By now, you’ve no doubt heard about OpenAI’s experiment hacking HuggingFace. It’s all anyone in tech is talking about. I’ve written a few thoughts about the hack for Modern CISO, and you can read that article here.
In the article, I make the following observation about OpenAI’s write-up.
OpenAI’s write-up of the incident reads more like a marketing document promoting a feature than an incident summary, while the quote from HuggingFace sounds more like someone accepting an award than someone who just got hacked. It’s a bit surreal.
This is strange, and there’s no doubt OpenAI is getting far more mileage out of the incident than they would have by publishing benchmark results. Someone could be forgiven for thinking this is a publicity stunt with the current lack of detail and OpenAI’s financial situation.
Ultimately, no. This isn’t the end of the world or of cybersecurity. Like so many things, we underestimate the complexities of the real world. More thoughts and what it means for cybersecurity defenders can be found in the article.
Update 7/24/26
The more I think about it, the more I think there are multiple possible scenarios here.
Scenario 1: The whole thing was a publicity stunt
Scenario 2: They noticed the experiment going off the rails and decided to see where it went, hoping later to use it for publicity
Scenario 3: Operational error, oversight, or poor configuration
Scenario 4: Exactly as they claim, and this is some novel emergence of capability
There is a high probability that the truth lies somewhere in the first three scenarios.
I feel like the headline should be, “Anthropic receives no publicity over hack due to being better at setting up environments.”
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 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.
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.
Social media is flooded with the same hot take: software is dead! Yup, that’s right, the world runs on software, but applications are either in the grave or the ICU with the cardiac monitor flatlining. It only takes a modicum of reflection to see through this illusion. But our modern world rewards reaction, not reflection, so everyone reacts. This is fueled by the fact that many tech journalists have abdicated their responsibility, leaving us with a world where people are consuming the equivalent of digital bath salts. Are we witnessing the death of software? Let’s find out.
Everyone Is Saying Software Is Dead
The new hotness to spout the phrase “software is dead.” Everyone is doing it. If you close your eyes and pretend to live in a fantasy world with unicorns and sorcerers, it almost makes sense. Unfortunately, in our modern world, a basis in reality is not a prerequisite for making an impact.
Over a week ago, the stock market began taking a major haircut on software stocks, with a one-day loss of 285 billion. This was dubbed the SaaSpocalpyse. It seems investors aren’t sure whether software products will exist in the future, and the AI bros are hyped. Honestly, when are the AI bros not hyped? Investors are convinced that, in the future, people will build their own software rather than purchase it. So long, SAP and Salesforce! You had a good run. If this were true, it would be a major shift, since the world runs on software. But as usual, this is mostly stoked by cluelessness and perverse incentives.
Here is the creator of OpenClaw saying that 80% of apps will disappear.
That’s right: reach for number, pull directly out of ass. His reasoning is fascinating, since he recycles the same tired examples we’ve heard for years: making a restaurant reservation. Which I’m pretty sure we have the technology to do today. Seriously, the guy built this viral agent with claims of transforming the world, and dinner reservations are the best he’s got? However, I do like the dystopian twist of having your agent get a human to stand in line for you.
Sam Altman thinks this guy is a genius. Just goes to show you how absolutely desperate OpenAI is. That Anthropic Super Bowl commercial really hurt him.
Not to be outdone, here’s Mustafa Suleyman pivoting this into an AGI prediction. Just when you thought we were done with the term AGI for a while, it’s back stronger than ever. He’s predicting “professional-grade AGI” in the next 12 to 18 months.
Hmmm. Why are these predictions always 12 to 18 months? It’s always 12 to 18 months because that’s long enough to generate hype that fuels investment and cannot be checked in the short term. It’s also long enough for people to forget the prediction.
At this point, it’s fair to assume the tech press has abdicated all responsibility. They just mindlessly parrot this nonsense without any questioning or due diligence. They repeat these statements knowing full well that hype is in their best interest.
And then there are all of the countless attention-mongering influencers selling their own unique brand of horseshit. Like this guy.
By the way, these people follow a familiar pattern to manipulate viewers. First, they make some dumb look on their faces with a clickbait headline, which psychology says increases the likelihood that people will click. Then they lay the foundation by stating some history or facts. By stating these up front, they lower your defenses and critical thinking skills, since the facts and foundation appear to give them credibility. This is followed by their own unique brand of nonsense that follows.
Here’s another rando agreeing with… checks notes… Mark Cuban? Well, both Mark Cuban and this person are dead wrong. The next decade belongs to security professionals because this technology is insanely insecure. More on this later. However, I do love the pitch that this dude is going to save your business with a single Mac mini and OpenClaw. Bold.
These claims are nothing but a combination of clueless ramblings and pure unadulterated bullshit. This doesn’t bode well if your goal is to align with reality.
The Software Environment
Let’s first define what we mean by “software” in this context. By software, we mean software that you purchase from a vendor or a SaaS (Software as a Service) solution you subscribe to. This could be everything from simple apps you purchase on the App Store to large enterprise applications like SAP.
So, what’s the claim? In short, the claim is that people and companies will stop buying software because they can just use AI to build it themselves.
There’s no doubt that tools like ClaudeCode and Codex are getting quite good. Many people are discovering software for the first time, writing what could be described as more elaborate examples of “Hello, World!” programs. Some may claim this is a disingenuous comparison because “Hello, World!” Programs merely print the words, Hello, World! and some of the things that people are building actually perform some task or tasks. Fair enough.
