Author: Nathan Hamiel

Democratizing AI Psychosis: Why Smart People Are Captured By AI Hype

April 23rd, 2026
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.
My Perspective On AI Advancement

April 8th, 2026
Since this site focuses on risks and trade-offs rather than shiny, utopian use cases, there is some confusion about my thoughts on AI. Be it the AI of the present or the AI of the future. With this post, I stake out my position on AI and its advancement so I don’t have to keep restating it. Also, it’s good to write down your beliefs and confront yourself with them. Sometimes what you think you believe isn’t what you actually believe. Maybe I’m a secret AI bro after all. Utopia, here we come!

AI acceleration has become a cult or religion, and offering any criticism of its advancement is taken as a personal attack. In many ways, if you swapped out AI for cryptocurrency, the theme would revive a familiar tone.

Even with this post, I’ll undoubtedly be pegged as an AI hater because I don’t have pictures of myself lying prostrate in front of a pile of GPUs. Any time you shed light on hype or bullshit, people are willing to label you a hater. That’s the easiest thing for them to do, and it takes no mental effort and requires no skill. With that said, here we go.

On AI Advancement Summary

I’ve been using this image in my conference presentations since 2023. The focus of my talks is on risk, which means talking about problems and challenges most of the time rather than amazing use cases. I wanted to show the audience that I don’t hate the technology.

The problem with being in the middle is that both sides typically frame you as an extremist. You don’t hate the technology enough for one side or love it enough for the other. Realities on the ground typically hover somewhere in the middle between extreme claims. This isn’t rocket science.

For those who honestly don’t care about the rest of this post and made it this far, here’s a quick summary:
- I’m not a skeptic, I’m a critic
- Yes, I think today’s AI can be useful
- Yes, there are some use cases that I’m hopeful for
- No, I don’t think today’s AI is AGI
- Yes, I think AGI is possible
- No, I don’t think LLMs will lead to AGI
- I think even AGI will have vulnerabilities
- I’m not so sure about the concept of ASI or the intelligence explosion
I’m Not a Skeptic, I’m a Critic

In the current era, I’ve seen people frame themselves as skeptics either of technology or of AI. This is not how I frame myself. I consider myself a critic, not a skeptic. I’ve joked that I’m a hype critic. Throughout my career, I’ve offered criticism of the state on cybersecurity, emerging technologies, and, especially, product manufacturers and their claims.

I believe that technology does need a better class of criticism. The tech press, in large part, has abdicated its responsibility, choosing instead to mindlessly parrot opinions from tech leaders. The entire world has been a gigantic sycophantic feedback loop. This is something I’ve called out many times myself and something that Karl Bode calls “CEO Said A Thing!” Journalism.

Most people criticizing the state of technology are insufferable. The few valuable points they present are wrapped in politics, bullshit, and, in some cases, conspiracy theories. Their goals aren’t to effect change but to pander to their audience. The very people who need to hear these points are the very ones who would never listen to them in the first place.

I have no vested interest in any technology’s success or failure, and I’m certainly not pandering to an audience. Hell, if I wanted to cultivate a large following, the last thing I’d be spending time on is writing. I’d start a podcast or YouTube channel, align my content with people’s biases, and go all out, telling them what they want to hear. I’d also use AI to write my content to up my pace. It’s the new definition of “productivity.”

One of the criticisms I get is that I shouldn’t be listened to because I don’t love AI enough, which is a strange perspective. Would you really trust someone who is selling you something, or who is completely head over heels in love with a technology? Are you going to get honest criticism? Of course you wouldn’t. But the whole premise of that argument doesn’t make sense. That’s a lot like saying someone isn’t religious enough because they don’t have enough religious bumper stickers on their car.

Framing The Conversation

Much of the debate around future AI advancement revolves around two questions after a claim is made:
- What specific technology is being discussed?
- When will it arrive?
So much confusion is caused by not clarifying the two follow-up questions. Many throw out the term “AI” as a catch-all, referring to any technology, present or future. For example, the claim that AI will cure cancer. Okay, but what specific AI technology? Is it technology we have today? Some future technology that hasn’t been invented yet? And of course, most importantly, when will this happen? The precision matters.

When asking people to provide some precision regarding their claims, it’s not uncommon to find that people aren’t talking about AI at all. They are talking about magic. For others, they are purely saying “something” will happen at “some point.” Which is basically saying nothing. Back in January of 2024, I published a framework for making sense of human AI predictions, which goes into a bit more detail on this topic.

I do believe that many of the claims made by proponents will be realized at some point through future technological advancements, some even with the technology we have today. I’m certainly hopeful about cures for debilitating illnesses, and a lot of work has already been done. I don’t think we are miles away from seeing those results. This is an example of something I’m hopeful for. Call me an optimist??? However, I don’t know what technology, under what circumstances, or when.

I’m sure so many people thought it was inevitable that by 2015, we’d have hoverboards in common use after watching Back To The Future 2. Our perspective on technological advancement is often skewed and off by a wide margin. It’s always good to keep this in mind.

LLMs

I certainly don’t hate LLMs. I find LLMs useful for various tasks, mostly coding tasks, basic research, and troubleshooting. I occasionally will use them to generate some AI slop images for a blog post or conference presentation. Pinning down my exact usage is a bit hard, since LLMs aren’t my first port of call for every problem. After all, I value my critical thinking skills, skills that people these days seem content to discard.

I never use LLMs for common cognitive tasks and never have an LLM decide for me or write anything on my behalf. I also never have an LLM summarize something I’m trying to understand, because knowledge and understanding aren’t generated from bullet points. The friction is the point in so many tasks where we look to reduce it.

The hype with LLMs hasn’t been commensurate with the realities on the ground. LLMs certainly have their uses. Just like me, people are finding them valuable for a variety of tasks. In my own industry (cybersecurity), there are positive examples in offensive security, vulnerability identification, and assisting analysts in security operations centers. You can also tune LLMs more effectively for specific tasks, which will have a positive effect. However, there are limiting factors to LLMs.

The first is the cost of failure for the use case. LLMs have relatively high failure rates, and when connected in agentic systems, these failures can cascade through the system. Failures compound like interest, to use the words of Demis Hassabis. I mean, the thought of ChatGPT running air traffic control is terrifying.

Second, they are highly manipulable. This is why everyone from startups to hyperscalers has had their AI-based applications hacked. This fundamental manipulability is baked into how LLMs operate. It’s why we have things like prompt injection, and adding LLMs to applications increases their attack surface. This condition is why I’ve described AI Security as a misnomer in the age of generative AI. You aren’t defending the AI. You are defending the application or use case against the effects of adding AI. This is a different problem.

These two factors can be misleading, though. We don’t need AGI-level capabilities for LLMs to be useful or to replace people in their jobs. The moment an LLM-based system is mediocre enough to replace someone, companies will rush to replace people. This is especially true if the cost of failure is lower for a particular job. Although reports of recent layoffs are nothing but AI washing, we are getting a glimpse of what will happen once capabilities arrive.

My biggest concern with LLMs isn’t what they can do for people, it’s what they do to people. I believe we are vastly underestimating the negative cognitive impacts these tools are having and will have on people in the future.

My biggest concern with LLMs isn’t what they can do for people, it’s what they do to people.

AGI

I do believe that AGI is possible, but I don’t think that today’s LLMs will be what gets us there. When do I believe AGI will arrive? 15 to 20 years. Put my precision on this at about 70%. I put the likelihood of LLMs alone becoming AGI at about 15%. But keep in mind, these are mostly guesses guided by intuition and actualities. Caveat: I’m not involved in developing AGI, and the world is a complex place that defies predictions. However, I do believe some factors will confound advancements for a while.

First, I don’t feel LLMs will lead to AGI, and this is where all of the focus seems to be at the moment. Second, I think there is a massive AI investment bubble. The amount of money being invested is nowhere near the value created. This bubble will pop at some point, hopefully not spectacularly. Companies like OpenAI will very likely go out of business. They are hemorrhaging money, and their shares are becoming almost impossible to unload on the secondary market. I mean, they put out a statement about focusing on business, and then just bought a podcast. Not exactly a shining indicator of future success.

