The past couple of years have been fueled entirely by vibes. Awash with nonsensical predictions and messianic claims that AI has come to deliver us from our tortured existence. Starting shortly after the launch of ChatGPT, internet prophets have claimed that we are merely six months away from major impacts and accompanying unemployment. GPT-5 was going to be AGI, all jobs would be lost, and nothing for humans to do except sit around and post slop to social media. This nonsense litters the digital landscape, and instead of shaming the litterers, we migrate to a new spot with complete amnesia and let the littering continue.
Pushing back against the hype has been a lonely position for the past few years. Thankfully, it’s not so lonely anymore, as people build resilience to AI hype and bullshit. Still, the damage is already done in many cases, and hypesters continue to hype. It’s also not uncommon for people to be consumed by sunk costs or oblivious to simple solutions. So, the dumpster fire rodeo continues.
Security and Generative AI Excitement
Anyone in the security game for a while knows the old business vs security battle. When security risks conflict with a company’s revenue-generating (or about to be revenue-generating) products, security will almost always lose. Companies will deploy products even with existing security issues if they feel the benefits (like profits) outweigh the risks. Fair enough, this is known to us, but there’s something new now.
What we’ve learned over the past couple of years is that companies will often plunge vulnerable and error-prone software deep into systems without even having a clear use case or a specific problem to solve. This is new because it involves all risk with potentially no reward. These companies are hoping that users define a use case for them, creating solutions in search of problems.
What we’ve learned over the past couple of years is that companies will often plunge vulnerable and error-prone software deep into systems without even having a clear use case or a specific problem to solve.
I’m not referring to the usage of tools like ChatGPT, Claude, or any of the countless other chatbot services here. What I’m referring to is the deep integration of these tools into critical components of the operating system, web browser, or cloud environments. I’m thinking of tools like Microsoft’s Recall, OpenAI’s Operator, Claude Computer Use, Perplexity’s Comet browser, and a host of other similar tools. Of course, this also extends to critical components in software that companies develop and deploy.
At this point, you may be wondering why companies choose to expose themselves and their users to so much risk. The answer is quite simple, because they can. Ultimately, these tools are burnouts for investors. These tools don’t need to solve any specific problem, and their deep integration is used to demonstrate “progress” to investors.
I’ve written before about the point when the capabilities of a technology can’t go wide, it goes deep. Well, this is about as deep as it gets. These tools expose an unprecedented attack surface and often violate security models that are designed to keep systems and users safe. I know what you are thinking, what do you mean, these tools don’t have a use case? You can use them for… and also ah…
The Vacation Agent???
The killer use case that’s been proposed for these systems and parroted over and over is the vacation agent. A use case that could only be devised by an alien from a faraway planet who doesn’t understand the concept of what a vacation is. As the concept goes, these agents will learn about you from your activity and preferences. When it’s time to take a vacation, the agent will automatically find locations you might like, activities you may enjoy, suitable transportation, and appropriate days, and shop for the best deals. Based on this information, it automatically books this vacation for you. Who wouldn’t want that? Well, other than absolutely everyone.
What this alien species misses is the obvious fact that researching locations and activities is part of the fun of a vacation! Vacations are a precious resource for most people, and planning activities is part of the fun of looking forward to a vacation. Even the non-vacation aspect of searching for the cheapest flight is far from a tedious activity, thanks to the numerous online tools dedicated to this task. Most people don’t want to one-shot a vacation when the activity removes value, and the potential for issues increases drastically.
But, I Needed NFTs Too
Despite this lack of obvious use cases, people continue to tell me that I need these deeply integrated tools connected to all my stuff and that they are essential to my future. Well, people also told me I needed NFTs, too. I was told NFTs were the future of art, and I’d better get on board or be left behind, living in the past, enjoying physical art like a loser. But NFTs were never about art, or even value. They were a form of in-group signaling. When I asked NFT collectors what value they got from them, they clearly stated it wasn’t about art. They’d tell me how they used their NFT ownership as an invitation to private parties at conferences and such. So, fair enough, there was some utility there.
In the end, NFTs are safer than AI because they don’t really do anything other than make us look stupid. Generative AI deployed deeply throughout our systems can expose us to far more than ridicule, opening us up to attack, severe privacy violations, and a host of other compromises.
In a way, this public expression of look at me, I use AI for everything has become a new form of in-group signaling, but I don’t think this is the flex they think it is. In a way, these people believe this is an expression of preparation for the future, but it could very well be the opposite. The increase in cognitive offloading and the manufactured dependence is precisely what makes them vulnerable to the future.
In a way, these people believe this is an expression of preparation for the future, but it could very well be the opposite. The increase in cognitive offloading and the manufactured dependence is precisely what makes them vulnerable to the future.
Advice Over Reality
Social media is awash with countless people who continue to dispense advice, telling others that if you don’t deploy wonky, error-prone, and highly manipulable software deeply throughout your business, then they are going to be left behind. Strange advice since the reality is that most organizations aren’t reaping benefits from generative AI.
Here’s something to consider. Many of the people doling out this advice haven’t actually done the thing they are talking about or have any particular insight into the trend or problems to be solved. But it doesn’t end with business advice. This trend also extends to AI standards and recommendations, which are often developed at least in part by individuals with little or no experience in the topic. This results in overcomplicated guidance and recommendations that aren’t applicable in the real world.
The reason a majority of generative AI projects fail is due to several factors. Failing to select an appropriate use case, overlooking complexity and edge cases, disregarding costs, ignoring manipulation risks, holding unrealistic expectations, and a host of other issues are key drivers of project failure. Far too many organizations expect generative AI to act like AGI and allow them to shed human resources, but this isn’t a reality today.
LLMs have their use cases, and these use cases increase if the cost of failure is low. So, the lower the risk, the larger the number of use cases. Pretty logical. Like most technology, the value from generative AI comes from selective use, not blanket use. Not every problem is best solved non-deterministically.
Another thing I find surprising is that a vast majority of generative AI projects are never benchmarked against other approaches. Other approaches may be better suited to the task, more explainable, and far more performant. If I had to take a guess, I would guess that this number is close to 0.
Generative AI and The Dumpster Fire Rodeo
Despite the shift in attitude toward generative AI and the obvious evidence of its limitations, we still have instances of companies forcing their employees to use generative AI due to a preconceived notion of a productivity explosion. Once again, ChatGPT isn’t AGI. This do everything with generative AI approach extends beyond regular users to developers, and it is here that negative impacts increase.
I’ve referred to the current push to make every application generative AI-powered as the Dumpster Fire Rodeo. Companies are rapidly churning out vulnerable AI-powered applications. Relatively rare vulnerabilities, such as remote code execution, are increasingly common. Applications can regularly be talked into taking actions the developer didn’t intend, and users can manipulate their way into elevated privileges and gain access to sensitive data they shouldn’t have access to. Hence, the dumpster fire analogy. Of course, this also extends to the fact that application performance can worsen with the application of generative AI.
The generalized nature of generative AI means that the same system making critical decisions inside of your application is the same one that gives you recipes in the style of Shakespeare. There is a nearly unlimited number of undocumented protocols that an attacker can use to manipulate applications implementing generative AI, and these are often not taken into consideration when building and deploying the application. The dumpster fire continues. Yippee Ki-Yay.
Conclusion
Despite the obvious downsides, the dumpster fire rodeo is far from over. There’s too much money riding on it. The reckless nature with which people deploy generative AI deep into systems continues. Rather than identifying an actual problem and applying generative AI to an appropriate use case, companies choose to marinade everything in it, hoping that a problem emerges. This is far from a winning strategy. Companies should be mindful of the risks and choose the right use cases to ensure success.
Weaved through the fabric of the hustle-bro culture, threaded with the drivel of influencers, lies one of the biggest cons of our current age. This is the false perception that everything we do has to be for some financial gain or public attention. With everything in life revolving around social currency or actual currency, removing friction enables us to reach value quickly. But don’t fret. The slop dealer is here with a plan to deliver us salvation, telling us that ideas are what’s important and everything else is pointless friction, needing to be optimized to reach full potential. Like so many things in our current moment, if only this were true.
Despite the decline in excitement for AI and the potential resulting market corrections, unfortunately, slop is here to stay. Although people outwardly complain about it, they are secretly glad it’s here. Being unique, thoughtful, and creative is hard. Slop allows people to swaddle themselves in a false comfort devoid of any real creativity. So, damn the torpedoes, full slop ahead.
Slop, Enshittification, and Brain Rot
Slop, enshittification, and brain rot are terms burned into our current lexicon. Although each term has a different definition, one referring to outputs, one referring to platforms, and one referring to what it does to us. When I use the generalized term slop here, I mean a mixture of all three together, a sort of thick, rancid mixture reminiscent of manure and White Zinfandel. This is because the combined term aligns better with the content and its overall impact.
The Slop Dealer
The slop dealer tells us everything is a hustle, and we need to get on board to reduce friction everywhere we can to accelerate value or be left in the dust by others using AI. They don’t talk of reasonable AI usage or prescriptions for specific tasks; it’s all or nothing. We need to surrender to the higher power. The slop dealer embodies everything that tech bro culture stands for. It’s the current equivalent of a get-rich-quick scheme, only instead of taking our money, they are stealing our attention and our satisfaction. Although sometimes they take our money too.
The slop dealer swindles us by telling us what we want to hear, that hard things are a thing of the past, and all we need is an idea. After all, everybody has ideas. These are the influencers, wanna-be influencers, and other AI useful idiots vomiting nonsense on social media. They aren’t peddling secret knowledge; they are peddling bullshit.
This pandering is done so we’ll follow them, subscribe to their newsletters, or buy their nonsense. But one of the biggest lies of all is the false impression that the value of creative pursuits lies in the end result.
Most of these people have no shame and not only believe in Dead Internet Theory, but also actively work to make it a reality. If you are wondering why people en masse find tech bro culture abhorrent, look no further than this stunning piece of work.
To quote this guy directly, “How I personally feel? I have no idea. The internet in my mind is already dead. I am the problem, right?” I get the impression this isn’t the first time he’s realized he’s the problem. Unfortunately, acknowledgement of this isn’t enough to change behavior.
The Slop Architect
The slop architect works not in traditional mediums but in ideas. To the slop architect, execution, skills, and experience are secondary, bowing at the pedestal of ideas. The fact is, most ideas are ill-thought-out, half-baked, or just plain fucking stupid. The slop architect doesn’t care because they don’t carry ideas to term; they birth them instantly, shoving them out into the world to fend for themselves as they move on to something else. I mean, the vape Tamagotchi was someone’s idea, too. Yes, please! Let’s accelerate these!
Ideas aren’t unique, precious resources, but common, run-of-the-mill, everyday occurrences for everyone on the planet. The slop architecture amplifies the fallacy that ideas are sacred and pushes the idea that if more ideas were executed, the world would be a better place. If only we had more apps, more books, more music, and the list goes on and on. This connects with people because everyone has ideas.
What most people who have thought about it for more than two seconds realize is that we don’t get to the value of an idea purely by having it. Ideas in isolation are senseless ramblings of the brain. Ideas forged and refined in the fire of execution, experience, and reflection are invaluable and fulfilling. Our ideas are never challenged in the slop architecture, leading us to new discoveries and paths, but are chucked out into the world and quickly discarded, like forgotten attempts at memes that nobody finds funny.
The AI Slop Architecture
The slop architect’s vision is implemented with the slop architecture, which presents itself as a process or application. The slop architecture is pitched as the way forward, the next-generation architecture fueling the future of humanity’s pursuits. But a simple scratch of the surface paint is all it takes to expose the entire thing as an empty shell.
When you see people pitching these types of things, it uncovers people who don’t understand creativity and certainly don’t understand where value exists in a process. Everything is a hustle for the sake of hustling. This person is hardly the only one.
Back in 2023, I jokingly created my own version of the slop architecture, which I referred to as IPIP, long before the AI influencers made it a reality.
This article was complete with a description of what would come to be known as vibe coding. “The hype has led to a new form of software development that appears to be more like casting a spell than developing software.”
Taking the slop architecture to heart, it’s not hard to find implementations already running. Books, slides, music, applications, nothing is off limits. Everything is fair game in the slop era.
Ah, Magic bookifier. Yeah, let me get on that. Any time someone puts magic in reference to AI, it’s bullshit.
People also fantasize about what advanced AI is or will be able to do. Take this use case for AGI, for example.
It reminds me of the Luke Skywalker meme where he’s handed the most powerful weapon in the galaxy and immediately points it at his face. This is informative for a couple of reasons. Movies can’t be exactly like the books for reasons other than length. They are different media with different tools. But look at the response. Human work isn’t worth protecting in the future. This is a far more common perspective than many think.
Even apps. It’s slop from all angles. So, if these tools already exist, why aren’t we all kicking back, receiving our profits? Maybe there’s something more to this than having an idea.
But we can’t just have a couple of people successfully making apps. It needs to be bigger! We are now told to await the arrival of the first billion-dollar solopreneur. Hark! The herald angels sing. Glory to the slop-born king! However, we shouldn’t get our hopes up. Setting aside how highly unlikely this is, people also win the lottery, so unless we have a mass of billion-dollar solopreneurs, it’s not proof of much. However, whenever people have strongly held beliefs, they will always point to exceptions as the rule.
It’s far more common for people to talk about a single person making a million-dollar app, and that we all can make them now. Even if this were true, it’s not like billions of people are going to make million-dollar apps or profit from a trillion new books. No degree in economics is necessary to see that the numbers don’t work. Besides, if billions of people can and will do something, then the whole enterprise becomes devalued.
The slop architecture deprives us of so much, sucking the soul out of activities until only the shriveled husk remains. There’s no learning with the slop architecture. No growth. No Reflection. No Satisfaction. It even robs us of a sense of style, something so foundational to the satisfaction of human artistic pursuits. But all things require sacrifice on the pyre of optimization. In the end, the slop architecture doesn’t democratize. It devalues, degrades, and destroys.
In the end, the slop architecture doesn’t democratize. It devalues, degrades, and destroys.
The Friction Is The Point
I’m going to let my friends in tech in on a secret, which isn’t a secret at all. The friction of an activity is directly related to the value you receive from it. The mistake being made is comparing an activity’s friction to the load time of an application or streamlining a user interface. I’ve written previously about how the next generation could be known as The Slop Generation and how we continue to devalue art. However, the removal of friction creates harmful follow-on effects.
Imagine telling Alex Honnold, “Dude, you don’t need to free solo El Capitan. We have a helicopter that can drop you off at the top.” People may see this example as silly because Alex obviously climbs mountains for reasons other than getting to the top, but it’s a mistake to assume other pursuits don’t contain similar value purely because they aren’t mountain climbing. Deep experiences don’t result from things that provide instant gratification or have little friction. Nobody finds meaning in a prompt or the resulting generation.
Deep experiences don’t result from things that provide instant gratification or have little friction.