I’d argue that these still represent Hello, World! applications because the people developing them have little understanding of the language and mechanics. The difference from a simple Hello World is that nobody writing one of these simple programs would say they were an expert in the language because they wrote one. However, now we have Hello, World! applications powered by Dunning–Kruger.
However, now we have Hello, World! applications powered by Dunning–Kruger.
The ease with which these tools create apparently working code has fooled many people. I mean, here are some folks from CNBC who know nothing about programming blowing their own minds.
The fact that they don’t know anything about software engineering is precisely the point. It’s the same kind of leap people made when they asked ChatGPT for a recipe in the style of Shakespeare and said that LLMs were more impactful on humanity than the printing press.
But to avoid any confusion, let me acknowledge a couple of things. One-off software and scripts can be incredibly useful. Also, experienced developers are finding LLMs useful in their development process too. So, I’m not claiming tools like ClaudeCode or Codex are useless or have no value in the software development lifecycle. I’m not even claiming that vibe coding is useless, especially for rapid prototyping. My point is that reality still exists, and reality is what’s constraining in this context. Much of what we are seeing is people just playing with toys.
Much of what we are seeing is people just playing with toys.
When it comes to individuals building their own software for personal use, I think tech people are in a bit of a bubble. For example, here is a statement I read from Andrej Karpathy this morning.
TLDR the "app store" of a set of discrete apps that you choose from is an increasingly outdated concept all by itself. The future are services of AI-native sensors & actuators orchestrated via LLM glue into highly custom, ephemeral apps. It's just not here yet.
Having a world of composable pieces scattered across the digital landscape, requiring users to connect and use them, is not the dream for end users. They don’t want ephemeral software that they have to construct themselves and then figure out how to host or run. They just want software that just works. Some people enjoy tinkering with software, while most people don’t. Just like some people enjoy tinkering with cars and changing their own oil, while most people don’t. The same could be said of IKEA furniture. However, at least with IKEA furniture, you get directions, not “Here’s a bunch of stuff, you figure it out.”
The Death of Software?
Given this, will software disappear? Of course not. There are many reasons for this, and it takes only a moment of reflection to surface. First of all, nobody is going to vibe code or gen smash Salesforce or SAP. This is true no matter how good the tools become. Development requires much more than just a UI, some simple functionality, and a few prayers.
Software engineering is a lot more than simply writing code. There is architectural work, debugging, feature enhancements, improvements, hosting, and more. There is also the human aspect of translating users’ requests into real features that meet their needs. It’s more than just copying what someone else did. Not to mention, there is value in incorporating other people’s inputs into a product. Other people from other companies, which you wouldn’t get by developing internally.
But ultimately, how much effort is someone willing to expend to save $10 a month? Are you really saving $10 a month in the end? Say it takes you $1000 to vibe code the application and a week of time squashing bugs. Now, you feel like you’ve begun to save money. Even if that were the end of the story, it would still take years to recoup your costs, and you’ve created a bunch of technical debt on day one that nobody is focused on fixing. While development and features continue to be added to the app you were using, they aren’t added to the application you created.
The impression that software is built and forgotten, like one-off applications, is a myth. This is especially true for enterprise applications. There is an ongoing process for updates and maintenance. Many have no idea how complex this becomes when no one knows what’s happening inside an application. Like what happens when you use AI to build the apps. This gets even more complex when the app itself also uses AI as part of its functionality. Creating conditions where nobody knows what code is going to execute at runtime. I’ve pointed this condition out before.
There’s no evidence that these tools can create robust applications over time as feature, functionality, and bug-fix needs arise. Imagine waking up one day to the enterprise applications you count on to make money not working, you don’t know why, and your human team doesn’t know why, and your AI agent doesn’t know why. All so you could save $10 a month.
There’s also a bit of a mirage here that software disappears with new workflows, when the opposite happens. Let’s take the OpenClaw dude and his example of using your agent to book you a reservation at a restaurant. You use your agent, but your agent may use a service like OpenTable to book a reservation for you. This doesn’t remove OpenTable; instead, OpenTable becomes middleware. In many of these cases, old applications become middleware and remain in place. So, more code, not less.
In many of these cases, old applications become middleware and remain in place.
For many, the issues I’m calling out are obvious. But here’s something not so obvious. Companies can’t operate properly when everyone has conflicting insights from the same data. This creates disorganization and leads to poor business decisions. When everyone is building their own apps, there’s a risk that the same data is interpreted in conflicting ways.
As far as success goes, on a small scale, with simple applications, it’s very possible that people could build their own applications with AI tools. Let’s consider the humble Pomodoro Timer. Building a simple application to count off 25-minute increments would be relatively simple. However, you can find these applications for free, and even the ones that cost money are like $1.99 for an app that adds functionality and runs in your computer’s taskbar. So, although possible, it may not be practical. There’s always a cost vs. effort trade-off.
There have been some genuinely cool examples, too. Like Nicholas Carlini, who built a C compiler in Rust. From the page:
I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kernel. Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V.
This isn’t some simple vibe coding example, and it’s impressive that we have tools to generate this today. However, even this cool example isn’t without its flaws, and that’s kind of the point of this post.