I bring this up because this crash will cause some reluctance to invest in the future. Maybe it won’t quite be an AI winter, but it will be an AI fall with colder weather and a lot fewer leaves on the trees. This may stall the advancement toward AGI.

I think some people think that LLMs will go away after the investment bubble pops, but this is nothing but wishful thinking on their part. LLMs are genuinely useful for certain tasks and will continue to be. Also, LLMs are so essential to some people’s identity now, you’ll have to pry them from their cold, dead hands.

When it arrives, I do believe AGI will have vulnerabilities, even if they are not immediately apparent. This would be especially true if AGI were built on today’s LLMs or if it weren’t a single large system but a network of systems. Once deployed, we’d be stuck with these vulnerabilities. This is a perspective I’ve shared publicly for years in my talks and keynotes. There may be something about generalizing to the world that contains inherent vulnerabilities. I have a draft post on this topic that I’ll publish in the future. Unfortunately, I have dozens of posts in draft and only so much time.

ASI and The Intelligence Explosion

Strangely, we haven’t even achieved AGI yet, but labs are already bragging about how we are close to artificial superintelligence (ASI). Okay, it’s not strange, that’s just how hype works. We seem to forget that ASI is a speculative technology, and speculative technology leads to speculative bullshit.

To sum it up, despite believing that AGI is possible, I’m not so sure about ASI. Or at least ASI as it’s traditionally been discussed. I’m not quite sure I can put my finger on exactly why. It’s more of an intuition I have rather than any one specific thing. Of course, I may be the one now talking nonsense.

I think my hesitancy stems from conceptions of ASI, the resources required, and the plateaus that would be encountered. We are told we get there by just packing in “more” and “better”, whatever the more and better happen to be, and this cycle will continue forever. But I don’t think we’ll scale our way there, and we still have to contend with the laws of physics and resource constraints. This is why Ray Kurzweil thinks we need to pave over the universe to create computronium.

I do believe that some recursive self-improvement is possible, but only to a point. Maybe we’ll get to something like an AGI+ but not ASI as it’s traditionally been discussed, with its planet-eating power requirements and its continual recursive self-improvement. However, there is one thing I can say for sure: something will be labeled ASI long before it’s possible. Maybe someone will buy a podcast to promote that perspective! Who knows.

Since I’m unsure if ASI, as it’s been defined, is even possible, I’ll put the odds of reaching ASI in the next 50 years at 10%. But feel free to chalk this up to me saying, “I don’t know,” and disregard everything I’ve said.

What Happens Next

I’ve left no doubt about my pessimism about what happens next and how it’s not good for humanity. Much of the content on this site focuses on that topic. And, no, I don’t think a super-capable AI will see humans as a nuisance and eliminate us. Sorry, Eliezer Yudkowsky, but our manifested problems will be much more mundane.

It’s we, humans, who plant the seeds of our own downfall. When massive unemployment occurs (which may happen well before reaching AGI), there will be no recourse. The so-called abundance movement won’t deliver the value it promises. Many will fall into the “sucks to be you” gap that I’ve defined previously. A segment of the population will remain pinned there, possibly for a generation. This is purely due to incentives and the reluctance or inability to do anything about it. I’ll have more to say on this in the future.

Also, people continue to cede their critical thinking skills to AI. By far my biggest concern is the collapse of culture amid homogenized AI outputs and people’s inability to think independently. I see people more concerned with collecting data than with understanding it. The idea that someone becomes wiser by collecting more data or by engineering a better retrieval system is nonsense. If you need an AI to tell you what you think or believe, you’ve made a fundamental error.

In a previous post, I mentioned the story of Calvisius Sabinus, who, in an attempt to appear learned, devised a shortcut. It didn’t work out so well for him, and this new strategy won’t for us. I have much more to say on this topic as well. But that’s all for now.
What Napster Can Teach Us About AI, Abundance, and the Future

March 31st, 2026

Talk to any tech bro, and they’ll be happy to tell you, scarcity is about to become a thing of the past. Abundance is coming and we are going to have so much awesomeness that we can’t even fathom how amazing it will be. Sam Altman recently said that society has been structured around managing scarcity, and now we need to get used to managing abundance. Hell yeah, bro! There’s just one problem. Well… there are many problems, but a big one that isn’t talked about is that nothing in abundance is worth anything. This fact alone can bring visions of utopian abundance crashing down like a house of cards in a tornado, and it’s a lesson we can learn from Napster. Yes, that Napster.

AI, Abundance, and the Future

We are told that scarcity is about to be in our rearview and the bright shiny utopia is out the windshield. With AI and robots doing all of the work, the cost of goods and services will crash to near zero. It’s yachts and Lambos for everyone! Oh, there’s just the small problem that nobody has a job. Damn, there goes my Lambo.

These takes are common, and you see them every day. For example, here’s a random one I saw this morning.

But this isn’t the only claim. They also claim it will cure all diseases and won’t just end poverty, but make everyone rich.

The “things will be free” argument is typically packed among heaps of bullshit, as seen in these examples. It’s intended to overwhelm your critical thinking skills. Let’s just acknowledge that none of this makes sense.

They state that people are going to lose their jobs, but don’t worry, because AI is going to make everything super awesome! They also claim that in this new environment, people are going to become obscenely rich by giving you free shit. Admittedly, I don’t have an MBA, but that doesn’t seem to be how business works.

I’ve covered this topic before in Techno-Communism and the Things Will Cost Nothing Fallacy. In that article, I attacked some of the core premises of this fallacy. I even covered the fact that, despite the premise, things will still cost something, and that nobody is investing in companies to produce zero-dollar goods and services. I may only lightly recover some of that ground, so please refer to the previous article for the core arguments.

This is an important topic to consider now because we will see better AI technologies. We will eventually get to AGI. Many of the nonsense claims people make about today’s LLMs will be true of this new technology, and the impacts will be far-reaching. We need to consider how this environment affects us as humans and our culture.

In this article, I focus on value and frame the lesson of Napster as it relates to abundance and the future.

Napster

I have to do some work here. Let me explain Napster to young millennials and Gen Z, who’ve always had reliable internet connections and never had to wait for anything. Napster was a peer-to-peer file-sharing application that mostly focused on music. If there was a song or album you wanted, you’d search for it and download the MP3s. The songs were encoded at varying quality levels, and depending on how many people had the files and their internet connection speeds, you may have to wait a day or two for the songs or album to download. Oh, and surprise! Sometimes the files you downloaded weren’t what they claimed to be at all.

A look back on the UI of Napster is enough to haunt your dreams.

I use the term “was” in reference to Napster, but apparently, they are still around and drumroll… They’ve pivoted into AI slop. Imagine that! Although you might think this is the lesson of this article, it’s not. It’s just a fun byproduct in the AI era. Enjoy.

Back in 2000, Metallica sued Napster. For clarification, Metallica was a band of aging gentlemen who sounded like Avenged Sevenfold. Sad but true.

At the time, I remembered thinking how backward Metallica looked, like they were just some aging rock band that didn’t understand technology. Most of all, they just looked greedy. People just wanted the freedom to use their music as they wished. Many downloaded MP3s of albums they already owned. For example, maybe you already had Metallica’s black album on tape or found downloading MP3s easier than ripping them from the CD you owned. CDs were also cumbersome, and taking a bunch with you on a road trip was a pain. Sure, people pirated music, but that was a minority. My perspective solidified as the space for digital music grew.

As digital music grew, it came with heavy-handed DRM (Digital Rights Management), which meant that, despite purchasing the music legally, you couldn’t use the music the way you wanted to or play it on devices you chose. Hell, Sony even went so far as to install malware on your computer in an attempt to stop piracy.

Then came streaming, and music essentially became free. The lesson that started with Napster now came full circle with services like Spotify and Apple Music. However, inheriting a gigantic problem.