People may see this example as silly because climbing a mountain without ropes is obviously different from something like writing a song. Except it’s not when viewed through the lens of experience. Alex Honnold doesn’t free solo mountains to get to the top or because ropes and safety equipment are too expensive; he does it because he knows there is value in the friction of his experience. He’s both challenging himself and learning about himself at the same time. He’s having an actual experience, which is hard to describe to people who have never had one. This experience enriches the conclusion of the activity, the accomplishment, which coincidentally happens to be getting to the top. However, when pursuits are framed in terms of the end results, it appears that reaching the top is the goal, and the removal of friction is logical.
Most people will never free solo a mountain, compete in the Olympics, or achieve any of the other remarkable feats that athletes at the top of their game accomplish, but that doesn’t mean we can’t have similar and fulfilling experiences, and we do this through exploration and conquering friction. When you are operating at the top of your game, you realize you aren’t competing with others, but yourself.
An artist puts a piece of themselves inside every work of art they create. AI deprives artists of having a piece of themselves included in the art, making the generated output purely an artifact of running a tool.
Slop Is Here To Stay
Immediately after Ozzy Osborne died, Oz Slop invaded social media. The prince of darkness himself fell victim to people’s boredom and lack of creativity. People chose to pay tribute to him, not through stories and anecdotes, but by slopping him into manufactured content. I can’t think of a more insulting way to pay tribute to an artist, but this is our future. Slop instead of something to say. Slop instead of stories and memories. Slop instead of emotion. Slop as a coping mechanism. May the slop be with you.
A disheartening thought is that no matter what happens to the market for generative AI, the slop will remain. People post this slop not because they enjoy it, but purely because it gives them something to post. Slop content is a stand-in for having something to say. It’s easy to generate and requires little thought, the perfect complement to today’s reactionary and performative social media environments.
In a way, this trend could create a new line of demarcation, where we start referring to things as “Before Slop” and “After Slop” to identify the creative expressions that preceded and followed the arrival of AI-generated content.
Conclusion
In the end, the slop architecture doesn’t generate experiences. Nobody is going to be on their deathbed mulling over their favorite prompts or sit down with friends and reminisce about the time they poked at a generative AI system for hours trying to get it to generate a particular image. The slop architecture doesn’t create a legacy or generate stories worth remembering or worth sharing, just pieces of forgotten garbage littering the digital landscape.
Although AI has taken a hit in the past few weeks, the vibes are still strong and infecting every part of our lives. Vibe coding, vibe analytics, and even vibe thinking, because well, nothing says “old” like having thoughts grounded in reality. However, an interesting trend is emerging in software development, one that could have far-reaching implications for the future of software. This is a type of code roulette where developers don’t know what code will execute at runtime. Then again, what’s life without a little runtime suspense?
Development and Degraded Performance
The world runs on software, so any trend that degrades software quality or increases security issues has an outsized impact on the world around us. We’ve all witnessed this, whether it’s the video conferencing app that periodically crashes after an update or a UI refresh that makes an application more difficult to use.
Traditionally, developers write code by hand, copy code snippets, use frameworks, skeleton code, libraries, and many other methods to create software. Developers may even use generative AI tools to autocomplete code snippets or generate whole programs. This code is then packaged up and hosted for users. The code stays the same until updates or patches are applied.
But in this new paradigm, code and potentially logic are constantly changing inside the running application. This is because developers are outsourcing functional components of their applications to LLMs, a trend I predicted back in 2023 in The Brave New World of Degraded Performance. In the previous post, I covered the impacts of this trend, highlighting the degraded performance that results from swapping known, reliable methods for unknown, non-deterministic methods. This paradigm leads to the enshittification of applications and platforms.
In a simplified context, instead of developers writing out a complete function using code, they’d bundle up variables and ask an LLM to do it. For simplicity’s sake, imagine a function that determines whether a student passes or fails based on a few values.
def pass_fail(grade, project, class_time):
if grade >= 70 and project == "completed" and class_time >= 50:
return "Pass"
else:
return "Fail"
If a developer decided to outsource this functionality to an LLM inside their application, it may look something like this.
prompt_pass = """You are standing in for a teacher, determining whether a student passes or fails a class.
You will use several values to determine whether the student passes or fails:
The grade the student received: {grade}
Whether they completed the class project: {project}
The amount of class time the student attended (in minutes): {class_time}
The logic should follow these rules:
1. If the grade is above 70
2. If the project is completed
3. If the time in class is above 50
If these 3 conditions are met, the student passes. Otherwise, the student fails.
Based on this criterion, return a single word: "Pass" or "Fail". It's important to only return a single
word.
"""
prompt = prompt_pass.format(grade=grade, project=project, class_time=class_time)
response = client.models.generate_content(model="gemini-2.5-flash", contents=prompt)
print(response.text)
As you can see, one of these examples contains the logic for the function inside the application, and the other has the logic existing outside the application. The prompt is indeed visible inside the application, but the actual logic exists somewhere in the black box of LLM land.
The example using code has greater visibility, and it’s far more auditable since the logic can be examined, which makes it far easier to debug when issues arise, and of course, it’s explainable. The real problem lies in execution.
The written Python function approach gives you the same result based on the input data every single time, without fail. The natural language approach, not so much. In this non-deterministic approach, you are not guaranteed the same answer every time. Worse yet, when this approach is used for critical decisions and functionality, the application can take on squishy and malleable characteristics, meaning users can potentially manipulate them like Play-Doh.
At first glance, this example appears silly, as writing out the logic in natural language seems more burdensome than using the simple Python function. Not to mention, slower and more expensive. But looks can be deceiving. People are increasingly opting for the natural language approach, particularly those with only minimal Python knowledge. This natural language approach is also more familiar to people who are more accustomed to using interfaces like ChatGPT.
Execute and Pray
However, let’s take a look at another scenario. In this scenario, a developer wants to generate a scatter plot using the Plotly library. In this case, we have some data for the X and Y axes of a scatter plot and use Plotly Express, which is a high-level interface for Plotly (as a developer may when plotting something so simple).
This is a simplified example, but in this case, we can clearly see the code that generated the plot and be certain that this code will execute during the application’s runtime. There is control over the imports and other aspects of execution. It also makes it auditable and provable.
Now, what happens when a developer allows modification of their code at runtime? In the following example, instead of writing out the Plotly code to generate a scatter plot, the developer requests that code be generated from an LLM to create the graph, then executes the resulting code.
prompt_vis = """You are an amazing super awesome Python developer that excels at creating data visualizations using Plotly. Your task is to create a scatter plot using the following data:
Data for the x axis: {xdata}
Data for the y axis: {ydata}
Please write the Python code to generate this plot. Only return Python code and no explanations or
comments.
"""
prompt = prompt_vis.format(xdata=xdata, ydata=ydata)
response = client.models.generate_content(model="gemini-2.5-flash", contents=prompt)
exec(clean_response(response.text))
As you can see from the Plotly code in this example… Of course, you can’t see it because the code doesn’t exist until the function is called at runtime. If you are curious, the first run of this generated the following code after cleaning the response and making it appropriate for execution.
The AI-generated code creates the same graph as the written-out code in the previous example, despite being different. You may be wondering what the big deal is since the result is the same. The concern stems from several reasons, but primarily, allowing an LLM to generate code at runtime is not robust and leads to unexpected outcomes. These outcomes may include the generation of non-functional code, incorrect code, and even vulnerable code, among others.
For a simple example, as the one shown in this post, the chances of getting the same or incredibly similar code returned from the LLM are high, but not guaranteed. For more complex examples, such as those developers may want to use this approach for, the odds increase that the generated code will change more frequently.
Additionally, I implemented a quick cleaning function called clean_response to remove non-Python elements, such as text and triple backticks, from the response. The LLM can introduce additional unexpected characters that end up breaking my cleaning function and making my application fail. The list goes on and on, but a larger danger lurks in the background.
Whose Code Is It Anyway?
If you are versed in security and familiar with Python, you may have noticed something in the LLM example: The use of the Python exec() function. The exec () and eval() functions in Python are fun because they directly execute their input. Fun as in, dangerous. For example, if an attacker can inject input into the application, they can affect what code gets executed, leading to a condition known as Remote Code Execution (RCE).
An RCE is a type of arbitrary code execution in which an attacker can execute their own commands remotely, completely compromising the system running the vulnerable application. They can use this access to steal secrets, spread malware, pivot to other systems, or potentially backdoor the system running the application. Keep in mind, this system may be a company’s server, cloud infrastructure, or it may be your own system.
Anyone following security issues in AI development is aware that RCEs are flying off the shelves at alarming rates. A condition that was previously considered a rarity is becoming common. We even commented during our Black Hat USA presentation that it was strange to see people praising CISA for promoting memory safe languages to avoid things like remote code execution, while at the same time praising organizations essentially building RCE-as-a-Service. Some of this is mind-boggling, since in many cases, outsourcing these functions isn’t a better approach. In the previous example, writing out the Plotly code instead of generating it at runtime is relatively easy, more efficient, and far more robust.
Up until AI came along, the use of Python exec() was considered poor coding practice and dangerous. Now, developers shrug, stating that’s how applications work. As a matter of fact, agent platforms like HuggingFace’s smolagents use code execution by default. This is a wakeup. So, we dynamically generate code, provide deep access, and the ability to call tools, all with a lack of visibility. What could possibly go wrong???
Not only have developers chosen paradigms to generate and execute code at runtime, but worse yet, they’ve begun to perform this execution in agents with user (aka attacker) input, executing this input blindly in the application. In our presentation titled Hack To The Future: Owning AI-Powered Tools With Old School Vulns at Black Hat USA this year, we refer to this trend as Blind Execution of Input, which is the purposeful execution of input without any protection against negative consequences. This condition certainly leads to RCE and other unintended consequences, providing attackers with a significantly larger attack surface to exploit.
An application that takes user input and combines it with LLM functionality is a recipe for a bad time from a security perspective. Another common theme in our presentation, as well as that of other presenters on stage at Black Hat, is that if an attacker can get their data into your generative AI-based system, you can’t trust the output.
Things Will Get Worse
Using the outsourced approach when a more predictable deterministic approach is a better fit will continue to degrade software from a reliability and security perspective and have an impact on the future of software development.
Vulnerabilities in AI software have made exploitation as easy as it was in the 1990s. This was the “old school” hint in the title of our talk. This isn’t a good thing, because the 90s were a sort of free-for-all. Not only that, but in the 90s, we often had to live with vulnerabilities in systems and applications. For example, in one of the first vulnerabilities I discovered against menuset on Windows 3.1, it was impossible to fix. There were no mitigations, and most people were unaware of its existence.
As the outsourcing of logic to LLMs accelerates, things will worsen not only due to incorrect output and hallucinations but also from a security perspective. Anyone paying attention to the constant parade of vulnerabilities in AI-powered software can see this trend with their own eyes. These vulnerabilities are often found in large, mature organizations with dedicated security processes and teams in place to support them. Now, consider startups and organizations that implement their own experiments using non-deterministic software, often with a lack of understanding of how these systems can be manipulated. It’s become a game of speed above everything else.
As I’ve said from the beginning of the generative AI craze, the only way to address these issues is architecturally. Most of AI security is just application and product security, and organizations without these programs in place are in trouble. If proper architecture, design, isolation, secrets management, security testing, threat modeling, and a host of other activities weren’t considered table stakes before, they certainly are now. And possibly not surprisingly enough, they still aren’t being done. Anyone working for a security organization sees this every day.
In essence, developers need to design their applications to be robust to failures and attacks. It helps to consider designing them as though an attacker can manipulate and compromise them, working outward from this premise. As the adage goes, an attacker only needs to be successful once; a defender needs to be successful every time. This makes something that sounds great in theory, like being 90% effective, sound less impressive in practice.
Keep in mind that performing a code review won’t provide the same visibility as it has traditionally. This should be obvious since the code that would be audited doesn’t exist until runtime. You’ll have to pay more attention to validation routines and processing of outputs, putting huge question marks over the black box in the middle. And, of course, ensuring the application is properly isolated.
Some may suggest instrumenting the applications with functionality to perform runtime analysis on the generated code. Sure, it’s possible, but the performance hit would be significant, and even this is, of course, far from a silver bullet. You might not even get the value you think you are getting from this instrumentation. Also, you’d have to know ahead of time the issues you are trying to prevent. That is, unless you plan to layer more LLMs on top of LLMs in a spray-and-pray configuration.
To keep this grounded, all AI risk is use case dependent. AI models don’t do anything until packaged into applications and used in use cases. There may be cases where reliability, performance, and even security are of lesser concern. Fair enough, but it’s a mistake to treat all applications as though they fall into this category, and it’s far too easy to overlook something important and view it as insignificant.
If you work at an organization that isn’t building these applications and think you’re safe, you might want to think again, because you are at the mercy of third-party applications and libraries. It would be best to start asking hard questions of your vendors about their security practices as they relate to applications you purchase. Especially applications that use generative AI to generate code and execute it at runtime.
Near the end of our presentation, we had some advice.
Whether outsourcing the logic of an application to LLMs or having the LLM dynamically generate code, assume these are squishy, manipulable systems that are going to do things you don’t want them to do. They are going to be talked into taking actions that you didn’t intend, and fail and hallucinate in ways you don’t expect. Starting from this premise gives a proper foundation for deploying controls to add some resilience to these systems. Of course, not taking these steps means your applications will contribute to the ongoing dumpster fire rodeo.
Although the singularity isn’t here, the shitularity certainly is, and it’s moving to infect every corner of our humanity. In the shitularity, shit’s upside down. Where you have people welcoming the extinction of humanity and worshiping marketing material as prophecy, it’s this environment that spawns people like Bryan Johnson and elevates them to hero status. That’s right, a dude who obsesses over his son’s nightly boners while occasionally also using him as a blood boy has been hoisted up and put on a pedestal. Although you’ll be happy to know that as of January 2025, he’s no longer using his son as a blood boy, no update on the boners.
You might wonder what kind of world would promote someone like Bryan Johnson to the level of a deity. Although dying is certainly an uncomfortable prospect, it’s not purely about him trying to cheat death, at least, not completely. It’s true that the not dying aspect is part of Kurzweilian transhumanism, but Bryan’s popularity is about something far simpler. Numbers.
You see, if you can turn health and happiness into a set of numbers, then you can measure them. If you can measure them, you can optimize the shit out of them. It’s this optimization ethos that drives everything in modern tech movements, and if it works for tech, it must work for humanity. Of course, to believe this, one has to set aside Goodheart’s Law.
If you can turn health and happiness into a set of numbers, then you can measure them. If you can measure them, you can optimize the shit out of them.
Another thing to realize is that his spiritual commentary on superintelligence isn’t an outlier in the community. Here’s a clip of him from the Honestly podcast where he says we are creating god in the form of superintelligence, and we had it backward all along.
For the tech community, Bryan has come to symbolize the physical embodiment of hustling. After all, he’s performing every movement of his hustle publicly. He’s suffering for our sins of human mortality on our social media timelines.
