Vibe coding creates a bus factor of zero

The article throws out a lot of potential issues with AI generated code, but doesn't stop for a moment to consider if solutions currently exist or might exist to these problems.

- Before LLMs, provided that your team did some of their due diligence, you could always expect to have some help when tackling new code-bases.

Has the author never worked on legacy code before?

- (oh, and it has forgotten everything about the initial writing process by then).

Does the author not think this can ever be solved?

- Because of the situation of a Bus Factor of zero that it creates, vibe coding is fundamentally flawed. That is, only until there is an AI that can generate 100% accurate code 100% of the time, and it is fed 100% accurate prompts.

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.

Everything you interact with on a daily basis is either natural, or designed by a human. Until now.

It's potentially the opposite. If you instrument a codebase with documentation and configuration for AI agents to work well in it, then in a year, that agent will be able to do that same work just as well (or better with model progress) at adding new features.

This assumes your adding documentation, tests, instructions, and other scaffolding along the way, of course.

Unfortunately the corporate machine has been converging on a bus factor of 0. I've been part of multiple teams now where I was the only one holding knowledge over critical subsystems and whenever I attempted to train people on it, it was futile. Mainly because they would get laid off doing 'cost-savings measures'.

There were times where I was close to getting fed up and just quitting during some of the high profile ops I had to deal with which would've left the entire system inoperable for an extended period of time. And frankly from talking to a lot of other engineers, it sounds like a lot of companies operate in this manner.

I fully expect a lot of these issues to come home to roost as AI compounds loss of institutional knowledge and leads to rapid system decay.

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

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

We tend to tackle the same problems the same way. So how something is solved, tells you a lot about why, who and when it was solved.

Still, it's a valid point that much of the knowledge is now obscured, but that could be said too of a high employee churn organization.