You’ve heard these two phrases uttered thousands of times. They creep into every conversation about AI and work, being mindlessly parroted as people nod in involuntary agreement. Two phrases that seem incredibly simple yet are loaded with a potential world of problems. They are:
AI won’t replace people. People using AI will replace those who don’t.
And.
Just use AI for everything.
These two phrases, the first a statement of truth and the second a piece of advice, shouldn’t be mindlessly heeded and require a closer look. The advice dispensed in these statements is not only untrue but also bad for you.
No Malicious Intent
To start, I don’t think most people using these phrases are malicious or deliberately misleading. It’s quite the opposite. I think they genuinely want to help people and have good intentions. After all, I often hear these phrases from well-meaning people, not from overhyping tech bros. Tech bros feel that a conversation around these two statements is beneath them, and anyone considering them is too stupid to exist in the future of work anyway.
The real problem is that nobody has spent much time reflecting on the meaning of these phrases or considering their implications in the grand scheme of things.
The Laziest Statement In AI
Let’s begin with the laziest statement in AI.
AI won’t replace people. People using AI will replace those who don’t.
There is a mental trick to the phrase that makes it sticky. When people repeat the phrase, it’s a way of letting others know that they are fine with technology. They are up to date and hip with the hype. This is one of the reasons for the phrase’s popularity. However, most using this phrase are merely parroting others. They haven’t given it much thought. It seems logical enough, so uttering the phrase in a conversation is almost an involuntary response, but this statement falls apart under the slightest scrutiny.
The first part of the phrase invokes a sigh of relief. With the current level of AI hype, many people are concerned about being replaced. This first part puts people at ease, but that ease is temporary.
The second part of the phrase issues a call to action with an implied sense of urgency, warning people that they had better get on board. The AI train is leaving the station, and you don’t want to be left behind.
Never mind the fact that neither the first nor the second part of the statement is true.
AI won’t replace people.
Although many of the AI layoff announcements are nothing but AI washing, if they are to be believed, then AI is absolutely replacing people. But even setting this reality aside, CEOs have made it clear they want to replace you. The moment an AI tool is mediocre enough to do your job, it’s done, done, doneski. They are like rabid dogs roaming the corporate directory in search of employees to maul.
In fact, people are being fired preemptively because of AI. Look at the recent layoffs of Oracle and Meta for examples of these. People are losing their jobs not because AI can do their jobs, but because of the mere idea of AI.
You don’t think investors are dumping truckloads of money into AI because it’s a productivity booster, do you? No, replacing people is absolutely the goal. As a matter of fact, replacing people may be the only viable path given the amount of investment. In the immortal words of Aldous Huxley, nothing short of everything will really do.
Everywhere possible, organizations have shoved generative AI into everything in an attempt to replace people, from newsrooms to Human Resources and everywhere in between. AI companies are even trying to replace your friends and loved ones. Yes, AI will absolutely replace people whenever capabilities allow. However, we aren’t there yet.
The thing workers have going for them is that today’s generative AI isn’t capable of replacing large swaths of the workforce. It’s much more likely that additional innovation will be required for that to happen.
People using AI will replace those who don’t
But what about the second part of the statement? People who use AI will replace those who don’t. This requires some deeper analysis.
To begin with, there’s a subtle, nefarious aspect to the second sentence in this phrase. It’s an attempt to make AI part of your identity. This is worse than it seems. Sorry, I know you were good at your job, but your skills have now been devalued. If you don’t slop, you’re gonna have to stop… working here.
But surely, tools tied to identities are common. What about something like a hammer to a carpenter? This is true, but the hammer doesn’t define the carpenter. A hammer is also one tool among many in the carpenter’s toolkit. It’s not like the carpenter brings the hammer to the dinner table to pass the mashed potatoes. A carpenter also doesn’t use the hammer as a confessional, a companion, or a lover. No, a carpenter has an identity without the hammer.
Even for something as specific as a pole vaulter, where “pole” is literally in the person’s title, the pole doesn’t generalize across tasks. Therefore, it’s only useful in one very narrow activity. The pole isn’t a tool for daily decision-making. You can’t cognitively offload to the pole.
What does all of this say about you? That your value lies in AI usage, not in your actual skills and capabilities. That you, as an employee, are no better than any other employee using AI. There’s no differentiation. And no, stating that you prompt better than someone else isn’t the differentiator people think it is.
If AI is doing everything, then what are you doing? No doubt people imagine themselves as the all-powerful puppet master pulling the strings, but the reality may very well be the opposite: the user is the one getting their strings pulled as they are transformed into a digital janitor. Cleanup in cubicle 5.
Companies themselves often don’t care. They are looking for someone to fill a position. The reality is that you and AI are no different than someone else with AI. In this situation, AI becomes an equalizer, but in the worst way.
In this situation, AI becomes an equalizer, but in the worst way.