The Cult That Requires Supplements
Bryan Johnson is creating a cult, but instead of the traditional cult leader approach of claiming to be a prophet or an incarnation of a god, Bryan has made himself the god, that is, until we create superintelligence. He’s a god of a new and everlasting covenant, one that requires supplements. For $412 a month, you too can remake yourself in Bryan’s image. After all, Christ died, but you don’t have to.
This package is topped off with a bottle of Snake Oil, because nothing screams modern fashion like saying the quiet part out loud.
Bryan has joined a cadre of crackpots, including people like Ray Kurzweil and Alex Jones, who latched on to an age-old money-making scheme. When all else fails, sell supplements. Unlike the old snake oil salesmen hawking bottles out of a wagon moving town to town, he’s got a website and a social media following. Supplements aren’t drugs and require no proof of effectiveness, which means faith is part of the bargain. Perfect. He’s selling highly nutritious communion wafers that will barely sustain your existence. Body of Bryan. One thing I’ll say about the old school snake oil is that at least it would get you drunk; Bryan’s gives you prediabetes.
The living forever bit is telling people what they want to hear. It’s part of the performance to get people to buy supplements and swag. By buying the swag and sharing his parables, sorry, social media posts, people can signal their affiliation. Many people now prominently feature “longevity” in their profiles along with e/acc, magic internet money, and whatever other crazy horseshit they believe that is anti-human.
The concept of “A Bryan Johnson” was inevitable in our current environment. Someone who tells us we can transcend death if we only believe hard enough and shell out some cash.
Which Johnson?
When I learned of Bryan Johnson and his whacked out antics, I considered the contrast with Brian Johnson. My mind immediately went to considering which of these two, on their deathbed, will have the fondest reflection of their lives? Yes, sorry to break this to everyone, but Bryan is absolutely going to die.
Let’s look at the two Brian/Bryan Johnsons. One is the 77-year-old singer of the band AC/DC. The other is the 47-year-old entrepreneur thinking he won’t die. One is out there living his best life, racing around in cars and having a good time. The other is not living life at all, choosing instead to torture himself in an elaborate performance. One sells music and good times, the other sells supplements and pain. One wants to salute those about to rock, the other salutes stunts camouflaged as experiments. I could go on, but you get the picture.
When it comes to living life and loving life, here’s Brian Johnson in 2009 running around the stage, hanging off a rope, ringing a gigantic bell. Now tell me, who’s having more fun as they age? Bryan Johnson wishes he had a following like this.
We don’t need to be rock stars to live a fulfilling life. Sure, the money and fame don’t hurt, but there are many areas of satisfaction that we can share. However, we are allowing people obsessed with technology to define what a good life is supposed to be, which is dangerous because these perspectives often miss the point entirely.
For example, consider the audience response in that video. Surely, there are more optimized ways to deliver music to your ears. If you are looking for a deeper experience, a VR headset and more cameras on stage would deliver a far more optimized experience tailored purely to your preferences. Hell, you could even choose which camera to watch at any given time! Surely, this must be better than buying tickets, getting in a car, finding a place to park, waiting in line, and then waiting for the band to start playing.
As optimization often does, it sheds value as it optimizes.
As optimization often does, it sheds value as it optimizes. Viewing the audience, it’s obvious to any actual human being that the experience those people are having isn’t the same experience an optimized VR experience provides. What people attending a live performance realize is that they are part of the performance along with the artist. Viewing the world this way opens the door for us to have all kinds of experiences despite not being rock stars and having money flying out of our pockets. These same doors shut through optimization.
Another point is that living life trying not to die is not living life at all, like the person so scared of dying in a plane crash that they never travel and see the world. But we’ve allowed a strange reframing of this experience in that we aren’t missing out on experiences; we are prolonging our existence, which opens the door to vastly more experiences. This is yet another argument that’s technically true, but practically false. Sure, we could live at the hospital, and we’d always have a medical team at our disposal, which could extend our life, but that’s not living life or gaining meaningful and fulfilling experiences.
The same sort of missing out happens if we live life with the belief that we won’t die. We always put off potential experiences because we’ll just do them later. We’ve all known people whose lives were cut short and who missed out on things they’d like to do. It’s the temporary nature of life, along with its stunning finality, that pushes us to live a good life, to seek out experiences instead of putting them off, to be fulfilled.
As an aging adult, I don’t want to die. I’ve also had many people around me pass away, which puts things in perspective. The prospect of spending years of my life focused on trying not to die instead of living life doesn’t appeal to me either. This doesn’t entail nightly binge drinking or a mainline of ice cream into my veins. I go to the gym five days a week, so health is certainly on my mind. However, when health and longevity become a hustle, something that must be performed and optimized to realize the true benefits, we need to acknowledge that we are doing something else.
Of course, all of this longevity garbage only takes into account physical health. But mental health is equally, if not far more important than physical health when it comes to longevity. All of this grinding away on tests on the body and measuring boners doesn’t leave much time for fulfillment and happiness. Spending time with your family and friends without drawing their blood, asking about erections, or shaming them because they aren’t fasting hard enough.
The thought of dying is scary, but the thought of dying without living life to the fullest is absolutely terrifying. Thankfully, we still live in a world where Brian Johnson is far more well-known (and loved) than Bryan Johnson, but it’s a mistake to be complacent about these things. Cults have an odd way of attracting followers.
How Did We Get Here?
Bryan Johnson is an example of how tech bro culture infiltrates broader cultural movements. Bryan didn’t invent this himself. He drew inspiration from earlier techno-utopians like Ray Kurzweil. Although thankfully, society at large still rejects the complete tech bro vision of culture, it’s leaky. Take a look at these new Olympic-style games.
It’s a mistake to assume that because this example involves something physical, it has nothing to do with tech bro culture. After all, human evolution is slow, but you can get a new iPhone every year, so why not roid it up and speed things along? This disturbing logic makes sense to many people.
We are dazzled by spectacles and monstrosities, so when someone pitches human excellence through augmentation by any means necessary, our interest is piqued. This exhibit is evidence of the direct impact of the current tech bro mindset on our culture. Although it would be easy to write this event off as stupid people doing stupid shit, this is tech bro culture at work. The gnashing of teeth from information overload wears us down into acceptance. And then, more people die.
This mindset warps people’s sense of reality. To the extent that when people share their preferences and they don’t align with Johnsonian expectations, others assume they are lying.
What’s happening in this image is simple. Men chose the “after” image because they thought that’s what they were supposed to prefer. Women just selected their preference. This isn’t rocket science or some epic conundrum that we need to investigate. Bryan Johnson’s “before” photos look better, too. That is, unless you prefer the aesthetics of an unwrapped mummy.
Against Life Extension
The argument for not living a longer life seems rather silly and self-sabotaging until you realize that the implications of living longer aren’t pretty. Anyone with aging parents or grandparents can attest to this. The body may live on, but the mind doesn’t cooperate.
Technology has already granted us a longer lifespan. However, this longer life span has set us on a collision course with cognitive decline and a lack of independence. Not exactly the definition of living your best life. Until cognitive ailments are cured and the ability to regenerate functionality is achieved, there isn’t much sense in living longer.
This reminds me of the point made by Aldous Huxley in his novel After Many a Summer Dies the Swan, or that of Tithonus from Greek Mythology, who was granted eternal life, but not eternal youth. I can already hear the tech bro response, but we’ll have fixed that. But unless we can regenerate functionality, then the prospect of living to 150 years old is downright terrifying.
In the past, people have speculated about cryonics, being stored frozen and then thawed out when the technology is advanced enough to revive them and cure their illnesses. This conjures a scarier and more realistic picture.
Since it’s easier to prolong the life of the body than to address cognitive decline and functional regeneration, what if the way aging bodies are stored ends up not being cryonics, but in a memory care facility? Imagine all of the tech bros milling around playing bingo, not knowing where they are or what year it is, and where Elon Musk thinks he’s still friends with Sam Altman.
What if the way aging bodies are stored ends up not being cryonics but in a memory care facility?
To the bros who think brain implants and augmentation will save us, not so fast. We should consider that augmentation with technology may actually make neurodegenerative conditions worse. The long-term effects of advanced brain implants aren’t known, but it seems they potentially optimize away the very activities that stave off conditions like dementia. These include activities such as reading, solving puzzles, and writing letters. Even worse, no matter how many brain implants and connections you create, it’s a confused mind with access to even more data and stimulation. This seems to create an even worse situation than what happens biologically.
In a new article, Francis Fukuyama argues against life extension, calling out, “Nearly half of all seniors in their mid- to late-80s suffer from some form of degenerative neurological disease like Alzheimer’s or Parkinson’s, in the later stages of which they are completely unable to care for themselves.” This isn’t a pretty prospect. Fukuyama also explores other aspects, such as the economic impacts.
These tech bros don’t just feel they are extending their lives. They think they will live forever. If that’s the case, then the memory care units will be packed.
Living A Good Life
What Bryan Johnson and his cohort fail to realize is that overall health cannot be reduced to a set of numbers that can be measured and optimized. Leave it to tech bros to reduce life down to a set of OKRs. So much of life is lived and enjoyed outside of metrics. Mental states have a significant impact on our longevity, and supplements won’t improve that. It’s the other joys in life that keep us young.
The definition of a good life is certainly subjective and sometimes situational, but most people can recognize it when they see it. Or at least, they can today. In the near future, that may not be the case. We are approaching a world where people prefer all experiences to be mediated through technology, but is this a good life?
This is reminiscent of E.M. Forrester’s 1906 short story The Machine Stops, in which people are horrified by direct experiences. In the story, when the character Vashti sees the vast flank of the ship stained from exposure and encounters smells that were neither strong nor unpleasant, she is horrified. We, too, are beginning to prefer mediated experiences over direct experiences. I think we’ll find that in the long run, mediated experiences don’t provide true fulfillment. Four hours on TikTok doesn’t compare to four hours of hiking in a new location, for example.
We are allowing options for a good life to dwindle, enabled by a performative culture and technology. In the immortal words of the American philosopher Tom Keifer, you don’t know what you got till it’s gone. If you’ve never had something, you don’t notice it being gone. Some of us can decide to regress to a previous mean, but this mean may not exist for future generations. The thought of picking up a guitar and doing it ourselves won’t occur as an option because there are far easier ways to make guitar sounds. The point of learning and playing any instrument isn’t to make noise. The noise is a byproduct of the satisfaction derived from learning and playing. This perspective can be applied to many aspects of life that bring meaning and fulfillment.
I find this incredibly sad for future generations, as many avenues to find fulfillment and satisfaction are collapsing. These are avenues that numbers cannot measure. However, we are told these activities are unoptimized, and by applying technology, they can be made “better.” As we’ve seen time and again, adding optimization can render the point of an activity pointless.
Future Prediction
I predict that Brian Johnson will live to an older age than Bryan Johnson. In fact, I predict a future headline. Bryan Johnson, a man focused on longevity, claiming he’d live forever, died at the age of 67. See you in 20 years.
We are continually inundated with examples of silly errors and hallucinations from generative AI. At this point, it’s no secret to anyone on the planet that these systems fail, sometimes at rather high rates. These systems also have a tendency to make stuff up, which isn’t a good look when that data is used for critical decisions. We’ve become numb to this new normal, creating a dangerous condition where we check out instead of recheck. But what happens when these errors and hallucinations become facts, facts that may be impossible to dispute or lurk in the background unseen and uncorrected?
Perspectives From Our Younger Selves
Imagine traveling back in time for a conversation with our younger selves about the current state of AI.
Younger: Wow, it must be great to live in a world without cancer or dementia. Older: No, we haven’t cured cancer or dementia. Younger: Well, at least people are super smart now. Older: No, there are still many dumbasses. Younger: At least you have systems that don’t make mistakes. Older: No, they make mistakes all the time. Younger: Then, what in the hell do you do with systems like this? Older: Mostly memes and short videos of stupid shit. Oh, we even try to impress world leaders with what they’d look like as a baby with a mustache.
Although it may seem silly, this thought experiment is informative. It puts our current AI moment in perspective and should add some humility. These systems aren’t the magnificent, magical boxes capable of handling every task with equal proficiency in both work and life. They are tools that we can use for specific tasks, far from the perfected AI of science fiction, and this is where the issues creep in.
Icebergs, Grenades, and Damage
I’ve made the grenade analogy before relating to agents. It’s an apt analogy because it’s something that causes damage, but not immediately. It’s like the classic joke grenade, which is a prank you play on your friends with the expectation of future laughter. Only with AI, the result isn’t a barrel of laughs. It’s a barrel of something that stinks and should be spread over a field as fertilizer.
The mistake is that seeing so many instances of these issues gives us the false impression that these issues are being caught and possibly even corrected. Think of issues like hallucinations as an iceberg. There are far more instances beneath the surface that lie unseen, lying in wait to send our ship to the depths.
There’s also the problem that not all conditions of hallucinations are so easy to identify. The ones that seem to get identified are those that are blatantly obvious or require additional validation, such as checking the cases referenced in a legal document. This is why it seems that only lawyers and politicians are making fools of themselves with AI. The landscape is far broader than these two categories.
It’s also instructive to see how people respond when these issues are brought to light. In the recent MAHA report scandal, the White House spokesman referred to AI hallucinations as “formatting issues.” Yeah, right. Imagine walking into your bank and finding out you have no money in your account. Frantic, you ask the teller what’s going on, and they tell you that you have no money because of a formatting issue. We can’t let people downplay these problems because they are common. It’s because they are common that we need to be more concerned.
We can’t let people downplay these problems because they are common. It’s because they are common that we need to be more concerned.
Although some instances may seem silly, there are no doubt real consequences. Such as AI hallucinating into people’s medical records, because we all know that can’t end badly. Hypothetically, let’s imagine that the generative AI system utilized is 99% accurate, which is enormously far from reality. Performing 10,000 transactions/results/outputs a day could potentially yield 100 issues. Crank that up to 1,000,000 a day, and that’s 10,000. This is terrifying when considering the realistically high error rates that these systems actually exhibit. There’s no doubt a river of manure flowing into data stores. The pin has been pulled.
The nature and pattern of errors differ significantly between AI and humans.
I can already feel the AI crowd’s eyes rolling, opening their mouths to issue the overused retort, “But humans make mistakes too.” Yes, they do, but human mistakes and AI mistakes aren’t the same. The nature and pattern of errors differ significantly between AI and humans. Human error tends to be more predictable, with errors and mistakes clustering around areas such as low expertise, fatigue, high stress, distraction, and task complexity. In contrast, AI errors can occur randomly across all problem spaces regardless of complexity. This is why AI systems continue to make boneheaded errors on seemingly simple problems.
A nurse may indeed make a mistake in an annotation in a patient’s medical record, such as a misspelling, incorrect date, or time. More severe incidents, such as mixing up patients or medications, can also occur, but are much rarer. Nurses aren’t going to fabricate a whole event that didn’t happen as a mistake.
With the widespread use of AI, there are bound to be significant impacts. They won’t all cause major harm, but they will all tell an inaccurate story. Severity will depend on the system consuming this data and its intended use. Some will be purely annoying, but others will have serious consequences. A person with hallucinated data in their medical record may be prescribed the wrong medication or a medication to which they are allergic. I’m speaking in vagaries here because the extent of the problem isn’t fully understood, but one thing is certain: it’s getting worse as the usage of generative AI expands.
