People love to utter phrases like “data is the new oil” or “data is the new gold.” When we hear these phrases, we immediately think of businesses and big data, not the simple aspects of our daily lives. However, this obsession with data seeps into every crevice of our existence, even when the data is worthless. The reality is that people find activities less valuable, or even pointless, unless they have data about them. This is like Nikola Tesla, who had a terrible meal if his portions weren’t divisible by three.
Many can’t just wake up well rested after a good night’s sleep. They need to confirm using data from a sleep tracker, ensuring that their sleep duration, O2 saturation, and heart rate variability fall within specific ranges. In many cases, reality is overridden by data, despite the fact that devices aren’t foolproof. When your device conflicts with your reality, the device wins because… data.
Modern readers are also data-obsessed. They hunger for a wealth of statistics and datapoints that they can poke, prod, and analyze. They seem more infatuated with data about their reading than the reading itself. As it so often does, our modern world, with its hyperfocus on optimization, shifts our attention away from what matters and leads us to optimize the wrong things. This result leaves us worse off while giving us the illusion of productivity.
Reading Data Obsession
In my previous post on reading, one thing I failed to mention was people’s obsession with data about their reading. As I mentioned in that post, I prefer physical copies of books, but I also have an eReader. In 2026, I switched from a Kindle to a Kobo as my primary eReader. As I poked around the Kobo community, I found that people were very excited about StoryGraph coming to Kobo.
For those unfamiliar, StoryGraph is an application that seems to suck the value and fun out of reading. StoryGraph has three main features: reading stats, personalized recommendations (including mood-based recommendations), and a read-with-friends feature. None of which enhances the reading experience. However, all of which are detrimental.
Read With Friends
The read-with-friends feature is a clear drawback. Yes, please interrupt the flow and focus of my reading so I can see what one of my dumb friends has to say about it. This provides the same type of distraction as social media, now available on your dedicated reading device.
Algorithmic Shoving Match
People will argue with me about algorithmic recommendations until the heat death of the universe. They tell me how AI is much better than other forms of recommendation. I’m sure some will swear by the value it provides. I mean, it’s not like I haven’t benefited from algorithmic recommendations myself, so fair enough. Despite this, mediating everything through algorithms isn’t the great idea it’s cracked up to be.
First of all, you take a social activity, getting recommendations from others, and turn it into an anti-social one, taking whatever recommendations the algorithm spits out. People may say, if the recommendations are good, who cares? Maybe. But other forms of discovery may provide even better value.
Ultimately, the algorithm removes any chance from the equation. There is never any room for happy accidents. Every decision is bent towards the mean, eliminating any chance of discovery as you are shoved towards the narrow selection of books that others “like you” have read. However, this article is about data, so let’s look at that.
The Data
The image below shows stats from StoryGraph. Not a single stat being tracked here is useful for your reading experience. In fact, I’d argue they are the opposite.
Describing the data from left to right and top to bottom, we have, who gives a shit, irrelevant, pointless, and stupid. What’s displayed is data for data’s sake and tells you nothing useful. Let’s start with Pace and end with the number of books and pages.
Pace
What does the Pace chart even tell you? Nothing. Not all books are created equal. Some are harder to read than others. Some authors even make up their own language, and still others contain dense technical content. There’s nothing to track and nothing to optimize. This data is irrelevant.
Despite its irrelevance, whenever data is shown, people tend to want to “optimize” that data. Taking actionable steps based on this pace data can mean you beat yourself up about nothing at all. Maybe you purposefully try to read faster to optimize your pace and, in doing so, degrade your comprehension. Maybe you chose different books that are easier to read. Regardless, this isn’t good for your reading experience.
Moods
What about Moods? It’s pointless to keep a historical record of this data. Just imagine what the chart would look like for someone who is goth. Like, what are they supposed to do? Balance their Poe with some comedy to provide levity for their tortured soul? Call me NightPain.
I guess Mood is intended to be a check yourself before you wreck yourself, but it’s just pointless. Read what you are in the mood for, and read what you want. Life is too short for anything else. Obsessing over balancing your reading mood can diminish the value of the reading experience, nudging you in different directions for absolutely no purpose.
Ratings
Tracking your average rating of books is just plain stupid. I mean, all of your values are going to be right-skewed, heavily weighted toward the high end. Why wouldn’t they? Who spends time reading a book they’d give 1.5 stars? The reality is, nobody keeps reading a bad book unless it’s for some other purpose, such as a class or research.
Number of Books and Pages
Of all the data shown, the number of books and pages will be the most contentious. Let’s strip reading down to its core and focus on the benefits. What does the number of books you’ve read tell you about the benefits you received from reading? More specifically, what does the number of pages read tell you about the benefits you receive? Does reading fewer books mean that you’ve gotten less value? By now, it should be obvious that this data tells you nothing.
For those who still think this data is relevant, let’s consider that War and Peace, with its approximately 560k words, is one book. It’s easy for one large book to throw off your statistics.
Regardless of length, as I previously mentioned, some books are more difficult to read than others, but this doesn’t make them less valuable. However, given that long books will throw off your stats, you may be tempted to only read smaller books to keep your stats up. That doesn’t sound very valuable for your reading experience.
I have no idea how many books I read last year, or the year before that. Not having data doesn’t bother me because it has nothing to do with the value I gained from my reading.
Despite being an irrelevant metric, people love tracking how many books they read. Knowing their number means they can talk about it online. Reading becomes something to be performed, and that performance is an important part of reading for these people.
Still, some may claim that setting a target for reading a number of books gives people a goal to shoot for. First of all, you don’t need an app for that. Second, how does that number relate to the value from reading? Some may say that setting this goal forces them to set time aside for reading. Okay, but if the number of books is just a proxy for something else, then measure the proxy.
Ultimately, if people are looking for a way to optimize their reading, it’s not about the number of books, words, or pages. It’s about time.
Optimizing Reading
True optimization of reading requires manipulating factors that lie outside the pages of a book. It involves an investment of time, space, and attention.
If you are looking to truly optimize your reading, the most important thing you can do is dedicate time to it. Without dedicating time, there’s no way to build a habit. It’s the same when you are trying to start a workout routine. Find the time and schedule that work for you, and do your best to stick with them. The more you do it, the easier it will get.
You also need to create the space for reading. Trying to read surrounded by distractions isn’t a recipe for success. However, as a positive effect, the better you get at focused reading, the better you get at tuning out distractions. In the meantime, do your best to keep distractions to a minimum. Silence devices and notifications. Utilize do-not-disturb mode if necessary. Better yet, keep your devices in a different room.
Attention is required for reading. I know, attention is in short supply these days. Deep reading is an exercise with multiple benefits, including increased attention. However, you need to be comfortable with being uncomfortable. If you haven’t read in a while, you’ll experience some discomfort. You may get easily distracted, your mind may drift, and you have to reread passages. This is because your brain has been rewired. Don’t get frustrated or beat yourself up about it. Just stick with it, you’ll see the change.
Conclusion
Data has become a security blanket. Something we are happy to have, whether it provides value or not. It’s there if we need it. However, whenever we are shown data, we need to ask how it’s valuable to whatever we are trying to accomplish. This will differentiate between data that’s truly helpful and data as a distraction.
Accidents aren’t always bad. That’s why we have the phrase “happy accidents.” When algorithms are involved, we never leave room for chance or happy accidents. Some would call this a feature, but I call it a bug.
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.
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.
It turns out that creating dystopias is rather easy. A smidgen of uncertainty, a dash of job loss, and a heaping of hopelessness topped with a dose of dehumanization will churn out dystopias quite nicely. Season to taste. However, getting people to accept that recipe without revolt is another thing entirely. As most people get flashes of the future pitched by influencers and tech bros, they immediately find it abhorrent.
Whenever people discuss utopias or dystopias, they thrust us right into the middle of them, but you find fewer people who talk about the transition period before these conditions arrive. Whether the result is a utopia or a dystopia, both conditions have what I call a dystopian lag, which I’ve referred to before as the “sucks to be you gap”. This article attempts to discuss this transition period.
Note: This post was written in January of 2025 as a follow-up to my Techno Communism post. I peeled this content off because it would have made the post too long. Not exactly sure why it took me so long to get back to this, I always meant to revisit, especially with all of the technoutopian narratives still swirling around.
Table of Contents:
Dystopian Lag
In my previous post, I called out a condition I referred to as the “Sucks To Be You” Gap. Not my greatest naming triumph. I think “dystopian lag” better sums up the condition. The dystopian lag is the period that exists before a utopia or dystopia is fully realized. The difference is that with a dystopia, things actually get worse. The period is of indeterminate length but could realistically be in years or even decades.
The example I gave was that unemployment was likely to reach something like 90% all at once. Early people displaced by automation would be the most harmed since they would be unable to support themselves and their families, and have no real recourse for their situation. Death and despair would result. This is an example of falling into the dystopian lag.
I even mentioned it wasn’t p(doom), it was p(shit). Which, even though it looks AI-generated with the form of “it’s not ‘x,’ it’s ‘y’”, I can assure you that came completely from my skull. I refuse to rewrite my jokes simply because of the patterns created by regurgitating slop machines.
Utopia Perspective
Whether the concept of AI taking everyone’s job is a utopia or a dystopia largely depends on your perspective. Technoutopians claim that AI taking everyone’s job will be one of the best things that’s ever happened to humanity, which is what you’d expect from people who sit around making stuff up all day. For everyone else who isn’t living in a delusional pipe dream, this kinda sucks. What happens when real systems arrive that displace large numbers of workers in one fell swoop? They have no real answers, other than AI will be so awesome that we’ll figure it out.
I believe that what people conceptualize as a utopia may not be possible. However, I do believe that any number of dystopias is certainly possible. We are always left with the fact that one person’s utopia is another person’s dystopia. Even if it could be conceptually boiled down to a single child, as in the case of Ursula K. LeGuin’s excellent short story The Ones Who Walk Away From Omelas.
Utopian talking points have evolved from thought experiments into narratives that push a selfish agenda. For example, when Elon Musk talks about robots making everybody rich, he actually doesn’t give a shit about making anyone rich other than himself. Not to mention, if everyone is rich by today’s standards, then everyone is poor by the future’s.
If everyone is rich by today’s standards, then everyone is poor by the future’s.
I’ve previously written that the economic and abundance arguments these people make don’t make sense. You can read those articles here and here.
This is an odd irony, since the point of a utopia is to benefit everyone, yet the people pushing it only care about themselves. Now, is it possible that something that benefits them also benefits us? Sure. But what I’m talking about is the motivation and manipulation behind the narrative.
We need to acknowledge that any attempt at a utopia involves trade-offs, making it not a utopia at all. For example, when Ray Kurzweil talks about brain-computer interfaces that allow us to store our memories in the cloud, he doesn’t acknowledge the massive potential for negative cognitive impacts, much less the reality of manipulation that would manifest. Ultimately, humans becoming nothing but machines isn’t a problem for him, but it’s a massive problem for everyone else. Kurzweil’s utopia is my dystopia.
All of this brings up another question. Assuming that something that resembles a utopia is attainable, is it sustainable? However, the answer to this question probably necessitates a post of its own.
Human Adjustment Problem
There’s a misconception that artificial general intelligence (AGI) or a fast takeoff scenario is necessary for mass unemployment. This isn’t the case. It’s possible for a less capable system to achieve performance sufficient to displace workers. CEOs are currently foaming at the mouth, hoping generative AI can be that technology.
People don’t evolve or adjust at the rate of a software update. We can’t expect to go from a life of purpose to having none, with no prospects, in a short period of time, without expecting severe backlash. However, if companies get their way, this is exactly what will happen.
There are even companies like Mercor that are working to turn every job into low-cost gig work by breaking jobs up into a collection of tasks. Everything gets devalued, so much that it’s causing homelessness. This devaluation is coming even before the big AI shift. The consequences of these activities aren’t given any attention because they assume it isn’t their problem. But it is their problem because it’s society’s problem.
Hannah Arendt described this condition in 1958.
“What we are confronted with is the prospect of a society of laborers without labor, that is, without the only activity left to them. Surely, nothing could be worse.” -Hannah Arendt, The Human Condition
If the rapid onset of AI technology into the workforce is realized with widespread human displacement, this will result in massive upheaval and violence. As a matter of fact, actual displacement isn’t necessary for the violence to occur. The thought of this displacement and what it means is enough. I wrote this article long before the attacks on Sam Altman. The reality is, almost every path along this road leads to violence.