Music Is Now Free and Worth Nothing

In the ignorance of my youth, I fell victim to a condition most young people do. I failed to see the big picture. When something is free, it is essentially worth nothing. Nobody values music today, and why should they? There was no friction to access it. Nobody had to go to a store and choose between one album and another. Nobody had to wait in line for a midnight release from their favorite artist. Nobody had to choose which albums to take with them on a road trip. Although this sounds like a major inconvenience, it enhanced the value of music for both the listener and the artist. These activities created loyalty and forged a bond.

Musicians were well aware that listeners had a choice when buying music and felt responsible for creating art to the best of their ability. Or at least, this concept was in the back of their mind. Artists also made money selling music, which gave them the freedom to spend more time on their craft, further enhancing their art/product. This environment didn’t require an artist to be as big as Taylor Swift to do so, either.

Purchasing music connected listeners to artists in a way that streaming doesn’t. When people bought an album, they listened to the album the way the artist intended. They could evoke emotion through ebbs and flows, taking people on a musical journey. With streaming, people just add things to playlists and hit shuffle. An artistic vision of an album is completely dismantled, chucked into the chaos of a musical vortex, spinning alongside Snoop Dogg and Conway Twitty.

Now, artists blast out music as fast as they can to increase their breadth because more music means more potential streams. Not releasing music at a steady cadence creates the impression that you’ll be forgotten in the vast sea of content.

Of course, artists now have to make money in other ways, spending less time on their music. They used to agonize over albums and songs, pouring their hearts and souls into them and taking the time to get things right. They also took creative chances. Now, music is collapsing into a formulaic structure where all music sounds the same, and artists bash the formula like the preprogrammed buttons in Mortal Kombat.

Music has become less something to be listened to and more something that provides background noise in daily life. It doesn’t take pride of place as an activity. It’s wallpaper.

Musical Abundance and Lost Value

What we have today is essentially musical abundance. There’s literally so much music on streaming platforms that people can’t find it. In 2024, it was reported that Spotify had over 100 million songs with over 60,000 new songs added per day. Want to guess just how much of that is good music? Certainly less than 1%, there is no way you can convince me that there are 1 million good songs on the platform. In this world of abundance, most things are shit.

As an artist, you basically have to give your music away. As a listener, music is nothing to you. If one song isn’t available, you just listen to something else without emotion. Music, possibly humanity’s very first art form and something that has meant so much to so many humans throughout history, is now stripped of its value and devoid of connection and meaning. In reality, musical abundance hasn’t led to positive outcomes for either the artist or the listener, and this is our lesson here.

In reality, musical abundance hasn’t led to positive outcomes for either the artist or the listener.

The healing effects, the satisfaction, the connection, the memories, and most of the factors that made music valuable and essential to humanity are now gone. Industrialized, homogenized, and mass-produced music has stripped the art of its value. For the far fewer than 1% of songs that aren’t terrible, we are left with momentary blips of completely forgettable, average sounds labeled as music that just happen to hit our ears. No wonder kids are rediscovering the classics.

There’s no going back. Once a technology is implemented, even if it makes things worse, there is no going back. We are stuck with it, which is why it’s so important to understand the trade-offs and implement mitigations before adopting a technology. We just can’t seem to do that.

Now, the music industry also has its share of blame here. It got greedy. A CD never should have cost anywhere near 20 dollars, unless it was a double album or some other special edition. $9.99 was a fair price and should have been the cost for an album. But that time has passed.

Abundance Math Doesn’t Make Sense

Let’s set a couple of foundations for abundance. The premise behind abundance is that companies will employ AI and robots instead of human workers. So, first and foremost, no jobs for most people on the planet.

We are told by futurists and AI bros that abundance will bring a utopia. That everything will be democratized. The cost of everything will be near zero, compute, goods, intelligence, development, and on and on. With so many things driven to essentially zero, not only does the math behind abundance not make sense, but its value as well, as we’ve seen with music.

With so many things driven to essentially zero, not only does the math behind abundance not make sense, but its value as well, as we’ve seen with music.

Let’s start off by addressing the use of the term democratize. Whenever an AI bro uses the term democratize, they actually mean something else: either devalue, degrade, or destroy. I call these the 3 D’s, and I’ve covered this before. But let’s get back to cost and value.

It doesn’t matter if the cost of things is driven to near zero. If they cost something and you have nothing, you still can’t afford it. If a brand new car costs a dollar and you don’t have a dollar, its inexpensiveness doesn’t change the fact that you have to walk. Assistance programs like UBI will hardly have us living lives of luxury.

When people have little to no money, they tend to focus on pure necessities. Pretty much the entire global economy is focused on selling us stuff that we want, not necessarily things we need. That means in this situation of supposed abundance, most companies on the planet disappear.

This shift will rewire our values. I’ve discussed how this may happen in retail through the implementation of agentic shopping. This rewiring isn’t lost on people who are thinking about these issues. The CEO of Walmart resigned because of AI’s potential to upend retail. If successful, more rewiring is on the way and will upend the entire fabric of the modern world. Just imagine, instead of lusting after material things, the dominant belief in the world was meditation and inner peace. Not great for business.

In a world of abundance, many large companies probably won’t exist. Diversification and competition will eat them alive. The abundance of tools and techniques will allow for quick imitation. Sure, some will remain, kind of like the company store in a mining town, but the economic landscape will look vastly different.

We are told that, despite things being so cheap, companies will make money from volume. But once again, with distribution spread across many small companies, it’s hard to see how that amounts to anything more than table scraps. This begs the question, then, where will the money for UBI come from? Even worse, instead of UBI, we may end up with company vouchers, forcing us to buy from a single company or to obtain vouchers for things we don’t need.

Abundance Equals Sameness

Henry Ford is quoted as saying of the Model T that a customer can have any color they want, as long as it’s black. There’s a lesson here for technological abundance. Many are laboring under the delusion that a world of abundance looks like the world of today, just with more. But the reality is this world will look totally different.

The world of technological abundance laid out before us isn’t a world of diverse beauty with green grass and open skies where people spend their days writing poetry and contemplating their existence in the universe. It’s a homogenized world of sameness where everything collapses into uniformity because this uniformity is predictable and can be mass-produced at scale. Individuality and one-offs are unpredictable and costly, and thus must be crushed. It’s a world where humans become little more than a burden.

Sure, people will try to control for this uniformity in the ways that they can. For example, if a family has a 3D printer at home, they have some control over what they print. However, they still need the resources to buy the printer and printing materials, and most would rely on the available designs. This concept of a utopia is starting to smell pretty rank.

New Business? Maybe.

I’m sure it will be said that I’m not envisioning the new businesses that will sprout up and how humans will change their behavior. Sure, the world is a complex place that defies prediction. I acknowledge that adaptation is possible, and surely, humans will change their behavior. They won’t have a choice. But it isn’t going to turn out the way people think.

Just shooting from the hip here, but a world in which people don’t have money and things are essentially free doesn’t seem like a robust environment for business. You won’t see a plethora of new startups all angling for that gigantic pot of nonexistent money.

You won’t see a plethora of new startups all angling for that pot of nonexistent money.

People invest in companies on the hopes of a big return. They take chances and are willing to take losses, but the gambler loses big in this new environment. Because even an investor with money and a successful business bet basically converts that investment into less money. In this environment, there are no incentives to improve, and everything will plateau at mediocrity to preserve existing margins.

Another thing to consider is that this environment is inherently fragile. Any problem, no matter how small, could cause a company to lose money since the margins would be so tight. Any amount of downtime, errors, or integrity issues, whether accidental or intentional, could be catastrophic. The predictable answer from the AI bro is: “But the AI won’t make mistakes.” To which I say, good luck with that.

Now, is it possible for us to evolve beyond money or capital toward a society that aligns its value with other meaningful activities and goals? Sure, it’s possible. But, once again, this wouldn’t be good for the business bros who are pushing this fallacy the hardest. In a world where abundance is truly successful and is beneficial to humanity, it’s bad for business.

Complexities and Negative Impacts

A full conversation on the negative impacts is outside the scope of this article. However, I have covered some of these before.