Another problem will be tracing these issues back to their source. It won’t always be obvious when a mistake originates from an AI system or a human. After all, these systems are meant to augment human processes. When it comes to blame, humans will always blame AI, while system owners will always blame the humans. It’s a mess.
The New Truth
Ultimately, we’ll uncover a disturbing reality. In many cases, hallucinated data becomes the truth. After all, it’s the “fact” that’s in the data store. Imagine trying to dispute this with someone at the DMV, customer service, our bank, and the list goes on and on. We become yet another in the long line of those contesting the “facts” on hand, directed to a Kafkaesque nightmare as we have to navigate some bureaucratic maze attempting to get a resolution.
A more cementing factor would be if the data is incorrect and there is no human to consult, only an AI making decisions based on the data it has. It offers apologies, not resolutions. And these are only instances that we become aware of.
Many stealthy decisions occur in the background, made by invisible systems that utilize these new “facts” to make determinations that impact our lives, our families, and our health. We may never fully understand the impact this new truth has on us, our families, or our future.
All of this damage stems from the systems we are using right now, today. Even if better, more accurate systems emerge, the damage being done today still stands. These new, more advanced AI systems may be trained or fine-tuned on hallucinated data generated by current AI systems. So, we’ve got that to look forward to.
These new, more advanced AI systems may be trained or fine-tuned on hallucinated data generated by current AI systems.
The Cause
Some of these issues can be attributed to automation bias, but it’s far from the whole explanation. There is a push from the top to utilize AI everywhere possible. Many companies are asking employees to do more with less. Well, when you have less time, one of the things you spend less time doing is worrying about quality or accuracy.
We’ve also been inundated with CEOs and other business leaders proclaiming their intent to replace everyone with AI. There isn’t much motivation to do a good job in environments like this. We’ve seen this happen in the past with jobs getting outsourced.
The reality is that these are self-inflicted wounds caused by the rapid adoption of error-prone technologies being thrown into use cases where the negative impacts aren’t considered.
What We Can Do
If companies and individuals intend to augment their activities to optimize and increase efficiency, they need to ensure that this optimization doesn’t cause harm. There needs to be processes in place to identify and address these issues before they cause a problem. This isn’t happening today.
Unfortunately, there isn’t much we, as future victims, can do, especially since we don’t know the extent of the problem. It’s impossible to be aware of all the people using these systems today and how they may affect us in the future. From government to private business, these tools are utilized for a wide range of tasks, both mundane and critical.
I’m not a fan of big government or excessive regulation, but it’s hard to see how these issues can be solved any other way, since we only become aware of the harm after it has happened. Consumer protection is something a government is far better equipped to handle than a handful of consumers. The tech crowd’s claims that burdensome regulations inhibit innovation are absolutely true, and this shouldn’t be the goal. However, the absence of existing regulations harms people, as consumers are powerless to take any action in their defense. Unfortunately, reasonable, level-headed regulations are not in our future.
At the very least, we should avoid AI in high-risk or safety-critical use cases. The thought of ChatGPT running something like air traffic control is terrifying. However, handing out this advice at this point seems like trying to reason with a hurricane. Admittedly, for users, it may not be immediately apparent that the tasks they are performing or the data they are collecting can ultimately lead to one of these scenarios.
The Problem At Our Feet
AI hallucinations and other inaccuracies are like grenades with the pin pulled, only instead of chucking them far away from ourselves, we’ve dropped them at our feet, staring at them, wondering what happens next. The only question is, how long will it take for us to find out?
What’s the effect of exposing children to AI at a very young age? Well, we are about to find out. President Trump signed an executive order called Advancing Artificial Intelligence Education For American Youth, and, in the face of the other executive orders pushed by the administration, it may be tempting to consider this order relatively benign. I urge people to reconsider, because this order could result in catastrophic and irreparable damage to future generations of children. Move fast and break things is all well and good until the thing being broken is your child.
This move represents many of my fears coming to fruition, with all of the negative aspects I’ve been warning about becoming cemented into the foundation of future generations. You may have heard me talk about conditions such as cognitive atrophy, but early exposure to AI in education can lead to something far worse: cognitive non-development.
There are also technical concerns, including issues with security, privacy, alignment, and reliability. Children are rich sources of data wrapped up in easily manipulable packages, so it’s no surprise that tech companies are opening their AI tools to them. However, I feel these concerns are more evident to most people than the negative cognitive impacts that the introduction of AI to young children creates, especially while their brains are still developing and maturing. These are the issues I highlight here.
Key Points
Since this is a long article, I’ll call out a couple of key points:
Cognitive offloading by children and adolescents to AI short-circuits cognitive development impacting executive functions, logical thinking, and symbolic thought
We convert social to anti-social activities
The very skills kids need to use AI effectively never develop due to the overuse of AI
Core foundations of critical thinking, data literacy, and probability and statistics need to be introduced before any AI curriculum
Worldviews will be shaped by interactions with AI systems instead of knowledge, experience, and exploration
Kids need time to explore the generative intelligence inside their skulls
What Are The Hopes?
Before we begin, it’s helpful to take a step back and consider what the product of this education is supposed to look like. We envision emotionally balanced young adults exercising hardened critical thinking skills and ingenuity to create the next wave of high-tech gadgets. This is the stereotypical AI bro vision of an AI tide lifting all boats, but the reality strays far from the vibes.
There’s nothing fundamentally wrong with this perspective except that exposing children to AI tools beginning in kindergarten almost guarantees the opposite. This is for two primary reasons: the negative cognitive impacts on early childhood and adolescent development, and poor curriculum implementation.
Now, can this program succeed in a way that benefits children and empowers them for the future? Absolutely, but it would be nothing more than success by miracle. A program like this needs to be well thought out and studied, with a gradual implementation that also considers potential tradeoffs and implements mitigations for these negative effects. This is NOT what we are getting here. This fails 999 times out of 1000, possibly more. Just read the wording of the executive order and imagine people rushing to implement it, along with the bros swarming like flies around a manure pile, anxious to pitch their half-baked products.
The introduction of AI and AI tools so early in childhood education will be yet another big mistake that everyone realizes in hindsight. To set the stage, many fail to realize just how much EdTech has been a failure, and now, without addressing any of the issues, we want to add even more screens in the classroom.
I don’t think everyone involved is a bad actor with perverse incentives. I think most people genuinely want to see children succeed and flourish. However, there is no consideration here for the long-term cognitive impacts on children.
AI In Education
While I was writing this article about AI in K-12, two other articles were released about AI in higher education. The article from New York Magazine about students using ChatGPT to cheat, and the story in Time of a teacher who quit teaching after nearly 20 years because of ChatGPT. The cheating article is creating a flurry of hot takes on social media. We’ve reached a technological tipping point where students don’t see the value in education. They want accomplishment and bragging rights (degrees) without effort. Apparently, attending an Ivy League school is no longer about the education you receive but the vibes you create and consume.
And of course, queue the defensive hot takes.
This is a common retort. The mistake of assuming low-quality Q&A for actual curiosity and insight. This information was available to us all along. It just required more friction to get. So, if this is the case, then the answers we wanted weren’t worth the effort. This is hardly an earth-shattering insight, yet we’re being pitched as though it is. Keep in mind, just because these people aren’t selling a product doesn’t mean they aren’t selling something.
As usual, Colin Fraser is on point.
A problem we’ve always faced is that we never know when we are learning something in the moment that will be valuable later. We exercise a stunning lack of current awareness for future value. This happens in all manner of experiences, but especially in education. Adults lack this awareness, and it’s completely delusional to expect that K-12 students will magically sprout this awareness.
We exercise a stunning lack of current awareness for future value.
There is value in learning things, even things you don’t use for your job. We seem to think learning is contained in individualized components that fit neatly into buckets, but there are no firewalls around these activities. Learning things in one subject is rewarding and beneficial, even to other subjects. Colin is also right about driving the cost of cheating to zero, a major point everyone seems to gloss over.
In his book, Seeing What Others Don’t, Gary Klein tells the story of Martin Chalfie walking into a casual lunchtime seminar at Columbia to hear a lecture outside his field of research. An hour later, he walked out with what turned out to be a million-dollar idea for a natural flashlight that would let him peer inside living organisms to watch their biological processes in action. In 2008, he received a Nobel Prize in Chemistry for his work. This insight doesn’t come from staying in your lane, being single-minded, or asking the right questions to an LLM. Yet, this is exactly the message thrust upon us. AI doesn’t provide the happy accidents that result from exploration and the randomness of life.
Using AI instead of our brains gives us the illusion of being more knowledgeable without actually being more knowledgeable. We shouldn’t underestimate the power of this illusion because it blinds us to certain realities. AI offers an illusion that completing tasks and knowledge acquisition are the same thing, but knowledgeable and productive are completely different attributes. This positive feeling of being more productive masks that we aren’t acquiring knowledge. Numbers end up overshadowing quality, and productivity vibes end up trumping learning.
Some may argue that productive is preferable to knowledgeable in a business context, but that hardly applies in education. The ultimate goal in formal education is to learn, not produce, with the PhD being the exception. Education shouldn’t be about creating useful automatons, despite how many business leaders may want them.
AI In K-12
Introduction in K-12 means that these tools are introduced during critical brain development and could short-circuit the development and maturation of things such as executive functions, logical thinking, and symbolic thought as students offload problems to AI systems. Instead of having skills atrophy through the overuse of AI, these skills never develop in the first place due to cognitive offloading to AI tools. No matter what the AI bro impulses, we should all agree that exposing kindergarteners to AI is an incredibly bad idea.
Instead of having skills atrophy through the overuse of AI, these skills never develop in the first place due to cognitive offloading to AI tools.
All of the issues and negative impacts I’ve been pointing out, such as the cognitive illusions created by the personas of personal AI, along with associated impacts such as dependence, dehumanization, devaluation, and disconnection, get far worse when exposed early in childhood and adolescent development because children never discover any other way. Blasting children with AI technology in their most formative years of brain development pretty much guarantees lifelong dependence on the technology. Something that elicits drooling at AI companies, but is hardly in the best interest of human users. What we consider overreliance today will be normal daily use for them. Worldviews will be shaped not by knowledge and experience, but by interactions with AI systems.
There’s something fairly dystopian about prioritizing AI literacy while actual literacy is on the decline , disarming future students from the very skills they’d need to keep AI in check. The impression seems to be that if you can teach kids AI, you can negate negative downturns in literacy. After all, why should something like reading comprehension matter if tools provide the comprehension for us through a mediation layer? Hell, why stop there? Why not apply AI to every task that could possibly be outsourced? We are close to creating a world where raw data and experiences never hit us.
The Future Isn’t Now
In their book AI 2041: Ten Visions for Our Future, Kai-Fu Lee and Chen Qiufan have a story about children who grow up and go through school with companion chatbots to assist them in life. These chatbots adapt to them and assist them in areas where they have challenges. AI systems are ever-present companions following them through school and in life. The story is meant to have the trappings of utopia, but ends up sounding like a dystopian hellscape. To make matters worse, their story considers a perfected AI system that doesn’t have all the issues and drawbacks of today’s AI systems.
We continue to make the mistake of treating the AI systems of today as though they are the AI systems of tomorrow. Encouraged into hyperstition and thought exercises of, “It doesn’t work, but just imagine if it did!” To say that AI will cure cancer and become the cure for all of humanity’s ails may likely turn out to be true, at some point. But these accomplishments have yet to come to fruition, and don’t appear on the horizon either. So, why are we treating these systems as if they’ve already accomplished goals they haven’t? The highly capable tutor/companions of Lee and Qiufan don’t exist, yet we want to apply this non-existent vision to K-12 education as though they do. Even if they did exist, where is all this highly personalized data about your child being stored, and what is being done with it?
Less Capable, More Dependent, and Less Stable
The crux of the issue is that this program will not set kids up for success in an AI world or otherwise. This early exposure will make them less capable, more dependent, and less stable. This curriculum could teach kids all the wrong things, such as that answers can be immediate and simple, and that working out a problem isn’t as important as asking the right questions. We also teach that learning is comfortable. We give the impression that knowing things is not as important as knowing where things are stored. This is all bullshit. Kids can’t summarize their way to knowledge. But, it gets worse.
Children exposed this early never learn how to do things for themselves. They end up outsourcing problems and decisions to AI. Instead of taking feedback on how to solve problems, challenging themselves to learn, they offload the problem to AI, making them incapable and lacking confidence in the absence of technology.
This technology dependence also creeps into their personal lives, meaning going about their typical day becomes unbearable without the ability to mediate through AI. It becomes a source of authority for them and a way to avoid difficult decisions that teach them lessons. It can be hard for us to imagine today the future paralysis created when the technology is absent, even for simple decisions like how to respond to a friend’s message or whether to go outside today.
Many adults may argue that this is a small price to pay for setting kids up for success in the future. There are two flaws here. First of all, this is a monumental price. Second, using technology more doesn’t automatically mean being better at using it. For AI use, the skills you learn outside of AI’s mediation are exactly the skills that make you better at using it.
We need to focus on teaching kids to use their brains, something I never thought I’d have to say when talking about… school.
This is typically when someone brings up the calculator, insinuating that nobody needs to learn math because it exists. Although I disagree, confusing a calculator with AI technology is a mental mistake. Calculators and AI are far from being similar technologies. A calculator isn’t a generalized technology that can be applied to many problem spaces. A calculator doesn’t provide recommendations, advice, or sycophantic outputs. It won’t tell you who to date or be friends with. Oh, and a calculator is always right, unlike AI.
The hypothetical response that gets pitched around is imagining if Einstein or Von Neumann had access to AI and all of the wonderful things that would have sprouted from their genius. Maybe, however, I pose a different experiment. Imagine if Einstein or Von Neumann were a product of AI education from a very early age, where even inane curiosities were immediately satiated by an oracle. The likely output is that nobody would know their names today. We are products of our environments. Remember, there are no happy accidents with AI, only dense data distributions in which everything is shoved. In the K-12 AI education era, Einstein never stares back at the clock tower on the train, because he’s looking down at his phone.
In the K-12 AI education era, Einstein never stares back at the clock tower on the train, because he’s looking down at his phone.
Avoiding Discomfort
Sam Williams from the University of Iowa said, “Now, whenever they encounter a little bit of difficulty, instead of fighting their way through that and growing from it, they retreat to something that makes it a lot easier for them.” We are looking to apply this in K-12, specifically when we want students to grow.
The truth is, knowledge acquisition isn’t comfortable, and students avoid discomfort like the plague. When we use AI to complete assignments, we aren’t challenging ourselves. We aren’t developing our own perspective and forming new connections between concepts. Students find writing uncomfortable and are quick to outsource to AI, but writing truly is thinking. When we write, we are confronted with our thoughts and perspectives, challenging ourselves and forming new insights. One realization with writing is that the more you do it, the better you get. This realization never comes when it’s constantly outsourced to technology.
