Vibe coding creates a bus factor of zero

I like the spirit of these, but there are waaaay more. Like you only mentioned the ones for professional and skilled coders who have another option. What about all the sub-examples for people all the way from "technically unskilled" to "baby-step coders". There's a bunch of things they can now just do and get in front of ppl without us.

Going from "thing in my head that I need to pay someone $100/h to try" to "thing a user can literally use in 3 minutes that will make that hypothetical-but-nonexistent $100/h person cry"... like there is way more texture of roles in that territory than your punchy comment gives credit. No one cares is it's maintainable if they now know what's possible, and that matters 1000x more than future maintenance concerns. People spend years working up to this step that someone can now simply jank out* in 3 minutes.

* to jank out. verb. 1. to crank out via vibe-coding, in the sense of productive output.

What's interesting is that I wouldn't really call any of the things you list software development. With the exception of the "testing starting point", they're mostly about translating from one programming language/API to another. Not really "the future of programming". Also, they all sound like the kind of thing that most "basic" models would do well, which means that the "thinking" models are a waste of money.

Finally, the productivity boost is significant from the perspective of the programmer, but I don't know how big it is from the perspective of the employer. Does this significantly shorten time-to-market?

> * I want to apply some repetitive change across a large codebase that's just too complicated for a clever regex, bam work you literally would have never bothered to do before done in 2 minutes.

You would naively think that, as did I, but I've tested it against several big name models and they are all eventually "lazy", sometimes make unrelated changes, and worse as the context fills up.

On a small toy example they will do it flawlessly, but as you scale up to more and more code that requires repetitive changes the errors compound.

Agentic loops help the situation, but now you aren't getting it done in 2 minutes because you have to review to find out it wasn't done and then tell it to do it again N times until it's done.

Having the LLM write a program to make the changes is much more reliable.

There's also a middle ground, where you have the AI generate PR reviews and then review them manually. So that 2 minutes of code you spat out (really more like 5-10 using CC) takes another hour or three to review, and maybe 5 to 10 more commits before it's merged in.

I've done this successfully on multiple projects in the 10-20k LOC, ~100 file area - fully LLM generated w/ tons of my personal feedback - and it works just fine. 4/5 features I implement it gets pretty close to nailing from the spec I provide and the work is largely refactoring. But the times it doesn't get it right, it is a slog that could eat the better part of a day. On the whole though it probably is a 3-5x speedup.

I'm a little less confident about doing this on projects that are much bigger... then breaking things up into modules begins to look more attractive.

> If you're using llms to shit out large swathes of unreviewed code you're doing it wrong

> bam, x days work done in 2 minutes

This is a bit of a misrepresentation, since those two minutes don’t account for the reviewing time needed (nor prorperly, which vastly exceeds that time. Otherwise you end up in the situation of “doing it wrong” described in your first paragraph.

personal favorite of mine - I want to switch data api but I dont have time to port 2 different services so here's their documentation. BAM. Done.

YES, this is the way to make AI tools pretty much a strictly positive productivity tool on large codebases.

Scaffolding is another area where LLM's work great.

I want to create a new project using framework XYZ. I already know how to do it, but I don't remember how to set up it since I only do that once, or I don't know how to set up a class that inherits from the framework because I usually just copy the other from another class in the same project. I can simply tell the bot to write the starting code and take it from there.

The sad thing is for a LOT of use cases an LLM is completely unnecessary. Like why do I even need an LLM for something like this? Why can't I just download a database of code examples, plug it into a search engine that apppears in the sidebar, and then just type "new project XYZ" or "new class XYZ.foo" to find the necessary snippet? A lot of NPM frameworks have a set up script to get you started with a new project, but after that you are practically forced to use Google.

It's crazy that a problem that could be solved so easily with a local file search has been ignored for so long and the only solution has been something impossibly inefficient for the problem it's supposed to solve.

Hello, author here. Thanks for your comment.

I agree with your first point, maybe AI will close some of those gaps with future advances, but I think a large part of the damage will have been done by then.

Regarding the memory of reasoning from LLMs, I think the issue is that even if you can solve it in the future, you already have code for which you've lost the artifacts associated with the original generation. Overall I find there's a lot of talks (especially in the mainstream media) about AI "always learning" when they don't actually learn new anything until a new model is released.

> Why does it require 100% accuracy 100% of the time? Humans are not 100% accurate 100% of the time and we seem to trust them with our code.

Correct, but humans writing code don't lead to a Bus Factor of 0, so it's easier to go back, understand what is wrong and address it.

If the other gaps mentioned above are addressed, then I agree that this also partially goes away.

Yea, I found the article to be overly reductive. I work on a shared ownership codebase, which at times, I am not going to be able to pull the original author at all times when I work on my branch.