Some of the proposed solutions to this problem are laughable at best and completely stupid at worst. For example, the thought that people could find meaning while being handed less than a living wage, but given video games to play instead. I’ve written previously about why this techno-communism won’t work out the way people assume, and even if it does, many will find themselves caught up in the dystopian lag.
But video games? Seriously? All it takes is a modicum of reflection to realize how silly this suggestion is. The thought that the meaning of life can be found in high scores is ridiculous. Video games provide activity, they don’t provide meaning, and that’s a pretty monumental difference. I’ve conquered many video games in my life, and this completion is certainly satisfying, but it’s a far cry from fulfilling. And, it’s only a momentary satisfaction at that.
The belief that video games will fill the void at the center of human existence left by the loss of human purpose demonstrates a fundamental misunderstanding of human nature and of humanity as a whole.
Despite this person’s ability to be completely fooled by every single press release, they do make a point. We do need a structure for meaning that isn’t based on traditional work. This is hardly a new perspective. People have held it for decades.
Technology and Meaning
Is it possible for technology to simulate some form of meaning? I doubt it. Meaning is something we create for ourselves. Often, this meaning involves a social component. Meaning is something deep, and technology is shallow. Then again, technology is making us more shallow creatures, so who knows. Regardless, it will take a lot more than video games.
Even with advanced technology and direct integration into the human brain to create a simulation indistinguishable from reality, this won’t necessarily create meaning. Despite being good at what it does, this technology would merely be a temporary distraction, allowing us to fool ourselves into living like couch potatoes. For technoutopians, The Matrix is viewed as a utopia because everyone can play Neo.
The Matrix is viewed as a utopia because everyone can play Neo.
However, there is a problem. Anyone alive today would ultimately reject this because they know it’s an illusion. After the novelty wears off, it begins to feel even more fake. This is similar to the way that it’s not fun to play all video games in god mode. Maybe some future human blank slate would accept this condition as a new reality, but that’s not us.
But all this is irrelevant because we don’t have this technology today, and we don’t know how to build it. The answer from the AI bros is that AI will be so smart that it will figure it out. But the larger problem is that all current efforts are being devoted to technology that displaces humans from the workforce. Nobody is expending any real effort to develop the technology for what comes next.
This means that there will be a gap of indeterminate length. Even assuming this technology would be an acceptable replacement, which is a massive assumption, the fact that there’s a gap at all guarantees there will be a backlash. Combine this with the fact that deep, human-integrated simulation technology is harder to create than many workforce automation solutions, as well as being less lucrative because, well, if people don’t have the money to buy and continue paying for your solution, it’s not worth building, and we have a massive problem.
The AI Adjustment Bureau
Addressing these challenges may require creating something like an AI Adjustment Bureau, since the government will want to appear to be addressing these issues. This organization may evaluate workforce levels at certain companies based on income until a proper tax/employment level is reached. This would allow a more gradual transition over time. Of course, this approach also makes massive assumptions.
We all know how much companies love paying taxes. Raising taxes may cause companies to relocate to more tax-friendly locations. Governments will have to get tough. Also, companies would need to buy into the bureau’s recommendations.
On a side note, it is interesting to consider what shifts in priorities would look like if the pool of money available to governments were reduced. What government services would be impacted or cut completely? How would this affect things like garbage collection, building upkeep, and a whole host of other issues? These shifts may result in making the real world resemble our vision of dystopian landscapes through urban decay. After all, servers don’t need office space, and every office can’t be transformed into a data center.
The AI Adjustment Bureau would be tasked with finding solutions to stave off upheaval and violence, which is almost universally an impossible task given the circumstances, but it’s possible. So, what are some options?
One method a government may try is authoritarianism. Sure, this is one way to go, but it’s most likely to provoke violence rather than stave it off. With fear and control, authoritarianism may be able to keep violence to a minimum. However, although governments may be tempted to use Orwell’s techniques, their mileage would be better with Huxley’s.
Drugs, Distraction, and Division
There’s no one method for addressing the issues here. Some would say, “Just give people money and let them do what they want.” But even if you give people more than enough money, the hedonic treadmill would kick in, creating discontent and further enflaming tensions. We can see this today as some of the most comfortable people in the world are the most discontent.
You’d certainly need to give people enough money to survive. That’s a given because no matter what you do otherwise, there’d still be problems. But money won’t solve the issues here. I’m going to outline something that could work despite being wholly dystopian. This approach involves a combination of drugs, division, and distraction.
Any one of these on its own wouldn’t be enough, but the combination of all three could be enough to make a major dent. Let’s call it the Triforce of Dystopia.
Aldous Huxley opined on the concept of consent in these environments and getting people to love their servitude. And people loving their servitude is what would be necessary in this situation.
One thing I think Huxley misses concerns the removal of hope. We are taught that if you remove hope, then all is lost, but that’s not the case in reality. Hope is never really lost. Once you condition people to a sense of inevitability, they seek hope elsewhere. Providing a diverse landscape of other comforts for people to take refuge in not only aligns with the diversity of people but may result in broken people who do actually love their servitude.
Drugs
Drugs will play a key role in the path forward, purely because the technology of the near and mid-term isn’t good enough. What I mean by this is that we don’t and won’t have advanced technology to directly connect our brains to systems to create the imperceptible experiences that technoutopians dream of. This tech may never be built because once workers are displaced and can’t afford both the technology and medical procedure for implantation, there’s no financial incentive for people to build it. Drugs, on the other hand, are cheaper.
You might think I’m talking about something like Soma from Aldous Huxley’s Brave New World, but I’m not. Soma was an impossible drug, something Huxley acknowledges in his reflection on the drug in Brave New World Revisited.
Soma was not only a vision-producer and a tranquilizer, it was also (and no doubt impossibly) a stimulant of the mind and body, a creator of active euphoria as well as of the negative happiness that follows the release from anxiety and tension. -Aldous Huxley, Brave New World Revisited
For Brave New World, Soma had to perform so much of the heavy lifting, but a drug of the future wouldn’t need to perform the same. Despite his critiques of technology and mass distraction, Huxley couldn’t have envisioned the amount of stimulation that happens to a typical teenager these days. It would no doubt terrify him. So, there is no need for this future drug to have all of the properties of Soma.
EVE-EE
Introducing EVE-EE! Enhanced Virtual Experiences for Emersion and Escape. The purpose of the drug would be to connect and immerse people in the digital experience. To chemically connect the user to a VR environment, further enhancing the experience. This would negate the need for surgery or advanced technology. Although temporary, the usage of this drug, combined with high-quality VR, would provide temporary escapism.
In a way, this drug more closely resembles the drug Can-D from Philip K. Dick’s The Three Stigmata of Palmer Eldritch. Can-D was a type of hallucinogenic drug that was used with something called a layout. These layouts were small model houses that Can-D users were transported into. VR provides a much more adaptable and modern upgrade of the layout.
Distraction
The technology developed over the past couple of decades, combined with algorithmic manipulation, has primed us for distraction. Many people can no longer perform activities that were easy 30 years ago. This obvious bug would become a feature for future governments and organizations looking to obliterate core features of our humanity while stopping a full-scale revolution.
Much like the Coliseum provided a distraction for everyday Romans, we’d have an assortment of digital distractions to take our minds off how bad our reality is, bombarding us with stimuli for indirect compliance.
The only caveat here is that distraction costs money. To quote the great American philosopher Stephen Pearcy, “Nobody rides for free.” This means that people without means would have to give up the last shred of dignity to pay for these distractions. What this payment for indignities means isn’t clear yet. Maybe they agree to be intrusively monitored for data collection, or worse, agree to have their whole family monitored, including their children.
Division
No matter how far society progresses, people still need people to hate. An other to fight against. We’ve become experts at dividing ourselves and creating factions. In this transitional period, this public-private partnership wouldn’t have to do much except provide occasional stoking of this division.
When I was young, I thought that as organized religions faded into the past, reason would prevail, and the world would be a better place. Oh, the naivety of youth. Instead, people have turned everything into a religion. Politics, vaccines, Bryan Johnson, e/acc, you name it, it’s a religion. Even the belief in AI is a religion now.
No matter what the scenario, we’ll find ways to create factions and others to hate. The usefulness of which was not lost on Orwell.
The horrible thing about the Two Minutes Hate was not that one was obliged to act a part, but that it was impossible to avoid joining in. -George Orwell, 1984
A government may allow these divisions to fester because we only have so much bandwidth for being mad at things. If we are busy being mad at “the others”, then we have less bandwidth for anger at government officials or companies that we don’t like.
One of the unfortunate side effects of declining literacy is the loss of empathy. Without empathy, we become far more tribal and far more likely to harbor hatred toward others.
Outliers
Despite this attempt at control and getting people to love their servitude, there will still be outliers. People who refuse to accept the path that lies before them. These people would cause trouble for governments and business leaders, whom they see as creating the problems.
These outliers would no doubt use violence as part of their arsenal of techniques, but being outliers means that this violence wouldn’t be widespread and would be more targeted. This doesn’t mean that they couldn’t do massive amounts of damage. It just means that there would be fewer people doing it. It may lead government leaders to assume they can more easily control them. Which would be a mistake.
Conclusion
Although I don’t think LLMs will lead to AGI and probably won’t lead to mass unemployment, I don’t think it’s impossible either. We’ve seen how companies are more than happy to replace humans with incredibly shitty automation solutions, making perfection unnecessary. They’d automate away everyone if they could. So, although LLMs may not be the solution, what we see is a dry run for what it will look like.
The conversation about the future often focuses on humans being wiped out by AI or the fact that LLMs are a silly technology. Nobody is focused on preparing for what comes next, and what comes next is the most important thing for those of us alive today.
It seems AI is becoming one of the most volatile and expensive dependencies in modern systems, and most organizations aren’t prepared for what comes next. I recently wrote an article for Modern CISO on AI cost volatility, offering observations and recommendations to mitigate this risk.
Token Ransom and High Cost
For years, we’ve been told to prepare for the cost of intelligence to crash to zero.
The narrative pushed in this tweet by Logan Kilpatrick is something I’ve called out in the past for its sheer ridiculousness. But many are seeing the light in the last few weeks. Everywhere you turn, AI services are getting more expensive. Every day, new providers are making announcements. One example below is from GitHub Copilot’s new pricing.
Even the all-you-can-eat AI buffet for $20 a month was always a myth. This was part of a larger narrative pushed by influencers, futurists, and AI leaders, but this narrative always made dollars and no sense.
What happens when you deploy solutions using these cutting-edge foundation models into production environments? You may end up with a dependency where you have a choice. Pay more or have the solution stop working. In the article, I refer to this as a token ransom.
Think of this as a token ransom. It’s scary to consider how the ransomware of the future may actually be an inflated token cost.
If companies aren’t prepared for these scenarios, they carry significant operational risk. In the article, I break down the issues with more examples and provide recommendations on how to start addressing them.
You can read the full write-up for Modern CISO here. Although framed toward security leaders, the advice is applicable beyond the cybersecurity space.
One interesting outcome of these price increases is that companies are very concerned. It seems no amount of security and reliability issues dissuaded these companies from chucking AI into everything, but the skyrocketing cost of AI may. I’ve heard far more grumblings about cost than security issues. Only time will tell.
For months now, I’ve been fascinated by seeing smart people completely captured by AI hype. The very people who should be pushing back against the hype are the most swept up in its rapture. But it’s starting to make sense to me. I believe I’ve pinpointed a few key features driving this phenomenon. As is the case whenever smart people get caught up in things, they don’t do things halfway.
In a larger context, we may be witnessing a glimpse of what critical thinking’s death might look like as the impacts of cognitive offloading become more widespread, with the technology’s numbing effects defying our ability to recognize them. These effects can create a democratization of AI psychosis. Time to get the shades, because it’s all vibes now.
Note: In this post, I admittedly do a bad job of defining “smart” people (I don’t even try) and the attributes that differentiate excitement from being captured by hype (I try). I realize that this makes things very subjective, but the goal here isn’t to apply definitions to specific people. It’s about highlighting the attributes that contribute to the condition.
Table of Contents:
Democratizing AI Psychosis
By now, most people have heard of AI psychosis. This is a term we typically associate with extreme cases, but the same features that create more extreme instances of psychosis are present in the regular usage of AI tools. Although it may not trigger extreme psychosis in most people, it does induce lesser delusions in some, leading to a warped worldview. It’s these lesser delusions we cover in this post.