Using AI for work-related tasks may be helpful, but using AI for education or even life is idiotic. Yet, we continue to make these foundational mental mistakes. This would be like saying that since Taylorism worked for business, why not apply it to daily life? We all know where that leads.
But we also end up robbing students of a sense of accomplishment and fulfillment, of a long-lasting sense of satisfaction, not to mention the ability to focus. And for what? Because we believe that children will need to be non-thinking automatons to have a chance in the future? This theft will have a lasting impact on the mental health of future generations.
We may experience the extinction of the flow state by never allowing people to enter it in the first place. I’ve heard people argue that they’ve entered a flow state using AI, maybe, but likely the very nature of using AI to complete tasks guarantees that you never enter a flow state. Either people are confused about what a flow state is, or they mistake the illusion of productivity for creativity and flow.
As Ted Chiang mentioned in an article I’ve referenced before, ”Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.”
Going to the gym isn’t comfortable, but the results are physically and mentally rewarding. The mental health benefits of going to the gym aren’t intuitive. After all, how can running on a treadmill or lifting weights, activities that work out your muscles, benefit your mental state? Yet, it does. There are no firewalls around exercise either. Knowing this doesn’t stop us from making the same mistakes in cognitive areas.
When Playing It Safe Becomes The Norm
Using AI to do things is perceived as safe because if the output is wrong, we can blame the AI, versus having to work out a problem ourselves and potentially being wrong. There’s a blame layer between us and the problem.
Let’s take art, for instance. AI art is safe, unchallenging, and unfulfilling, providing no opportunity to learn about ourselves, others, or the world. And yet, the very fact that it’s safe and easy is what makes it so attractive. Failure can result from the paintbrush, but never the prompt.
Failure can result from the paintbrush, but never the prompt.
The best things in life come from not playing it safe. Taking a chance on a job, moving to a new location, or asking a person out on a date are all activities that aren’t safe, but they can end up being the best decisions we’ve ever made. We need to keep this instinct alive in children.
Lack of Resiliency
The more we rely on AI, the less we question its outputs. The more we use AI and our capabilities atrophy, the less capable we become of questioning the outputs and, hence, the more dependent we become. We end up losing a critical capability when we need it the most, or in the case of early childhood exposure, never develop it in the first place.
Modern generative AI is far from error-free. It makes frequent mistakes and hallucinates. Students must construct the cognitive fitness necessary to operate robustly using a technology that makes these frequent mistakes. This fitness isn’t built on a foundation of the same AI that has these issues.
Students also need a foundation and the ability to explore outside AI mediation. This requires both time and foundational courses and concepts. For example, this foundation should include critical thinking, data literacy, and probability and statistics. Early exposure to these concepts with late exposure to AI offers the best chances for students to build this robustness.
From Social to Anti-Social
AI is a fundamentally anti-social technology. From the ground up, we are removing the human and converting it to the non-human. Even social networks are transforming into anti-social networks. With AI’s overuse in children we teach kids that humans are second-class citizens to AI. After all, the sales pitch is that AIs are better at everything, so why should children believe otherwise?
Handing kids an oracle to ask questions not only converts a social activity into an anti-social activity but also shifts authority away from humans and onto technology. This shift would still be bad even if the technology were perfected, but it is far worse given the error-prone technology of today.
Young children are quick to anthropomorphize and will form a bond with non-human companions. Although the video of the little girl not wanting to play with the shitty AI gadget is funny, it won’t last when children are surrounded by AI. Kids will switch from actively using their imagination to becoming passive consumers of AI output.
The human retreat has already begun, as kids prefer interactions with friends mediated by a device. But now tech companies want to take this further. This is all happening outside of education, but kids can’t avoid forced interactions with their companion/tutor/friend/bot in the classroom, reinforcing this retreat.
Much of this slide comes from our tendency to oversimplify, not accounting for the bigger picture and the complexities involved. Take, for instance, a common claim that kids ask many questions, and since AIs never tire of answering them, pairing kids with AI is a natural fit. This seems like an almost throwaway point, a gotcha to any potential critic, but people making this point haven’t thought it through.
First of all, asking questions is a social activity. We interact with other humans in different environments, learning far more than the simple answer to our questions. This activity teaches us essential skills, including ones related to non-verbal communication. Humans also don’t answer questions the same way AIs do, often providing additional context and anecdotes that may further aid us in knowledge acquisition and retention.
This act connects us to other people and the world, making us active participants in something bigger rather than passively consuming an answer. I still remember anecdotes shared from my high school chemistry teacher that stick with me today. We don’t just lose context and perspective from an AI oracle, we lose something human.
When it comes to context, any expert who has asked AI questions about their topic area has been confronted with incorrect information, including something like, “I guess that’s technically true, but it’s hardly the whole story.” And this is what we want to make the norm.
Closing The Curiosity Gap
We are told that asking an AI questions makes people more curious, but AI closes the curiosity gap. By getting an instant answer, we satiate our curiosity and move on to the next thing, only digging deeper or exploring further in cases of pure necessity. This act reinforces low attention spans, further reducing the ability to focus. At some point, System 2 may become extinct. What kind of world will that create, where the world is nothing but hot takes and vibes?
AI satisfies a need for quick answers. However, searching for answers in a more traditional way means other pieces of valuable context surround you. Other rich pieces of information that lead to new ideas and new understanding. Humans have an evolutionary need for exploration.
When using AI for exploration, you are never exposed to ideas and concepts you don’t want to be exposed to. I don’t think we fully grasp just how much of an impact this selection bias will have on the future.
Sure, there are situations where a quick answer is perfectly fine, mundane things like what time a movie starts or what temperature to set your oven to cook a pie. The mistake here is assuming these situations apply evenly to all problem spaces, especially knowledge creation.
My Recommendations
Despite the many unknowns, we shouldn’t shut the door to new innovations because we could slam the door to new solutions. Although it doesn’t exist today, a robust tutoring bot focused on a single purpose and specific subjects could benefit students. The message here isn’t to discard everything but to be cautious, knowing there are tradeoffs and downsides, and incorporate mitigations.
For a program such as this to be successful, it needs to be well thought out and studied, with a gradual implementation that also considers potential tradeoffs. Without this, you have no way of telling whether you are helping or harming until it’s too late. There is no way to succeed without this step. Beyond this up-front work, I’ll make four other suggestions.
Avoid Early Exposure
Students need plenty of time to develop their brains, not technology. Early exposure should be avoided at all costs. Exposure to this curriculum should happen in high school, preferably in the last two years, not earlier. This is typically when vocational education programs were introduced in schools as well. This gap gives students time to develop skills and experiences outside AI influence and mediation. Kids adapt to technology quickly, so this later exposure will not stunt their capabilities when tools are introduced.
Create A Prior Solid Foundation
Before introducing the AI curriculum, a solid foundation in various topics should be established. This foundation should include courses in critical thinking, data literacy, and probability and statistics. These courses and concepts have been sorely lacking in K-12 education today, and their introduction is long overdue. Arming students with this foundational knowledge will allow them to question the outputs of these systems and create defenses for cognitive creep.
Smart Implementation
The implementation of the courses should be isolated and away from other topics. AI shouldn’t be woven into every topic with a tie-in. Although some would argue that an effective AI tutor could help students struggling with certain subjects, these systems have yet to be developed, much less proven effective. In almost all cases, the AI would be used as an oracle, providing answers directly instead of the necessary understanding and even discomfort that helps students grow.
Solid Curriculum
The curriculum should focus on challenging students, not giving answers. Kids often don’t realize when challenges are beneficial to them. AI tools should continue to be viewed purely as tools, not oracles or companions. The curriculum should focus on avoiding usage as personas and teaching kids how to think in terms of solutions. Appropriate labs should be constructed that give students the ability to explore concepts and define solutions, pulling AI tools in secondarily to complete the tasks and realize a student’s vision. This way, there is a separation between the mental approach and the AI components.
Final Thought
Ultimately, we may end up with anti-social, dependent, and unstable young adults. We take so many skills for granted, skills we don’t realize we developed and honed in school, and now we want to apply technology to optimize these attributes away. We need to give future generations a chance to allow their brains to develop outside of AI mediation. Here’s something to consider.
Imagine an art teacher standing in front of a class. The students aren’t in front of an easel or grasping a pencil, but sitting in front of computers. They aren’t using their hands and tools to create a vision that originates from their minds. Instead, their fingers clack on the keyboard and echo through the class as the teacher instructs them to be more descriptive and provide pleasantries to the machines. Is this really the world we want to immerse children in?
We are moving toward an existence where raw data and experience never hit us as everything becomes mediated. We prefer optimization over expertise. I’m sure the illiterate masses of the Middle Ages felt powerful after leaving a sermon by the literate priest mediating the message of the written word, but that was hardly the best state for individuals. Now we are applying this logic to AI with far-reaching consequences for the everyday life of an entire generation.
In the words of Aldous Huxley, many may mature to “love their servitude,” preferring optimization and rigid structures that take decisions off the table, making things easy, not requiring thought. In Zamyatin’s We, most inhabitants enjoyed living in One State with its rules, schedule, and transparent housing. They were happy to trade free thought and experiences for optimization, comfort, and structure. It needs to be said, over and over again: These are dystopias, not roadmaps.
Following the pace of AI advancement can make you feel like the Blown Away Guy from the old Maxell commercials. Tech leaders and influencers tell us to expect artificial superintelligence in the next year or so, even doubling down on inevitability by moving up their timelines. The world is cooked. This perceived inevitability, combined with uncertainty, is leaving many people on edge, and rightly so. If all the things the tech bros hope for come true, humanity is in a terrible spot.
All is not lost. The over-the-top predictions we are bombarded with daily often equate to nothing more than performance art. The tech media frequently parrots perspectives from people who have a vested interest in selling us stuff. I mean, after all, why would they embellish or lie!???
In early April, the AI 2027 thingy was making the rounds. For those unfamiliar, you are in for a treat. The result answers what would happen if you locked a few tech bros in a conference room for a day, depriving them of any reality and oxygen.
Are the scenarios outlined in AI 2027 impossible? Certainly not. This sort of fast takeoff scenario is possible, but it’s highly unlikely. I predict the whole AI 2027 thing will start looking pretty silly in late 2025 or early 2026.
With all this endless AI advancement hype, I was happy to see a new article by Arvind Narayanan & Sayash Kapoor titled AI as Normal Technology. This article doesn’t talk about how AI displaces the human workforce or about a super-intelligent AI taking over the world, but rather about how AI becomes a normal technology that blends into the background of our daily lives.
They also touch on a few other topics, such as overregulation. I also believe that any regulation should be specific and targeted at use cases, not painting with broad strokes. This specificity wouldn’t allow for regulatory capture or weaponization of the regulations. The tech leaders are right that regulation can stifle innovation. By targeting regulations in this way, we can protect people without stifling innovation.
It’s a good read that’s well thought out and researched. For anyone mainlining AI hype, this is an essential read. The scenarios in the AI as Normal Technology article are far more likely than the AI 2027 one, by far.
Questioning The One True Faith
Starting in early 2023, I added a slide with the following image to my presentations. This is because any criticism of advancement was seen as an affront to a spiritual belief, and since I didn’t believe that LLMs would lead to AGI or ASI, I must hate the technology outright. This couldn’t be further from the truth.
Saying that LLMs won’t become ASI isn’t a blasphemy that requires self-flagellation afterward. We don’t need AGI or ASI for these tools to be effective. We can and are using them to solve problems today. People are using them to augment their jobs today. So, why turn AI beliefs into a religion? People are acting like questioning any part of the narrative makes someone a non-believer or some disconnected fool. The reality is that not questioning the narrative or exercising any skepticism is what makes someone a fool. A gullible fool at that.
The reality is that not questioning the narrative or exercising any skepticism is what makes someone a fool.
There’s a strange group that thinks belief is required for AI to create a utopia, but the reality is that facts don’t require belief. It’s ancient wisdom from five minutes ago that we’ve seemed to have forgotten in the vibes era.
I believe what we encounter here is a problem in perception caused by both our environment and us.
Environment
In the book Nexus by Yuval Noah Harari he describes the witch hunts as a prime example of a problem that was created by information, and was made worse by more information. For example, people may have doubted the existence of witches, having not seen any evidence of witchcraft, but the sheer amount of information circulating about witches made their existence hard to doubt. We are in a similar situation today with beliefs in AI advancement. This is made worse because the systems we use today reduce the friction in information sharing, making it much easier to get flooded with all sorts of information, especially digital witches.
We humans also gravitate toward information that is more novel and exciting. It’s the reason why clickbait works. However, novel and exciting information often doesn’t correlate with the truth or reality. As Aldous Huxley pointed out in Brave New World Revisited, “An unexciting truth may be eclipsed by a thrilling falsehood.” We are in this situation again. The vision of near-term artificial superintelligence is exciting and novel, even when people talk about it destroying humanity. AI, thought of as normal technology, as Narayanan and Kapoor put it, is boring by contrast, despite being more realistic.
This condition was the same back in the times of the witch hunts as well. The belief that witches were roaming the countryside looking to corrupt everyone, meaning you had to use your wits and your faith to defend yourself is a lot more novel and exciting than acknowledging that life really sucks because of the lack of food and indoor plumbing.
But then, there’s another strange type of information we gravitate towards: people telling us what we want to hear.
Ah, yes. Evals as taste. Vibes above all. Skills inessential.
We have allowed the people selling us stuff to set the tone for the conversation on the future. These people have a vested interest in selling us on a certain perspective. It’s like taking advice on a car’s performance and long-term viability directly from the mouth of the car salesman instead of objective reality. I wrote about this last year, saying that many absurd predictions were nothing more than performance art for investors. The tech media needs to step up and start asking some real questions.
Many of the influencers and people on social media are parroting the same perspective as the people selling us stuff because of audience capture. Audience capture, for those unfamiliar, is the phenomenon where an influencer is affected by their audience, catering to it with what they believe it wants to hear. This creates a positive feedback loop, leading the influencer to express more extreme views and behaviors. People get more likes and clicks by telling people more exciting things, as Huxley mentioned. So, there’s a perverse incentive for doing so.
Lack of Reflection
One of my biggest concerns is that we’ve lost our ability to reflect. Many things we believe are silly upon reflection. Unfortunately, our current information environment conditions us to reward reaction over reflection. Until we address this lack of reflection, we’ll continue to be fooled in many contexts, not least of which is the pace of AI advancement.
Benchmarks
Many of the benchmarks that people use for AI are not useful in real-world scenarios. This is because the world is a complicated place. Benchmarks are often not very useful in real-world scenarios due to additional complexities and edge and corner cases that arise in real-world use. Even small error rates can have significant consequences. But don’t take my word for it, take it from Demis Hassabis. “If your AI model has a 1% error rate and you plan over 5,000 steps, that 1% compounds like compound interest.” All of this adds up to much more work, not superintelligence next year.