At least having a partner explain some aspects is a huge unlock for me. Maybe the OP shadowboxing a world with no humans, but no humans is already a status quo issue that my team and I face sometimes.

> Why does it require 100% accuracy 100% of the time?

I've said this before, but I'd say it again: anti-AI people, instead of AI users, are usually those who expect AI to be magical panacea.

The vibe reminds me of some people who are against static typing because "it can't catch logical error anyway."

The Bus Factor was indeed an issue before LLMs, and in fact it's a jargon term that has been in use since forever.

What TFA is arguing is that never before we had a trend towards Bus Factor zero. Before, the worst was often 1 (occasionally zero, of course, but now TFA argues we're aiming for zero whether we're aware or not).

Yes, its very much a goldfish problem, where work needed grows to fill what is possible, not what is advisable or good. The only way I have seen people "solve" this is by putting a bunch of speed bumps in a process, and generally it just makes everyone lazy and deliver stuff at the last second anyway, not use the additional time to make something polished.

There is a really important point here, and it's critical to be aware of it, but we're really at the end of these tools and workflows and these issues can't be solved.

The author though neglects what a bus factor of 0 means in real terms and how it gets there, aside from the description of the definition upfront where all knowledge lost.

A company acceptable with a bus factor of zero is a company that is not willing to pay the economic advantage to the expertise required to do the work.

The economic demand, of humanity competing with AI is zero because AI does things its good at with an order of magnitude difference in cost, and the deception and lies surrounding the marketing coupled with communications channel jamming lead to predictable outcomes. What happens when that demand and thus economic benefit go to zero? Any investment in the knowledge in the first place has no return. No one goes into it, no one learns, and that's quite dangerous in economies based in money-printing.

So while there may not be a problem right now, there will no doubt be problems in the next proverbial quarter. Career development pipelines are years in the making. They are sequential pipelines. Zero into a sequential pipeline is zero out with the time-lag between the two (which is years).

~2 years without economic incentive is when you lose your best and brightest. From there is a slow march to a 10 year deadline after which catastrophic losses occur.

I had a chance to have a interesting discussion with a local community college Dean. Apparently they have had to lower the number of Computer Science related program sections because of lack of demand. In intro courses, for 18 sections there were 13 people who had declared for the major, most students when asked citing AI concerns and lack of career development pipeline.

What happens when you have no expertise that you can pay at any price to fix the processes involved because you put them all out of business using what amounts to a semi-thinking non-sentient slave.

Without supply, there can be no demand. Where supply becomes infinite because of external parties, there can be no demand. There can be need, but demand is not need.

So this all started in 2022. Best and brightest are re-skilling. There's a glut of lower competency talent too. Bad things happen when you mess with the foundations of economics, and they happen on a time lag, where you can't react fast enough after-the-fact.

What will it take? At some point there will be a crisis where they will have to treat it as triage on the battlefield. The people in charge didn't want to hear about this when it could have made a difference.

> I think the article underestimates how much intent can be grasped from code alone. Even without comments.

I agree, the human thought process always ends up getting embedded in a which of several possible ways any one thing might be done. But it's still a process, and a vastly inferior one to having a knowledgeable person on hand. Reverse-engineering has up until now been reserved to times when it is necessary. (I mean, we all still do it, especially on legacy codebases, but it's not good for productivity at all.)

> Humans (and I strongly suspect LLMs, since they're statistical synthesis of human production) are fairly predictable.

I disagree with the parenthetical. That's what stands out to me the most about LLM code: there are definitely intentions embedded in the code, but they're a hodgepodge of different intentions all tangled up together. You can't infer much of anything from a small snippet, because the intention behind that snippet was likely relevant to a completely different origin codebase that was piled up into a compost heap to grow an LLM out of. It's actually harder to make sense of an LLM codebase because the intentions distract and confuse the reader -- just as with text, we implicitly assume that a generated artifact means the same thing as if a human wrote it, and it doesn't.

> I think the article underestimates how much intent can be grasped from code alone.

That's very scale related.

I rarely have any trouble reading and understanding Arduino code. But that's got a hard upper limit (at least on the common/original Arduinos) of 32kB of (compiled) code.

It's many weeks or months worth of effort, or possibly impossible, for me to read and understand a platform with a hundred or so interdependent microservices written in several languages. _Perhaps_ there was a very skilled and experienced architect for all of that, who demanded comprehensive API styles and docs? But if all that was vibe coded and then dropped on me to be responsible? I'd just quit.

I wonder how soon (or if it's already happening) that AI coding tools will behave like early career developers who claim all the existing code written by others is crap and go on to convince management that a ground up rewrite is required.

(And now I'm wondering how soon the standard AI-first response to bug reports will be a complete rewrite by AI using the previous prompts plus the new bug report? Are people already working on CI/CD systems that replace the CI part with whole-project AI rewrites?)