I’d argue that these people vastly underestimate the world’s complexities and the sheer number of negative impacts this change brings, not just on the economics but on humanity as a whole. There are basically only one or two ways this can kind of go right, and an incalculable number of ways it can go wrong. For example, this is an environment tailor-made for surveillance and totalitarianism. How can it not when your existence is completely dependent on external factors like the state or the generosity of a benefactor?

If you think that people will tolerate a few trillionaires while the world suffers mass unemployment, you are dead wrong. This is so obvious it shouldn’t need to be stated. I can completely envision a group calling themselves The Children of Ludd wreaking havoc. Only their anger won’t be isolated to the machines in data centers and their embodiments out in the world. They’ll direct their anger at the people they see exploiting the situation as well. It won’t be pretty.

I can completely envision a group calling themselves The Children of Ludd wreaking havoc.

In many cases, this new environment will make people far more tribal and extreme and willing to believe just about anything, but this is a topic for another day.

Conclusion

The vision of abundance defined by the tech bros is an absolute fallacy. It’s a world in which AI is talked about more like magic and less like an actual technology. From this tap spouts outlandish claims with no foundation in reality. As we’ve seen, even when abundance works, it has a degradation effect.

A world of successful abundance appears to be bad for business, which raises the question: Why are so many business leaders pushing this concept? If I had to guess, it’s that they hope to make their money before this big crash and leave humanity to pick up the pieces.
The Great AI Washing and the Mere Idea of AI

March 19th, 2026

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.

AI Washing Layoffs

The biggest layoff news recently is Block. Jack Dorsey is laying off nearly half of his workforce. The reason given is, of course, AI!

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.

This playbook was repeated with Atlassian.

And of course, this poor soul from Atlassian was promoted and laid off on the same day. All thanks to the overwhelming power of AI!

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.
Transforming Books Into Statistics Won’t Bring Wisdom or AGI

March 10th, 2026

Here’s a secret: turning books into statistics won’t bring AGI, cures for cancer, utopia, or any number of useful inventions that we are told are merely 12 to 18 months away. This activity also won’t bring their users wisdom. As a society, we are told that if we don’t let companies freely pillage the intellectual work of our past and present, we won’t get the life of leisure we are promised. But turning War and Peace into statistics won’t lead to significant breakthroughs. It won’t even bring knowledge… for you.

I believe that many of the people who work at the big AI companies know that training on a large corpus of non-domain-related works won’t lead to AGI or significant breakthroughs in areas such as cancer research, but they do know it may lead to breakthroughs in manipulation, and that has them interested.

Project Panama: Books Into Statistics

Recently, the Washington Post had an article about Anthropic’s Project Panama, a secret project to destructively scan every book on the planet. The image is shocking, and the whole situation feels dirty, which is probably why Anthropic tried to keep it a secret. Although it was found that they didn’t break any laws, they tried to keep it secret because they knew this would have a negative public perception. Mission accomplished.

In one sense, this is an attempt to obtain untainted training data. The internet is submerged in AI slop after the launch of ChatGPT. AI slop is good enough for the internet, but not so good for AI training. AI models tend to degrade when trained on their own outputs, a condition known as model collapse. So, instead of a model getting better, it gets worse. Seems models know what’s better for them than we do.

But the quest for untainted training data isn’t the whole story. If you are trying to scan every book on the planet, then you’ve made a decision to ingest and train AI on books of all kinds, inaccurate books, dated books, and even “bad” books. In short, accuracy isn’t the goal here.

Okay, so what’s a “bad” book? I mean books with universally accepted bad ideas, poor stories, poor writing, and many other issues. Trying to train on all books means you’ve made a conscious decision to also train on material such as Mein Kampf and The Turner Diaries. That’s right, you’ve “trained” on it, not assigned it to an AI model as homework for a classroom discussion. There are a few things I can say with 100% certainty, although I can say this: there is nothing in the works of books like Mein Kampf that will cure cancer.

Bad books shouldn’t be eliminated, although bad for AI, they can be beneficial for humans. You can always stop reading a poor novel or other books you feel aren’t providing proper value for your effort. As for books with bad ideas, when a human reads one, they can do so from a given perspective, trying to formulate a certain understanding. They can even be read with the intent of identifying and avoiding certain conditions in the future. Only a fool would think reading a book with bad ideas is always bad.

It’s basically just nom nomming the data, creating statistical grenades.

When an AI trains on a bad book, it incorporates the ideas and even the poor sense of style. It isn’t providing any perspective. It’s basically just nom nomming the data, creating statistical grenades. I’m certainly not claiming that training on Mein Kampf creates an AI Hitler, there are other ways that can happen. What I am saying is that the ideas and concepts contained in these books are kicking around in there somewhere, even if they are shoved way down in the statistical distribution. What this ultimately means is unclear.

I don’t mean to be disingenuous here. The definition of a “bad” book is highly subjective. This quickly devolves into a who decides scenario, which could lead to unintended consequences of its own. My point is that there should be more purpose to the activity.

There were also plenty of idiotic takes on Project Panama. Never underestimate the true cluelessness of the e/acc community. One thing they effectively accelerate their own idiocy.

One of my favorite arguments from the e/acc community was that they weren’t destroying the books, they were preserving them. To which I joked that it was preservation through destruction. Preservation through destruction sounds like a quote that could be ripped from Orwell, just like the pages of his books for Project Panama. Turning books into statistics to monetize them doesn’t preserve them in any sense of the word. This is a silly argument that can be destroyed by one simple question. If they are preserved through this process, then where are they?

Preservation through destruction sounds like a quote that could be ripped from Orwell, just like the pages of his books for Project Panama.

Manipulation and Imitation

So, why are AI companies foaming at the mouth to get their hands on books that seem to have nothing to do with their goals? If I had to guess, it has to do with a couple of factors.

The more of this type of data ingested for training, the more the system may be able to imitate humans under a variety of conditions. This can be used by users of the system to create a “personality” from the tool, or, more importantly, to manipulate people, fooling them into thinking the AI is actually a human. This manipulation could be applied in situations like customer service. I experienced this recently.

A broken water pipe forced me to call some local plumbing companies after hours. Quite a few of them were using a call service with what sounded like a human in a call center, complete with office background noise. This was clearly done to manipulate users. Oddly enough, when asked if they were AI, only a couple responded that they were. This is the type of manipulation that I find unacceptable. Gary Marcus recently wrote an article about this as well.

Of course, these conditions can also be used to claim that the AI has a consciousness or a self that needs to be protected. This is pure SciFi bullshit meant to ramp up the hype.

Another interesting and related condition is pastiche. The more of this type of data the model is trained on, the better it may get at imitating specific forms of human style. People can use these to fool themselves into thinking they are being creative.

As an example, generative AI can reliably generate books. They aren’t good books, or well-informed books, or well-written books, or accurate books, or present new information, or new perspectives, or any of the other countless characteristics we associate with a good book. But words slathered onto pages… This it can do reliably.

Generative AI hasn’t cured cancer yet, but it excels at creating slop. Slop is literally the number one use case for generative AI today, arguably more than coding. There’s no doubt that AI companies want more of this behavior to keep people engaged.

But let’s get back to books.

Next-Gen Nerds

When I was growing up, being called a nerd was considered a bad thing, and nerds read a lot. Now, they are popular, wear black t-shirts and blue jeans, and claim that reading is for losers. Their perspective is warped by an “optimization-at-all-costs” mindset. But don’t take my word for it.

In November of 2022, notorious tech bro and crypto con man Sam Bankman-Fried told a writer interviewing him, “I would never read a book.” He went on to say, “I’m very skeptical of books. I don’t want to say no book is ever worth reading, but I actually do believe something pretty close to that. I think, if you wrote a book, you fucked up, and it should have been a six-paragraph blog post.”

That’s the next-gen nerd’s perspective. An entire book should be six paragraphs. But why stop there? Why not six bullet points? Seems I just out-optimized the optimizer! In fact, many of their points can be addressed by reductio ad absurdum.

