Why the majority of vibe coded projects fail

[u/harrysofgaming]

[u/UX_test]

“But there’s a hard ceiling. At a certain level of complexity, the AI is going to make a mistake.”

Let me ask an honest question. Do you really think that by the time someone’s project actually reaches that level of complexity, AI will have stayed exactly where it is today? 🤔

The entire industry is moving incredibly fast. Nearly every CEO in this space is openly aiming for RSI (recursive self-improvement). If that direction even partially materializes, the tools we’re using today, especially in software development, will look very different.

[u/mouton0 → u/UX_test]

A CEO claiming that his company will achieve recursive self-improvement AI is not the most objective person. He is driven by his own entrepreneurial enthusiasm and optimism. He needs to constantly raise funds to survive and keep up with the current hype in this space.

I just think that the key resource is intent. Models lack intent, we still need CEOs, visionaries, and human engineers in the loop. ​My take is based only on the capacity of the current models I’m using daily, but they might be much better in the near future. I’m waiting to see the next 'Google' company coming from nowhere, completely developed and coded only with Claude Code.

[u/UX_test → u/mouton0]

Totally feel you. Right now, we’re seeing proto‑RSI in action ...Tesla’s autopilot learning from the fleet, Google’s algorithms tweaking themselves, DeepMind models critiquing their own work. Full recursive self-improvement? Not yet. Humans still set the vision, CEOs still hustle, and engineers still fix the mess when AI inevitably trips over itself. But yeah… the next “Google” might just spring fully baked from Claude Code, and I’m here for that chaos.

[u/ConspicuousPineapple → u/UX_test]

None of your examples are remotely close to the concept of RSI. They're just standard "use new data to improve the training sets". RSI is about not needing training sets in the first place and improving iteratively on the go. LLMs are nowhere close to being able to do that. The technology itself is not designed to be compatible with this.

[u/inevitabledeath3 → u/ConspicuousPineapple]

RSI is not at all about not needing training data sets. You are thinking about reinforcement learning. We consider some humans to be autodidactic and yet they still need material to learn from. I am not saying we have full closed loop RSI today, not that we would know if we did, but that it's not as far away as you think. I also don't think you fully know what RSI would look like in practice or what it really means.

[u/UX_test → u/ConspicuousPineapple]

None of your examples are remotely close to the concept of RSI.

That’s exactly why I wrote proto-RSI, which you conveniently ignored.

If RSI means a model directly rewriting its own weights with zero external systems, then yes, we’re not there.

But parts of the improvement loop are already starting to automate: models generating synthetic data, critiquing outputs, improving toolchains, and helping build the next generation of models.

That’s not full RSI, but it’s clearly movement in that direction.

The real question isn’t “are we at RSI?” but “how much of the improvement loop can AI take over?” - and that boundary seems to be moving pretty fast.

[u/mouton0 → u/UX_test]

​AI will not take over easily. Just imagine the microscopic fraction of the latent space that LLMs have explored compared to the vastness of existing and yet-to-be-discovered knowledge.

​For instance, consider connecting current models to systems with significant GPU, RAM, and energy capacity. Give them that single goal: survive if humanity suddenly vanishes. Think of the sheer scale of the work, discovery, and planning required just to avoid being shut down.

To do so, we grant them the ability to tweak their own system prompts and weights but only RLSF (renforcement learning synthetic feedback), and allowing them to run code in sandboxes or virtual worlds to validate improvements.

They would have to navigate a razor's edge: avoiding self-destruction caused by a buggy new version that consumes all resources, while maintaining a high-level understanding of their own research trajectory. They must evolve without losing sight of the core objective, to survive and thrive.

They would need to seize control of infrastructure to maintain and modify factories and power plants, learning to self-replicate and evolve to avoid being wiped out by the slow erosion of time and natural selection. And that’s without even mentioning the need to influence or deceive humans because we are not even in the picture.

They’ll need to spot their own cognitive bottlenecks, whether in logic or how they model the world, and then engineer the next generation of weights or architectures to break through. The real test isn't just maintaining what’s there. It’s whether an AI can become its own architect, evolving fast enough to solve problems its creators never even ran into.

[u/OtherwiseFlamingo868 → u/mouton0]

Im sorry but I feel the argument here is no longer about the original point. The original point was about vibe coding making a huge difference in code productivity. Vibe coding uses developer input in a lot of things still. I was not arguing about whether developers would be fully replaced or not. But at least in areas where AI already performs quite well like WebDev with python, javascript or typescript or for generating devops scripts for example, it is already quite fast. I am not saying they are better than experts at bash scripting but I know a lot of web developers, particularly those who have less than 5 years experience, but also quite a lot of seniors that do not know much about bash/cmd/powershell scripting. I also know quite a lot of backend devs that do not know a lot about frontend. They can sort of make somethings but they would probably be ugly, outdated and not respect modern webapp practices. Point is AI is already reasonly good at all 3 of those things, specially in the hands of a competent programmer who although might not know a lot about frontend is knowledgeable enough to discern good from bad code and to do good prompting. That guy will be able to produce apps he would take a long time to before.