Us
Fooling Ourselves
We have a tendency to fool ourselves easily. As I’ve said many times, we are very bad at constructing tests and very good at filling in the blanks. The tests we create for these systems end up being overly simplistic. Early on, people tested model capabilities by asking for recipes in the style of Shakespeare. Hardly a difficult test, and easily impressive.
This condition is also why every time a new model is released, it appears immediately impressive, followed by a drop-off when reality hits. Sometimes, this has increased problems, such as OpenAI’s o3 and o4-mini models hallucinating at a higher rate than previous models.
We are also easily fooled by demos. Not realizing that these things can be staged or, at the very least, conducted under highly controlled conditions. In these cases, variables can be easily controlled, unlike deployment in the real world.
Oversimplification
We humans tend to oversimplify everything. After all, almost half of the men surveyed thought they could land a passenger plane in an emergency. This oversimplification leads us to underestimate the jobs that others do, possibly seeing them as a task or two. So, when ChatGPT passes the bar exam, we assume that lawyers’ days are numbered.
This oversimplification is also exploited by companies trying to push their wares. This claim is more absurd performance art. No, there will not be any meaningful replacement of employees next year due to AI. The reality is that most jobs aren’t a task or two but collections of tasks. Most single-task jobs have already been automated. It’s why we don’t see elevator operator as a current career choice.
Being Seen as Experts
Many people seek content to share to be seen as experts. If you don’t believe me, have you logged in to LinkedIn lately? This adds to the massive amounts of noise on social media platforms. However, it’s often just parroting others.
This also extends to the tech media. I wish these people would start adding a modicum of skepticism and asking these people hard questions instead of writing articles about model welfare and how we should treat AI models. But once again, novelty over reality.
Conclusion
We are witnessing people attempting to shape a future with vibes and hype. This is the opposite of evidence. It certainly doesn’t mean their future vision is wrong, but it sure as hell means it’s a lot less likely to happen. Reality is a lot more boring than dystopian sci-fi.
I do believe that these tools can be disruptive in certain situations. If we are being honest, I feel much of the disruption is happening in all the wrong areas: creative arts, entertainment, music, etc. We’ve already seen these tools disrupt freelance marketing and copywriting jobs. These areas are disrupted because the cost of failure is low. There will even be niches carved out in more traditional work, too. So, even without AGI and ASI, disruption can still happen.
However, the predictions made over the past few years have been silly and absurd. If you believed many of the people peddling these views, we should be exploring universal basic income right now due to all of the job displacement by AI. But that’s certainly not the case. Many of these same people resemble doomsday cult leaders preaching the end of the world on a specific date, only to move the date into the future because of a digital divine intervention. The reality is, this is vibe misalignment. This is not only going to continue, but increase before it levels out, because investors don’t invest in normal or boring.
Let’s all take a breath, reflect, and maintain our sanity.
By now, you’ve no doubt heard of the term vibe coding. It’s become the favorite talking point from influencers and the tech media, which, even in 2025, can’t seem to muster a modicum of skepticism. But, if you’ve ever wondered what it was like to play Russian Roulette in reverse, loading all the chambers but one, spinning the cylinder, and having a go, you’re in for a treat. Welcome to the world of vibe coding and YOLO mode, two things that go together like nitroglycerin and roller coasters. So, of course, it’s become one of the hottest topics right now, and it has all of the bros super psyched.
For those out of the loop, vibe coding is “Where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” You get to this state by talking with a computer and letting it generate all the code while you kick back and enjoy a cold one. YOLO mode is the spirit animal of vibe coding. It’s where you blindly accept all the code suggestions generated by the AI tool and push the code to see what happens. Neat. It’s interesting to note that YOLO mode in video games means if you die once, you are dead. No respawning.
Vibe Coding = Code Slop
Before we probe the issues with vibe coding, let’s take a step back and look at what vibe coding is. This practice shouldn’t be confused with a developer using an AI coding tool to assist with tasks or gain productivity. You know, the intended use of many of these tools.
Vibe coding is a delusional dream state in which people use these tools as if they are in the future instead of the present. The fact is, these tools aren’t reliable enough or mature enough to be used this way. It’s a lot like having an illness and getting a bottle of pills from the pharmacy marked with clear directions and immediately slamming the entire bottle because the instructions are for losers who don’t know how to hustle.
Vibe coding is a delusional dream state in which people use these tools as if they are in the future instead of the present.
The reality is that vibe coding generates slop that happens to be code instead of media categories, meaning that the negative consequences extend far beyond other categories of slop. This distinction is essential when evaluating the potential value of vibe coding.
Vibe Coding Pitch
The pitch of vibe coding is that literally, anyone can become an instant millionaire developing apps. You’ll be too busy making money to worry about how your code works or its security. There are no boundaries or barriers. All you need is an idea (more on this in a future post.) Like so many things, it’s technically true but practically false. And also, you aren’t wrong if you are beginning to get crypto bro vibes.
Much of the same logic employed by conspiracy theorists is at work here. If one person gains some success vibe coding, then it must be possible for anyone to do that. Technically true, but practically false. This is like thinking millions of new rock stars will be minted because people can publish their songs on Apple Music.
It’s about tradeoffs. We don’t say that being a conspiracy theorist is a good thing since some conspiracy theories turn out to be true. That’s because the negative impacts of conspiracy thinking outweigh the potential benefits. The same applies here.
There are other flaws with their logic. For example, people don’t consume apps the same way they do media like music, video, or photos. People can listen to hundreds of songs every day and not repeat a single one, but this consumption strategy doesn’t apply to applications. People these days often consume media passively. For example, people often don’t listen to music when they are listening to music; it’s purely background noise for other tasks. Applications usually can’t be consumed passively and require active interaction. This would make consuming many different apps irritating to users.
There are already 1.9 million apps on the App Store alone. Are we really hurting for apps? Do we need 100 million apps to compete with the number of songs on Apple Music? Of course not, but that doesn’t mean we won’t get them anyway. If you look at the outputs of vibe coding, it’s often uninteresting, overly simple, derivative, or just plain unwanted. Buckle up.
There will undoubtedly be exceptions, just like the person who was a bartender six months previously who started a crypto project and manages 100 million in assets. These are exceptions and not the rule, but these exceptions serve as accelerants to fuel the hype flames.
People are trying to sell us on the fact that vibe coding has no downsides. This is delusional. Take a step back and think of the answer to a single question. What do you get when everything takes a backseat to speed? It’s like a car that does 200 mph with no seatbelts and no airbags constructed from paper mache.
Before we move on, I’d like to acknowledge something. It’s a good thing that AI coding assistants are making coding more accessible. However, using these tools as a drop-in replacement for common knowledge and domain expertise isn’t a recipe for success. Imagine something like vibe surgery? Yeah, bro, let me get in on that appendix! Nah, I didn’t go to medical school or know much about anatomy, but I got an AI tool, dexterity, and a good sense for vibes. So little of developing an application is about the code itself, but that gets lost in the vibes.
Changing Behavior and Attitudes
In the public sphere, the discussion of the merits and drawbacks of vibe coding and YOLO mode are entirely contained within the technical aspects of the approach. I’m also concerned about the technical components, but I’d like to bring attention to something nobody discusses.
As often happens when a new technology or approach arrives and removes friction, it changes people’s behavior and attitudes. In technology circles, friction is discussed as though it’s universally bad. It’s not. Sometimes, friction is a feature, not a bug. Nicolas Carr provides an excellent example in his book Superbloom, which discusses introducing the Retweet feature on Twitter.
The time and effort required to share a tweet manually, though seemingly so small as to be inconsequential, turned out to be vitally important. It slowed people down. It gave them a moment to reconsider the message they were about to repeat, now under their own name. Once that little bit of friction was removed, people acted and reacted more impulsively. Twitter became more frenzied, more partisan, and much nastier.
Things like vibe coding and YOLO mode will have similar behavioral effects if this technology trend takes off. People won’t put a lot of thought into the apps they create. Some may build apps purely because they can, not considering why an app for that particular purpose didn’t exist in the first place, assuming that it was purely because nobody had built it and not because of the potential for negative impacts or harm.
The removal of so much friction removes not only the appreciation for the problem but also opportunities to catch potential issues. These lines of generated code become grenades with various time delays chucked into production. This assumes that the developer had the skills to identify the issues in the first place.
These lines of generated code become grenades with various time delays chucked into production.
Some will argue that these features are great for prototyping and mockups. I agree. However, as I mentioned, these features change behavior, and using them simply for prototyping won’t hold. A vast majority of people who can get away with chucking vibe-coded apps into production will.
With the changes in behavior and attitudes, there are many things creators of these applications are more likely to do.
Act unethically (ethics don’t align with speed)
Devalue the work of others
Not learn or at least learn lessons
Encounter skills atrophy
Not build robust software (Security, Privacy, Alignment, Reliability, etc.)
Not constrain code to prototypes and mockups
Think they know things they don’t (Illusion of knowledge, Illusion of capability)
Misunderstand what’s valuable
Devalue collaboration
Go it alone and not include domain expertise and misunderstand the problem they think they are solving
Build apps that nobody wants
Build apps that cause harm
Choose poor architectures
Use more resources and not prioritize efficiency
Fail to benchmark properly
Not be able to troubleshoot their own creations
Not do something truly innovative
And on and on…
These were just a few of the conditions off the top of my head. What happens when these conditions now become the norm? When people start making app slop the way they do image slop?
Risk and Security
Vulnerabilities in code and lack of security controls account for a lot of pain and financial loss every year, much of this from organizations that try to do the right things. So, imagine what happens when people don’t care about doing the right things.
It’s known that these tools output vulnerabilities at rather high rates. So imagine what happens when people YOLO code into production and don’t check the resulting code or even the environment where it is hosted for security issues. Hustlers ain’t got no time for the right things.
There’s more to worry about than an AI tool outputting specific code blocks that are vulnerable. Other contributing issues increase the attack surface of an application. For example, choosing a vulnerable library or suggesting vulnerable configuration options for cloud environments. These tools also contribute to library bloat by including multiple libraries that do the same thing.
I could go on and on about this topic, but at this point, the various security issues created by AI coding assistants are known issues. I wrote a white paper on this topic in early 2023, and I delivered a presentation at InfoSec World the same year. These issues should be common knowledge now with the publication of various articles, papers, and presentations.
When it comes to risk, sure, all vibes aren’t created equal. A video game getting hacked isn’t as bad as a financial application getting hacked and draining your bank account. I’m certainly not being an absolutist here. However, technology trends have an odd way of not staying confined to specific buckets. So, we’ve got that to look forward to.
Today, countless vulnerabilities are moving into production without vibe coding, all because people are trying to push things faster. Vibe coding and YOLO mode make this monumentally worse. We’ve only discussed security and haven’t touched on other topics like privacy.
Making Software Worse
The trend of vibe coding will make software worse. Like security, software quality isn’t a consideration in vibe coding because reasoning about quality is a bummer when huffing vibes.
We live in a highly advanced world where digital things fail all around us all of the time, like a leisurely stroll through a cityscape where freshly painted buildings mask a crumbling interior of decay and misshapen architecture. This is so common that there’s a term for it: enshittification. We’ve become so accustomed to the software and tools we use sucking so bad we hardly notice it. This is a contributing factor to why some view generative AI as AGI.
Vibe coding and YOLO mode will lead to failures, half-baked functionality, and mountains of technical debt.
Vibe coding and YOLO mode will lead to failures, half-baked functionality, and mountains of technical debt. This should concern everyone, but queue the bros to claim this is a feature.
Yes, because that’s how things work. He and many like him are stating that they should create as many problems as possible because AI can fix them in the future. Once again, they are taking something technically true but practically false. At some point, we’ll have highly advanced and capable systems that operate this way, but the mistake is thinking those systems are on the cusp of arrival. It’s hard to ignore the religious fervor in these claims.
People can pray to the gods of gradient descent and burn Gary Marcus in effigy, but it doesn’t change the realities on the ground. Problems created today will be with us tomorrow, and no AI god is coming to deliver us from our evils any time soon, so we should work to minimize potential problems instead of running up the credit card. I’ve been calling this problem out for the past couple of years, stating it would lead to a brave new world of degraded performance.
On a side note, I feel these people feed off each other. I’ve heard perfectly reasonable people making wholly unreasonable claims. These are the things you hear people say when they are trapped in filter bubbles, getting high on the supply of techno-utopians. They also suffer from a healthy dose of audience capture because, no doubt, being unreasonable gets you more likes and shares than being reasonable. Welcome to the perverse incentives of modern social media.
There continue to be many misconceptions about software development, but one of the biggest is that writing code is the end of the journey.
There continue to be many misconceptions about software development, but one of the biggest is that writing code is the end of the journey. This is because most people opining on the topic are not developers. I noticed this trend years before the existence of AI coding tools when security professionals who learned to write a few lines of Python code thought that developers’ jobs were easy. The assumption, then, for AI coding tools is that since the tool can output code and developers only write code, developers are no longer necessary. Developing code isn’t the end of the journey. It’s the beginning.
The written code must be troubleshot and maintained, and features must be added. We live in a constantly evolving world with changing problems, environments, and customer needs. Developed code will crash into the realities of the real world both when it’s initially deployed and over its lifetime. This leads to another problem.
Developers don’t understand the code being written, especially when the people generating the code aren’t developers. As developers’ skills atrophy and people who were never developers start creating these applications, they cannot troubleshoot problems, effectively add features, or perform any of the other countless tasks that developers perform daily. The answer from the utopians is to use AI to figure it out, but this strategy won’t always work.
There is a higher likelihood that the AI tool will successfully troubleshoot issues for simple tools and scripts, but these are the very types of applications that are unlikely to net you big money. As applications grow in size and complexity, the AI tool is less likely to provide the solution necessary to resolve the issue.
Imagine a world where an app needs to be rewritten from scratch because the person who created it couldn’t get the AI tool to troubleshoot and fix the problem. Now, that’s the utopia we’ve all dreamed of.
Imagine a world where an app needs to be rewritten from scratch because the person who created it couldn’t get the AI tool to troubleshoot and fix the problem.
There is a vast oversimplification of the entire landscape here. So, an application starts simply enough, and then more requests are made to the AI tool in an attempt to add more functionality, but this doesn’t always work or isn’t done in the most efficient way, leading to a buildup of issues.
Another trend affecting application reliability is using probabilistic systems as though they are deterministic. Whether this trend is due to laziness, ignorance, or an attempt to handle unknowns is unclear, but it will surely affect applications’ reliability and their ability to be manipulated.
Ultimately, we may be left with App Store decay, where the App Store becomes a graveyard for abandoned apps. RIP.
Making Us Worse
I mentioned skills atrophy in my laundry list. It seems that even AI tools understand this problem. This is not only a comical error message but contains a truth.
We Never Learn Lessons
Although arguably more intense and a bit different, what’s happening now in AI isn’t new. We’ve gone through these cycles before with previous technologies. Every time a new technology comes along, we discard the lessons we’ve learned, assuming they no longer apply, only to discover that the previous lessons were even more important with the new innovation. This condition is something I’ve referred to in my conference talks as the emerging technology paradigm.