This perspective does not serve people well and further devalues books. The theory is that if books can be reduced to numbers, then the ideas they contain can be made “useful” in a programmatic or more efficient way. But ideas from books can’t be reduced to numbers, just like The Hitchhiker’s Guide to the Galaxy can’t be reduced to 42.

Oddly enough, when reducing books to numbers, you remove the content from context. Context, the very thing both humans and AIs need to make sense of things.

The idea that books are nothing but bloated friction is nonsense and could only be cooked up by delusional idiots. I acknowledge that poorly written books exist, and some books are 12 chapters when they should have been 6, but applying this perspective equally across all books is just plain stupid.

The Decline of Reading

Here’s a chart that should surprise no one.

Everyone knows you don’t become an influencer by reading. Or reflecting. Or thinking. You become an influencer by reacting. Thinking before you do something takes too much time. What should be concerning is the rate of the lines. Kids who don’t read turn into adults who can’t read. And we are seeing this play out.

Kids who don’t read turn into adults who can’t read.

Gen Z is showing up to college unable to read.

And teachers are taking notice, labeling it a crisis, as it should be.

Wisdom Takes Work

Here’s another secret: no matter how good the AI gets, you’ll never become wise without doing the work yourself. Wisdom manifests from reading, writing, and a whole lot of reflection. All activities that are devalued today. I’ve previously covered how knowledge and understanding aren’t generated from bullet points, but let’s go a bit deeper.

In letter 27 of Seneca’s Letters on Ethics, he discusses how real joy depends on real study. In this letter, he describes a man named Calvisius Sabinus, a wealthy man who wanted to appear learned, who devised a shortcut. He spent a great deal of money on slaves, one to know Homer, another Hesiod, and nine more for each of the lyric poets.

After he assembled this group, he would pester his dinner guests by having these slaves at his feet and regularly ask them for verses to quote. Even with this assistance, it was observed that he’d often stop mid-sentence. When a man named Satellius Quadratus made fun of him, saying he should train his busboys to be literary scholars too, Sabinus responded that the slaves had cost him a hundred thousand sesterces apiece. To which Quadratus said, “You could have bought as many libraries for less.”

Sabinus’ optimization led to a mistaken assumption that the knowledge possessed by anyone in his household was his own. This perspective is not only wrong, but it also led to ridicule. Today, we have a similar situation with AI, and many would claim that the information contained in an AI tool is knowledge they themselves possess, except that it isn’t. This resembles a situation we had previously with Google search, so we shouldn’t be fooled merely by upgraded tech.

Excellence of mind cannot be borrowed or bought. -Seneca

Wisdom isn’t recall. After all, someone who memorizes things wouldn’t be considered wise. It’s the perspective that’s gained from study and reflection across a variety of sources. It’s the ability to connect the dots between concepts and form new ideas. Wisdom manifests in someone who puts in the work, which includes reading, writing, and a healthy dose of reflection, all things labeled by the tech bros as “friction.”

When Zeno of Citium (The founder of Stoic philosophy) visited the Oracle of Delphi with the question of what he should do to live his best life, the god replied, “He should have intercourse with the dead.” This is recorded in Diogenes Laertius’ Lives of the Eminent Philosophers. In modern times, people have changed this to “converse with the dead,” no doubt because of how intercourse is used today, but I think intercourse has a much deeper meaning. No pun intended.

The oracle’s response didn’t mean Zeno needed to engage a psychic medium or have a seance. The only true way to converse with the dead is to read. This is the meaning Zeno inferred as well.

I’m obsessed with the works of long-dead authors such as Seneca, Aldous Huxley, Marshall McLuhan, Neil Postman, Montaigne, and many others. I can’t send them letters or call them on the phone. I can grasp their perspective through their writing, through books, letters, and other artifacts they’ve left behind. I can highlight passages, write my own notes, create my own modern perspective, and even challenge these authors in my own way, given the time that has passed from them to me.

Reading is a superpower that we seem eager to relinquish, which is a shame, because in many cases, reading is the remedy for so much of what ails us today. Reading does broaden the mind. It demonstrates that even ancient people encountered many of the same problems we have today. It creates room for reflection that extends far beyond the act of reading. The quest for wisdom and conversing with the dead create a satisfaction that can’t really be matched by other activities. It’s a feeling that isn’t possible to explain, and it’s something that needs to be experienced.

Getting Useful Information From Books

There’s a time-tested way to get the information from books that doesn’t require destroying them and turning them into a statistical distribution: you can actually read them. Dated concept, I know. I have a large piece in draft on literacy more broadly, so we’ll keep this piece focused on reading.

People might claim that the joke is on me because I only have knowledge from the books I’ve read, whereas someone with generative AI has knowledge from all of the books, despite never reading any of them. This is an idiotic statement to make. Setting aside the issues with retrieval, hallucinations, and other technical issues, the user doesn’t actually doesn’t have the knowledge of any of these books. At best, they have pieces torn from context. These people are like Sabinus, without knowledge but happy to annoy dinner guests.

The great thing about books is that you don’t need to read them all. Just like an explorer doesn’t have to explore every square inch of the globe, a reader is free to explore their unique interests and forge their own path of knowledge and wisdom.

Another response could be, since the AI has been trained on the content of someone like Seneca, you could have a Seneca bot and ask it questions. But this approach doesn’t make sense either. First of all, even if this were an effective approach, you’d have to know the right questions to ask. Since you haven’t read the source material and aren’t confronted with concepts in the writing, the “right” questions would escape them.

Second, none of the responses from the bot would stick with you, or in some cases, make any sense. The responses won’t form a connection in the way the content is encountered in the context of reading a book. The bot is going to “tell” you something, while reading will “show” you something. Reading is a true experience. Being told something is ephemeral and throwaway. Experiencing something can last a lifetime, while being told something may last only seconds.

Experiencing something can last a lifetime, while being told something may last only seconds.

Finally, the bot will approximate a pastiche response based on what it may have encountered during its training. It’s going to fill in the blanks in whatever statistical way makes sense. It won’t be the true response based on what the author really knew or felt. However, this response does actually fool people, and we’ve seen it time and time again in the generative AI era.

Much of this makes so much more sense when stated out loud. What do you think is the best way to get value from George Orwell? Do you think throwing 1984 into a massive statistical distribution or reading the book? Reading a book sticks with you in a way other methods can’t match.

There’s a mismatch in the application of technical thinking here. The goal of reading a book is to change the landscape of your mind, not have immediate recall over chapter and verse. Thinking that the point of reading a book is to remember everything is a warped perspective caused by our modern technological environment.

Unable To Read

Many people find it difficult to read long-form content, things like books and longer essays, articles, maybe even this very one. This may be due to attention hijacking, inability to focus, or discomfort, for lack of a better term.

People often tell me they wish they had time to read. I tell them, “Yeah, like I have boatloads of free time.” When viewed this way, zoomed out, it appears that nobody has time to read. However, this isn’t the case.

It’s true that getting information from books requires purposeful action and commitment. However, once started, it’s not as bad as people make it out to be. In many ways, it’s like an exercise routine. The best way to get started, or to get back into reading, is to create a habit. Start with the intention and don’t beat yourself up about it when you miss the mark.

What do you typically do before you go to bed? For many, this is swiping through social media, reading news stories, or maybe watching TV. This is a prime block of time to target for a reading habit.

You don’t need to start big. Maybe try 15 or 30 minutes of uninterrupted time. You may get distracted or feel uncomfortable. It’s fine. Like any habit, it will take some adjustment. At some point, it will click.

Don’t let some sort of idealized conception of reading throw you off. There’s so much reading advice out there, and much of it is bullshit. For example, nothing is more bullshit than speed reading. If you were worried about wasting your time reading, then speed reading will confirm your worries.

All of the things you are told are bad habits, such as vocalizing as you read, re-reading sections, and reading slowly, are all positives that reinforce the concepts contained in the book. Your goal is to develop an understanding of the material, form new connections between concepts in the book and in the world, and even develop new ideas based on these. None of that happens during speed reading.