And of course, there will be outliers, too. I’m not claiming that using AI to develop alternatives is somehow impossible. It’s certainly possible, but what we are asking is whether it’s practical or well-advised. There will undoubtedly be companies that demonstrate how they saved money by developing their own in-house alternatives. This may happen in very specialized situations for very specific tasks, but the mistake is assuming these outliers are the norm. AI bros love to point to outliers as proof to justify their perspectives. Don’t fall for it. The question here is, does this happen at scale? Which I believe is highly unlikely.
Keep in mind that the world and the use cases to which software is applied are highly complex. So many unforeseen circumstances surface when applying software to problems.
100% Chance Of Vulnerabilities
No matter what happens, I can say with 100% certainty that software vulnerabilities will be everywhere, from code generated by coding agents to the generative AI functionality built into applications. This is regardless of the success of applying coding agents and vibe coding.
Security is the cost of this spray-and-pray style of development. We never solved the problem with developers introducing vulnerabilities into software, and now we are encouraging everyone to be a developer using tools they don’t understand, creating more code than ever. This was a condition I called out before with the introduction of Copilot apps.
To summarize, we now have tools that people configure insecurely, introduce vulnerabilities into code, apply them to insecure architectures, and create outputs that the creators don’t understand. What could possibly go wrong? We will have a patchwork of vulnerable applications, which means anyone with minimal knowledge can manipulate the systems in unexpected ways.
The Other Side
So, what would my detractors say? First of all, they would tell you not to believe me because I just don’t love AI enough. Which is a very cryptocurrency way of dealing with criticism that makes no point whatsoever.
They may also claim that I don’t understand the current moment. To this, I’d say they are confused and possibly trapped in a filter bubble. They are extrapolating capabilities from simple functionality. We aren’t there yet, to which they’d reply, “Soon.”
Finally, they will claim that AI will just figure it out. This perspective treats AI far more like a magic wand than a technology. AI really hasn’t been figuring it out in the past few years. We haven’t solved any of the major issues with the technology, such as hallucinations and prompt injection. We’ve just been getting products that pretend these issues don’t exist.
At some point, we’ll have technology capable of doing all the things these people claim, but not soon, and probably not built on top of Generative AI. Admittedly, this is speculation on my part, but at least it’s speculation based on observation.
In short, these aren’t easy problems to solve. Otherwise, they’d be solved already.
Conclusion
It’s certainly possible that I’m wrong, and we see GenAI crush software. The world is an uncertain place, and sometimes innovations have a moment and snap into place. However, I wouldn’t run to Polymarket with this bet. Success would require much of the world’s complexity to evaporate. Enterprise software engineering is far more complex than people building simple tools give it credit for. My guess is that SAP and Salesforce will still be with us five years from now, barring idiotic business decisions. The death of software is greatly exaggerated.
People making predictions fall into three general camps: those selling something, delusional ignoramuses, and the rare case of thoughtful reflectors. I’d like to think I fall into the last category, but since I’m not selling anything, I fear I may be part of the former. Regardless of category, we seem to forget that the world confounds prediction through complexity, even for the most ardent of reflectors.
Another common playbook in our era is to make so many predictions that some are bound to come true, then cite those cases as proof that you are an oracle. I see this happening frequently. It’s the exploitation of our short attention spans. This isn’t magical foresight, it’s statistics.
Regardless of my opinion on tech predictions, people seem to love hearing them. While I was at the AI Security Summit in London, several people asked me for my predictions for 2026, since in my keynote, I described hype shifting back to embodied systems. I guess I asked for it. But, please don’t listen to me or anyone else making predictions about 2026. Well, at least I’m not trying to sell you anything.
I think people have an instinct that 2026 feels more uncertain than 2025. There is a sense of desperation in the air as companies push to prove there is no AI bubble by wallpapering everything with AI.
Now that I’ve complained about making predictions and how uncertain 2026 feels, here are my predictions/vibes/observations for 2026.
1. Agent Double Down
“No, no. Last year wasn’t the year of the agent. THIS year is going to be the year of the agent.” I can already hear people course-correcting from their predictions last year. 2025 was the year generative AI was going to take off, resulting in massive layoffs and tons of revenue. Instead, we hear speculation about the AI bubble about to pop.
Despite the ongoing issues and high manipulability of agents, people will continue to double down. We didn’t resolve any issues with agents in 2025, so they’ll be with us again in 2026. But with the doubling-down efforts, people will try to convince you that the issues are solved or didn’t matter much in the first place.
Most business leaders who ask for agents and insist on using AI have no idea how the technology works, what it’s capable of, or the associated risks. This is not a recipe for success. Deploying this technology successfully requires a firm understanding of capabilities and realities on the ground. Of course, having appropriate expectations helps too. This isn’t happening, as MIT found when they identified that 95% of GenAI pilots failed.
I’m not claiming that agents are useless. They have their uses and can be employed in certain scenarios to augment human activities. And yes, this can be done successfully. What I’m saying is, they aren’t the utopian, headcount-reducing technology we were promised in 2025, and the data bears this out.
The truth is, if your use case has a low cost of failure and can tolerate errors and manipulations, you don’t need to wait for a new innovation. You can deploy agents today. How well they perform, on the other hand, is a different story. Performance will vary by use case and environment.
2. Embodiment Hypes Again
Although the hype of generative AI will continue in 2026, we’ll see much more hype of embodied systems. Embodied systems are those that interact and learn from the real world. Think robots, self-driving cars, drones, etc. This category is certainly no stranger to hype.