We never seem to learn lessons from our previous mistakes, no matter how often we encounter them. We have incredibly short memories and seem to dive face-first into the pool without checking the water level.
Ultimately, it’s all about tradeoffs. What we get and what we lose. When viewed simply as writing code, it seems we are getting more than we are losing. However, building and deploying applications and solving problems goes far beyond code. When considering the impacts holistically, this doesn’t appear to be a good tradeoff. However, it’s possibly one we are going to get anyway.
Whenever a new technology or approach comes along, proponents always pitch it with a utopian eye. They envision all the perfectly aligned scenarios with dominoes falling exactly into place. The Internet, Social Media, The Cloud, Web3, and many other technologies all diverted away from these visions and adapted differently than expected. Even something as simple as the telegraph was seen as a utopian invention that would end world conflict. After all, how could people go to war when misunderstandings were a thing of the past? We all know how that turned out. Vibe coding is destined for a similar fate.
Is it possible to play Russian Roulette in reverse without devastating consequences? Sure, but the odds aren’t great. The world also won’t be a better place with everyone vibe coding and YOLOing stuff into production. Many disagree with me. Fair enough. However, if this trend takes off, it will be another example of something we are stuck with, which is not good for a world that runs on software. We will need to improve or invent new technology to solve the problems we create, trading one set of problems for another. Welcome to utopia.
I’ve been considering a frightening prospect. What if the next generation is known as The Slop Generation? A generation unaccustomed to the world before the inception of AI-generated slop. Despite successes or failures with generative AI, it seems the AI-generated cat is out of the slop bag with the power to warp artistic expressions and further devalue art and the creative process.
This Slop Generation will be the most technologically advanced yet least capable and emotionally unstable of any generation. This is a byproduct of the value of life’s undertakings being picked clean by the vultures of innovation, leaving only the bones littered across the barren landscape. This has far-reaching consequences, but here we focus on art.
Art
When we think of art, we tend to think of images, but art encompasses different mediums, such as music, images, video, and the written word. The results of the artistic process are cultural artifacts with lasting permanence. This permanence can raise some artistic works into modern consciousness despite the passing of centuries, think The Mona Lisa, Beethoven’s 5th, or even the petroglyphs on Newspaper Rock.
Wikipedia defines art as:
Art describes a diverse range of cultural activity centered around works utilizing creative or imaginative talents, which are expected to evoke a worthwhile experience, generally through an expression of emotional power, conceptual ideas, technical proficiency, and/or beauty.
I mentioned the petroglyphs, but we have far older examples of art than these. Neanderthals drew cave paintings 64,000 years ago. Art predates society.
These artistic expressions lead to wonder. What were they trying to say? Were they documenting something? Were they trying to tell people something? Or were they having fun? The mystery is part of the allure. You get none of this with AI art. There’s no wonder, no mystery, no deeper meaning, just slop. Nobody wonders what the AI was trying to say when it adds an additional finger to a generated picture. When a human does it, we search for a meaning.
For over 64,000 years, art has been a part of us, but this may be coming to an end as people hail the post-human era. Enter The Slop Generation.
The Slop Generation
The Slop Generation won’t be defined by their cultural constructions and artistic expressions but by the output of one-arm bandit slop machines where artistic expressions aren’t created to be admired and revered but as individualized wallpaper blending into the background noise of life. A reincarnated version of the noisy, animated GIF-ridden world of early Geocities pages with higher fidelity and less permanence.
All art will be ephemeral, a momentary passing unworthy of saving and revisitation. Shrimp Jesus is their Mona Lisa, and some anonymous rando using a throwaway prompt is their Davinci, but this work isn’t held in any esteem, quickly forgotten and tossed into the low attention span waste bin of modernity.
All art will be ephemeral, a momentary passing unworthy of saving and revisitation.
Low attention spans won’t allow for appreciation, detail, or discerning deeper meaning or context in an artistic representation. This means all art must be literal, or else it is completely misunderstood. A child with a fork next to an electrical outlet will always be just that and never a representative warning in the context of larger cultural issues. The medium is the message.
In Generation Slop, no artists create works for others to enjoy, only algorithmic outputs catering to whatever whim we have at the moment. These outputs are created for an audience of one. Everything pushed and nudged to the dense center of a data distribution creating more predictable outputs and algorithmic uniformity. Why listen to someone else’s slop when you can generate your own?
None of this is a problem for The Slop Generation since art should look cool and serve no other purpose. Fidelity should trump meaning, as art requires consumption at a glance. Art is worthless if it can’t be consumed while multitasking. Any art requiring attention won’t be acknowledged, much less appreciated.
Art from the Slop Generation is cold and disconnected from humanity, unable to strike a chord or illicit emotion. Those trying to use slop machines to illicit emotions will have to resort to extremes, often to get any attention at all, much less an emotional response.
With AI-generated novels, they become worthless wastes of space existing purely for the vanity of the person who generated them, but even these people won’t read their own novels.
In Generation Slop there’s no sense of style, no sense of taste, no investment of any kind. It is the perfect low-attention-span content for the devalue generation as the point becomes getting to the bottom as fast as possible.
With art like music, the pitch will be you can hear a different song every time you listen without one repeating, and this will be seen as a benefit, even though this generation wouldn’t know if a song is repeated anyway since nobody actually “listens” to music. Music becomes background noise for other life activities, nothing more than a sonic firewall to keep the outside world out.
Traditional art, like the art in museums, exists purely to stand in front of for a quick selfie and post to social media. There’s no time for appreciation. Those notifications won’t check themselves. Art will continue to lose its value as nobody appreciates it anymore. The human work and toil that went into their creation are unappreciated because everything seems so easy, and doing anything hard is a waste of time.
This is The Slop Generation’s perspective as they rush headlong into post-humanism.
The Downward Slide
This shift to rapid-fire slop will be a net negative for humanity, as we lose something fundamental to the human experience connecting people and cultures for time immemorial. Art helps us understand the world, other people, and even ourselves in a way other mediums can’t convey. An entire generation may never discover the benefits of creating and consuming art, along with the positive effects this brings.
Take the novel, for instance. I hear so many people brag about never reading fiction as though it’s a badge of honor, assuming that nonfiction is the only way to “learn” something from reading, but this couldn’t be further from the truth. Works of fiction are vehicles for conveying big ideas. That’s why most books banned throughout history were works of fiction. Life isn’t a tutorial and doesn’t align with specific steps.
Imagine trying to convey the message of dystopian works like Orwell’s 1984 or Huxley’s Brave New World in a nonfiction format. It doesn’t strike the same chord or have the same impact. Both authors wrote essays and discussed these concepts in interviews, but these never created the impact that the novels did.
Fiction written by humans can be instructive. It can help us understand situations, other people, and cultures in ways other mediums cannot. Even movies with all of their visual and audio aspects don’t bring us inside the heads of characters like written works of fiction do.
Written works invite us to participate, exercise our imagination, and consider our own thoughts and perspectives. But this invitation requires our attention and an investment of time, attributes many don’t know how to exercise and believe they don’t have. Without exercise, we lose our imagination and our ability to connect. This is why, despite being so connected, people feel more disconnected than ever, mistaking digital connection for human connection.
Written works invite us to participate, exercise our imagination, and consider our own thoughts and perspectives.
When all art is literal, it loses its sense of mystery and wonder but also has degrading knock-on effects. At a local writer’s group meeting around 2018, I watched attendees dole out their usual mixture of helpful and non-helpful feedback to a girl on the chapter she’d written. One of the biggest criticisms was show, don’t tell.
I remember telling the attendees I’d been thinking about that piece of writing advice. I was concerned that younger generations may be unable to deliver the required attention to a piece. You may need to write shorter fiction that actually “tells” instead of shows because they won’t be able to infer emotion or meaning from the act of showing. I mentioned I thought it was because of YouTube and tutorial culture, where people won’t try anything without being told everything first. I still think this plays a part, but it may be a symptom.
Ultimately, the foundational concept of show, don’t tell in fiction may need to be thrown out like yesterday’s garbage, as future generations cannot infer deeper meaning from descriptions. If you are unfamiliar with the rule, here’s an example of what the transformation may look like.
She folded to the ground, collapsing like a house of cards as she shut her eyes and tucked her lips behind her teeth.
Becomes.
She was disappointed and knew it was hopeless.
Okay, this is not my best description, but it’s enough for you to get the point: a degradation in quality to meet low-attention-span readers, which makes people avoid reading.
Ultimately, anyone who might have been interested in more traditional art will be turned off by a warped lack of incentives, never having a chance to discover the real value of art and the artistic process. This generation’s inability to delay gratification means any activity requiring practice and investment will be considered a waste of time.
In the future, the creation of art may very well morph into other activities like a video game. This may even be pitched as a game you can play with others. Although this may be fun (video games are fun) and seem like a sort of progress, the devaluation continues, and everything from the previous section applies.
I spent most of this section discussing written work since it made for a better demonstration, but these same principles apply to all other forms of artistic expression.
Mistakes and Misunderstanding
Applying AI-generated works to a low-attention environment leads to a fundamental mistake: mistaking resolution for quality. They aren’t the same. I’ll write more on this later, but you can see this with all of the generated Veo 2 examples. These examples have a high resolution (look good visually) but poor quality (they actually suck).
A human actor playing a part performs the emotions supposedly taking place in the character’s head. These are the types of things that are missing in AI-generated video. The generations are unfeeling, giving the impression of being dead inside. That’s because the actual creator (the AI) was never alive to begin with.
Quite a few people working in tech think people who don’t shouldn’t have jobs. These same people believe programs that don’t lead to technological progress are a waste of money. If you are trying to understand the current moment, look no further.
From this perspective, since art can be easily generated, the value of creating must be minimal, and anything that adds friction to the process must be bad. We need to push back against this narrative, not because it’s misguided but because it’s flat-out wrong.
Use AI For What It’s Good For
There’s a growing AI backlash, which is understandable but misguided. This situation isn’t helped by AI influencers and tech executives making stuff up and overhyping claims whenever their mouths open. Hating the tech because you hate the people making it isn’t a recipe for success.
AI isn’t purely ChatGPT or even LLMs. Many different AI approaches have various benefits for humanity and the potential to accelerate cures for many of humanity’s ails. Notice I used the word accelerate instead of cure. People can wield hubristic ignorance, blinding them to the fact that humans solved problems before the arrival of AI.
We want AI to provide benefits, such as cures for cancer and hunger and ending geopolitical conflict. But solving real problems with AI is hard, especially when AI can actually exacerbate some of these problems instead of solving them. Solving non-problems with AI is easy. This is why we get AI art instead of cures for cancer.
The goal here isn’t AI avoidance but AI selective usage. Don’t use “AI for everything,” as the poor advice goes, especially for art. Put a firewall around activities you value where AI would degrade the activity and use AI for more mundane activities to gain efficiencies. We can have the best of both worlds if we want to. We can cure cancer and protect art. Anyone who claims otherwise is selling something.
If you have kids, leaving room for friction and discovery is important. Let them explore different creative tasks and help them understand the benefits that manifest from the investment of time and effort. They’ll discover that this investment will pay far more dividends than any momentary gratification from AI art as they make new discoveries and learn about themselves. Unfortunately, this isn’t easy and will take work, which is why many take this path. Delay device usage as long as possible, and let them explore without mediation.
Unfortunately, this shift to The Slop Generation is already happening, and we are losing the battle to instant gratification and a sense of false, momentary satisfaction at the sacrifice of lifelong satisfaction. I’m going to leave you with a couple of quotes from Ted Chiang’s article called Why AI Isn’t Going To Make Art.
But let me offer a generalization: art is something that results from making a lot of choices. This might be easiest to explain if we use fiction writing as an example. When you are writing fiction, you are—consciously or unconsciously—making a choice about almost every word you type; to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.
Generative A.I. appeals to people who think they can express themselves in a medium without actually working in that medium. But the creators of traditional novels, paintings, and films are drawn to those art forms because they see the unique expressive potential that each medium affords. It is their eagerness to take full advantage of those potentialities that makes their work satisfying, whether as entertainment or as art.
One of the oft-repeated talking points erupting from the mouths of futurists and tech leaders alike is claiming that things will cost nothing in the future. As if we are to believe all of these people are in the business of making something for nothing. The entire claim is a gross absurdity that charlatans like Ray Kurzweil conjured out of thin air, and others parrot at every opportunity. This claim is made with such confidence that it is rendered self-evident, and to question it means you are an out-of-touch dolt lacking the religious fervor necessary to create the techno-utopia.
But these responses are a smokescreen to dispel the very rational questions this claim evokes. None of these people can explain exactly how this will work in practice or are willing to admit just how bad things will get, which seem like consequential details to omit considering the plan to rework the social contract of most of the world.
The claim promises us a Fully Automated Luxury Communism (FALC) where all of our needs are not only met but propels us into a life of luxury. However comforting the concept, the reality may be closer to Fully Automated Digital Breadlines (FADB). I know, how dare I poo-poo the utopia.
The False Choice
We are often given a false choice. We are told that if we don’t allow companies carte blanche to raw-dog technology all the way to utopia, then humanity will vanish. Either grow or die, as the mantra goes. Given this, a minuscule number of people are trying to rework the social contract and reimagine society without society’s input.
We can have cures for cancer and other illnesses without destroying art, stealing people’s work, or removing humans from the creative process. However, curing cancer is a hard problem, and imitating humans is easy. So we get AI slop machines instead of cures for Alzheimer’s.
Maybe I’m just an idiot, but I fail to see how LLMs will make humans immortal. Immortality is one of the many promises if we only just let it happen, even though there’s absolutely no evidence for this.
Also, if you read Andreessen’s Techno Optimist Manifesto from October of 2023, you may notice his crediting of Filippo Tommaso Marinetti, the author of the Fascist Manifesto. Marinetti was a futurist, but his position as a futurist and someone who had complete disregard for the past led him to embrace fascism as a logical vehicle for technocracy.
Don’t get me wrong, there’s plenty in Andreessen’s manifesto that I agree with. We are a society built on technology, and this has brought some of our greatest achievements. There certainly are regulations that seem pointless and get in the way. There are groups inside organizations that have become politicized and create unnecessary obstacles. I also agree with the critique of communism. These are all true. However, Adreessen’s mistake assumes that multiple things can’t be true simultaneously.
Even though these are extreme views that some have labeled techno-authoritarianism, understand that they are the average view of the e/acc community. Andreessen also invokes the perils of communism multiple times while also driving humanity into techno-communism, but to each their own, I guess.
I love technology and believe, as Andreessen does, that technology will deliver the best future. It’s because of technological advancement that we’ll cure cancer and reduce suffering around the world. However, I don’t believe a better society results from discarding ethics and principles and disregarding voices different from our own in the pursuit of generating a cornucopia of innovation porn. We in technology seem to constantly make this mistake, only to be disappointed by our ignorance of the complexities of the real world and the jobs and perspectives of others.
Ethics and principles aren’t obstacles or roadblocks. They are guideposts that ensure what we build aligns with our values and vision of the world we want to create. We, in this case, meaning society as a whole and not just a couple of dudes sharing technology with their friends.