It’s true, people read at different paces. You may feel like you read slower than other people, but that’s probably not true. Besides, who cares? You are reading for you.

As you begin reading again, you’ll find what works for you and what doesn’t. For example, maybe you prefer physical copies of books or the convenience of an eBook reader. The format is irrelevant if it works for you. Also, maybe you need to put your devices in do-not-disturb mode or make other purposeful interventions. Be intentional, find what works, and push forward.

My Approach To Reading

Let me share my approach, because I feel it’s pretty simple. I’ve explored using book tabs and other reference techniques, but I don’t use them consistently. Some people use reference cards, but I’ve never felt the need to go this far. I read for about an hour and a half every night before I go to bed. I typically have at least one fiction and one non-fiction book I’m reading at a time.

I prefer physical copies of books because they feel more engaging to me, and I don’t have yet another “device” in my hands. However, for nonfiction books I’m really trying to dig into, I’ll buy all three formats: physical, ebook, and audiobook. The audiobook is mostly for use while running on a treadmill or driving on a road trip.

For the physical copy of the book, I have three things: a Zebra Mildliner in lemon yellow, a pen, and a notebook. As I encounter interesting content, it could be concepts, things I’d like to quote, or anything else, I highlight it. If there is an entire section of a page, then I’ll highlight the first sentence and make a note with my pen in the margin, so when I revisit it, I have the context.

As I have ideas or make connections, I’ll stop reading and capture my thoughts in the notebook. I may include a piece of the book’s content there, but not always. I will always annotate the page number for reference, though. This way, it’s easier to revisit in the future.

After I’m done reading, I’ll take all the highlights from the physical book and apply them to the same sections in the eBook. I then sync those highlights to Readwise for both ease of reference and spaced repetition. I also have a physical commonplace book where I write the highlighted items. However, I’m not very consistent with this activity.

You may question the efficiency of my approach, as it seems I’ve added unnecessary extra work for myself. It appears that using the ebook and syncing the highlights is far more efficient. Once again, the optimization is a trap. The friction is the point.

In the act of transferring the highlights to the ebook, I’m once again confronted with all of the concepts I’ve highlighted, this time, after reading the whole book. Although I don’t remember things word-for-word, when I see the highlight, I’m reminded of the context in which the highlight was created. New ideas form, and I note those in my notebook. This activity further reinforces the content in my mind. I will sometimes reread the page or section during this activity. This activity provides much value.

Someone may argue that an AI can digest the entire book and provide relevant highlights without having to read it. Hopefully, by now you can recognize the issue. Even if it did this accurately, you’d be confronted with highlights out of context. The meaning and important features would be unavailable to you mentally.

Conclusion

The current AI age is making us wisdom-poor and manipulation-rich. The damaging consequences of the devaluation of reading are on the horizon for an entire generation and generations to come. It’s separating us from the very skills we need to defend ourselves and keep us robust in the modern environment. It’s removing our ability to reflect as modern technology pushes us to react.

Many believe they can’t read long-form content anymore, but that’s only because they haven’t tried. By creating a habit and some purposeful interventions, we can get back on track to finding wisdom.
Confusing Innovation With Progress

March 5th, 2026

One mistake we continue to make, time and time again, is confusing innovation with progress. It’s true that in many cases, innovation is progress. We see the results of this every day, in the development of vaccines, antibiotics, and even indoor plumbing. The results of innovation have improved people’s lives across the globe, allowing them to live longer, healthier lives. These are examples of progress fueled by innovation, but this is hardly always the case. It’s a mistake to assume that innovation and progress are the same, despite people using the terms interchangeably. Thinking this way is a trap that lulls us into complacency.

Confusing Innovation With Progress

When people use the term “innovation,” they often mean technological innovation. People imagine everything from hoverboards to intergalactic space travel. It’s these larger-than-life imaginings implanted in our brains that companies exploit to sell products as progress. Apparently, the road to utopia is paved with forced obsolescence and mandatory upgrades. However, nowhere in the definition of technological innovation is there a qualifier that it must be beneficial to humans.

Innovation is about making money, after all, companies are in business to make money. This isn’t some terrible, disgusting secret. And no, we shouldn’t burn it all down because companies are trying to turn a profit. This is how the world works. However, we need to recognize this fact because it figures into our calculus of progress. Because when innovation makes life better for us, the making life better part is a byproduct of the innovation activity. There are certainly exceptions and exceptional people like Jonas Salk, but this is hardly the norm.

Progress, on the other hand, is advancement toward the betterment of humanity. Or, at least, that is what the definition should be. Granted, these are subjective measures, which make them difficult to pin down, but progress is typically something we know when we see, for example, reducing poverty or medical advancements that reduce disease. Basically, anything that makes human lives better as a whole, preferably ones that lead to human flourishing, although this is rarely the case.

The fact that progress is subjective is what many technologists don’t like. There’s no number to optimize. Of course, since it’s subjective, people’s perspectives on progress vary. This is what gets exploited to sell nonsense as progress. When innovation is packaged as progress by default, then there’s something to optimize.

Let’s take the example of brain-computer interfaces, a space that is certainly going to heat up. Here’s Elon Musk claiming that Neuralink will allow us to become one with AI.

This is certainly innovation, but is it progress? In some cases, the claim could be made that it is progress. This would be true if it corrected some impairment or disability. But otherwise, let’s think about the scenario. Imagine a world where you never know whether a thought or memory you have is truly yours. A world that signals the end of private thoughts and the beginning of a whole new world of manipulation and unintended consequences. Maybe it’s just me, but this doesn’t sound like progress. I’ve pointed out these risks in my public presentations on emerging technology, as well as my addressing Kurzweil’s nonsense.

The lines can certainly get blurry. Self-driving cars seem like progress until they take away the self-driving features and instead charge you a monthly fee to access them. Apparently, a monthly subscription fee is how we get to Mars. I’m not claiming technology should be free. I just don’t think monetization should be a rug pull.

Apparently, this is how we get to Mars, through a monthly subscription fee.

Detractors often bring their own set of problems. No innovation that leads to true progress benefits everyone on the planet equally and simultaneously. The idea that it can and should is delusional, and insisting on this as the criterion is disingenuous and won’t lead to any progress. It’s the mark of a performance by someone living in an alternate reality.

Much of what we’ve seen over the past couple of years is innovation for its own sake, with associated negative consequences.

Why Does This Distinction Matter?

Many would have us believe that this is a distinction without a difference, and that I’m being provocative, but the reality is, these are two separate terms with two separate definitions. Progress is a general term that all humans can understand, whereas innovation is a specialized term used by technologists. The blurring of the lines between the two makes innovation in whatever form easier to digest. Quite often, innovation is immediately labeled and packaged as progress to exploit this condition.

This packaging and blurring of the lines between progress and innovation have been with us for quite some time. In Aldous Huxley’s post-apocalyptic novel from 1948 titled Ape and Essence, the Arch-Vicar of the satanic church describes progress and nationalism as the two great ideas that Belial put into human heads. In this scene, innovation has clearly been labeled as progress.

Progress—the theory that you can get something for nothing; the theory that you can gain in one field without paying in another; the theory that you alone understand the meaning of history; the theory that you know what’s going to happen fifty years from now; the theory that, in the teeth of all experience, you can force all the consequences of your present actions; the theory that Utopia lies just ahead and that since ideal ends justify ideal means, it is your privilege and duty to rob, swindle, torture, enslave, and murder all those who, in your opinion (which is, by definition, infallible), obstruct the onward march to the earthly paradise. -Aldous Huxley (Ape and Essence)

There’s no doubt that Huxley isn’t describing progress as human flourishing; he’s describing technological innovation packaged as “progress.” This is no doubt because the term “innovation” was not a common one flaunted in the faces of the general public the way it is today. This matters for all the reasons Huxley points out in this very line, and it applies just as well to technologists today as it did back in 1948. We are seeing the conditions Huxley noted in this passage today.

Technology Is Never Neutral

People love to claim that technology is neutral, but this demonstrates a stunning lack of awareness of reality. Technology is never neutral. The mere presence of the technology changes people and their environments. This has been known for over half a century. Here’s Marshall McLuhan writing about it in the 1960s.