Embodied systems are always ripe for hype because they tend to be more tangible and less behind-the-scenes. There will undoubtedly be some real improvements in this area. Unfortunately, these real improvements will provide ammunition for the hype cannon. Any modest improvement will be pointed to as exponential. For example, Elon Musk recently said robots wouldn’t just end poverty, but also make everyone rich. Utopian abundance is often talked about but never rationally explained.
3. Security Issues Continue To Rise
Security issues will not only persist but also accelerate. How can they not? With more AI writing more code and more code being pushed by inexperienced people, that’s a recipe for security issues. But to quote the late American philosopher Billy Mays, “But wait, there’s more!” As more applications are developed to outsource functional components to generative AI, the application itself becomes highly vulnerable.
Unknowns will continue to plague applications and products, leading to security issues. If you’ve seen any of my conference presentations over the past couple of years, you’ll have heard me talk about these unknowns. For example, we now have conditions in which developers don’t know what code will execute at runtime.
We security professionals aren’t doing ourselves any favors. Much of the guidance on AI security is overly complex, doesn’t align with real-world use cases, and doesn’t help organizations realize value quickly. We are not rising to the occasion.
4. AI Backlash Builds
AI backlash will continue to build in 2026. A vast majority of people on the planet find tech bros abhorrent. Talking about technology as if it’s magic and CEOs foaming at the mouth to replace people leaves a bad taste in the mouth. Also, the shoving AI into every possible crevice of our existence isn’t a condition that a vast majority of people want. We are getting AI in everything, whether we want it or not.
2026 will be a challenging year for tech companies. They have to prove their investments are paying off. As we enter the fourth year of the generative AI craze, companies are still hemorrhaging money. This will lead to more intense claims, hype, and AI in everything. Backlash will certainly result. As to what form this backlash takes or how big it becomes, it’s anyone’s guess.
5. Negative Human Impacts Gain More Attention
When you mention the topic of AI’s negative impacts on humans, people almost universally think of job displacement. However, this isn’t even the most impactful effect on humans. The human impacts of AI have been a focus of mine for years. This is the main focus of Perilous.tech where I’ve covered topics such as cognitive atrophy, skills decline, devaluation, dehumanization, and on and on.
I believe more people are recognizing the human impacts of AI, and it will receive far more attention in 2026. Today, the most extreme examples, such as people committing suicide or AI psychosis, get all of the attention, but this is starting to shift.
I recently saw Jonathan Haidt mention these cognitive and developmental issues, referencing both Idiocracy and The Matrix. Two references I’ve also made in the past couple of years. These are natural conclusions once you consider the facts on the ground. AI can make you stupid and overconfident in an environment that seems like it’s already saturated with stupid and overconfident people.
6. OpenAI’s Device Flops
OpenAI is working on a device, and it’s going to be the most world-changing thing ever. It will demonstrate that OpenAI absolutely has a moat. After all, they’ve hired Johnny Ive! You sense my sarcasm.
I’m not sure what form OpenAI’s device will take or even if it will be launched in 2026, but it’s rumored to be a small, screenless device with a microphone and camera. This road has been traveled before, a couple of examples are the Humane pin and the 01 light. These devices failed for the same reason OpenAI’s will. It’s not that these devices lacked capabilities, it’s that they directly conflicted with culture. We have a screen-based culture, and now OpenAI expects people to give up the screens? No chance.
People are accustomed to having their experiences mediated, and screens are a large part of that. There’s an idealized vision that people will wear these devices and use them to make sense of the world. Unfortunately, in our current culture, people aren’t curious about the world or look at it with a sense of wonder. They want to transform the world into content. Everyone on the planet now has camera eye, and nobody is going to trust a wearable to frame content.
The device will also be visible to others, so it will signal something about you as a person, and what it signals is nothing good. In addition, if the device has a microphone and camera, public shaming will further lead people to either abandon it or avoid purchasing it altogether, regardless of its functionality.
There’s also the verification aspect. People have become accustomed to degraded tech performance, and they will just not want to talk to their neck and hope that the device takes some action on their behalf. They’ll want to verify.
Remember the GPT Store? Yeah, nobody else does either, including the influencers who claimed it was the new AppStore. We’ll get overwhelming hype followed by a belly flop the size of the US economy, regardless of whether the device is launched in 2026 or 2027.
Conclusion
Buckle up, we aren’t through the hype yet. We are in an era where faith in gods is replaced by faith in tech, and people can gamble on the mundane aspects of daily life. 2026 is going to be weird.
AI security is a hot topic in the world of cybersecurity. If you don’t believe me, a brief glance at LinkedIn uncovers that everyone is an AI security expert now. This is why we end up with overly complex and sometimes nonsensical recommendations regarding the topic. But in the bustling market of thought leadership and job updates, we’ve seemed to have lost the plot. In most cases, it’s not AI security at all, but something else.
Misnomer of AI Security: It’s Security From AI
I recently delivered the keynote at the c0c0n cybersecurity and hacking conference in India. It was truly an amazing experience. One of my takeaways was encouraging a shift in perspective on the term “AI Security,” highlighting how we often approach this topic from the wrong angle.
The term “AI Security” has become a misnomer in the age of generative AI. In most cases, we really mean securing the application or use case from the effects of adding AI. This makes sense because adding AI to a previously robust application makes it vulnerable.
