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.