Now, there are certainly exceptions and exceptional people. Companies may be hiring for an AI developer role. There may be other roles where AI usage aligns more with job tasks, too. Keep in mind, these are exceptions, and we are talking about rules. Even in these exceptional cases, people need to consider differentiation outside of AI.
How will you differentiate in the current environment? What’s your story? What are your passions? What do you bring to the table that isn’t AI? How do you apply your skills and expertise to the job to differentiate yourself from the AI-dependent? This probably requires a whole post of its own.
There’s more to say here, but that involves looking at our next piece of advice.
Use AI For Everything?
And now, everybody’s favorite phrase.
Just use AI for everything.
That’s right. Don’t be selective. Don’t differentiate tasks. Damn the torpedos it’s full slop ahead. There are so many issues with this statement that it’s hard to choose a place to begin. But let me start by saying the phrase “Just use AI for everything” and “People using AI will replace people who don’t” are two sides of the same coin.
I’m not claiming that today’s generative AI doesn’t have its uses. It certainly does, but it’s a tool one can utilize for tasks. So, use it for everything? Seriously? Should we let ChatGPT run air traffic control? Should we replace our loved ones with AI? Should we use AI to write a sympathy email? The list goes on and on, and the answer to all of these should be no. Unfortunately, it looks like that’s exactly what we are getting in the air traffic control use case. This is absolutely insane, since it can’t even manage inventory at a Starbucks. The ATC scenario is my go-to for highlighting idiotic use cases, so I guess I need to find another one.
There are three immediate reasons to question using AI for everything: it devalues the activity, degrades your skills, and dehumanizes you and others. For a deeper dive, see my Four Ds of Personal AI Risk article, where I also cover disconnection.
Given the potential negative consequences, we should be selective in our use of AI for tasks and processes, using it where it is most appropriate and not for everything. After all, there may be tradeoffs we are willing to accept. Fair enough, but often these tradeoffs are made without a single thought.
Devaluation
Every time AI is added to a task, the value of that task lowers. For example, let’s look at sentiment analysis. Let’s say we have a human analyzing a host of reviews of a company’s products. The human determines whether the sentiment is positive or negative and forwards feedback to product teams.
AI has been capable of performing sentiment analysis for quite some time. The value of having a human do this decreases, even in conditions where the human is better, for example, in sensing sarcasm. If an algorithm only sends negative feedback to the product team, it may miss valuable insights in positive reviews as well. This is a tradeoff and one a company may happily make.
This isn’t universally a bad thing. Sometimes, this is beneficial. Maybe there’s a process in which, every time a condition is met, a check is put in a box inside a document. It may seem hard to argue that we need to add more value to this process. Yes, we are making a bunch of assumptions about the task, error rates, and a host of other factors, but the point still holds.
Now, let’s say we automate this checking of the box. The process will continue the same way until the heat death of the universe. Maybe this is perfectly okay. Fair enough. However, when a human performs the task (or a human is at least in the loop), questions may continue to surface as the business itself changes. Does the activity make sense? Maybe the activity itself provides no value. Maybe the activity can be enriched to provide even more value. All of this is lost once the human is removed from the equation.
It may be argued that other people in the chain could also come to these conclusions, yes, that is true, but often these insights come from people closest to the task, the very ones that have been removed with automation. It’s this insight that spells bad news for companies that want to get rid of people and replace them with AI.
There’s also the case where people in the chain create slop and send it on to their coworkers to fix a condition dubbed workslop. This actually creates more work for humans, despite using AI. This only gives the appearance of productivity, but it moves tasks around like a shell game.
Of course, all of this is moot when CEOs and other executives demand that their employees use AI. When this happens, out of fear and a need to demonstrate they are using AI, people will try to use AI for everything, further accelerating the creation of workslop.
Here’s the CEO of Box with a bit of insight.
He’s right, this distance is something CEOs and other executives don’t realize exists. In other cases, they spend far too much time reading nonsense news articles and half-baked analyst reports, thinking they are being left behind. Most CEOs don’t have the time (or won’t make time) for meaningful AI use. They play around, run a few experiments, and think they need far fewer employees. Of course, even more usage and experimentation could also fuel more delusions.
When executives demand that their employees maximize their use of AI, it further devalues what people do on a daily basis. Then again, not respecting your employees has become a bit of a theme lately.
Degradation
When it comes to degradation, there are two types of degradation we are concerned with. The degradation of the task or process being performed and the degradation of our own cognitive abilities.
Process Degradation
Let’s start with a question. Does adding AI to a task make it better or worse? I know, what is the definition of “better” in this context? Let’s say, for the sake of our conversation, that better refers to quality.
In a monumental number of cases, there’s absolutely no attempt to answer this question. It’s just that if AI can do it, people apply AI to it. It’s the Jeff Goldblum Jurassic Park meme approach to applying AI. Back in 2023, I wrote a whole post covering this degradation in applications.
In many cases, AI makes things worse or, at the very least, has no impact. In the cases where it’s made things worse, the output is acceptable enough for the task. An example of this degradation would be replacing a product’s search feature with an AI-powered one, which can lead to failures in simple pattern matching. Which, I don’t know, seems to be the entire purpose of the search feature. I mean, have you tried to use the search functionality on X lately?