In most cases, we really mean securing the application or use case from the effects of adding AI.
For most AI-powered applications, the AI component isn’t the end target, but a manipulation or entry point. This is especially true for things like agents. An attacker manipulates the AI component to achieve a goal, such as accessing sensitive data or triggering unintended outcomes. Consider this like social engineering a human as part of an attack. The human isn’t the end goal for the attacker. The goal is to get the human to act on the attacker’s behalf. Thinking this way transforms the AI feature into an actor in the environment rather than a traditional software component.
There are certainly exceptions, such as with products like ChatGPT, where guardrails prevent the return of certain types of content that an attacker may want to access. An attacker may seek to bypass these guardrails to return that content, making the model implementation itself the target. Alternatively, in another scenario, an attacker may want to poison the model to affect its outcomes or other applications that implement the poisoned model. Conditions like these exist, but are dwarfed in scale by the security from AI scenarios.
Once we start thinking this way, it makes a lot of sense. We shift to the mindset of protecting the application rather than focusing on the AI component.
AI Increases Attack Surface
Another thing to consider is that adding AI to an application increases the attack surface. Increase in attack surface manifests in two ways: first, functionally through the inclusion of the AI component itself. The AI component creates a manipulation and potential access point that an attacker can utilize to gain further access or create downstream negative impacts.
Second, current trendy AI approaches encourage poor security practices. Consider practices like combining data, such as integrating sensitive, non-sensitive, internal, and external data to create context for generative AI. This creates a new high-value target and is a poor practice that we’ve known from decades of information security guidance.
Also, we have trends where developers take user input, request code at runtime, and slap it into something like a Python exec(). This not only creates conditions ripe for remote code execution but also a trend where developers don’t know what code will execute at runtime.
Vulnerabilities caused by applying AI to applications don’t care whether we are an attacker or a defender. They affect applications equally. This runs from the AI-powered travel agent to our new fancy AI-powered SOC. Diamonds are forever, and AI vulns are for everyone.
It’s Simpler Than It Seems
Here’s a secret. In the real world, most AI security is just application and product security. AI models and functionality do nothing on their own. They must be put in an application and utilized in a use case, where risks materialize. It’s not like AI came along and suddenly made things like access control and isolation irrelevant. Instead, controls like these became more important than ever, providing critical control over unintended consequences. Oddly enough, we seem to relearn this lesson with every new emerging technology.
In the real world, most AI security is just application and product security.
The downside is that without these programs in place, organizations will accelerate vulnerabilities into production. Not only will they increase their vulnerabilities, but they’ll be less able to address them properly when vulnerabilities are identified. Trust me, this isn’t the increase in velocity we’re looking for.
I’ve been disappointed at much of the AI security guidance, which seems to disregard things like risk and likelihood of attack in favor of overly complex steps and unrealistic guidance. We security professionals aren’t doing ourselves any favors with this stuff. We should be working to simplify, but instead, we are making things more complex.
It can seem counterintuitive to assume that something a developer purposefully implements into an application is a threat, but that’s exactly what we need to do. When designing applications, we need to consider the AI components as potential malicious actors or, at the very least, error-prone actors. Thinking this way shifts the perspective for defending applications towards architectural controls and mitigations rather than relying on detecting and preventing specific attacks. So much focus right now is on detection and prevention of prompt injection, and it isn’t getting us anywhere, and apps are still getting owned.
I’m not saying detection and prevention don’t play a role in the security strategy. I’m saying they shouldn’t be relied upon. We make different design choices when we assume our application can be compromised or can malfunction. There are also conversations about whether security vulnerabilities in AI applications are features or bugs, allowing them to persist in systems. While the battle rages on, applications remain vulnerable. We need to protect ourselves.
There is no silver bullet, and even doing the right things sometimes isn’t enough to avoid negative impacts. But if we want to deploy generative AI-based applications as securely as possible, then we must defend them as though they can be exploited. We can dance like nobody is watching, but people will discover our vulnerabilities. Defend accordingly.
The past couple of years have been fueled entirely by vibes. Awash with nonsensical predictions and messianic claims that AI has come to deliver us from our tortured existence. Starting shortly after the launch of ChatGPT, internet prophets have claimed that we are merely six months away from major impacts and accompanying unemployment. GPT-5 was going to be AGI, all jobs would be lost, and nothing for humans to do except sit around and post slop to social media. This nonsense litters the digital landscape, and instead of shaming the litterers, we migrate to a new spot with complete amnesia and let the littering continue.
Pushing back against the hype has been a lonely position for the past few years. Thankfully, it’s not so lonely anymore, as people build resilience to AI hype and bullshit. Still, the damage is already done in many cases, and hypesters continue to hype. It’s also not uncommon for people to be consumed by sunk costs or oblivious to simple solutions. So, the dumpster fire rodeo continues.
Security and Generative AI Excitement
Anyone in the security game for a while knows the old business vs security battle. When security risks conflict with a company’s revenue-generating (or about to be revenue-generating) products, security will almost always lose. Companies will deploy products even with existing security issues if they feel the benefits (like profits) outweigh the risks. Fair enough, this is known to us, but there’s something new now.
What we’ve learned over the past couple of years is that companies will often plunge vulnerable and error-prone software deep into systems without even having a clear use case or a specific problem to solve. This is new because it involves all risk with potentially no reward. These companies are hoping that users define a use case for them, creating solutions in search of problems.