AI hype, when manufactured by continuous AI usage, becomes an artifact of AI psychosis. This isn’t as extreme as the cases you’ve read about in news articles, but it creates delusions nonetheless and is fueled by some of the very same attributes.
AI hype, when manufactured by continuous AI usage, becomes an artifact of AI psychosis.
Years ago, I sat through a presentation on human manipulation by an expert in cults. He mentioned that when smart people got caught up in cults, they were the most effective members. They’d fully committed and had a way of rationalizing misgivings. They also made the best cases to attract new members through their devotion. It was also damn near impossible to get them to change their minds. This always stuck with me.
Smart people certainly have more faculties to resist being sucked into cults or, more broadly, to resist hype. I believe what caught many smart people off guard was due to the erosion of our cognitive defenses, as well as the packaging of AI as “just another tool.”
I noticed the phenomenon of smart people and AI hype ramping up in late 2025, with full acceleration in 2026. When someone laid out a scenario for using AI for a task or use case, I often found myself saying, “I can’t tell if you’re joking or serious.” To which the reply of awkward laughter or an “lol” would result, depending on the communication medium. But my favorite is when people would lay out scenarios where they had a task to do, then brag that the AI outperformed them.
I’ve been writing about the cognitive effects of AI for a few years now, and the speed at which these impacts arrived caught me off guard. I didn’t expect we’d see these effects so soon. There’s something about the generalized nature of generative AI and its increased use that has accelerated negative cognitive effects. I’m certainly not the only one who’s noticing this.
So, what’s the difference between finding AI useful and being captured by AI hype? Many people (including myself) are finding today’s AI useful and even believe it can be disruptive in certain areas more than others, but disruptive nonetheless. Believing this doesn’t necessarily mean someone is captured by AI hype. For anyone confused about my perspective or who thinks I’m an AI hater, please see my post here.
A few characteristics of being captured by AI hype may be starting with AI and working backward to find problems, perpetually believing the next version will unlock the true value, jumps immediately to catastrophizing, describing AI in terms of revolution versus specific task outcomes, being blown away by outputs despite the issues, discounting complexities, treating outputs as authoritative and delegating judgment to AI, extrapolating to futuristic predictions, and on and on.
Admittedly, I haven’t done a great job of distinguishing between excitement about AI and being captured by hype. Mainly because it’s something that you know when you see it. And trust me, someone who’s captured by AI hype is more than happy to tell you about it.
Two groups of users are the most likely to be caught up in AI hype. These are AI power users and people with little AI experience. People with little AI experience are the ones who merely parrot others’ opinions, and we won’t focus on them here. Power users, on the other hand, are the most susceptible due to the amount of cognitive offloading and constant interactions with AI tools. They assume they are “witnessing” a revolution that others simply don’t see.
The being captured by hype scenarios is clearly evident in the wake of the Claude Mythos announcement and project GlassWing. I wrote this article before this announcement, but it proves a solid example.
GlassWing Example
The number of cybersecurity people genuinely depressed over the Mythos and Glasswing announcement is strange. Everywhere I turn, speculation is rampant, with countless people claiming this is the end of cybersecurity.
The other claim is that we are on the verge of a Vulnpocalypse, where Heartbleed-style vulnerabilities occur every week. This is speculation devoid of critical thinking. The realities are far more mundane.
I remember when cybersecurity people were more skeptical. We used to make vendors prove their claims before we took them at face value. We would do our own evaluation and see the results for ourselves, but that’s not the environment we are in. Now people are falling all over themselves to be the marketing arm for these companies.
For more details on this topic, see my reasoned take on Mythos and Glasswing for the ModernCISO.
I admit, I may be totally wrong, and all the speculation may be true. Maybe cybersecurity is about to be solved. It’s certainly not impossible, nor is the prospect of a Vulnpocalypse, but it’s not likely. The advancements are more likely a step improvement than an exponential one. There are plenty of problems to go around, and the world is a complex place. So, let me make a prediction: cybersecurity isn’t about to be solved. At least, not anytime soon.
Erosion of Defenses
We need to reclaim the ability to keep two thoughts in our heads at once. The fact that AI can be incredibly useful and simultaneously overhyped. This is difficult in the current era, which has deteriorated our defenses.
We need to reclaim the ability to keep two thoughts in our heads at once. The fact that AI can be incredibly useful and simultaneously overhyped.
I believe that three things have contributed to the erosion of our cognitive defenses.
The shift to a post-literate culture
The effects of modern communication technologies
The destruction of our attention.
These cultural changes are leaving us defenseless in the age of hype and doom. The craziest thing is that people don’t realize this is happening to them. Marshall McLuhan stated that every augmentation is a self-amputation, creating a numbing effect that eludes recognition. We are witnessing this play out in real-time.
I break down what’s causing this capture into a few categories. Some may affect certain people more than others, but a combination of all of these factors is what’s driving smart people to be captured by AI hype.
Local bias
Information Bubbles
Dark flow
Overconfidence
Playing Around
Warped Rewards
Local Bias and Blowing Yourself Away
This is one of the earliest factors of AI hype. Back in 2023, at various conferences and events, I described the massive uptick in hype more broadly as people being bad at constructing tests and good at filling in the blanks. This leads people to blow themselves away with their experiments. So, when people asked ChatGPT for a recipe in the style of Shakespeare and received it, they were so blown away that they claimed LLMs will be more impactful on humanity than the printing press. What we are seeing today is just a more advanced version of this.
In my completely unscientific observation, I seem to have isolated the rise in smart people getting captured by AI hype to the uptick in Claude Code usage. Many underestimate the extent to which people are losing their minds over Claude Code. In some cases, their usage is fueling delusions. There are people publishing markdown files, thinking that they are changing the world or revolutionizing business. We are led to believe that markdown files will create the first billion-dollar solopreneur.
People are also blown away by other people being blown away. Every day, it seems people are happy to share that a family member, significant other, parent, or anyone without technical skills was able to generate something. Mind blown. 🤯
These people then carry this perspective forward into all sorts of predictions about business and the world. This ends up in perspectives like the SaaSpocalypse, the SOCpocalypse, and the idea that AI is eating, destroying, and reducing to rubble “x” industry. All of this demonstrates a lack of awareness of how the world actually works, as well as the discounting of the massive complexity involved. Aspects these smart people used to recognize, but the results of their experiments have caused them to suspend disbelief in much the same way as watching Matt Damon successfully survive on Mars.
We’ve completely lost our ability to reflect because anyone who reflects on these topics would see these obvious issues. For a further breakdown, I’ve covered these issues in relation to the SaaSpocalypse in The Death of Software is Greatly Exaggerated.
I can already hear the response now, “But the software works!” Of course, the software works. If it didn’t work, it wouldn’t warp perspectives. Functional software in small experiments isn’t the point. Even the fact that people find the applications they built useful isn’t the point. The point is the lack of awareness of what this actually means in the grand scheme of things, and of how insignificant an individual’s experiments and one-off applications are to the world as a whole.
To extrapolate a tiny experiment out into the perspectives that companies in the future won’t buy software because they’ll just build it themselves on the fly, or to think that companies won’t have employees in the near future, is where the delusion enters.
We have something that resembles a software self-esteem movement. Everyone is told that an idea and some vibe coding are all they need to make millions of dollars. Is it impossible? Of course not. However, is this something likely to scale? Absolutely not. Remember, exceptions will always be pointed to as the rule. People win the lottery, too.
We have something that resembles a software self-esteem movement.
We have smart people who now believe that code is the only thing that matters at a company. Or even that code is the hard part at the company, and if the code is right, the rest will fall into place, never mind the use case or problem to be solved in the first place.
This condition reminds me of people who stated that global warming couldn’t be real because it was cold where they were at that moment. Regardless of anyone’s perspectives on climate, the reasoning behind the response is silly. First of all, it mistakes weather for climate. Second, it assumes the effects are equal and stable across geographic locations. Reality wouldn’t change the very real perception of the person who was cold that day, just like it won’t change a person with the successful Claude Code experiment. In many ways, vibe coding and major economic predictions completely align with our attention-poor environment.
This condition reminds me of people who stated that global warming couldn’t be real because it was cold where they were at that moment.
People are outsourcing their entire thought process and even memories to these tools. Only someone laboring under a delusion would think this would end well for them. To a certain extent, this may come down to the feeling of productivity. I’ve written about this illusion of productivity before, both here and here.
Being productive means more than just doing stuff or doing more stuff. Someone can vibe code for an entire weekend and write more code than they’ve ever written, but it doesn’t mean they were productive. But somehow, the feeling of doing more has counteracted the critical ability to evaluate productivity.
Information Bubbles
Information bubbles are an effect of modern communication technologies. These can be traditional filter bubbles from social media, as well as bubbles that people create themselves in private chat groups on platforms like Discord.
People are encasing themselves in these bubbles, planning to burst forth like butterflies from cocoons as billion-dollar solopreneurs. Except they burst forth into a complex world that doesn’t resemble the simplistic one they created. Social media filter bubbles certainly play a role, but the bubbles people proactively choose to enter may have a greater effect.
There are private chat groups where members jazz each other up. Quite often, they aren’t exposed to contradictory information and perspectives. When contradictory evidence makes it into the bubble, they explain it away as a group.
Being in an information bubble doesn’t automatically make someone wrong, but it significantly increases the likelihood that they are. People in bubbles are often surprised when things they believed turn out to be wrong, but they often reframe the evidence and their perspective to claim they were right all along. I know, welcome to the Internet.
Overconfidence
Overconfidence is a foregone conclusion in the age of AI. It doesn’t matter how smart you are. Overconfidence is one of the inevitable byproducts of the cognitive illusions created by the personas of personal AI.
I remember reading a paper a couple of years ago in which researchers showed participants a trivially informative video of a pilot landing a plane, inflating participants’ confidence that they could do the same. This is Dunning-Kruger in full effect.
Frank Landymore had a great line in one of his articles. He said AI was democratizing the Dunning-Kruger effect. Which is one of those lines you hate yourself for not coming up with first, but it really does summarize what we are seeing in the AI era.
This effect was obviously going to be a foundational aspect of AI usage. And we are seeing people overestimate their abilities when using AI. But this isn’t constrained to having confidence in the presence of a tool. It’s the tool’s psychological effects outside of its usage as well.
Addiction and Dark Flow
We often underestimate the addictive nature of AI tools. When people think of tools like Claude Code, many things spring to mind. Addiction is probably not one of them, but this is something I’ve witnessed myself. It’s now common to hear of people not sleeping and not eating, binging on all-night coding sessions with AI tools. The FOMO is real, but what they are building is not.
Slot machine memes related to vibe coding have been around for a while now.
Fascinatingly enough, the comparison between slot machines and technology dates back to the 1950s. Jacques Ellul made this very same analogy back in 1954, and it fits right into the current conversation. Ellul was commenting on how humans participate less and less in technological creation, reduced to a catalyst. He went on to say, “Better still, he resembles a slug inserted into a slot machine: He starts the operation without participating.”
Ellul points out the true lack of human participation in the process, but the addition of gambling takes this to another level.
In her excellent article on dark flow, Rachel Thomas from fast.ai makes some key points relating these issues to AI coding tools.
The first is loss disguised as a win. The article discusses this in the context of a multi-line slot machine, stating that:
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.
This same condition happens with vibe coding and requires subsequent pulls of the one-armed bandit. The signals are just as misleading, too, as it may not be apparent for quite some time whether the code produced is actually any good.
Second, Thomas points out that “With ‘junk’ (or ‘dark’) flow we lose our ability to accurately assess our productivity levels and the quality of our work.” This condition contributes to the other categories we’ve discussed, mainly, blowing yourself away with your experiments.
Thomas goes on to state that vibe coding often violates the same characteristics of flow that fail with gambling, with three points:
Vibe coding does not provide clear clues of how well one is performing (and even provides misleading losses disguised as wins).
The match between challenge level and skill level is murky.
It provides a false sense of control in which people think they are influencing outcomes more than they are.
This final point aligns well with the one Ellul made in the 1950s, aligning with a misconception of agency. The article contains many more points and is a must-read.
Playing Around
At a recent conference, in reference to OpenClaw and Moltbook, I said that what we were seeing was just people playing around with toys, and that I wouldn’t sit around watching people play with Legos either.
There are millions of people running OpenClaw. What are they actually doing with it? Who knows. They are just playing around. OpenClaw, like many agents of its type, has no killer use case, so you end up with people doing things to do things, like hooking up their email or price-checking items. All things they do because they built the system to do it, not because it actually solved a problem. It’s Maslow’s Hammer, enhanced by a strong emotional attachment to the hammer.
