenario. This step can also be difficult because humans are cognitively lazy, and once something feels “optimized,” it’s hard to go back. Mitigation doesn’t necessarily mean abstinence. However, you may choose to have a no-AI rule for specific activities or communication methods.
Choose an approach that you feel protects what you care about. For example, you can exercise some tool-free practice days to keep your skills sharp. Or you could implement a human first-pass rule and only add AI after you’ve done a first pass yourself. Use the strategy that works for you.
By now you are probably thinking, “Wow, that seems like a lot of work.” It certainly can be. That’s why you are only implementing these strategies for skills you care about protecting. These strategies can also be critical for workers new to a job or industry, allowing them to practice foundational skills before AI tooling abstracts them away. Something to think about whether you are the new employee or a people manager.
Let me throw out a few examples.
Examples
I don’t use AI to write anything for me. Writing is thinking. I painstakingly write every word either by putting pen to paper or in an editor like Ulysses. This is because I’ve seen how it sharpens my thinking and lets new insights emerge. Insights that wouldn’t have come about otherwise.
The response may be, “But isn’t that time-consuming?” Yes! It absolutely is, and that’s the point. As for the inevitable question about time, it’s simple. Don’t spend time on things you don’t care about. Life is too short.
In another example, maybe you have a repetitive task, like searching through a group of files for certain conditions and extracting them. In many cases, this isn’t a skill you want to protect, and you’re happy handing it off to a tool and making better use of your time.
Finally, what about reporting? On the surface, reporting may seem similar to the file search case, but it really depends. You want to ensure the analysis and insights are coming from you and your expertise. Don’t pull an Autenrieth. If the tool is simply taking care of the formatting and other miscellaneous wrapper content and the analysis and insights are yours, then this may be fine. Obviously, as long as you check the work product.
However, if additional insights emerge while you write the report yourself, you may want to keep the task rather than outsource it. It depends on what the report is for, who will consume it, and how valuable it is to you.
It Doesn’t Have To Be
The sad part is that systems don’t have to be designed like this, to devalue the human and degrade their skills. It’s possible to design systems that augment and strengthen the human involved in the process. And yes, using AI. It’s just that no one has the appetite to address this. The whole emphasis of AI investment is on removing humans from various processes completely.
However, if we look closer, the benefit is obvious. The approach leaves room for new discoveries, new innovation, and the opportunity to be far more robust when unexpected situations arise, creating a best-of-both-worlds scenario. But I wouldn’t hold my breath that this will happen on purpose.
You can also use AI tools without experiencing cognitive degradation. People do it every day. However, this isn’t how most people use the AI tools at their disposal. They may not have gone full Autenrieth yet, but they are on the path. In the immortal words of the great American philosopher Ice Cube, “You better check yo self before you wreck yo self.”
Conclusion
In a recent article, Minh-Hoang Nguyen argues that a new kind of brain rot is “intellectual cowardice.” This is useful framing. We’ve gotten away from using language like this. We need to elevate the concept of “honorable” activities and call out the dishonorable or cowardly ones. This framing mirrors my conversations. I often describe activities as “honorable” or “dishonorable.” For example, spending twelve hours a day on social media and ignoring your family is not an honorable activity. We should be elevating more honorable activities.
People often talk about the utopian freedom that AI can bring, but in many cases they describe a life of dependence and confinement. We are offered the illusion of choice when the path has already been chosen, and we remain vulnerable to every unknown thrown across our path. Ellul was right: technique is coming for us all. We must protect what we care about, because nobody else will.
Note About McLuhan and Amputation
Some who have never read Understanding Media may nitpick that McLuhan never actually uttered the words “every augmentation is an amputation.” Although this is true, it’s also certainly what he meant. McLuhan mentions “amputation” or “amputated” seventeen times alone in his chapter called “The Gadget Lover.” Below is the closest line to this:
Any invention or technology is an extension or self-amputation of our physical bodies, and such extension also demands new ratios or new equilibriums among the other organs and extensions of the body. -Marshall McLuhan, Understanding Media (1964)
So, yes. Although he never said the exact phrase, anyone who has spent time reading Understanding Media knows that’s certainly what he meant.