What we’ve learned over the past couple of years is that companies will often plunge vulnerable and error-prone software deep into systems without even having a clear use case or a specific problem to solve.
I’m not referring to the usage of tools like ChatGPT, Claude, or any of the countless other chatbot services here. What I’m referring to is the deep integration of these tools into critical components of the operating system, web browser, or cloud environments. I’m thinking of tools like Microsoft’s Recall, OpenAI’s Operator, Claude Computer Use, Perplexity’s Comet browser, and a host of other similar tools. Of course, this also extends to critical components in software that companies develop and deploy.
At this point, you may be wondering why companies choose to expose themselves and their users to so much risk. The answer is quite simple, because they can. Ultimately, these tools are burnouts for investors. These tools don’t need to solve any specific problem, and their deep integration is used to demonstrate “progress” to investors.
I’ve written before about the point when the capabilities of a technology can’t go wide, it goes deep. Well, this is about as deep as it gets. These tools expose an unprecedented attack surface and often violate security models that are designed to keep systems and users safe. I know what you are thinking, what do you mean, these tools don’t have a use case? You can use them for… and also ah…
The Vacation Agent???
The killer use case that’s been proposed for these systems and parroted over and over is the vacation agent. A use case that could only be devised by an alien from a faraway planet who doesn’t understand the concept of what a vacation is. As the concept goes, these agents will learn about you from your activity and preferences. When it’s time to take a vacation, the agent will automatically find locations you might like, activities you may enjoy, suitable transportation, and appropriate days, and shop for the best deals. Based on this information, it automatically books this vacation for you. Who wouldn’t want that? Well, other than absolutely everyone.
What this alien species misses is the obvious fact that researching locations and activities is part of the fun of a vacation! Vacations are a precious resource for most people, and planning activities is part of the fun of looking forward to a vacation. Even the non-vacation aspect of searching for the cheapest flight is far from a tedious activity, thanks to the numerous online tools dedicated to this task. Most people don’t want to one-shot a vacation when the activity removes value, and the potential for issues increases drastically.
But, I Needed NFTs Too
Despite this lack of obvious use cases, people continue to tell me that I need these deeply integrated tools connected to all my stuff and that they are essential to my future. Well, people also told me I needed NFTs, too. I was told NFTs were the future of art, and I’d better get on board or be left behind, living in the past, enjoying physical art like a loser. But NFTs were never about art, or even value. They were a form of in-group signaling. When I asked NFT collectors what value they got from them, they clearly stated it wasn’t about art. They’d tell me how they used their NFT ownership as an invitation to private parties at conferences and such. So, fair enough, there was some utility there.
In the end, NFTs are safer than AI because they don’t really do anything other than make us look stupid. Generative AI deployed deeply throughout our systems can expose us to far more than ridicule, opening us up to attack, severe privacy violations, and a host of other compromises.
In a way, this public expression of look at me, I use AI for everything has become a new form of in-group signaling, but I don’t think this is the flex they think it is. In a way, these people believe this is an expression of preparation for the future, but it could very well be the opposite. The increase in cognitive offloading and the manufactured dependence is precisely what makes them vulnerable to the future.
In a way, these people believe this is an expression of preparation for the future, but it could very well be the opposite. The increase in cognitive offloading and the manufactured dependence is precisely what makes them vulnerable to the future.
Advice Over Reality
Social media is awash with countless people who continue to dispense advice, telling others that if you don’t deploy wonky, error-prone, and highly manipulable software deeply throughout your business, then they are going to be left behind. Strange advice since the reality is that most organizations aren’t reaping benefits from generative AI.
Here’s something to consider. Many of the people doling out this advice haven’t actually done the thing they are talking about or have any particular insight into the trend or problems to be solved. But it doesn’t end with business advice. This trend also extends to AI standards and recommendations, which are often developed at least in part by individuals with little or no experience in the topic. This results in overcomplicated guidance and recommendations that aren’t applicable in the real world.
The reason a majority of generative AI projects fail is due to several factors. Failing to select an appropriate use case, overlooking complexity and edge cases, disregarding costs, ignoring manipulation risks, holding unrealistic expectations, and a host of other issues are key drivers of project failure. Far too many organizations expect generative AI to act like AGI and allow them to shed human resources, but this isn’t a reality today.
LLMs have their use cases, and these use cases increase if the cost of failure is low. So, the lower the risk, the larger the number of use cases. Pretty logical. Like most technology, the value from generative AI comes from selective use, not blanket use. Not every problem is best solved non-deterministically.
Another thing I find surprising is that a vast majority of generative AI projects are never benchmarked against other approaches. Other approaches may be better suited to the task, more explainable, and far more performant. If I had to take a guess, I would guess that this number is close to 0.
Generative AI and The Dumpster Fire Rodeo
Despite the shift in attitude toward generative AI and the obvious evidence of its limitations, we still have instances of companies forcing their employees to use generative AI due to a preconceived notion of a productivity explosion. Once again, ChatGPT isn’t AGI. This do everything with generative AI approach extends beyond regular users to developers, and it is here that negative impacts increase.
I’ve referred to the current push to make every application generative AI-powered as the Dumpster Fire Rodeo. Companies are rapidly churning out vulnerable AI-powered applications. Relatively rare vulnerabilities, such as remote code execution, are increasingly common. Applications can regularly be talked into taking actions the developer didn’t intend, and users can manipulate their way into elevated privileges and gain access to sensitive data they shouldn’t have access to. Hence, the dumpster fire analogy. Of course, this also extends to the fact that application performance can worsen with the application of generative AI.
