Four Uncomfortable Truths About the Impeding Collapse of AI

The cracks are starting to form, and they’re visible. After GitHub introduced metered pricing to its Copilot subscription models, developers across the board realized just how much text that model generated for each request. And soon, they had to realize that the golden times of vibe coding were over. No more letting LLMs run rampant on a code base for hours on end to implement features or chase bugs. Now it’s all about being efficient, ensuring that the LLM doesn’t burn too many tokens so that the allowance reaches until the end of the month.

It’s kind of mindboggling to think about that: You are writing a bunch of text and submit it to some machine learning model, and the provider is going to bill you based on how many words it generated. But there is no way for you – or the provider, for that matter! – to really control how many tokens it will generate. Instead, you have to think hard about your prompt, and pray to the gods of probabilistic computing that the model does not think for too long. You can control what you provide to the model, but you have no way of knowing whether the model will accidentally enter an infinite loop and eat up your credit without doing anything of what you’ve asked it to.

But I digress. This is not about the insanity that is connected with the widespread adoption of LLMs. Instead, in this article, I want to provide a prognosis of what is going to happen going forward. And regardless of whether you’re an apologetic vibe coder or an “LLM-Luddite,”¹ you are not going to like that.

The AI Bubble is Popping

First things first: The AI bubble is currently starting to crack. You don’t have to be a fervent reader of Ed Zitron to know that: AI companies are slowly running out of VC money; data center builds are postponed; all companies are tightening the thumb screws around their usage plans. In a year, give or take, the AI bubble will fully burst, and it will take down a lot of startups with them. But not all of them, and that’s the first uncomfortable truth.

None of the “big names” will go out of business. Open AI will probably stay around due to market penetration. Anthropic, too, but for developers. Microsoft, Google, and Meta will similarly stick around, because they have business models that also include non-AI services (read: advertisements). Even if the AI bubble exterminates 90 % of the AI market, none of the companies that we all know and love/hate will go down.

It helps to compare the AI bubble with the Dot-com bubble in the early 2000s. Microsoft was very entrenched in the Dot-com market, and it still survived. Just because something turns from free money printer to a normal business activity, this does not mean that the cause of it will go away. Or did we stop living in houses after the 2007 subprime crisis?

Besides this big, primary point, there are a few other trends that will continue to flourish even post-bubble. And these are not positive trends. Instead, they are deeply worrisome. These are: (1) Software quality will stay at a degraded, lower standard; (2) There will not be any meaningful post-crisis re-hiring of laid off software developers; and (3) Atrophy of both skills and knowledge will continue to stay an issue for decades.

Atrophy of Skills and Knowledge will Continue

There have been debates that LLM usage deteriorates skills and knowledge. If you externalize any thought process to an LLM, you won’t be keeping your mind sharp. The more we rely on LLMs for basic tasks, the less we will be capable to do things by ourselves. And even if the bubble finally pops, this won’t suddenly make people crawl out of their caves and see the light. There will still be some LLM providers out there, even if they aren’t as many. And people will continue to rely on them for the tasks they have forgotten how to do.

This is the same principle as with Uber, Airbnb, Netflix, Instagram, or Spotify: No matter the enshittification of a service, once it is foundational to our social lives, it is impossible to fully get rid of it. The same will happen with LLM providers — once they have become muscle memory, people will continue to use them instead of (re)learning the necessary skills. Think of OpenAI as the next Spotify.

Skill atrophy is not going to go away. As long as there is a handy button for people to compose emails for them, many really won’t feel the need to write an email by themselves. And even if their favorite email client suddenly no longer has a button, I feel that many will reach for another provider rather than going through the — quite uncomfortable — experience of having to write an email with the correct tone and choice of words.

Because that’s the thing: Having to decide what to do, and rolling with that decision can be scary, depending on the stakes involved. And if there is something, like an LLM, that can do some of the work for you, it doesn’t feel as if you have to make the decision yourself. And this externalization can be quite comfortable. The main difference between LLMs and other technical breakthroughs in the past is that in the past technology has primarily made things easier, but still required knowledge to operate. Now, technology – for many people – automates the thought process itself.

No Meaningful Re-Hiring of Software Developers

And this atrophy is already re-shaping society. More specifically, if everyone can use an LLM for a task, this will gradually become the new norm. If you think, say, about an advertisement text, rather than letting an AI come up with a catchy slogan, you’ll possibly be more witty, but also slower than your competitors. And once AI-generated advertisement has become the norm, there won’t be much use for people who can think about the problem anymore. Exactly the same will also happen to software developers, many of whom are currently laid off.

Here’s the next uncomfortable truth: Even if all AI providers went bankrupt tomorrow, there will be no complete re-hiring of the software developers who have been laid off since the start of the AI bubble. There are two reasons for this. First, there had been a lot of over-hiring during the pandemic, because everyone suddenly relied on software for everything. Those “surplus” positions are gone for good.