I’ve said this many times over the past few years, but in an attempt to make hard things easy, many have made easy things hard. Welcome to the brave new world of degraded performance.
In an attempt to make hard things easy, many have made easy things hard.
Once AI appears to work, companies high-five and move on with life. If you don’t believe me, AI is being shoved into every conceivable crevice of our existence. Where has it made things meaningfully better? The AI phone representative, the AI features in applications, the AI operating system, and the list goes on and on. None of which we asked for and all of which we got.
I don’t mean to make this sound like there aren’t successful AI use cases. These certainly exist, and you can find them in places like software engineering or even cybersecurity. However, even in these successful use cases, the tradeoffs are rarely addressed. Only recently have people begun to talk about things like technical debt and cost.
There are certainly other cases where AI makes a meaningful positive impact. These may be due to volume, complexity, or other factors that humans struggle with. These are good candidates for AI applications. Imagine having to manually review 10,000 product reviews a day. Where companies run into issues is that they don’t have a way to measure the success of their experiments with any meaningful metric other than whether it appears to work.
Cognitive Degradation (Cognitive Atrophy)
AI is a tool that augments human tasks and activities through outsourcing. What sets AI apart from other, more common tools is that it is a generalized cognitive tool. Rather than augmenting a part of our body for focused tasks, as a hammer does, it augments our cognitive processes across a wide range of tasks. The benefit is also a tremendous detriment.
I’ve discussed cognitive offloading and cognitive atrophy many times throughout the years. It’s one of my biggest AI concerns. A few examples can be found here, here, and here.
The best way to think about AI is that it’s a competitive technology, and every time we use it, we are also competing with it. This isn’t as negative as it sounds. As humans, we collaborate with people we may be competing with, but we bring a different mindset to this activity under such circumstances. However, this is not the same mindset we bring to using an AI tool. We can claim it’s all us, without doing the work.
The best way to think about AI is that it’s a competitive technology, and every time we use it, we are also competing with it.
At best, AI rounds off the corners of human skills, and at worst, it atrophies them to the point of uselessness. As Nicolas Carr said in his book The Shallows, the brighter the software, the dimmer the user.
When I first started talking about the cognitive impacts of AI, it was a pretty lonely position. Now it seems you can’t go a couple of days without these issues being highlighted. A few examples from the past few months can be seen here, here, here, and here. There’s plenty more.
The challenge manifests when you try to add interventions to protect your cognitive capabilities and skills. Once the friction is removed, it all seems like additional work. And it is. However, if you want to continue using AI tools while protecting yourself, you will need to do additional work.
You don’t become a better coder by not coding or a better writer by not writing. Not doing makes you worse at these things, which certainly isn’t a benefit in the job market.
Ed Zitron, the firebrand of AI criticism, had this observation.
This needs to be tweaked and, in some cases, inverted. LLMs impress the non-writers who want to write, the non-coders who want to code, the researchers who simply want to boost their publication count, and the lawyers who’d rather be drinking.
In the end, using AI for creative tasks impresses only the people who use it, and those are people with no particular taste or talent.
Dehumanization
The use of AI dehumanizes you and others. It does this almost by its very nature of use, removing humans from the process. It numbs your senses to other people’s conditions and treats them more like apps than humans. I wrote about this condition and the dehumanization that comes from simulating emotions with AI back in early 2023.
To summarize, let’s take the example of a sympathy card. I’d take a poorly worded, human-written card over a perfectly worded AI-written card any day. It really is the thought that counts. This is something that every human innately understands. In the previous post, I used the example of a sympathy email on the loss of a child. Heavy.
The point is that the activity isn’t supposed to be comfortable, and its true value comes from the discomfort. While writing, we are forced to reflect on the situation, put ourselves in the person’s shoes, and connect with our feelings and our fellow humans. This makes us better people, far more appreciative of what we have and less likely to take things for granted. None of that happens when AI is used.
AI, in many cases, carries the potential to turn us into automatons performing tasks devoid of emotion. We shouldn’t allow ourselves to be turned into machines.
I mean, if we are using AI for everything, why not use AI to interview people for jobs? Hopefully, it is clear that this is fairly dystopian. Here’s a video of a guy doing a mock interview with an AI tool. Some people consider this progress and on the path to utopia, but I consider it the shitularity.
Conclusion
Lazy thinking dressed up as wisdom is the currency of our era. No time for reflection, only for reaction. As we’ve seen, the two phrases we examined aren’t harmless. Your hard-won expertise and domain experience remain valuable, but are absolutely things you can lose if not properly exercised.
None of this means rejecting the use of AI outright. It means being selective in your usage and application. The problem is that many aren’t considering the trade-offs. This needs to change. The next few years will bring challenges to both our humanity and our dignity. Lean into your strengths, find your differentiators, and defend your humanity.