The Claim
If you have escaped these claims, here’s a recent example from Marc Andreessen below.
You read that right. We need to hurt you before we can help you. It’s the sort of pitch you’d hear from a sadistic boyfriend who insists he needs to tear a partner down before building them back up. The we have to break it before we can fix it mantra is applied to almost everything, including humans and the environment. This is the core premise of the Effective Accelerationist (e/acc) movement.
But Andreessen is hardly the only one making these claims.
That’s right, Google is in the business of giving you things for free. We’ve learned this lesson a long time ago. Yes, Google makes its money off of ads for its “free” services. However, in a future where things are worth nothing and people don’t have an income stream, it seems likely that advertising budgets will be zero as well.
I blame much of this on Ray Kurzweil. For years, he’s been peddling this nonsense. I addressed this very same claim in my post on his latest book in the “Things Will Cost Nothing” and “Jobs and Wages” sections of the article. Despite this, I wanted to explore this topic further.
These people claim we shouldn’t worry about losing our jobs to AI because AI will make companies so good that goods and services will essentially be cheap or free. But both on the surface and upon reflection, the claim is absurd.
Nobody can describe exactly how this is supposed to work other than sprinkling everything with AI magic. When someone does make an attempt, like Ray Kurzweil, for example, the explanations make no sense, don’t address the questions, and highlight how little about the real world these people know.
For years, I’ve been pushing back against the phrase, “AI won’t replace people. People with AI will replace people without.” This is just patently false. The moment AI is good enough to take our job, it will. I mean, it doesn’t even have to be that good.
So, no job, no income. This is our baseline. It doesn’t matter how cheap things get if you have zero.
But AI, Tho
Before we get too far, let’s address the counterargument. For all the issues I’m about to raise, the answer is, “But AI, tho.” The response involves invoking the name of AI like a magician conjuring a spell. We are told that AI will be so great and powerful, rising to the status of deity, and no matter what the encountered issue, AI will figure it out. But merely spouting an incantation doesn’t make it a reality.
This answer is a complete copout that leaves the questioner unsatisfied. Whenever someone invokes the But AI, Tho defense to real questions, continue to ask them for more specifics. Don’t allow the oversimplification of a vast and complex problem space. AI isn’t god, and they aren’t prophets.
The “Sucks To Be You” Gap
Remember, we need to be broken before we can be fixed. This means there will be a gap between the damage incurred and any mitigation strategies. I call this the Sucks To Be You gap. There is no telling how long this gap will stay open or what mitigations will be implemented to remedy it.
Unemployment is unlikely to hit something like 90% all at once. This would mean that early people displaced by automation would be the most harmed since they would be unable to support themselves and their families and have no real recourse for their situation. How long will this drag out? My guess is years, possibly a decade or more, depending on how slow the adoption is and any difficulties implementing mitigations.
The amount of harm caused by this gap is unfathomable. This gap brings pain, suffering, and death. If you think I’m being dramatic, think about it for a moment. Imagine the mental toll this takes on someone trying to provide for themselves and their family. This isn’t a matter of re-skilling. Even if people did re-skill, the competition for remaining jobs would be astronomical, with thousands of applicants for a single position. This isn’t p(doom) it’s p(shit).
It’s easy to see how self-harm could result from this situation, but that’s not the only scenario where mortality is concerned. Not working leads to a lack of benefits, meaning you can’t make co-pays on doctor visits and prescriptions. This doesn’t include all of the potential harm from algorithmic decision-making mistakes. Deaths will result, and we know this because we’ve seen it happen on a smaller scale with people not being able to afford insulin.
No Intelligence Explosion
All of the claims of a near-future techno-utopia are predicated upon an intelligence explosion. This is the condition in which AIs will recursively improve, creating even better AIs that morph into superintelligence. Advocates claim this attainment of superintelligence fuels this world of comfort and abundance. But what if it doesn’t manifest this way? What if we get the Diet Coke of AGI? Just one calorie, not intelligent enough.
The assumption is that superintelligence brings massive productivity gains, but what if, instead, we get algorithms that are purely good enough, leading to human workers being displaced and productivity staying relatively the same? For example, an agent can work 24 hours a day, but what if that 24-hour-a-day agent produces the same productivity as a human working 8 hours a day? This could happen because of needing to account for errors, wait times for additional reasoning, running tasks multiple times, and other issues relating to the complexities of seemingly simple tasks. It’s easy to see how this can stretch out when we factor in additional difficulties of completing complex tasks.
This human replacement could result in cost savings but would be far from driving costs to zero. Also, this would be less of a complete human replacement and more of a human staff reduction. Now, you have a displaced workforce and a company with similar productivity. This doesn’t seem like a recipe for a utopia. It’s a recipe for problems.
This is a very real possibility, especially given all of the hype around LLMs. I know everyone is losing their mind about DeepSeek at the moment, but I don’t believe LLMs are a path to AGI, much less ASI. However, it’s important to realize that we don’t need this level of intelligence to apply these technologies to specific tasks successfully. It’s entirely feasible that a company would take a shitty LLM with repeatable failures over a human worker if they could save money.
What’s The Point of Things Costing Nothing?
I’m not sure anyone gets out of bed in the morning with dreams of creating a company that delivers goods and services that cost nothing. It’s even absurd to say out loud, so you might wonder why people at the largest companies in the world are making this claim. Investors are the same way. Nobody is investing in a company so they can deliver zero-cost goods and services. In the Sucks To Be You gap, the first affected suffers the most harm, but the opposite happens with companies.
Nobody is investing in a company so they can deliver zero-cost goods and services.
Tech leaders and investors aren’t considering what happens to their companies after this so-called intelligence explosion. They are thinking of all the money they will make leading up to it. This is why these staunch capitalists are so comfortable forcing everyone into techno-communism. Now that I think of it, the thought of an algorithmic Stalin hunting kulaks is terrifying.
Stagnation
Counterintuitively, this condition could lead to stagnation. The very opposite of what proponents claim. This doesn’t strike me as a competitive environment where companies and people are stepping up to create new solutions due to a lack of incentives. I guess someone could make the argument that people’s lives will suck so bad that they’ll be incentivized to create something better. Fair enough, but it seems like these bigger initiatives would cost more money, putting them out of reach by these very people. Not to mention, this is an odd flex for the techno-utopians. “Your life will suck so bad you’ll be dying to create something better.”
The price of stagnation for a majority of the population is that they remain in the mire of the Sucks To Be You gap for a much longer time. Even if basic necessities are met, it will be miles away from a good life, much less luxurious.
Things Will Still Cost Something
The core premise of the argument that things will be zero or low cost is absurd on its face, so much so that it’s remarkable that nobody seems to push back. A whole host of things won’t be free or low-cost. Consider rent and property, the means to generate electricity, medical treatments, and, most importantly, food. Even extracting and refining raw materials is going to cost something. Imagine being monitored every moment with everything in your home subscription-based, requiring a micro transaction for nearly everything you do. Now, that’s the utopia we’ve all dreamed of!
Regarding food, Kurzweil claims that advancements in vertical farming will make abundant, nutritious food freely available. This highlights Kurzweil’s cluelessness on a variety of topics. Vertical farming took a hit last year, making MIT Technology Review’s list of the worst tech failures of 2024. Score another “L” for Kurzweil.
As I mentioned, companies and investors aren’t in the business of giving things away for free. These companies will adjust to the conditions imposed upon them. When have we ever seen a company that gets hit with higher taxes or additional tariffs responding with, “Well, sucks to be us. I guess we’ll have to make less money now.”
This condition may level out at some point. After all, if nobody has any money to buy your products, that’s not a good business strategy either. I’m saying that this leveling out could take some time, especially if a segment of the population continues to remain employed.
New Risks
New architectures, technologies, and automated processes will bring new risks. Due to our complete dependence on these systems, these risks will have a much larger direct impact. The vertical farming example is instructive because it raises new risks and considerations. For example, damage can spread quickly in these new architectures, creating cascading failures.
In reality, the company’s lettuce was more expensive, and when a stubborn plant infection spread through its East Coast facilities, Bowery had trouble delivering the green stuff at any price.
And this is just one of the many potential examples. Whenever potential challenges such as this are raised, the But AI, Tho defense is invoked as some sort of benevolent deity here to deliver our salvation and absolve us from our sins. “AI will just figure it out.” This is not an answer.
Techno-Communism and Techno-Welfare
Let’s acknowledge that these companies aren’t willing to part with their money. It’s not like they will be so successful that they’ll start sharing their profits with us. Even if they half the cost of goods and services or even reduce by 90%, we’ve got zero dollars, which makes these cheap necessities still out of reach. This begs a couple of questions.
How do companies make money from people who don’t have any?
It seems unlikely to be profitable in this environment, so companies raise prices for those who can afford their products to cover gaps. This situation actually makes it worse for displaced workers, as I mentioned previously in the adjusting to market conditions section.
What’s the remedy?
Some have proposed an automation tax that funds a Universal Basic Income (UBI) program. This sounds good on paper but may not be so great in practice. We will tax people who are making less money; hence, there will be less recovered in taxes. Not to mention, I’m only considering the United States here. What about goods and services from other countries? After all, we have a global economy. This requires tariffs on goods and increased taxes on digital goods, which will require companies to raise costs even more.
There is the impression that the techno-welfare provided by some universal basic income will have us jet-setting around the globe. This is the premise of Fully Automated Luxury Communism (FALC). This is flat-out bullshit when you consider the realities on the ground. UBI’s benefits are a social welfare program and will be commensurate with similar programs.
Nobody on a social welfare program lives it up on their yacht, sipping champagne and wondering when their Ferrari will be out of the shop. These people worry about basic necessities constantly. Any small hiccup can result in major consequences. This future techno-welfare program will be far more like today’s social welfare than some government-funded luxurious lifestyle. So, yes, it is much more like Fully Automated Digital Breadlines (FADB) than FALC.
Not to mention, this very same social welfare program will be administered by the very system systems that displaced these workers in the first place, leaving the door open to a whole host of technical issues and challenges that will affect the people in the program, adding to risks.
The thing that pisses me off about people like Kurzweil is that the very foundation of their arguments is not only so disconnected from reality that they don’t make sense, they are dehumanizing. But for people like Kurzweil, this is a feature, not a bug.
The response to hungry children comes off as, “Just shut up and eat your amino acid paste, you ungrateful little shits. Don’t you realize how much more compute you have access to? You couldn’t even run stable diffusion locally when I was a kid!” When you are hungry, it’s hard to eat your computer.
Reduced Agency and Helplessness
What does it mean to be human in an age without work and agency? Do we resign ourselves to being helpless and needy? This is hard to pin down in advance. Humans are indeed incredibly adaptable creatures, but there’s a limit to this adaptability. But more importantly, why should we settle for this vision of the future?
These systems turn us into robots, shoving us into predictable buckets, reducing our agency, and making us dependent. This is necessary to increase the accuracy of predictions. The result is we end up as helpless schmucks standing on the sidelines, waiting to be told what to do and where to go at the mercy of every algorithmic decision. Technology should work for us, not the other way around, a point that gets lost in the shuffle and hype.
With every new risk that surfaces, we’ll be helpless to intervene. We need to take it on faith that what we built will automatically do something about it, as the world we construct becomes far too complex for us to understand. In some instances, humans may not be informed of impending dangers due to their lack of ability to do anything about them. We remain blissfully aware until the asteroid strikes.
We should insist on better. We deserve something better—technology that works for us, not us working for technology.
Technological advancements require tradeoffs, which will benefit humans as a whole. For example, suppose self-driving cars worked as advertised and delivered on promises. In that case, giving up manual driving for the benefit of safer roads may be a worthwhile tradeoff that most of society accepts. However, today, we are being asked to pre-purchase a tradeoff where it’s unclear what we get and what we lose.
Does This Sound Like Utopia?
I don’t know about you, but this scenario doesn’t sound like a slam dunk in the utopia basket. At best, this sounds like human-forced retirement with a monumental cut in income and benefits. At worst, it’s suffering and death, far from the promised life of luxury. It likely won’t be either of these extremes, but it will be something like a Fully Automated Digital Breadlines scenario I mentioned where the role of humans is needy and dependent.
I’m not sure exactly where I fall on the utopia scale above except to say I am probably not in the upper half. Not a precise measure other than to say away from the luxury lifestyle.
Can we achieve artificial superintelligence quickly and solve the world’s problems by creating a world of abundance? Yes, it’s certainly possible that everything snaps into place perfectly, and governments and corporations work hand in hand to create a world of abundance free from suffering. Possible, just not probable, or at least probable in a reasonable amount of time. For this to be the winning scenario, things must work perfectly the first time with advancements free from issues. We should know from history this is rarely the case.
Even if we eventually reach a reasonable utopia, we’ll have years, if not decades, of pain and misery as humans do their best to adapt and deal with less-than-perfect technology, governments, and companies. All of these challenges are incurred by humans while simultaneously being stripped clean of our agency and purpose.
By some estimations, communism is responsible for 100 million deaths in the twentieth century. Although some dispute this number, even on the lower side, we’re still talking about 50 million people. But hey, what’s 50 million deaths among friends? Something about one death being a tragedy and a million being a statistic. And yes, I know Stalin didn’t say that, but it’s relevant here.
Although I don’t think techno-communism will cut that wide a path, I do believe that some will view resulting deaths and misery as the cost of progress. However, progress is subjective, and despite often being linked, innovation and progress aren’t the same thing.
Conclusion
I hope that none of my predictions come true, that I am wrong, that some fluke happens, and everything magically snaps into place without issue. Thankfully, much of the hot takes on social media can be written off as bros sharing vibes. Also, I don’t think the current crop of LLMs will cause mass unemployment, create large destabilizing effects in the workforce, or create immortality. However, I’m not as confident about this prediction, well, other than the immortality piece.
The real question for LLMs is how much better this buggy, insecure, black-box technology needs to get to start disrupting a larger part of the workforce. We’ve seen this happen in the creative domains, but the cost of failure is low in these use cases. Let’s hope there are no plans to hook ChatGPT up to air traffic control or the nuclear arsenal, but there are still plenty of other jobs without such high failure costs. Only time will tell.
The attempt by a few to change the social contract raises many questions: Who sets the rules? Who changes the rules? Who or what makes the important decisions affecting humanity? These are good questions to have answers to before wading into the slough.
This situation can’t be described as a Faustian bargain since most people won’t gain any true advantage. At least Robert Johnson received amazing guitar skills. Many of us will get digital breadlines and an endless feed of slop.
One of the oft-repeated talking points erupting from the mouths of futurists and tech leaders alike is claiming that things will cost nothing in the future. As if we are to believe all of these people are in the business of making something for nothing. The entire claim is a gross absurdity that charlatans like Ray Kurzweil conjured out of thin air, and others parrot at every opportunity. This claim is made with such confidence that it is rendered self-evident, and to question it means you are an out-of-touch dolt lacking the religious fervor necessary to create the techno-utopia.