Our conventional response to all media, namely that it is how they are used that counts, is the numb stance of the technological idiot. For the “content” of a medium is the juicy piece of meat carried by the burglar to distract the watchdog of the mind. -Marshall McLuhan (Understanding Media)

To quote another sage on the same topic, back in the 1980s:

Only those who know nothing of the history of technology believe that a technology is entirely neutral. -Neil Postman (Amusing Ourselves To Death)

Technology, by some measure, always changes the environment in which it’s found. Take social media, for instance. Social media changes the communications landscape, forcing even people who opt out to reckon with the possibility of being left out of activities when a group decides to organize on a social media platform.

There’s not much we as individuals can do about this, other than be aware of the phenomenon so we can implement mitigations. If we recognize that technologies are not neutral, we must acknowledge trade-offs, even if trade-offs don’t appear in the short term. Once again, we can look to social media. Early in social media’s life, there didn’t seem to be any real trade-offs. This perspective seems quaint today.

This is the point of this post: we never get something for nothing. The bill comes due at some point. When innovation, regardless of impact, is automatically packaged as progress, it becomes a vehicle to shove it down our throats, to make us get in line, to label us Luddites, to strip us of our humanity, and disregard our concerns.

For example, I’m still struggling to understand how destroying art and artists and dehumanizing everyone on the planet is somehow a prerequisite to getting a cure for cancer. If you don’t let us loot the intellectual and creative property of everyone on the planet, then you won’t get a cure for cancer or eternal life. Seems Huxley was right.

Conclusion

We should want and insist upon innovation that’s actually progress, with progress defined as the betterment of humans. So many these days are bedazzled by technology because it is neat or cool and never ask any larger questions about what problems it actually solves and what the tradeoffs are.

The next few years will require vigilance because much of the innovation packaged as progress comes at the cost of stripping us of our humanity and agency, further pushing us toward becoming machines. We need to become much better at envisioning trade-offs and not blindly take every new application or technology for only its surface-level functionality. The real dangers lurk behind the curtain and are rarely exposed until it’s too late. Awareness of potential trade-offs allows us to keep our guard up, choose our technology use cases wisely, and defend our humanity to the best of our ability. In the current environment, it’s up to us to defend ourselves.
The Death of Software Is Greatly Exaggerated

February 20th, 2026
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.

However, it is funny to consider that maybe the thing Anthropic actually broke was ClaudeCode, right after their latest funding round.

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.
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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.
7 Books For More Robust Humans In The AI Age

January 28th, 2026

The entire modern world seems to be doing everything it can to rob us of our humanity. Everywhere we turn, every reaction we have is either mediated or a byproduct of our current technological environment. Pretty much every piece of content we encounter online is a form of manipulation. This tiresome cacophony of content overwhelms our senses and wears us down. But we have a superpower, it’s called literacy. Literacy creates robust humans and is our greatest weapon to defend our humanity. It creates space for us to reflect and withhold our reactions. However, maybe not for long, as the kryptonite of content, entertainment, and instant reactions destroys this superpower, turning us into mindless, predictable automatons.

I know, I read so I must be some kind of loser who doesn’t know how to prompt. However, if books are so worthless, then why are AI companies frothing at the mouth to scan every book they can and don’t want you to know about it?

I do make time for this “friction” every day. This isn’t said to sound elitist, after all, I still watch TV shows and movies too. I wouldn’t even say I’m a particularly good reader, but I make do. And making do with reading is the whole point. Reading isn’t like baseball, where trying hard isn’t enough to be a starter. Reading makes you better, and trying hard pays off no matter how good you are.

As a matter of fact, if you want to maintain your humanity in a world that’s doing everything to steal it from you, then literacy is your greatest weapon.

What Orwell feared were those who would ban books. What Huxley feared was that there would be no reason to ban a book, for there would be no one who wanted to read one. -Neil Postman

The Reading List For Robust Humans

It’s really hard to find books these days that provide a critical lens of technology or AI without also being filled with politics, biases, and other assorted bullshit. I feel that the world really does need a better class of critic.

I’ve read some highly recommended, well-reviewed AI books that were absolutely terrible. Mostly filled with junk food for biases. The few good points that were made were mercilessly beaten like a dead horse, revived, and then beaten again, which I can only assume happened for the sake of page count or the author’s own personal indulgences.

This post is my attempt to save you some time. There are many great books that didn’t make the list because it would have made it too long. So what I tried to do with this list is provide some objective books that examine the impact technology has on us humans, with a focus on impact, experience, background, and data. Although some of these books may cover similar ground, they are all worthy reads.

Technopoly: The Surrender of Culture to Technology – (1992) – Neil Postman

You may wonder how a technology book from 1992 can be relevant today. That’s because Neil Postman was a true oracle. He was the Marshall McLuhan of the 80’s and 90’s. He could cut through the noise and see the reality of things. Every single chapter is filled with insight, and the dated examples could be easily swapped out for today’s. Mostly, it’s a book that demonstrates people of the past saw the problems of our current time, long before they surfaced. It’s an amazing book, which is why it’s the first on my list.

Amusing Ourselves to Death: Public Discourse in the Age of Show Business – (1985) – Neil Postman

Okay, I couldn’t help myself. Here is a bonus Neil Postman book. Quite often, this book is the first port of call for Neil Postman. Despite the references to television, it’s an easy swap out for other technology. There’s so much interesting history here, such as the history of advertising, with some examples from the early days when our culture was more literate. Fascinating read.

I am saying we are losing our sense of what it means to be well informed. Ignorance is always correctable. But what shall we do if we take ignorance to be knowledge? -Neil Postman

Superbloom: How Technologies of Connection Tear Us Apart – (2025) – Nicolas Carr

Pretty much any of Nicolas Carr’s books are good, but this is his most recent book. I’d kind of describe it as a modernized, more approachable work on the impacts of communication technology than Marshall McLuhan. Although McLuhan’s work is referenced multiple times, Carr offers his own modern perspective on today’s technology and its impact on us.

More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade To Control The Fate of Humanity – (2025) – Adam Becker

If some of these other books are about the what, then Adam Becker’s book is about the why. It demonstrates just how kooky some tech leaders’ beliefs are. Such as the need to go to Mars to survive, despite its toxic soil. Or some of Kurzweil’s greatest hits, such as we should pave over the universe to convert atoms into computronium. I’ve covered some of this myself and highlighted Kurzweil’s nonsense, but this is a whole book dedicated to exposing the odd beliefs of the people who create the technology we use every day. He’s an astrophysicist, so the odd space beliefs are something he’s uniquely capable of addressing.

I will say, Becker gets a little preachy near the end of the book, but the work is solid. It’s a summary and origin story of the worst ideas to emerge from techno utopians.

Dark Data: Why What You Don’t Know Matters – (2020) – David J. Hand

I’ve been recommending this book to people since it was published in 2020. This isn’t a book about AI or technology’s impact on humanity, but about our inability to properly make sense of data. In this book, Hand identifies multiple categories of dark data that confound our ability to make sense of data. You’ll never look at a survey the same way again.

The Extinction of Experience: Being Human in a Disembodied World – (2024) – Christine Rosen

This book covers our preference for mediated experiences and what this means when we lose them. She covers experiences that are on the verge of extinction and makes us think about exactly what we are giving up.

Understanding Media: The Extensions of Man – (1964) – Marshall McLuhan

I struggled to add this book to the list because both Postman and Carr refer to and build on McLuhan’s work. I even referred to McLuhan multiple times in this very post. It’s for this reason, and because McLuhan is a true visionary, that I had to add this book to the list.

McLuhan was someone who could see the impact of technology on humans, despite no one else noticing. In this book, he covers the difference between hot and cold media, human impacts of augmentation, and the shrinking of the world due to electric technology. A world that has only further shrunk in the internet and social media age. Remember, the medium is the message.