But second, and more importantly, GPT models won’t suddenly stop working. As such, companies will continue to attempt to make their small core-team of developers achieve inhuman feats by pairing them with a school of agents to work on their software. Middle management has a reputation for good reason: They aren’t trained in any of the actual productive tasks that need to happen in a company. They are trained to keep the numbers look good. And as long as one developer can work through the same amount of tasks than two with the help of some LLM, management will happily keep only one developer. Because green-lighting a new server to run some big model locally is cheaper than paying one server every year because it’s a human being and looks more expensive on balance sheets.

Both an AI server and a developer might cost $100,000. However, the developer will need to be paid every year, while the AI server looks as if it’s just a one-off price. I’m not going to elaborate on all the levels this is wrong, but you hopefully get the point.

But there is more. The balance sheets and the inhumane appeal of fixed capital over human capital for middle managers is just one part of the equation. What will really convince managers that they can continue to operate with a reduced team is lowered standards. Which leads me to the next point.

Software Quality Will Likely Never Recoup

On social media, in personal chats, or in blog articles, many have already uttered concerns about a degradation in software quality. Vibe coding has objectively degraded the software quality of many products, and it has supercharged the principle “quantity over quality” for software, judging by the amount of new Markdown editors/readers/writers in r/Markdown over the past twelve months.²

Unfortunately, this will be a persistent trend. Again, GPT models won’t stop working overnight, and you can do a surprising amount of coding with local models.³ The “tech enthusiast-to-vibe coder” pipeline will continue. As John Gruber recently quoted Jason Snell, “if you can dream an app, you can probably build it.” And this will remain true even after the AI bubble bursts.

But just because some randos on the internet decided they want to become indie developers now doesn’t suffice as a reason for a general degradation in software quality. What is needed is a general, widespread degradation of software quality. Luckily, Microsoft is doing the lord’s work in this regard. Microsoft is really at the forefront of depressing the expectations of quality for software products. And it doesn’t matter where. Windows, VS Code, Active Directory, corporate licenses of Office — all of them are either stagnant, or actively degrading in software quality to a degree I have not witnessed in my entire life.

“What does this have to do with permanently lowered standards?” you may ask now. “Won’t users be able to tell good from bad?”

Let me tell you: We all have to interact with Microsoft products. We cannot avoid it. I am a Mac user, and have been for the past decade. But even I interact with at least one Microsoft product multiple times a day. Because I have no choice. Many people will just shrug and continue as they were. If you’re not a tech-savvy person, what are you going to do about it? Nothing. You can’t possibly know whether some bug is a stupidity that no sane developer would have let run past Quality Control, or whether you’re just doing it wrong.

This, gradually, leads to a lowering of standards that we, on a societal level, have towards software. And this will stick. Once people are “trained” to accept buggy or sluggish software, there is no incentive for anyone to improve their software. Because that doesn’t make money. Software standards are driven by large companies, not indie developers with a devotion to their craft. In turn, these lowered standards will empower companies to continue mandating AI coding agents for their developers.

The basic problem with the argument that software standards degraded is the notion of what a “standard” even is. “Standard” is whatever people see as the norm. And there is no objective baseline to determine what a “good” or “bad” standard is. A hundred years ago, the norm was that, if you were badly ill, you would just die. Nobody had an issue with that because there was no way to prevent that from happening. And vaccine acceptance shows that, even if it’s possible to prevent death, if people stop seeing the benefits of access to vaccines, they won’t care, until dying before the age of 18 suddenly becomes the norm again. The same phenomenon holds true for software.

Unless software degrades to state where it quite literally prevents users from doing their job at all, this trend will continue. Because the hard truth is that, as long as people can somewhat do what they need to do with software, they won’t rebel. If a software does its job, then — even if it is the most horrible experience you can have on this planet — people will accept it. Society does not operate on the principle of “I won’t use something that’s inferior.” It operates on the principle of “As long as it works, don’t touch it.”

Final Thoughts

When I talk with people or read discussions online about what people expect after the bubble pops, I see a lot of misconceptions about how the economy works. And I believe it is important to stay as realistic as possible. The AI frenzy has already altered society and the tech we use profoundly, and anything that has become culturally ingrained is much more likely to stick, even if the original cause of it vanishes. There is this famous notion in causal analysis that the cause that leads to a phenomenon to emerge in the first place can be entirely disconnected from the cause that perpetuates this phenomenon. That’s why social changes remain “sticky.”

Being honest about the changes that AI has already introduced to society and which are likely to remain persistent even after the economy goes back to “normal” is crucial to manage expectations. Atrophy, no rehiring of software developers and a persistent quality degradation of software are not the only things that the AI hype has brought. But they are the most visible ones.

With all of that being said, I’m not an oracle, and as such all of what I wrote here might turn out wrong. I would wish for that. But I have lived through two global economic crises already, and when the AI bubble pops, it’s going to be somewhere in the ballpark of the Dot-com or subprime crisis. And I have seen which changes have stuck around. I sincerely do hope that I’m wrong, but I’m not optimistic.