It’s Maslow’s Hammer, enhanced by a strong emotional attachment to the hammer.
This “game” aspect isn’t lost on some people.
This scenario isn’t necessarily bad as long as you recognize what it is. Unfortunately, many people follow this path to a delusion. They assume that what they are playing with will change the world, have some massive external effect, or make them rich. Instead, we get code for the sake of code.
They also assume that the same iota of satisfaction they have will scale equally across people, and they mistakenly believe that building and producing code are measures of being “productive.” As I previously mentioned. Measuring productivity by lines of code has become a technological fallacy.
Where is all this code? Where are all the new killer applications? So many commits, so little effect. In a vast majority of cases, it’s like chucking pennies into a digital wishing well, only instead of pennies, it’s thousands upon thousands of dollars in tokens. This aligns with what I’ve dubbed the Slop Architecture and with the misconception that “ideas” are what’s truly important in any of these scenarios.
It’s like chucking pennies into a digital wishing well, only instead of pennies, it’s thousands upon thousands of dollars in tokens.
There are and will certainly be exceptions. This isn’t the point. The point is that people will cite exceptions and claim they’re the rule.
In a certain sense, this whole thing is a game or at least gamified. The playing around, generating code, and then talking about it publicly has the feeling of everyone playing a gigantic MMORPG. It’s like watching people talking about playing Warcraft all day and discussing their campaigns. Only, it’s much more isolating than Warcraft. It’s just you and a sycophantic non-human entity.
In reality, you are engaged far more in playing a game than you are in vibe coding. As Ellul points out, you are just a catalyst.
To be fair, playing around is an essential part of learning. When it comes to new technologies, arguably, it’s the most essential part. The problem here isn’t the playing around, it’s the accompanying delusion. Nobody playing Guitar Hero thinks, “Wow, I’m Steve Vai now! Let me book my world tour.” But add AI, and it’s all vibes now.
Warped Rewards
Simply put, smart people want to be seen as being ahead of the curve. This is a powerful intoxicant and shouldn’t be underestimated. They want to point back to things and say, “See. I was right!”
Due to their successful experiments and the mountain of positive press, they feel they know which way the wind is blowing, so they attempt to move to the head of the line. Critics like myself, on the other hand, are left feeling like Diogenes walking into a theater.
This is also a result of the warped reward systems of the modern communication environment. We often reward people for being bold, not for being right. We also reward people for posting hot takes and being reactive instead of reflective.
Conclusion
We need to recapture the ability to keep more than one thought in our heads at the same time. There’s no doubt that AI can and will be disruptive, yet it can also be overhyped. In five years, will the landscape change? Sure. Things are moving fast, and we’ll have to be adaptable. But, will it be completely unrecognizable? I doubt it.
AI is all about trade-offs, and we need to be mindful that outsourcing so much of our cognitive processing to AI tools can have far-reaching negative impacts. This is something that many are unprepared for today due to the erosion of defenses and the inability to recognize the conditions.
This whole article is about recognizing these conditions. It’s not that vibe coding is bad, or any of the conditions outlined are automatically bad. It’s when we don’t recognize what they are and allow them to warp our perceptions of reality that things get bad. Unfortunately, we are very bad at recognition. Welcome to the democratization of AI psychosis.
Since this site focuses on risks and trade-offs rather than shiny, utopian use cases, there is some confusion about my thoughts on AI. Be it the AI of the present or the AI of the future. With this post, I stake out my position on AI and its advancement so I don’t have to keep restating it. Also, it’s good to write down your beliefs and confront yourself with them. Sometimes what you think you believe isn’t what you actually believe. Maybe I’m a secret AI bro after all. Utopia, here we come!
AI acceleration has become a cult or religion, and offering any criticism of its advancement is taken as a personal attack. In many ways, if you swapped out AI for cryptocurrency, the theme would revive a familiar tone.
Even with this post, I’ll undoubtedly be pegged as an AI hater because I don’t have pictures of myself lying prostrate in front of a pile of GPUs. Any time you shed light on hype or bullshit, people are willing to label you a hater. That’s the easiest thing for them to do, and it takes no mental effort and requires no skill. With that said, here we go.
On AI Advancement Summary
I’ve been using this image in my conference presentations since 2023. The focus of my talks is on risk, which means talking about problems and challenges most of the time rather than amazing use cases. I wanted to show the audience that I don’t hate the technology.
The problem with being in the middle is that both sides typically frame you as an extremist. You don’t hate the technology enough for one side or love it enough for the other. Realities on the ground typically hover somewhere in the middle between extreme claims. This isn’t rocket science.
For those who honestly don’t care about the rest of this post and made it this far, here’s a quick summary:
I’m not a skeptic, I’m a critic
Yes, I think today’s AI can be useful
Yes, there are some use cases that I’m hopeful for
No, I don’t think today’s AI is AGI
Yes, I think AGI is possible
No, I don’t think LLMs will lead to AGI
I think even AGI will have vulnerabilities
I’m not so sure about the concept of ASI or the intelligence explosion
I’m Not a Skeptic, I’m a Critic
In the current era, I’ve seen people frame themselves as skeptics either of technology or of AI. This is not how I frame myself. I consider myself a critic, not a skeptic. I’ve joked that I’m a hype critic. Throughout my career, I’ve offered criticism of the state on cybersecurity, emerging technologies, and, especially, product manufacturers and their claims.
I believe that technology does need a better class of criticism. The tech press, in large part, has abdicated its responsibility, choosing instead to mindlessly parrot opinions from tech leaders. The entire world has been a gigantic sycophantic feedback loop. This is something I’ve called out many times myself and something that Karl Bode calls “CEO Said A Thing!” Journalism.
Most people criticizing the state of technology are insufferable. The few valuable points they present are wrapped in politics, bullshit, and, in some cases, conspiracy theories. Their goals aren’t to effect change but to pander to their audience. The very people who need to hear these points are the very ones who would never listen to them in the first place.
I have no vested interest in any technology’s success or failure, and I’m certainly not pandering to an audience. Hell, if I wanted to cultivate a large following, the last thing I’d be spending time on is writing. I’d start a podcast or YouTube channel, align my content with people’s biases, and go all out, telling them what they want to hear. I’d also use AI to write my content to up my pace. It’s the new definition of “productivity.”
One of the criticisms I get is that I shouldn’t be listened to because I don’t love AI enough, which is a strange perspective. Would you really trust someone who is selling you something, or who is completely head over heels in love with a technology? Are you going to get honest criticism? Of course you wouldn’t. But the whole premise of that argument doesn’t make sense. That’s a lot like saying someone isn’t religious enough because they don’t have enough religious bumper stickers on their car.
Framing The Conversation
Much of the debate around future AI advancement revolves around two questions after a claim is made:
What specific technology is being discussed?
When will it arrive?
So much confusion is caused by not clarifying the two follow-up questions. Many throw out the term “AI” as a catch-all, referring to any technology, present or future. For example, the claim that AI will cure cancer. Okay, but what specific AI technology? Is it technology we have today? Some future technology that hasn’t been invented yet? And of course, most importantly, when will this happen? The precision matters.
When asking people to provide some precision regarding their claims, it’s not uncommon to find that people aren’t talking about AI at all. They are talking about magic. For others, they are purely saying “something” will happen at “some point.” Which is basically saying nothing. Back in January of 2024, I published a framework for making sense of human AI predictions, which goes into a bit more detail on this topic.
I do believe that many of the claims made by proponents will be realized at some point through future technological advancements, some even with the technology we have today. I’m certainly hopeful about cures for debilitating illnesses, and a lot of work has already been done. I don’t think we are miles away from seeing those results. This is an example of something I’m hopeful for. Call me an optimist??? However, I don’t know what technology, under what circumstances, or when.
I’m sure so many people thought it was inevitable that by 2015, we’d have hoverboards in common use after watching Back To The Future 2. Our perspective on technological advancement is often skewed and off by a wide margin. It’s always good to keep this in mind.
LLMs
I certainly don’t hate LLMs. I find LLMs useful for various tasks, mostly coding tasks, basic research, and troubleshooting. I occasionally will use them to generate some AI slop images for a blog post or conference presentation. Pinning down my exact usage is a bit hard, since LLMs aren’t my first port of call for every problem. After all, I value my critical thinking skills, skills that people these days seem content to discard.
I never use LLMs for common cognitive tasks and never have an LLM decide for me or write anything on my behalf. I also never have an LLM summarize something I’m trying to understand, because knowledge and understanding aren’t generated from bullet points. The friction is the point in so many tasks where we look to reduce it.
The hype with LLMs hasn’t been commensurate with the realities on the ground. LLMs certainly have their uses. Just like me, people are finding them valuable for a variety of tasks. In my own industry (cybersecurity), there are positive examples in offensive security, vulnerability identification, and assisting analysts in security operations centers. You can also tune LLMs more effectively for specific tasks, which will have a positive effect. However, there are limiting factors to LLMs.
The first is the cost of failure for the use case. LLMs have relatively high failure rates, and when connected in agentic systems, these failures can cascade through the system. Failures compound like interest, to use the words of Demis Hassabis. I mean, the thought of ChatGPT running air traffic control is terrifying.
Second, they are highly manipulable. This is why everyone from startups to hyperscalers has had their AI-based applications hacked. This fundamental manipulability is baked into how LLMs operate. It’s why we have things like prompt injection, and adding LLMs to applications increases their attack surface. This condition is why I’ve described AI Security as a misnomer in the age of generative AI. You aren’t defending the AI. You are defending the application or use case against the effects of adding AI. This is a different problem.
These two factors can be misleading, though. We don’t need AGI-level capabilities for LLMs to be useful or to replace people in their jobs. The moment an LLM-based system is mediocre enough to replace someone, companies will rush to replace people. This is especially true if the cost of failure is lower for a particular job. Although reports of recent layoffs are nothing but AI washing, we are getting a glimpse of what will happen once capabilities arrive.
My biggest concern with LLMs isn’t what they can do for people, it’s what they do to people. I believe we are vastly underestimating the negative cognitive impacts these tools are having and will have on people in the future.
My biggest concern with LLMs isn’t what they can do for people, it’s what they do to people.
AGI
I do believe that AGI is possible, but I don’t think that today’s LLMs will be what gets us there. When do I believe AGI will arrive? 15 to 20 years. Put my precision on this at about 70%. I put the likelihood of LLMs alone becoming AGI at about 15%. But keep in mind, these are mostly guesses guided by intuition and actualities. Caveat: I’m not involved in developing AGI, and the world is a complex place that defies predictions. However, I do believe some factors will confound advancements for a while.
First, I don’t feel LLMs will lead to AGI, and this is where all of the focus seems to be at the moment. Second, I think there is a massive AI investment bubble. The amount of money being invested is nowhere near the value created. This bubble will pop at some point, hopefully not spectacularly. Companies like OpenAI will very likely go out of business. They are hemorrhaging money, and their shares are becoming almost impossible to unload on the secondary market. I mean, they put out a statement about focusing on business, and then just bought a podcast. Not exactly a shining indicator of future success.
I bring this up because this crash will cause some reluctance to invest in the future. Maybe it won’t quite be an AI winter, but it will be an AI fall with colder weather and a lot fewer leaves on the trees. This may stall the advancement toward AGI.
I think some people think that LLMs will go away after the investment bubble pops, but this is nothing but wishful thinking on their part. LLMs are genuinely useful for certain tasks and will continue to be. Also, LLMs are so essential to some people’s identity now, you’ll have to pry them from their cold, dead hands.
When it arrives, I do believe AGI will have vulnerabilities, even if they are not immediately apparent. This would be especially true if AGI were built on today’s LLMs or if it weren’t a single large system but a network of systems. Once deployed, we’d be stuck with these vulnerabilities. This is a perspective I’ve shared publicly for years in my talks and keynotes. There may be something about generalizing to the world that contains inherent vulnerabilities. I have a draft post on this topic that I’ll publish in the future. Unfortunately, I have dozens of posts in draft and only so much time.
ASI and The Intelligence Explosion
Strangely, we haven’t even achieved AGI yet, but labs are already bragging about how we are close to artificial superintelligence (ASI). Okay, it’s not strange, that’s just how hype works. We seem to forget that ASI is a speculative technology, and speculative technology leads to speculative bullshit.
To sum it up, despite believing that AGI is possible, I’m not so sure about ASI. Or at least ASI as it’s traditionally been discussed. I’m not quite sure I can put my finger on exactly why. It’s more of an intuition I have rather than any one specific thing. Of course, I may be the one now talking nonsense.