The generalized nature of generative AI means that the same system making critical decisions inside of your application is the same one that gives you recipes in the style of Shakespeare. There is a nearly unlimited number of undocumented protocols that an attacker can use to manipulate applications implementing generative AI, and these are often not taken into consideration when building and deploying the application. The dumpster fire continues. Yippee Ki-Yay.
Conclusion
Despite the obvious downsides, the dumpster fire rodeo is far from over. There’s too much money riding on it. The reckless nature with which people deploy generative AI deep into systems continues. Rather than identifying an actual problem and applying generative AI to an appropriate use case, companies choose to marinade everything in it, hoping that a problem emerges. This is far from a winning strategy. Companies should be mindful of the risks and choose the right use cases to ensure success.
Weaved through the fabric of the hustle-bro culture, threaded with the drivel of influencers, lies one of the biggest cons of our current age. This is the false perception that everything we do has to be for some financial gain or public attention. With everything in life revolving around social currency or actual currency, removing friction enables us to reach value quickly. But don’t fret. The slop dealer is here with a plan to deliver us salvation, telling us that ideas are what’s important and everything else is pointless friction, needing to be optimized to reach full potential. Like so many things in our current moment, if only this were true.
Despite the decline in excitement for AI and the potential resulting market corrections, unfortunately, slop is here to stay. Although people outwardly complain about it, they are secretly glad it’s here. Being unique, thoughtful, and creative is hard. Slop allows people to swaddle themselves in a false comfort devoid of any real creativity. So, damn the torpedoes, full slop ahead.
Slop, Enshittification, and Brain Rot
Slop, enshittification, and brain rot are terms burned into our current lexicon. Although each term has a different definition, one referring to outputs, one referring to platforms, and one referring to what it does to us. When I use the generalized term slop here, I mean a mixture of all three together, a sort of thick, rancid mixture reminiscent of manure and White Zinfandel. This is because the combined term aligns better with the content and its overall impact.
The Slop Dealer
The slop dealer tells us everything is a hustle, and we need to get on board to reduce friction everywhere we can to accelerate value or be left in the dust by others using AI. They don’t talk of reasonable AI usage or prescriptions for specific tasks; it’s all or nothing. We need to surrender to the higher power. The slop dealer embodies everything that tech bro culture stands for. It’s the current equivalent of a get-rich-quick scheme, only instead of taking our money, they are stealing our attention and our satisfaction. Although sometimes they take our money too.
The slop dealer swindles us by telling us what we want to hear, that hard things are a thing of the past, and all we need is an idea. After all, everybody has ideas. These are the influencers, wanna-be influencers, and other AI useful idiots vomiting nonsense on social media. They aren’t peddling secret knowledge; they are peddling bullshit.
This pandering is done so we’ll follow them, subscribe to their newsletters, or buy their nonsense. But one of the biggest lies of all is the false impression that the value of creative pursuits lies in the end result.
Most of these people have no shame and not only believe in Dead Internet Theory, but also actively work to make it a reality. If you are wondering why people en masse find tech bro culture abhorrent, look no further than this stunning piece of work.
To quote this guy directly, “How I personally feel? I have no idea. The internet in my mind is already dead. I am the problem, right?” I get the impression this isn’t the first time he’s realized he’s the problem. Unfortunately, acknowledgement of this isn’t enough to change behavior.
The Slop Architect
The slop architect works not in traditional mediums but in ideas. To the slop architect, execution, skills, and experience are secondary, bowing at the pedestal of ideas. The fact is, most ideas are ill-thought-out, half-baked, or just plain fucking stupid. The slop architect doesn’t care because they don’t carry ideas to term; they birth them instantly, shoving them out into the world to fend for themselves as they move on to something else. I mean, the vape Tamagotchi was someone’s idea, too. Yes, please! Let’s accelerate these!
Ideas aren’t unique, precious resources, but common, run-of-the-mill, everyday occurrences for everyone on the planet. The slop architecture amplifies the fallacy that ideas are sacred and pushes the idea that if more ideas were executed, the world would be a better place. If only we had more apps, more books, more music, and the list goes on and on. This connects with people because everyone has ideas.
What most people who have thought about it for more than two seconds realize is that we don’t get to the value of an idea purely by having it. Ideas in isolation are senseless ramblings of the brain. Ideas forged and refined in the fire of execution, experience, and reflection are invaluable and fulfilling. Our ideas are never challenged in the slop architecture, leading us to new discoveries and paths, but are chucked out into the world and quickly discarded, like forgotten attempts at memes that nobody finds funny.
The AI Slop Architecture
The slop architect’s vision is implemented with the slop architecture, which presents itself as a process or application. The slop architecture is pitched as the way forward, the next-generation architecture fueling the future of humanity’s pursuits. But a simple scratch of the surface paint is all it takes to expose the entire thing as an empty shell.
When you see people pitching these types of things, it uncovers people who don’t understand creativity and certainly don’t understand where value exists in a process. Everything is a hustle for the sake of hustling. This person is hardly the only one.
Back in 2023, I jokingly created my own version of the slop architecture, which I referred to as IPIP, long before the AI influencers made it a reality.