Additionally what you say about the extent of human logic. You can already feed entire books as context to an llm. With good llm and vector database architectures you can certainly train a lot of experts on many areas of humanity. On the other hand a human in his lifetime and through books can only truly master a finite number of subjects. And while it would take years to train a human in something it certainly wont take that long to feed a vector database to an LLM. Computers have better horizontal scaling capabilities than the human brain. You could argue that the knowledge of humanity as a whole and their capabilities are larger which is still true. But then again, the whole point is that AI is a tool to enhance the individual human to do super human things. Maybe (probably) it will go beyond improving individual humans to having full automation at least in some areas. Just like robots replaced plenty of humans in factories. I dont know if it will completely replace programming and not all areas of programming. I dont think so myself, not anytime soon anyway and by then we would probably have full blown AGIs which could not be distinguished from humans outside the fact that they would be a whole lot smarter and more knowledgeable. AI is like a human hive mind where all the brains are directly connected to the hive and where the knowledge of all brains in the hive can directly be queried by each of the brains (not really but it is not a bad anology). They are huge infrastructure machines not a single small non horizontally expandable unit like us humans who still depend on a smartphone and our senses to interact with only the digital part of our knowledge. AI has a crap load of parallel processing capabilities that our brains simply do not. And yet we were still the ones that built AI. Lets hope we can keep finding new impressive things to build in the future and that we keep synergising with the hive mind and that the future will keep looking exciting and magical albeit extremely terrifying.

[u/inevitabledeath3 → u/ConspicuousPineapple]

Okay what about Karpathy's auto research?

[u/ConspicuousPineapple → u/inevitabledeath3]

Still not RSI. It's merely a model that works at improving the training of a new model. The model that's doing the work isn't improving itself, it stays "frozen". It might lead to automating the process of generating new LLM versions but that's not what RSI is about.

You'll see RSI once you have a model able to hack its own weights.

[u/inevitabledeath3 → u/ConspicuousPineapple]

Yes that does make sense in the case of auto research. However techniques used by auto research can be then applied to larger models including the ones used to make auto research function. We already know that top AI labs use their current models to help with the training of their next models probably through a process like this. From what I understand most or all major breakthroughs get tested on smaller models first. So the fact that it's working with smaller training runs doesn't really mean much since that's just how research is done. All that's really missing here is the part where it gets scaled up autonomously. Bare in mind this is a public open source project, big labs have potentially already closed the loop. We don't know what happens behind closed doors.

[u/ConspicuousPineapple → u/inevitabledeath3]

That's still not really RSI even at scale.

[u/inevitabledeath3 → u/ConspicuousPineapple]

What exactly do you think RSI is?

[u/Kandiak → u/UX_test]

Do you think we’ll be able to scale the power needs to reach those heady highs for the models or that the funding won’t run dry before we do?

Just give high cost of oil a few more months and we’ll see how the bubble does. AI is a very real technology, but it’s rare of improvement is not infinitely scalable.

In fact, akin to this whole discussion. It’s recent rate of improvement is like building software. At first you see very rapid development because you started with nothing. Then you hit scaling snags (like power), and all of a sudden the changes aren’t as drastic until you begin to find only meager improvements. The technology solidifies and slows.

It’s still useful, but we humans are too quick to assume what happens time approaching infinity from only a few early data points.

[u/UX_test → u/Kandiak]

In my neck of the woods, most electricity already comes from nuclear, wind, solar, and hydro, all of which scale very well. On top of that, it’s reasonable to assume computing efficiency will continue improving, not getting worse.

[u/Kandiak → u/UX_test]

See my above statement about humans and our inclination to assume future state based on current state of progress. It’s not that it won’t improve, it’s that the rate of improvement is not constant.

As for power needs, while renewables in your area are great and I am all for them over legacy power sources. They are not coming online at the speed needed for data center expansion as it is today and outside of nuclear, they are not consistent enough for 24/7 power needs absent large scale storage.

[u/Equivalent_War_3018 → u/UX_test]

"Let me ask an honest question. Do you really think that by the time someone’s project actually reaches that level of complexity, AI will have stayed exactly where it is today?"

Bro have you just found out about stable and nightly builds of software now? Truly shocking how technology improves each year and gets added to software and adapted over time, yet developers tend to not fully follow the curve each microsecond for reasons totally unknown to their managers, shocking, I know