I think my hesitancy stems from conceptions of ASI, the resources required, and the plateaus that would be encountered. We are told we get there by just packing in “more” and “better”, whatever the more and better happen to be, and this cycle will continue forever. But I don’t think we’ll scale our way there, and we still have to contend with the laws of physics and resource constraints. This is why Ray Kurzweil thinks we need to pave over the universe to create computronium.
I do believe that some recursive self-improvement is possible, but only to a point. Maybe we’ll get to something like an AGI+ but not ASI as it’s traditionally been discussed, with its planet-eating power requirements and its continual recursive self-improvement. However, there is one thing I can say for sure: something will be labeled ASI long before it’s possible. Maybe someone will buy a podcast to promote that perspective! Who knows.
Since I’m unsure if ASI, as it’s been defined, is even possible, I’ll put the odds of reaching ASI in the next 50 years at 10%. But feel free to chalk this up to me saying, “I don’t know,” and disregard everything I’ve said.
What Happens Next
I’ve left no doubt about my pessimism about what happens next and how it’s not good for humanity. Much of the content on this site focuses on that topic. And, no, I don’t think a super-capable AI will see humans as a nuisance and eliminate us. Sorry, Eliezer Yudkowsky, but our manifested problems will be much more mundane.
It’s we, humans, who plant the seeds of our own downfall. When massive unemployment occurs (which may happen well before reaching AGI), there will be no recourse. The so-called abundance movement won’t deliver the value it promises. Many will fall into the “sucks to be you” gap that I’ve defined previously. A segment of the population will remain pinned there, possibly for a generation. This is purely due to incentives and the reluctance or inability to do anything about it. I’ll have more to say on this in the future.
Also, people continue to cede their critical thinking skills to AI. By far my biggest concern is the collapse of culture amid homogenized AI outputs and people’s inability to think independently. I see people more concerned with collecting data than with understanding it. The idea that someone becomes wiser by collecting more data or by engineering a better retrieval system is nonsense. If you need an AI to tell you what you think or believe, you’ve made a fundamental error.
In a previous post, I mentioned the story of Calvisius Sabinus, who, in an attempt to appear learned, devised a shortcut. It didn’t work out so well for him, and this new strategy won’t for us. I have much more to say on this topic as well. But that’s all for now.
Talk to any tech bro, and they’ll be happy to tell you, scarcity is about to become a thing of the past. Abundance is coming and we are going to have so much awesomeness that we can’t even fathom how amazing it will be. Sam Altman recently said that society has been structured around managing scarcity, and now we need to get used to managing abundance. Hell yeah, bro! There’s just one problem. Well… there are many problems, but a big one that isn’t talked about is that nothing in abundance is worth anything. This fact alone can bring visions of utopian abundance crashing down like a house of cards in a tornado, and it’s a lesson we can learn from Napster. Yes, that Napster.
AI, Abundance, and the Future
We are told that scarcity is about to be in our rearview and the bright shiny utopia is out the windshield. With AI and robots doing all of the work, the cost of goods and services will crash to near zero. It’s yachts and Lambos for everyone! Oh, there’s just the small problem that nobody has a job. Damn, there goes my Lambo.
These takes are common, and you see them every day. For example, here’s a random one I saw this morning.
But this isn’t the only claim. They also claim it will cure all diseases and won’t just end poverty, but make everyone rich.
The “things will be free” argument is typically packed among heaps of bullshit, as seen in these examples. It’s intended to overwhelm your critical thinking skills. Let’s just acknowledge that none of this makes sense.
They state that people are going to lose their jobs, but don’t worry, because AI is going to make everything super awesome! They also claim that in this new environment, people are going to become obscenely rich by giving you free shit. Admittedly, I don’t have an MBA, but that doesn’t seem to be how business works.
I’ve covered this topic before in Techno-Communism and the Things Will Cost Nothing Fallacy. In that article, I attacked some of the core premises of this fallacy. I even covered the fact that, despite the premise, things will still cost something, and that nobody is investing in companies to produce zero-dollar goods and services. I may only lightly recover some of that ground, so please refer to the previous article for the core arguments.
This is an important topic to consider now because we will see better AI technologies. We will eventually get to AGI. Many of the nonsense claims people make about today’s LLMs will be true of this new technology, and the impacts will be far-reaching. We need to consider how this environment affects us as humans and our culture.
In this article, I focus on value and frame the lesson of Napster as it relates to abundance and the future.
Napster
I have to do some work here. Let me explain Napster to young millennials and Gen Z, who’ve always had reliable internet connections and never had to wait for anything. Napster was a peer-to-peer file-sharing application that mostly focused on music. If there was a song or album you wanted, you’d search for it and download the MP3s. The songs were encoded at varying quality levels, and depending on how many people had the files and their internet connection speeds, you may have to wait a day or two for the songs or album to download. Oh, and surprise! Sometimes the files you downloaded weren’t what they claimed to be at all.
A look back on the UI of Napster is enough to haunt your dreams.
I use the term “was” in reference to Napster, but apparently, they are still around and drumroll… They’ve pivoted into AI slop. Imagine that! Although you might think this is the lesson of this article, it’s not. It’s just a fun byproduct in the AI era. Enjoy.
Back in 2000, Metallica sued Napster. For clarification, Metallica was a band of aging gentlemen who sounded like Avenged Sevenfold. Sad but true.
At the time, I remembered thinking how backward Metallica looked, like they were just some aging rock band that didn’t understand technology. Most of all, they just looked greedy. People just wanted the freedom to use their music as they wished. Many downloaded MP3s of albums they already owned. For example, maybe you already had Metallica’s black album on tape or found downloading MP3s easier than ripping them from the CD you owned. CDs were also cumbersome, and taking a bunch with you on a road trip was a pain. Sure, people pirated music, but that was a minority. My perspective solidified as the space for digital music grew.
As digital music grew, it came with heavy-handed DRM (Digital Rights Management), which meant that, despite purchasing the music legally, you couldn’t use the music the way you wanted to or play it on devices you chose. Hell, Sony even went so far as to install malware on your computer in an attempt to stop piracy.
Then came streaming, and music essentially became free. The lesson that started with Napster now came full circle with services like Spotify and Apple Music. However, inheriting a gigantic problem.
Music Is Now Free and Worth Nothing
In the ignorance of my youth, I fell victim to a condition most young people do. I failed to see the big picture. When something is free, it is essentially worth nothing. Nobody values music today, and why should they? There was no friction to access it. Nobody had to go to a store and choose between one album and another. Nobody had to wait in line for a midnight release from their favorite artist. Nobody had to choose which albums to take with them on a road trip. Although this sounds like a major inconvenience, it enhanced the value of music for both the listener and the artist. These activities created loyalty and forged a bond.
Musicians were well aware that listeners had a choice when buying music and felt responsible for creating art to the best of their ability. Or at least, this concept was in the back of their mind. Artists also made money selling music, which gave them the freedom to spend more time on their craft, further enhancing their art/product. This environment didn’t require an artist to be as big as Taylor Swift to do so, either.
Purchasing music connected listeners to artists in a way that streaming doesn’t. When people bought an album, they listened to the album the way the artist intended. They could evoke emotion through ebbs and flows, taking people on a musical journey. With streaming, people just add things to playlists and hit shuffle. An artistic vision of an album is completely dismantled, chucked into the chaos of a musical vortex, spinning alongside Snoop Dogg and Conway Twitty.
Now, artists blast out music as fast as they can to increase their breadth because more music means more potential streams. Not releasing music at a steady cadence creates the impression that you’ll be forgotten in the vast sea of content.
Of course, artists now have to make money in other ways, spending less time on their music. They used to agonize over albums and songs, pouring their hearts and souls into them and taking the time to get things right. They also took creative chances. Now, music is collapsing into a formulaic structure where all music sounds the same, and artists bash the formula like the preprogrammed buttons in Mortal Kombat.
Music has become less something to be listened to and more something that provides background noise in daily life. It doesn’t take pride of place as an activity. It’s wallpaper.
Musical Abundance and Lost Value
What we have today is essentially musical abundance. There’s literally so much music on streaming platforms that people can’t find it. In 2024, it was reported that Spotify had over 100 million songs with over 60,000 new songs added per day. Want to guess just how much of that is good music? Certainly less than 1%, there is no way you can convince me that there are 1 million good songs on the platform. In this world of abundance, most things are shit.
As an artist, you basically have to give your music away. As a listener, music is nothing to you. If one song isn’t available, you just listen to something else without emotion. Music, possibly humanity’s very first art form and something that has meant so much to so many humans throughout history, is now stripped of its value and devoid of connection and meaning. In reality, musical abundance hasn’t led to positive outcomes for either the artist or the listener, and this is our lesson here.
In reality, musical abundance hasn’t led to positive outcomes for either the artist or the listener.
The healing effects, the satisfaction, the connection, the memories, and most of the factors that made music valuable and essential to humanity are now gone. Industrialized, homogenized, and mass-produced music has stripped the art of its value. For the far fewer than 1% of songs that aren’t terrible, we are left with momentary blips of completely forgettable, average sounds labeled as music that just happen to hit our ears. No wonder kids are rediscovering the classics.
There’s no going back. Once a technology is implemented, even if it makes things worse, there is no going back. We are stuck with it, which is why it’s so important to understand the trade-offs and implement mitigations before adopting a technology. We just can’t seem to do that.
Now, the music industry also has its share of blame here. It got greedy. A CD never should have cost anywhere near 20 dollars, unless it was a double album or some other special edition. $9.99 was a fair price and should have been the cost for an album. But that time has passed.
Abundance Math Doesn’t Make Sense
Let’s set a couple of foundations for abundance. The premise behind abundance is that companies will employ AI and robots instead of human workers. So, first and foremost, no jobs for most people on the planet.
We are told by futurists and AI bros that abundance will bring a utopia. That everything will be democratized. The cost of everything will be near zero, compute, goods, intelligence, development, and on and on. With so many things driven to essentially zero, not only does the math behind abundance not make sense, but its value as well, as we’ve seen with music.
With so many things driven to essentially zero, not only does the math behind abundance not make sense, but its value as well, as we’ve seen with music.
Let’s start off by addressing the use of the term democratize. Whenever an AI bro uses the term democratize, they actually mean something else: either devalue, degrade, or destroy. I call these the 3 D’s, and I’ve covered this before. But let’s get back to cost and value.
It doesn’t matter if the cost of things is driven to near zero. If they cost something and you have nothing, you still can’t afford it. If a brand new car costs a dollar and you don’t have a dollar, its inexpensiveness doesn’t change the fact that you have to walk. Assistance programs like UBI will hardly have us living lives of luxury.
When people have little to no money, they tend to focus on pure necessities. Pretty much the entire global economy is focused on selling us stuff that we want, not necessarily things we need. That means in this situation of supposed abundance, most companies on the planet disappear.
This shift will rewire our values. I’ve discussed how this may happen in retail through the implementation of agentic shopping. This rewiring isn’t lost on people who are thinking about these issues. The CEO of Walmart resigned because of AI’s potential to upend retail. If successful, more rewiring is on the way and will upend the entire fabric of the modern world. Just imagine, instead of lusting after material things, the dominant belief in the world was meditation and inner peace. Not great for business.
In a world of abundance, many large companies probably won’t exist. Diversification and competition will eat them alive. The abundance of tools and techniques will allow for quick imitation. Sure, some will remain, kind of like the company store in a mining town, but the economic landscape will look vastly different.
We are told that, despite things being so cheap, companies will make money from volume. But once again, with distribution spread across many small companies, it’s hard to see how that amounts to anything more than table scraps. This begs the question, then, where will the money for UBI come from? Even worse, instead of UBI, we may end up with company vouchers, forcing us to buy from a single company or to obtain vouchers for things we don’t need.
Abundance Equals Sameness
Henry Ford is quoted as saying of the Model T that a customer can have any color they want, as long as it’s black. There’s a lesson here for technological abundance. Many are laboring under the delusion that a world of abundance looks like the world of today, just with more. But the reality is this world will look totally different.
The world of technological abundance laid out before us isn’t a world of diverse beauty with green grass and open skies where people spend their days writing poetry and contemplating their existence in the universe. It’s a homogenized world of sameness where everything collapses into uniformity because this uniformity is predictable and can be mass-produced at scale. Individuality and one-offs are unpredictable and costly, and thus must be crushed. It’s a world where humans become little more than a burden.