This article was complete with a description of what would come to be known as vibe coding. “The hype has led to a new form of software development that appears to be more like casting a spell than developing software.”
Taking the slop architecture to heart, it’s not hard to find implementations already running. Books, slides, music, applications, nothing is off limits. Everything is fair game in the slop era.
Ah, Magic bookifier. Yeah, let me get on that. Any time someone puts magic in reference to AI, it’s bullshit.
People also fantasize about what advanced AI is or will be able to do. Take this use case for AGI, for example.
It reminds me of the Luke Skywalker meme where he’s handed the most powerful weapon in the galaxy and immediately points it at his face. This is informative for a couple of reasons. Movies can’t be exactly like the books for reasons other than length. They are different media with different tools. But look at the response. Human work isn’t worth protecting in the future. This is a far more common perspective than many think.
Even apps. It’s slop from all angles. So, if these tools already exist, why aren’t we all kicking back, receiving our profits? Maybe there’s something more to this than having an idea.
But we can’t just have a couple of people successfully making apps. It needs to be bigger! We are now told to await the arrival of the first billion-dollar solopreneur. Hark! The herald angels sing. Glory to the slop-born king! However, we shouldn’t get our hopes up. Setting aside how highly unlikely this is, people also win the lottery, so unless we have a mass of billion-dollar solopreneurs, it’s not proof of much. However, whenever people have strongly held beliefs, they will always point to exceptions as the rule.
It’s far more common for people to talk about a single person making a million-dollar app, and that we all can make them now. Even if this were true, it’s not like billions of people are going to make million-dollar apps or profit from a trillion new books. No degree in economics is necessary to see that the numbers don’t work. Besides, if billions of people can and will do something, then the whole enterprise becomes devalued.
The slop architecture deprives us of so much, sucking the soul out of activities until only the shriveled husk remains. There’s no learning with the slop architecture. No growth. No Reflection. No Satisfaction. It even robs us of a sense of style, something so foundational to the satisfaction of human artistic pursuits. But all things require sacrifice on the pyre of optimization. In the end, the slop architecture doesn’t democratize. It devalues, degrades, and destroys.
In the end, the slop architecture doesn’t democratize. It devalues, degrades, and destroys.
The Friction Is The Point
I’m going to let my friends in tech in on a secret, which isn’t a secret at all. The friction of an activity is directly related to the value you receive from it. The mistake being made is comparing an activity’s friction to the load time of an application or streamlining a user interface. I’ve written previously about how the next generation could be known as The Slop Generation and how we continue to devalue art. However, the removal of friction creates harmful follow-on effects.
Imagine telling Alex Honnold, “Dude, you don’t need to free solo El Capitan. We have a helicopter that can drop you off at the top.” People may see this example as silly because Alex obviously climbs mountains for reasons other than getting to the top, but it’s a mistake to assume other pursuits don’t contain similar value purely because they aren’t mountain climbing. Deep experiences don’t result from things that provide instant gratification or have little friction. Nobody finds meaning in a prompt or the resulting generation.
Deep experiences don’t result from things that provide instant gratification or have little friction.
People may see this example as silly because climbing a mountain without ropes is obviously different from something like writing a song. Except it’s not when viewed through the lens of experience. Alex Honnold doesn’t free solo mountains to get to the top or because ropes and safety equipment are too expensive; he does it because he knows there is value in the friction of his experience. He’s both challenging himself and learning about himself at the same time. He’s having an actual experience, which is hard to describe to people who have never had one. This experience enriches the conclusion of the activity, the accomplishment, which coincidentally happens to be getting to the top. However, when pursuits are framed in terms of the end results, it appears that reaching the top is the goal, and the removal of friction is logical.
Most people will never free solo a mountain, compete in the Olympics, or achieve any of the other remarkable feats that athletes at the top of their game accomplish, but that doesn’t mean we can’t have similar and fulfilling experiences, and we do this through exploration and conquering friction. When you are operating at the top of your game, you realize you aren’t competing with others, but yourself.
An artist puts a piece of themselves inside every work of art they create. AI deprives artists of having a piece of themselves included in the art, making the generated output purely an artifact of running a tool.
Slop Is Here To Stay
Immediately after Ozzy Osborne died, Oz Slop invaded social media. The prince of darkness himself fell victim to people’s boredom and lack of creativity. People chose to pay tribute to him, not through stories and anecdotes, but by slopping him into manufactured content. I can’t think of a more insulting way to pay tribute to an artist, but this is our future. Slop instead of something to say. Slop instead of stories and memories. Slop instead of emotion. Slop as a coping mechanism. May the slop be with you.
A disheartening thought is that no matter what happens to the market for generative AI, the slop will remain. People post this slop not because they enjoy it, but purely because it gives them something to post. Slop content is a stand-in for having something to say. It’s easy to generate and requires little thought, the perfect complement to today’s reactionary and performative social media environments.
In a way, this trend could create a new line of demarcation, where we start referring to things as “Before Slop” and “After Slop” to identify the creative expressions that preceded and followed the arrival of AI-generated content.
Conclusion
In the end, the slop architecture doesn’t generate experiences. Nobody is going to be on their deathbed mulling over their favorite prompts or sit down with friends and reminisce about the time they poked at a generative AI system for hours trying to get it to generate a particular image. The slop architecture doesn’t create a legacy or generate stories worth remembering or worth sharing, just pieces of forgotten garbage littering the digital landscape.