It’s not a quick or easy read. Maybe it’s just me, but I found myself reading something, then going back to reread it. The references are a bit dated, as to be expected, but the content contained is as relevant today as it was back then.

Conclusion

This is just a start. Make 2026 the year you rekindle a reading habit. I set aside at least an hour a day before I go to bed. Once the habit forms, it’s easy to continue. Start with a few minutes a day if that’s all you have, and work from there. Welcome to the robust revolution!
Agentic Shopping: How Silicon Valley Accidentally Destroys Retail

January 16th, 2026
Recently, Google, along with Shopify, Etsy, Wayfair, and Target, created Universal Commerce Protocol. A protocol that retailers can use in their AI agents to support product discovery, purchasing, and even support. However, I don’t think retailers understand the full impacts of agentic shopping. When viewed through the autonomous lens, this approach presents the act of shopping as friction, then removes it. Removing the shopping experience from the purchase of products will have the opposite economic effect than retailers hope for.

Companies are betting big that people will want agents to buy things on their behalf. But if this takes off, it could backfire, rewiring the shopping impulse in people’s brains and causing them to buy even less. I don’t think agentic shopping will take off because, once again, innovation is competing with culture. However, this requires a closer look.

Innovation Failures

One of the big reasons innovations fail has nothing to do with a technology’s capabilities and everything to do with the individuals building it not understanding people and culture. A perfect example of this is the vacation agent.

I’ve made fun of the vacation agent before. A nonsensical idea that could only be dreamed up by someone locked away in a room, having no idea what a vacation is. The big point is that the planning is part of the vacation. People don’t view researching activities for a vacation as a burden. It’s part of the fun.

These proposed innovations conflict with culture, which is why I predict that OpenAI’s device will fail. Introducing a device without a screen in a screen-based culture. Here again, we have people thinking it will be different. Sure, innovations come along that break the culture, but they have to be overwhelmingly compelling.

Now, the wonderful folks of Silicon Valley are here to give us agentic shopping, something that nobody actually wants. Every step of the way, proving yet again that they not only don’t understand humans but also don’t understand existing technology.

Shopping Technology

Let’s start with technology. We already have technology today that allows people to check prices to get the best deals. Whether it be flights, hotels, or products. In the US, you are bombarded with Trivago commercials while watching television. Browsers also save address and payment details, and you have options like Apple Pay to make the checkout process even more painless. The friction that’s left is much of what people find enjoyable and what retailers find necessary. (More on this shortly.)

It’s true that people today are using AI as a research tool on products, or at least there is no reason to think they aren’t. After all, they are using AI to self-diagnose their medical conditions. So, there’s no reason to think they aren’t also using it to research the products they may purchase. Companies view the purchase as purely the last step, connecting the dots, if you will. But there’s a big difference between connecting the final dots when a human is doing research and making decisions, and when an autonomous shopping agent does so.

Shopping

The joys of shopping come from outside the technical workflow. Simply put, shopping is fun for people, and our economy depends on that. But what people fail to realize is that shopping with autonomous agents isn’t shopping at all. You’ve amputated the impulse and transformed a fun experience into a purely utilitarian one by viewing shopping as friction. However, even though shopping seems simple, there are complexities that confound the autonomous shopping experience. Let’s start with price.

Shopping with autonomous agents isn’t shopping at all.

Is buying the cheapest thing really the best? Price is purely a number, easy to sort and prioritize. However, thinking that product selection is a matter of price is fooling yourself. What if you don’t get the cheapest thing for a month? What if the company has a bad habit of poorly packaging products? What if the company has poor support? The follow-up questions are endless. It’s true, you could try to account for these countless variables based on personal preference in the agent, but at some point, it becomes too tedious and varies from product to product.

In some cases, even when shopping for the same product, you might prefer the markings, such as the wood grain pattern, on one product over another, even though they are identical. The list here can be endless, and this choice is only for selecting between different options from the same vendor.

Often, you aren’t comparing apples to apples but apples to oranges, and you’re trying to decide between them. Similar products from different vendors or even different formats. For example, when choosing your next vehicle, you might be deciding between a car and a truck. Both vehicles are apples and oranges. In many cases, you might not be able to explain exactly why you made the choice you did.

Nobody likes dealing with salespeople at car dealerships, but browsing different interiors and options is actually fun. But don’t take my word for it, take our entire economy as proof. Advertisements are purely the bait. The hook comes when the browsing starts.

When purchasing services, it becomes even more complex. “Book the cheapest plumber for Tuesday” isn’t a prompting for success. However, let’s keep the conversation on products.

There is still tedium in the shopping process, and edge cases may emerge. For example, I think the idealized view of agentic shopping is something like this:
```
“Put together five dinners for the week based on my preferences and order all of the ingredients. Have them delivered on Monday.”
```
I can see where some would find this attractive. Grocery shopping is a far more utilitarian shopping activity than other forms of shopping. However, I’m far too picky about my ingredients, and I’d never trust a stranger to choose an apple or an onion for me. Actually, I’m far too picky about everything, so the question is: are picky consumers the norm or the outlier? Maybe dinners are the exception, but generalized technology rarely stays confined to specific use cases, and enough edge cases are needed to push it into mainstream use.

Will people use agents to outsource the shopping experience? Maybe. But technology choices like this are all about trade-offs, and none of those trade-offs are being considered, especially by retailers. Let’s talk about those now.

Manipulation and Gaming

The more automation is applied in the shopping experience, the more it opens the door to manipulation and gaming. It won’t be the best product vendors and products resorting to dirty tricks. Just like today, people have manipulated search engine optimization (SEO) to rank higher in search results. It’s never the best content at the top, but the people who used the right words to game the algorithm. At least with search engines, all of the content is visible, and we can tell what’s garbage. With an agent, it’s not only invisible to the user but also costs them money.

It’s never the best content at the top.

Of course, this opens the door to scammers as well, giving them new ways to exploit people. Generative AI is highly manipulable, and it’s extremely unlikely that scammers will not find unique ways to exploit these agents. Scammers are typically one step ahead, and it’s likely that the techniques they use will be exploited by marketers and advertisers.

Negative Economic Impacts

By optimizing the purchasing experience, AI agents remove the friction, in this case, known as shopping. The result could create devastating economic impacts. By removing the joy of shopping and turning purchasing into a utilitarian activity, this could cause people to buy fewer things or focus only on necessities. Our entire economy is based on people buying things they want, not necessarily things they need.

Removing the friction from purchasing may seem productive when viewed purely in terms of optimization, and if the trend picks up, there may be a short-term spike as people test the approach and impulse-buy items. However, this won’t last. The use of agents could rewire the brain’s reward system in a way that’s devastating for businesses.

Agentic shopping also removes a vital metric for tech companies, time on platform. More time on platform means more viewing ads for other products that customers may buy or encountering other products that are more preferable. The point of agentic shopping is to avoid time on platform altogether. Advertisers won’t be happy. They’ll insist that agents be further enshitified adding friction to the shopping process, possibly by adding ads or other interventions.

Adding friction to the shopping experience is actually preferable for companies. There’s a reason your local grocery store decides to rearrange the products periodically. It’s not to optimize the store, it’s to un-optimize it. The additional friction of walking around the store leads to additional purchases.

If the shopping impulse gets rewired in people’s brains, this could lead to devastating economic impacts. If I were someone who hated capitalism and wanted to see it fall, I’d be a huge fan of agentic shopping. It would be ironic if the innovations created to build more capital end up being its downfall. Technology has a powerful impact on humanity and can transform or destroy culture. Just look at Gutenberg, television, radio, the Internet, etc., as examples.

Conclusion

Companies are investing in technology that may cause their demise, driven by the fear of leaving revenue streams on the table. It’s that simple. They don’t want to be left behind. The irony is that the search for additional revenue may lead people to buy less.

Ultimately, I don’t think agentic shopping will take off. The shopping impulse ingrained in our culture is too strong, but if it does, the unintended consequences may have the opposite effect. In an attempt to get people to buy more, by removing the friction, they buy less.
6 AI Predictions For 2026

January 12th, 2026

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.