Sure, people will try to control for this uniformity in the ways that they can. For example, if a family has a 3D printer at home, they have some control over what they print. However, they still need the resources to buy the printer and printing materials, and most would rely on the available designs. This concept of a utopia is starting to smell pretty rank.
New Business? Maybe.
I’m sure it will be said that I’m not envisioning the new businesses that will sprout up and how humans will change their behavior. Sure, the world is a complex place that defies prediction. I acknowledge that adaptation is possible, and surely, humans will change their behavior. They won’t have a choice. But it isn’t going to turn out the way people think.
Just shooting from the hip here, but a world in which people don’t have money and things are essentially free doesn’t seem like a robust environment for business. You won’t see a plethora of new startups all angling for that gigantic pot of nonexistent money.
You won’t see a plethora of new startups all angling for that pot of nonexistent money.
People invest in companies on the hopes of a big return. They take chances and are willing to take losses, but the gambler loses big in this new environment. Because even an investor with money and a successful business bet basically converts that investment into less money. In this environment, there are no incentives to improve, and everything will plateau at mediocrity to preserve existing margins.
Another thing to consider is that this environment is inherently fragile. Any problem, no matter how small, could cause a company to lose money since the margins would be so tight. Any amount of downtime, errors, or integrity issues, whether accidental or intentional, could be catastrophic. The predictable answer from the AI bro is: “But the AI won’t make mistakes.” To which I say, good luck with that.
Now, is it possible for us to evolve beyond money or capital toward a society that aligns its value with other meaningful activities and goals? Sure, it’s possible. But, once again, this wouldn’t be good for the business bros who are pushing this fallacy the hardest. In a world where abundance is truly successful and is beneficial to humanity, it’s bad for business.
Complexities and Negative Impacts
A full conversation on the negative impacts is outside the scope of this article. However, I have covered some of these before.
I’d argue that these people vastly underestimate the world’s complexities and the sheer number of negative impacts this change brings, not just on the economics but on humanity as a whole. There are basically only one or two ways this can kind of go right, and an incalculable number of ways it can go wrong. For example, this is an environment tailor-made for surveillance and totalitarianism. How can it not when your existence is completely dependent on external factors like the state or the generosity of a benefactor?
If you think that people will tolerate a few trillionaires while the world suffers mass unemployment, you are dead wrong. This is so obvious it shouldn’t need to be stated. I can completely envision a group calling themselves The Children of Ludd wreaking havoc. Only their anger won’t be isolated to the machines in data centers and their embodiments out in the world. They’ll direct their anger at the people they see exploiting the situation as well. It won’t be pretty.
I can completely envision a group calling themselves The Children of Ludd wreaking havoc.
In many cases, this new environment will make people far more tribal and extreme and willing to believe just about anything, but this is a topic for another day.
Conclusion
The vision of abundance defined by the tech bros is an absolute fallacy. It’s a world in which AI is talked about more like magic and less like an actual technology. From this tap spouts outlandish claims with no foundation in reality. As we’ve seen, even when abundance works, it has a degradation effect.
A world of successful abundance appears to be bad for business, which raises the question: Why are so many business leaders pushing this concept? If I had to guess, it’s that they hope to make their money before this big crash and leave humanity to pick up the pieces.
The cool thing right now seems to be to tell the world you are reducing headcount because of AI, regardless of the reason. Although not a recent development, it’s picking up steam. There’s even a term for it, “AI washing.” Although this term began life as a reference to products and services, it’s now right at home in companies’ layoff messaging. The future is bright 😎
There is no doubt that AI is having an impact on the job market, but not necessarily for the reasons people think. It’s not due to massive gains from deploying AI technology, but because of something far simpler, the mere idea of AI.
Before We Start
I try to keep my information diet balanced. As such, I follow a cavalcade of haters and AI hype bros. In this group, some people think LLMs will disappear. For example, if the AI investment bubble pops, LLMs will evaporate, much like the metaverse did. This perspective demonstrates a fundamental misunderstanding of realities on the ground.
Generative AI is seeing some success across various use cases. Two examples are cybersecurity and software development. Sure, the amount of success and the extent to which these use cases can be driven are open to speculation, but denying they exist is delusional. LLMs don’t need to be AGI to be useful. Hell, they can even be kind of bad at something and still be useful as long as you understand the capabilities and limitations.
The disconnect people see is the undertone of the marketing, which casts it as a complete labor-saving device rather than a productivity tool. This is partly what we’ll look at in this article.
Oh, and I’d say the metaverse is down, but not out. Never underestimate people’s desire not to live in reality. It will be back at some point. Now on to AI washing.
Not only is he doing it, but he sees most companies doing the same next year. The issue being he’s not the first. Many tech companies overhired during the pandemic, and they’ve already reduced headcount, specifically Meta and Amazon.
The AI washing of layoffs is something that Sam Altman himself acknowledges, and he specifically uses the term in relation to layoffs when speaking at a recent summit. However, Sam Altman disingenuously uses the term “blame” when he says, “Almost every company that does layoffs is blaming AI, whether or not it really is about AI.”
Business leaders aren’t “blaming” AI for layoffs. They are praising it. Celebrating it even. There’s a pretty wide gap between blame and celebration. In much the same way I celebrate my birthday, I don’t blame my parents for the fact that I have one.
Altman strategically uses the word “blame” here because he’s attempting to rework AI’s image in the face of growing backlash. This is a manipulation. Everyone needs to remain vigilant against these manipulations in our current era. However, I love how Altman goes right back to spouting abundance nonsense, always on-brand.
AI Washing: Performance Art Yields Rewards
Telling the world you are laying people off because of over-hiring, your financials are down, you expect an uncertain market, increased competition, or any number of other factors would cause your stock to drop. Pretty much the only positive way to frame layoffs these days is to say that it’s because of AI.
When you say it’s because of AI, you are sending a positive signal to the market. You are saying, “We didn’t cut headcount; we gained efficiency.” We are now set up to reduce even more headcount in the future, which translates into greater potential profit for investors. Reality has no business here. As I’ve said before, much of this is performance art for investors, and the performances are paying off. Throwing AI in front of layoffs works… for now. Block shares surged after the announcement.
And we are back to Meta again as they consider cutting 20% of their workforce because AI is so capable right now. I’m joking, of course, they’ve made some terrible AI investments (and hires), and investors aren’t happy. But they are happier now that they are considering laying off 20% of their workforce.
However, it’s not working out so well for Oracle. Oracle’s situation is playing out more realistically. They overspent and then needed to cut jobs to cut costs. This could be more difficult for them to reframe because so much is publicly known about their data center project and their relationship with OpenAI.
Are you sensing a trend yet? Whether AI actually works and replaces staff is irrelevant. More companies will see this and follow suit. AI will be attributed to every layoff from here on out. Even non-publicly traded companies will follow, seeing a more positive framing, even if it doesn’t work out in the end.
The Idea of AI
AI is coming for jobs, but not before the mere idea of AI does. There are people right now either getting laid off or not getting new opportunities, not because of AI’s capabilities, but because of the mere thought of AI doing their jobs in the future. Companies are betting their future on the hype that AI companies are pitching. This is like jumping off of a perfectly good boat in the middle of the ocean because some dudes on the internet claim a better boat is coming soon.
Even if companies aren’t laying people off because of AI, they are certainly slow to open new positions, hoping that AI will alleviate the need. This places more work on the shoulders of current employees, intensifying their workload rather than reducing it.
Look, I’m not delusional. There is no doubt that AI is having some impact on the labor market. How much impact and the reason are hard to decipher. It’s difficult to distinguish decisions made between true capabilities and pure hope. In some cases, where it would seem to impact certain jobs more negatively, the opposite happens. For example, instead of hiring fewer developers, companies are hiring more.
Generative AI models are great at generating initial code. However, it remains to be seen how these tools fare in maintenance over time, especially for larger, more complex codebases. There’s reason to believe it won’t work out as well as people hoped. In a way, we get a glimpse of what could be, but still isn’t. Companies are hoping this gap closes.
However, the hype catches fire because many business leaders have no idea how the technology works. They read news articles, many of which are nonsense, and then assume everyone is doing something except for them. So, they force-feed half-baked technology to everyone at the company and delay hiring in the hope that AI swoops in as a savior.
Layoffs Are The Point
Even if the current spate of layoffs is mostly AI washing, it should be noted that the total reduction of staff with AI is the point. Even if the business leaders won’t admit it, the influencers certainly do. If you ask them, they’ll tell you that the ideal number of employees at a company is zero. This can be accomplished with a far less-than-perfect AI technology.
The pseudo-utopian sales pitch is that the goal of AI in the workplace is to reduce workload, freeing people up to focus on more meaningful and creative tasks. This pitch was always 100% bullshit. The goal of AI in the workplace isn’t to reduce workload. You don’t think people are dumping billions upon billions in investment into AI because it’s a productivity booster, do you?
The famous saying, “AI won’t replace people. People with AI will replace those without,” was always silly. I’ve been saying for years that we don’t need AGI for companies to replace workers. The moment the AI is mediocre enough to pass muster, it will be adopted. Bugs, errors, issues, vulnerabilities, and all. Period. Doesn’t matter if the person has AI or not.
What we are seeing is a dress rehearsal for how a more capable AI offering would unfold. Businesses would replace people as fast as they could. We are already on the precipice of people falling into what I call the “Sucks to be you gap,” a condition in which workers are displaced from the workforce by AI with no alternatives and no support. The sad thing is, they may fall into this gap not because of legitimate AI capabilities, but because of the mere idea of AI.
The Negative Consequences
There are plenty of trade-offs in replacing employees with today’s AI tools, as well as the misconception that we are one iteration away from complete success. As usual, what shouldn’t be surprising to anyone is apparently mind-blowing.
First of all, organizations are cutting headcount, leaving fewer people, and AI doesn’t replace their jobs. So you have fewer people doing more, even with AI tools. This is leading to a kind of AI burnout being labeled “brain fry.” This isn’t sustainable or productive. Most companies are already efficient and can limp along for a bit after drastic cuts, but it catches up with them quickly after a few quarters or even a year. In the long run, these short-term gains turn into long-term losses.
AI adoption over human talent can lead to stagnation. This may seem counterintuitive, but LLMs don’t generate novel ideas. The tools contain a mishmash of already known things. This is like expecting an industrial robot on an assembly line to come up with a new way of working. Humans are where true creativity and novelty still exist, and after cuts, companies may be missing the very people who can move the business forward. Most modern organizations aren’t like factories, but making them more like a factory could be a recipe for disaster.
From a human perspective, an AI-powered organization is fairly uninspiring. So your best people don’t stick around, and attracting new talent may become problematic. Imagine telling someone that their job will mostly be managing a fleet of AI agents. Super fun! Especially since that implies the technological equivalent of a janitor. They wouldn’t be exploring or creating, they’d be cleaning up.
In many cases, companies would be reducing the quality of the products and services they offer. Moving fast, vibe coding, replacing people with agents that have errors, and many other cases cause a degradation in delivery. I’ve written about this before… back in 2023. Companies are running face-first into a wall of technical debt.
For all the talk about competition with China and the EU, it seems our US tech companies may be putting themselves at a disadvantage in pursuit of short-term balance-sheet wins. The sentiment of US tech companies is at an all-time low as countries around the world scramble for alternatives. This will be a space to watch over the next couple of years to see how much damage it causes.
Of course, a huge issue with the public praising of AI as the reason for layoffs is the massive negative sentiment it engenders. The AI backlash is only going to get a lot worse. Please, everyone, Sam Altman can’t handle this much backlash on his own. 😆
At Some Point
At some point, a technology will come along that delivers on all of the promises the AI companies are making. Call it AGI or whatever. The real questions are, will it be built atop LLMs, and how soon will this arrive? Despite their usefulness for specific tasks, I personally don’t think LLMs are the technology to deliver on these promises, though many people disagree. Fair enough.
As far as timing goes, I don’t have a good read on this, and anyone who claims otherwise is full of it and drinking marketing Kool-Aid. If I had to speculate, I think another 15 or 20 years, to which every AI bro on the planet just collapsed on the floor laughing. The running claim in tech circles is 12 to 18 months (it’s always 12 to 18 months), but I believe a reckoning is coming that the AI bros fail to recognize. I’m not saying that AI won’t have an impact on jobs during this time. I’m talking about major employment disruption and workforce displacement due to AI.
