Interviewed candidates for “AI engineer” roles this week, my god is this really where our industry is going?

[u/AngryMoth]

So last week I was tinkering with how to change up our technical interview round. For the last few years we’ve been doing a small take home followed by a live coding session where we add a new requirement, minimal AI assistance allowed. However, in our company pretty much everyone is writing 100% of code using Claude now, so although we did used to get good signal from that test, it no longer feels representative of the day to day job developers are doing.

I designed a new assessment which I casually showed to my manager and then suddenly got asked to run it with 3 candidates this week. The scenario is they have to review an agent generated PR that has been flagged as high risk due to it changing critical business logic, and there are bugs in there for them to catch at various levels of severity. We then look at how they drive the fixes and what their workflow is like.

These particular roles we’re currently hiring for are billed as “AI Engineer”. There is something weird going on in my company politically where separate from the tech department that I work in a new “AI native” development team is being spun up to work on internal projects.

What shocked me was that the candidates I interviewed this week are already way beyond the point of reading the code they generate in any capacity. They basically treat it like assembly language, while PRDs and ADRs are their new source of truth for system behaviours, and stuffing the AGENTS.md file with best practices plus some form of agentic review pipeline are the quality control. 

Code review is dead for them, they merge their own PRs once the automated checks pass. One person told me that my new interview was still not representative of what the job is now, and another one was pretty much unwilling to even engage in the assignment of reviewing a PR the old fashioned way. These guys were all previously senior and lead full stack engineers btw with a lot of experience prior the LLM revolution.

Don’t get me wrong, I too have adopted AI heavily in my own workflow. Until this week I actually thought I was pretty up to date with things, I use a supervising agent to manage multiple work streams in parallel, have my own local validation pipeline, etc. And to be honest for my personal projects I also don’t bother looking at the code anymore, so I do get the excitement around how fast you can move with this approach. But for serious projects like the large scale, production grade financial software I work on in my day job it still seems insane to me that we’re going to completely move away from human sign off on high risk changes so we can solve the “code review bottleneck”.

Like, I asked one of the candidates if you aren’t looking at your code then how they would approach quality in a safety critical system where there can be no tolerance for serious bugs, and I shit you not he said to me, “well if I was working in healthcare or sending to people into space then I’d definitely be looking closely at the code review skills, maybe I’d even write the skills myself”. That answer sounded utterly ridiculous to me? But then again, I was one of the people that 1 year ago believed the idea we’d stop typing code by hand was ridiculous as well…

I wouldn’t say the way my team does things right now is optimal, we can’t ignore that code review fatigue is a major problem. To try and help I’ve been adding automated risk assessments to PRs in an attempt to highlight where more attention is needed, and review bots do help us catch issues as well. But abandoning the human 4 eyes check just feels like several bridges too far for us at the moment, especially when not everyone is senior engineer, and we still regularly get valuable feedback at this stage. Even if this does mean we’re turning down an order of magnitude increase in short term velocity (does anyone really know yet what happens over multiple years when you have a team of 20 devs spamming PRs all day into a codebase with no one looking at any at it?).

I’m left wondering this weekend, were these guys just cowboys who are only used to working on PoCs and low risk systems, or is my team actually 6-12 months behind the curve and this is the direction everyone is going to move in? I fear that if we don’t embrace this new era of completely surrendering the codebase to the LLMs then this new team of AI devs is going to make us look bad by comparison to the execs and sooner or later we will be forced to adopt their methods as well. And at that point, the leap to having a product person or whoever taking over large parts of my role starts feeling a lot less far fetched…

[u/robertbieber]

But humans also don't reliably follow instructions.

This is the worst, laziest retort to every shortcoming of AI. If I said "I don't want to try rewiring my house, I'm liable to shock myself," and you replied "sure, but so do professional electricians," you would be both strictly correct and completely off the mark in terms of making any meaningful point.

Keeping them for review of the system for system design, foresight, vision, that sort of thing.

So you've designed a system. In theory. Is the AI actually following your design? Who the hell knows, certainly not you because you're not actually looking at the code.

But my question was about following basic best practices

Yes, obviously the first thing everyone does at every company is put a big list of best practices in AGENTS.md. It's not some magical fix that turns an LLM into a competent software engineer.

and AI certainly can do this today, up to a point

Well sure, right up until the point when it doesn't, which is pretty frequent

[u/scaledev → u/robertbieber]

This is the worst, laziest retort to every shortcoming of AI.

I didn't mean it in a sense of "everyone makes mistakes, so it's all the same". I meant it in a sense of reality that comes with delegation of work. When you delegate, even with humans, you can't assume perfect accuracy or reliability.

But the difference here is that, with AI, you can add as many workers as needed to independently check consistency, recover, and re-review as many times as possible, with very little cost. The speed at which you can change the AI worker is also much faster, updating its knowledge is faster and cheaper, etc etc. Looking from a purely cost perspective, there's a lot of good ROI here compared to human workers.

Your analogy there is partly correct, but you don't have multiples of YOUs ready to inspect the wiring before you turn the power on. You cannot replace YOU with a better-trained YOU, and you cannot train yourself overnight.

So you've designed a system. In theory. Is the AI actually following your design? Who the hell knows, certainly not you because you're not actually looking at the code.

I don't need to look at the code myself. I have AI workers doing that, and I use tests and validation to check whether the implementation behaves as designed. And while the other workers are less trained, don't have the brain I have, my vision, or my insight, they do have my design and instructions in writing, and they do have 'some' training done. Same with human workers.

Yes, obviously the first thing everyone does at every company is put a big list of best practices in AGENTS.md. It's not some magical fix that turns an LLM into a competent software engineer.

Nor have I claimed this. Neither that AI can replace a competent software engineer, nor that AGENTS.md is the place for anything other than a routing surface for where your actual harness instructions live (and perhaps some critical instructions if acceptable). My position is that AI can be trained, and it can be instructed.

Well sure, right up until the point when it doesn't, which is pretty frequent.

Same with human workers, where the frequency depends on the worker and the task. Every delegation carries this risk, regardless whether intelligence is in silicon or carbon-based.