This post is the piece those two were circling around: what happens to the bottom of the chart, and whose job it is to fix it. Spoiler — it’s yours, senior engineer. It’s mine. And most of us are about to get it wrong in a very specific, very tempting way.

BS. Most good seniors I know would love to hire more juniors, feel badly for the mass of recent college grads who are unemployed, and enjoy mentoring new engineers without needing a wall of AI slop to tell them to do so. When you give us control over the hiring budget and let us hire juniors again, you can blame us for the future senior engineer shortage. Until then, blame the people actually at fault (management, executives, the investor class), not us.

I have a hard time telling AI authored LinkedIn thought leadership apart from formulaic LinkedIn thought leadership sometimes, but this feels AI written, which makes it fairly amusing considering the point it’s making.

(Does vibe coding feel incredible? Also, “Nobody’s asking what happens in Month 18 when the original dev has left and nobody understands the codebase” implies that the vibe coder original dev understood the codebase at any point, which isn’t an argument I’d feel comfortable making for any of the ones I’ve worked with)

A couple observations on reading this (as someone who heavily uses agentic coding at work and is mostly OK with it):

• Maybe I’m weird, but I don’t find “we always plan the implementation and make decisions first, and only then do we write the code” to describe my work style that well. If I don’t know how I want something to look, I will frequently just start writing whatever code comes to mind about that something. Most of the code will suck, and I’ll probably throw much of it away, but I find that it’s often a much more efficient way to get to a design that I like than sitting and debating tradeoffs in the abstract. Me writing the code might be slower than an agent writing the code, but that misses the point of the exercise: me writing the code is a way for me to build my own understanding of the problem.
• The text translation analogy at the beginning is illustrative, I think. A piece of writing has information, but also elements of the author’s wit, humor, tone, their assumptions about us as readers, and the cultural frame they write within. Good writing combines all of those things in an appealing way. At some basic level translation is about translating words correctly, but “correctly” there does a lot of work. It can be very hard to translate a work from one language to another without losing those less obvious characteristics (wit, tone, etc) that make it appealing, especially if you’re translating (e.g.) idioms or slang that don’t have a clear counterpart in your destination language. This is probably familiar to folks who read authors who’ve been translated outside of their native language: the right (or wrong) translator can have a huge impact on what you experience while reading. That’s not a bar we hold for all text (we’ve all seen very poorly translated manuals for cheap consumer electronics that are nonetheless fine because we can figure out what they’re trying to say), and similarly isn’t a bar that holds for all code, but there is some code where the nuances that automatic translation misses are actually important, and where a human touch is useful.

There are also enough engineers looking for jobs that companies can be picky and only hire the ones who seem agreeable and inoffensive, which reinforces the dynamic. “Idealistic” folks accumulate employment gaps that they have to explain or which cause AI ATS systems to auto reject their application before a human ever sees it, meanwhile management and hiring panels become increasingly corporate and less tolerant of difference because that’s who gets hired.

I don’t disagree with a lot of the advice here as a practical thing (e.g., having a scrubbed public image online, assuming that someone evaluating your job application will research you), but I also remember a time when you didn’t have to do that to work in the industry. A lot of the smartest, most creative folks in my CS program were also outspoken on (e.g.) privacy or civil rights in ways that could be tied to their real name, and it makes me kind of sad to think that they’d probably have to hide that today if they wanted to get hired as a new grad.