I believe that at some point, there will be a significant setback. A reset will cause a reckoning. The buildup of technical debt, the degradation of service, the brain drain from companies, stagnation, the bursting of the AI investment bubble, AI data center sunken cost, or any number or combination of factors will cause a reset. As companies try to reset themselves, competitors without this baggage will swoop in to steal market share, further damaging the organization. It could lead to a situation in which smaller, more agile organizations overtake large competitors.
This may happen because the company spent so much time reworking things for AI that it’s not working for humans. You can certainly do both, but that’s not what companies are doing right now.
And no, LLMs won’t go away. If that’s what you were hoping for, I have bad news for you. Beyond the use cases and tools where LLMs are genuinely useful, LLMs have become a comfort blanket for people. You’ll have to pry it out of their cold, dead hands.
Conclusion
AI washing is here to stay, and pretty much every future layoff announcement will be framed as AI-related. This trend will continue until something breaks. One thing is for sure: the next year is gonna be wild.
Here’s a secret: turning books into statistics won’t bring AGI, cures for cancer, utopia, or any number of useful inventions that we are told are merely 12 to 18 months away. This activity also won’t bring their users wisdom. As a society, we are told that if we don’t let companies freely pillage the intellectual work of our past and present, we won’t get the life of leisure we are promised. But turning War and Peace into statistics won’t lead to significant breakthroughs. It won’t even bring knowledge… for you.
I believe that many of the people who work at the big AI companies know that training on a large corpus of non-domain-related works won’t lead to AGI or significant breakthroughs in areas such as cancer research, but they do know it may lead to breakthroughs in manipulation, and that has them interested.
Project Panama: Books Into Statistics
Recently, the Washington Post had an article about Anthropic’s Project Panama, a secret project to destructively scan every book on the planet. The image is shocking, and the whole situation feels dirty, which is probably why Anthropic tried to keep it a secret. Although it was found that they didn’t break any laws, they tried to keep it secret because they knew this would have a negative public perception. Mission accomplished.
In one sense, this is an attempt to obtain untainted training data. The internet is submerged in AI slop after the launch of ChatGPT. AI slop is good enough for the internet, but not so good for AI training. AI models tend to degrade when trained on their own outputs, a condition known as model collapse. So, instead of a model getting better, it gets worse. Seems models know what’s better for them than we do.
But the quest for untainted training data isn’t the whole story. If you are trying to scan every book on the planet, then you’ve made a decision to ingest and train AI on books of all kinds, inaccurate books, dated books, and even “bad” books. In short, accuracy isn’t the goal here.
Okay, so what’s a “bad” book? I mean books with universally accepted bad ideas, poor stories, poor writing, and many other issues. Trying to train on all books means you’ve made a conscious decision to also train on material such as Mein Kampf and The Turner Diaries. That’s right, you’ve “trained” on it, not assigned it to an AI model as homework for a classroom discussion. There are a few things I can say with 100% certainty, although I can say this: there is nothing in the works of books like Mein Kampf that will cure cancer.
Bad books shouldn’t be eliminated, although bad for AI, they can be beneficial for humans. You can always stop reading a poor novel or other books you feel aren’t providing proper value for your effort. As for books with bad ideas, when a human reads one, they can do so from a given perspective, trying to formulate a certain understanding. They can even be read with the intent of identifying and avoiding certain conditions in the future. Only a fool would think reading a book with bad ideas is always bad.
It’s basically just nom nomming the data, creating statistical grenades.
When an AI trains on a bad book, it incorporates the ideas and even the poor sense of style. It isn’t providing any perspective. It’s basically just nom nomming the data, creating statistical grenades. I’m certainly not claiming that training on Mein Kampf creates an AI Hitler, there are other ways that can happen. What I am saying is that the ideas and concepts contained in these books are kicking around in there somewhere, even if they are shoved way down in the statistical distribution. What this ultimately means is unclear.
I don’t mean to be disingenuous here. The definition of a “bad” book is highly subjective. This quickly devolves into a who decides scenario, which could lead to unintended consequences of its own. My point is that there should be more purpose to the activity.
There were also plenty of idiotic takes on Project Panama. Never underestimate the true cluelessness of the e/acc community. One thing they effectively accelerate their own idiocy.
One of my favorite arguments from the e/acc community was that they weren’t destroying the books, they were preserving them. To which I joked that it was preservation through destruction. Preservation through destruction sounds like a quote that could be ripped from Orwell, just like the pages of his books for Project Panama. Turning books into statistics to monetize them doesn’t preserve them in any sense of the word. This is a silly argument that can be destroyed by one simple question. If they are preserved through this process, then where are they?
Preservation through destruction sounds like a quote that could be ripped from Orwell, just like the pages of his books for Project Panama.
Manipulation and Imitation
So, why are AI companies foaming at the mouth to get their hands on books that seem to have nothing to do with their goals? If I had to guess, it has to do with a couple of factors.
The more of this type of data ingested for training, the more the system may be able to imitate humans under a variety of conditions. This can be used by users of the system to create a “personality” from the tool, or, more importantly, to manipulate people, fooling them into thinking the AI is actually a human. This manipulation could be applied in situations like customer service. I experienced this recently.
A broken water pipe forced me to call some local plumbing companies after hours. Quite a few of them were using a call service with what sounded like a human in a call center, complete with office background noise. This was clearly done to manipulate users. Oddly enough, when asked if they were AI, only a couple responded that they were. This is the type of manipulation that I find unacceptable. Gary Marcus recently wrote an article about this as well.
Of course, these conditions can also be used to claim that the AI has a consciousness or a self that needs to be protected. This is pure SciFi bullshit meant to ramp up the hype.
Another interesting and related condition is pastiche. The more of this type of data the model is trained on, the better it may get at imitating specific forms of human style. People can use these to fool themselves into thinking they are being creative.
As an example, generative AI can reliably generate books. They aren’t good books, or well-informed books, or well-written books, or accurate books, or present new information, or new perspectives, or any of the other countless characteristics we associate with a good book. But words slathered onto pages… This it can do reliably.
Generative AI hasn’t cured cancer yet, but it excels at creating slop. Slop is literally the number one use case for generative AI today, arguably more than coding. There’s no doubt that AI companies want more of this behavior to keep people engaged.
But let’s get back to books.
Next-Gen Nerds
When I was growing up, being called a nerd was considered a bad thing, and nerds read a lot. Now, they are popular, wear black t-shirts and blue jeans, and claim that reading is for losers. Their perspective is warped by an “optimization-at-all-costs” mindset. But don’t take my word for it.
In November of 2022, notorious tech bro and crypto con man Sam Bankman-Fried told a writer interviewing him, “I would never read a book.” He went on to say, “I’m very skeptical of books. I don’t want to say no book is ever worth reading, but I actually do believe something pretty close to that. I think, if you wrote a book, you fucked up, and it should have been a six-paragraph blog post.”
That’s the next-gen nerd’s perspective. An entire book should be six paragraphs. But why stop there? Why not six bullet points? Seems I just out-optimized the optimizer! In fact, many of their points can be addressed by reductio ad absurdum.
This perspective does not serve people well and further devalues books. The theory is that if books can be reduced to numbers, then the ideas they contain can be made “useful” in a programmatic or more efficient way. But ideas from books can’t be reduced to numbers, just like The Hitchhiker’s Guide to the Galaxy can’t be reduced to 42.
Oddly enough, when reducing books to numbers, you remove the content from context. Context, the very thing both humans and AIs need to make sense of things.
The idea that books are nothing but bloated friction is nonsense and could only be cooked up by delusional idiots. I acknowledge that poorly written books exist, and some books are 12 chapters when they should have been 6, but applying this perspective equally across all books is just plain stupid.
The Decline of Reading
Here’s a chart that should surprise no one.
Everyone knows you don’t become an influencer by reading. Or reflecting. Or thinking. You become an influencer by reacting. Thinking before you do something takes too much time. What should be concerning is the rate of the lines. Kids who don’t read turn into adults who can’t read. And we are seeing this play out.
Kids who don’t read turn into adults who can’t read.
Here’s another secret: no matter how good the AI gets, you’ll never become wise without doing the work yourself. Wisdom manifests from reading, writing, and a whole lot of reflection. All activities that are devalued today. I’ve previously covered how knowledge and understanding aren’t generated from bullet points, but let’s go a bit deeper.
In letter 27 of Seneca’s Letters on Ethics, he discusses how real joy depends on real study. In this letter, he describes a man named Calvisius Sabinus, a wealthy man who wanted to appear learned, who devised a shortcut. He spent a great deal of money on slaves, one to know Homer, another Hesiod, and nine more for each of the lyric poets.
After he assembled this group, he would pester his dinner guests by having these slaves at his feet and regularly ask them for verses to quote. Even with this assistance, it was observed that he’d often stop mid-sentence. When a man named Satellius Quadratus made fun of him, saying he should train his busboys to be literary scholars too, Sabinus responded that the slaves had cost him a hundred thousand sesterces apiece. To which Quadratus said, “You could have bought as many libraries for less.”
Sabinus’ optimization led to a mistaken assumption that the knowledge possessed by anyone in his household was his own. This perspective is not only wrong, but it also led to ridicule. Today, we have a similar situation with AI, and many would claim that the information contained in an AI tool is knowledge they themselves possess, except that it isn’t. This resembles a situation we had previously with Google search, so we shouldn’t be fooled merely by upgraded tech.
Excellence of mind cannot be borrowed or bought. -Seneca
Wisdom isn’t recall. After all, someone who memorizes things wouldn’t be considered wise. It’s the perspective that’s gained from study and reflection across a variety of sources. It’s the ability to connect the dots between concepts and form new ideas. Wisdom manifests in someone who puts in the work, which includes reading, writing, and a healthy dose of reflection, all things labeled by the tech bros as “friction.”
When Zeno of Citium (The founder of Stoic philosophy) visited the Oracle of Delphi with the question of what he should do to live his best life, the god replied, “He should have intercourse with the dead.” This is recorded in Diogenes Laertius’ Lives of the Eminent Philosophers. In modern times, people have changed this to “converse with the dead,” no doubt because of how intercourse is used today, but I think intercourse has a much deeper meaning. No pun intended.
The oracle’s response didn’t mean Zeno needed to engage a psychic medium or have a seance. The only true way to converse with the dead is to read. This is the meaning Zeno inferred as well.
I’m obsessed with the works of long-dead authors such as Seneca, Aldous Huxley, Marshall McLuhan, Neil Postman, Montaigne, and many others. I can’t send them letters or call them on the phone. I can grasp their perspective through their writing, through books, letters, and other artifacts they’ve left behind. I can highlight passages, write my own notes, create my own modern perspective, and even challenge these authors in my own way, given the time that has passed from them to me.
Reading is a superpower that we seem eager to relinquish, which is a shame, because in many cases, reading is the remedy for so much of what ails us today. Reading does broaden the mind. It demonstrates that even ancient people encountered many of the same problems we have today. It creates room for reflection that extends far beyond the act of reading. The quest for wisdom and conversing with the dead create a satisfaction that can’t really be matched by other activities. It’s a feeling that isn’t possible to explain, and it’s something that needs to be experienced.
Getting Useful Information From Books
There’s a time-tested way to get the information from books that doesn’t require destroying them and turning them into a statistical distribution: you can actually read them. Dated concept, I know. I have a large piece in draft on literacy more broadly, so we’ll keep this piece focused on reading.
People might claim that the joke is on me because I only have knowledge from the books I’ve read, whereas someone with generative AI has knowledge from all of the books, despite never reading any of them. This is an idiotic statement to make. Setting aside the issues with retrieval, hallucinations, and other technical issues, the user doesn’t actually doesn’t have the knowledge of any of these books. At best, they have pieces torn from context. These people are like Sabinus, without knowledge but happy to annoy dinner guests.
The great thing about books is that you don’t need to read them all. Just like an explorer doesn’t have to explore every square inch of the globe, a reader is free to explore their unique interests and forge their own path of knowledge and wisdom.
Another response could be, since the AI has been trained on the content of someone like Seneca, you could have a Seneca bot and ask it questions. But this approach doesn’t make sense either. First of all, even if this were an effective approach, you’d have to know the right questions to ask. Since you haven’t read the source material and aren’t confronted with concepts in the writing, the “right” questions would escape them.
Second, none of the responses from the bot would stick with you, or in some cases, make any sense. The responses won’t form a connection in the way the content is encountered in the context of reading a book. The bot is going to “tell” you something, while reading will “show” you something. Reading is a true experience. Being told something is ephemeral and throwaway. Experiencing something can last a lifetime, while being told something may last only seconds.
Experiencing something can last a lifetime, while being told something may last only seconds.
Finally, the bot will approximate a pastiche response based on what it may have encountered during its training. It’s going to fill in the blanks in whatever statistical way makes sense. It won’t be the true response based on what the author really knew or felt. However, this response does actually fool people, and we’ve seen it time and time again in the generative AI era.
Much of this makes so much more sense when stated out loud. What do you think is the best way to get value from George Orwell? Do you think throwing 1984 into a massive statistical distribution or reading the book? Reading a book sticks with you in a way other methods can’t match.
There’s a mismatch in the application of technical thinking here. The goal of reading a book is to change the landscape of your mind, not have immediate recall over chapter and verse. Thinking that the point of reading a book is to remember everything is a warped perspective caused by our modern technological environment.
Unable To Read
Many people find it difficult to read long-form content, things like books and longer essays, articles, maybe even this very one. This may be due to attention hijacking, inability to focus, or discomfort, for lack of a better term.
People often tell me they wish they had time to read. I tell them, “Yeah, like I have boatloads of free time.” When viewed this way, zoomed out, it appears that nobody has time to read. However, this isn’t the case.
It’s true that getting information from books requires purposeful action and commitment. However, once started, it’s not as bad as people make it out to be. In many ways, it’s like an exercise routine. The best way to get started, or to get back into reading, is to create a habit. Start with the intention and don’t beat yourself up about it when you miss the mark.
What do you typically do before you go to bed? For many, this is swiping through social media, reading news stories, or maybe watching TV. This is a prime block of time to target for a reading habit.
You don’t need to start big. Maybe try 15 or 30 minutes of uninterrupted time. You may get distracted or feel uncomfortable. It’s fine. Like any habit, it will take some adjustment. At some point, it will click.
Don’t let some sort of idealized conception of reading throw you off. There’s so much reading advice out there, and much of it is bullshit. For example, nothing is more bullshit than speed reading. If you were worried about wasting your time reading, then speed reading will confirm your worries.
All of the things you are told are bad habits, such as vocalizing as you read, re-reading sections, and reading slowly, are all positives that reinforce the concepts contained in the book. Your goal is to develop an understanding of the material, form new connections between concepts in the book and in the world, and even develop new ideas based on these. None of that happens during speed reading.
It’s true, people read at different paces. You may feel like you read slower than other people, but that’s probably not true. Besides, who cares? You are reading for you.
As you begin reading again, you’ll find what works for you and what doesn’t. For example, maybe you prefer physical copies of books or the convenience of an eBook reader. The format is irrelevant if it works for you. Also, maybe you need to put your devices in do-not-disturb mode or make other purposeful interventions. Be intentional, find what works, and push forward.
My Approach To Reading
Let me share my approach, because I feel it’s pretty simple. I’ve explored using book tabs and other reference techniques, but I don’t use them consistently. Some people use reference cards, but I’ve never felt the need to go this far. I read for about an hour and a half every night before I go to bed. I typically have at least one fiction and one non-fiction book I’m reading at a time.
I prefer physical copies of books because they feel more engaging to me, and I don’t have yet another “device” in my hands. However, for nonfiction books I’m really trying to dig into, I’ll buy all three formats: physical, ebook, and audiobook. The audiobook is mostly for use while running on a treadmill or driving on a road trip.
For the physical copy of the book, I have three things: a Zebra Mildliner in lemon yellow, a pen, and a notebook. As I encounter interesting content, it could be concepts, things I’d like to quote, or anything else, I highlight it. If there is an entire section of a page, then I’ll highlight the first sentence and make a note with my pen in the margin, so when I revisit it, I have the context.
As I have ideas or make connections, I’ll stop reading and capture my thoughts in the notebook. I may include a piece of the book’s content there, but not always. I will always annotate the page number for reference, though. This way, it’s easier to revisit in the future.
After I’m done reading, I’ll take all the highlights from the physical book and apply them to the same sections in the eBook. I then sync those highlights to Readwise for both ease of reference and spaced repetition. I also have a physical commonplace book where I write the highlighted items. However, I’m not very consistent with this activity.
You may question the efficiency of my approach, as it seems I’ve added unnecessary extra work for myself. It appears that using the ebook and syncing the highlights is far more efficient. Once again, the optimization is a trap. The friction is the point.
In the act of transferring the highlights to the ebook, I’m once again confronted with all of the concepts I’ve highlighted, this time, after reading the whole book. Although I don’t remember things word-for-word, when I see the highlight, I’m reminded of the context in which the highlight was created. New ideas form, and I note those in my notebook. This activity further reinforces the content in my mind. I will sometimes reread the page or section during this activity. This activity provides much value.
Someone may argue that an AI can digest the entire book and provide relevant highlights without having to read it. Hopefully, by now you can recognize the issue. Even if it did this accurately, you’d be confronted with highlights out of context. The meaning and important features would be unavailable to you mentally.
Conclusion
The current AI age is making us wisdom-poor and manipulation-rich. The damaging consequences of the devaluation of reading are on the horizon for an entire generation and generations to come. It’s separating us from the very skills we need to defend ourselves and keep us robust in the modern environment. It’s removing our ability to reflect as modern technology pushes us to react.
Many believe they can’t read long-form content anymore, but that’s only because they haven’t tried. By creating a habit and some purposeful interventions, we can get back on track to finding wisdom.
One mistake we continue to make, time and time again, is confusing innovation with progress. It’s true that in many cases, innovation is progress. We see the results of this every day, in the development of vaccines, antibiotics, and even indoor plumbing. The results of innovation have improved people’s lives across the globe, allowing them to live longer, healthier lives. These are examples of progress fueled by innovation, but this is hardly always the case. It’s a mistake to assume that innovation and progress are the same, despite people using the terms interchangeably. Thinking this way is a trap that lulls us into complacency.
Confusing Innovation With Progress
When people use the term “innovation,” they often mean technological innovation. People imagine everything from hoverboards to intergalactic space travel. It’s these larger-than-life imaginings implanted in our brains that companies exploit to sell products as progress. Apparently, the road to utopia is paved with forced obsolescence and mandatory upgrades. However, nowhere in the definition of technological innovation is there a qualifier that it must be beneficial to humans.
Innovation is about making money, after all, companies are in business to make money. This isn’t some terrible, disgusting secret. And no, we shouldn’t burn it all down because companies are trying to turn a profit. This is how the world works. However, we need to recognize this fact because it figures into our calculus of progress. Because when innovation makes life better for us, the making life better part is a byproduct of the innovation activity. There are certainly exceptions and exceptional people like Jonas Salk, but this is hardly the norm.
Progress, on the other hand, is advancement toward the betterment of humanity. Or, at least, that is what the definition should be. Granted, these are subjective measures, which make them difficult to pin down, but progress is typically something we know when we see, for example, reducing poverty or medical advancements that reduce disease. Basically, anything that makes human lives better as a whole, preferably ones that lead to human flourishing, although this is rarely the case.
The fact that progress is subjective is what many technologists don’t like. There’s no number to optimize. Of course, since it’s subjective, people’s perspectives on progress vary. This is what gets exploited to sell nonsense as progress. When innovation is packaged as progress by default, then there’s something to optimize.
Let’s take the example of brain-computer interfaces, a space that is certainly going to heat up. Here’s Elon Musk claiming that Neuralink will allow us to become one with AI.
This is certainly innovation, but is it progress? In some cases, the claim could be made that it is progress. This would be true if it corrected some impairment or disability. But otherwise, let’s think about the scenario. Imagine a world where you never know whether a thought or memory you have is truly yours. A world that signals the end of private thoughts and the beginning of a whole new world of manipulation and unintended consequences. Maybe it’s just me, but this doesn’t sound like progress. I’ve pointed out these risks in my public presentations on emerging technology, as well as my addressing Kurzweil’s nonsense.
The lines can certainly get blurry. Self-driving cars seem like progress until they take away the self-driving features and instead charge you a monthly fee to access them. Apparently, a monthly subscription fee is how we get to Mars. I’m not claiming technology should be free. I just don’t think monetization should be a rug pull.
Apparently, this is how we get to Mars, through a monthly subscription fee.
Detractors often bring their own set of problems. No innovation that leads to true progress benefits everyone on the planet equally and simultaneously. The idea that it can and should is delusional, and insisting on this as the criterion is disingenuous and won’t lead to any progress. It’s the mark of a performance by someone living in an alternate reality.
Much of what we’ve seen over the past couple of years is innovation for its own sake, with associated negative consequences.
Why Does This Distinction Matter?
Many would have us believe that this is a distinction without a difference, and that I’m being provocative, but the reality is, these are two separate terms with two separate definitions. Progress is a general term that all humans can understand, whereas innovation is a specialized term used by technologists. The blurring of the lines between the two makes innovation in whatever form easier to digest. Quite often, innovation is immediately labeled and packaged as progress to exploit this condition.
This packaging and blurring of the lines between progress and innovation have been with us for quite some time. In Aldous Huxley’s post-apocalyptic novel from 1948 titled Ape and Essence, the Arch-Vicar of the satanic church describes progress and nationalism as the two great ideas that Belial put into human heads. In this scene, innovation has clearly been labeled as progress.
Progress—the theory that you can get something for nothing; the theory that you can gain in one field without paying in another; the theory that you alone understand the meaning of history; the theory that you know what’s going to happen fifty years from now; the theory that, in the teeth of all experience, you can force all the consequences of your present actions; the theory that Utopia lies just ahead and that since ideal ends justify ideal means, it is your privilege and duty to rob, swindle, torture, enslave, and murder all those who, in your opinion (which is, by definition, infallible), obstruct the onward march to the earthly paradise. -Aldous Huxley (Ape and Essence)
There’s no doubt that Huxley isn’t describing progress as human flourishing; he’s describing technological innovation packaged as “progress.” This is no doubt because the term “innovation” was not a common one flaunted in the faces of the general public the way it is today. This matters for all the reasons Huxley points out in this very line, and it applies just as well to technologists today as it did back in 1948. We are seeing the conditions Huxley noted in this passage today.
Technology Is Never Neutral
People love to claim that technology is neutral, but this demonstrates a stunning lack of awareness of reality. Technology is never neutral. The mere presence of the technology changes people and their environments. This has been known for over half a century. Here’s Marshall McLuhan writing about it in the 1960s.
Our conventional response to all media, namely that it is how they are used that counts, is the numb stance of the technological idiot. For the “content” of a medium is the juicy piece of meat carried by the burglar to distract the watchdog of the mind. -Marshall McLuhan (Understanding Media)
To quote another sage on the same topic, back in the 1980s:
Only those who know nothing of the history of technology believe that a technology is entirely neutral. -Neil Postman (Amusing Ourselves To Death)
Technology, by some measure, always changes the environment in which it’s found. Take social media, for instance. Social media changes the communications landscape, forcing even people who opt out to reckon with the possibility of being left out of activities when a group decides to organize on a social media platform.
There’s not much we as individuals can do about this, other than be aware of the phenomenon so we can implement mitigations. If we recognize that technologies are not neutral, we must acknowledge trade-offs, even if trade-offs don’t appear in the short term. Once again, we can look to social media. Early in social media’s life, there didn’t seem to be any real trade-offs. This perspective seems quaint today.
This is the point of this post: we never get something for nothing. The bill comes due at some point. When innovation, regardless of impact, is automatically packaged as progress, it becomes a vehicle to shove it down our throats, to make us get in line, to label us Luddites, to strip us of our humanity, and disregard our concerns.
For example, I’m still struggling to understand how destroying art and artists and dehumanizing everyone on the planet is somehow a prerequisite to getting a cure for cancer. If you don’t let us loot the intellectual and creative property of everyone on the planet, then you won’t get a cure for cancer or eternal life. Seems Huxley was right.
Conclusion
We should want and insist upon innovation that’s actually progress, with progress defined as the betterment of humans. So many these days are bedazzled by technology because it is neat or cool and never ask any larger questions about what problems it actually solves and what the tradeoffs are.
The next few years will require vigilance because much of the innovation packaged as progress comes at the cost of stripping us of our humanity and agency, further pushing us toward becoming machines. We need to become much better at envisioning trade-offs and not blindly take every new application or technology for only its surface-level functionality. The real dangers lurk behind the curtain and are rarely exposed until it’s too late. Awareness of potential trade-offs allows us to keep our guard up, choose our technology use cases wisely, and defend our humanity to the best of our ability. In the current environment, it’s up to us to defend ourselves.
