Two narratives about AI

Or, am I a "devops engineer"?

or.. a "SRE"...

or... am I a platform engineer?

You know what, I don't know.

What I do know, is that people keep trying to make my job obsolete, only to hire me under a different title for more money later. The practices and the tools are the same too, yet to justify it to themselves they'll make shit up about how the work is "actually, a material difference from what came before" (except, it's really not).

I'm still employable after 20 years of this.

I'm not a software engineer, and I'm not a tech CEO - I don't care, but people have been trying to replace me my whole career (even myself: "Automate yourself out of a job” is a common sysadmin mantra after all). Yet, somehow, I'm still here.

Eventually, things will stabilize to a point where we will know what the boundaries of all the new LLM based tooling is good for, and where humans add lots of value to that.

That will then drive a maximalist hiring spree, as the more people you have working at an increase in velocity, the faster you ship, and for once, perhaps the quality of code won't substantially decrease, assuming LLMs improve another few leaps in output quality and engineering workflows adjust in a normalized way.

Thats the hopeful side of the equation anyway.

I feel incredibly bad about customer oriented jobs, like white glove customer service. I already saw a trend (especially since 2020 but certainly before as well) where these AI chat bots and AI support lines will decimate that job category. These are pretty common white collar jobs, lots of people even in our industry got their starts on support lines.

Those are groups defined by something other than actual LLM usage, which makes them both not particularly interesting. What is interesting:

You have people who've tried using LLMs to generate code and found it utterly useless.

Then you have people who've tried using LLMs to generate code and believe that it has worked very well for them.

AI code completion is awesome, but it's essentially a better Stack Overflow, and I don't remember people worrying that Stack Overflow was going to put developers out of work, so I'm not losing sleep that an improved version will.

There’s going to be a need for smart people with a good work ethic unless literally everyone loses their jobs, and at that point we’re living past the singularly event horizon as far as I’m concerned and all bets are off.

I also see the non-development jobs as much more in peril from AI; I can go generate marketing copy, HR policies, and project plans of comparable (or better!) quality today.

There is no “how we’ve always worked”; there was no steady state. Constant evolution and progressive automation of the simpler parts has been the norm for software development forever.

> with hope of getting back to pre 2022 levels of software hiring and income. AI stands in the way of that.

It doesn't, though. Productivity multipliers don't reduce demand for the affected field or income in it. (Tight money policies and economic slowdowns, especially when they co-occur, do, though, especially in a field where much of the demand and high income levels are driven by speculative investment, either in startups or new ventures by established firms.)

That’s why the sentiment among most people and also HN is highly negative against AI. If you’re reading this you likely have that bias.

Yes, well said - with one caveat: "Money and social status" is subtly but crucially different from "livelihood". You correctly identify the group of people whose lives are affected, but lump those people along with CEOs and AI companies under the umbrella of "money and social status" in your summarization, which maybe undersells the role of the masses in this equation.

The software job - traditionally one of the few good career options remaining for a large chunk of Americans - is falling. There are many different reasons, and AI, while not the apocalypse, is a small but crucial part of it. We need to get over the illusion that a large piece of that fall consists of wildly luxurious incomes reducing to simply cushy incomes - "boo hoo", we say, sarcastically. But the majority of software folks are going from "comfortable" to "unhappy but livable", while some are going from "livable" to "not livable", while others are no longer employed at all. There were already too many people across the job spectrum in these buckets, and throwing another gigantic chunk of citizens in there is going to eventually cause big, bad things to happen.

We need to start giving a shit about our citizens, and part of that is avoiding the implication that just because something disruptive is inevitable, doesn't mean the affect isn't devastating and we should just do absolutely nothing about the situation. Another part of that is avoiding the implication that the average person can just successfully change careers without enormous suffering. We can ease, assist, REGULATE (which the party in power would like to make illegal), etc. It's important to understand that none of that means "stopping" AI or something ridiculous like that.

We need to start giving a shit about our citizens.

A third time: We need to start giving a shit about our citizens.

And sorry, most of this was not directed personally at you. Just the first note about your wording.

I think your analysis is sound from a technical perspective, but your closing statement is why AI is going to be mass adopted. The people who want to be rich now and don't care about what will happen in 5 years have been calling the shots for a very long time now, and as much as we technical folks insist this can't possibly keep going on forever, it's probably not going to stop sometime soon.

Ironically you don't need AI to see this pattern. Maybe AI makes it a little bit more obvious who's thinking long term and who's not (both at the top and in the trenches)

> Use AI if you want to go bankrupt in 5 years but be rich now

Or, as some would put it "Use AI if you want to be rich now, exit, and have someone else go bankrupt in 5 years"

Conception -> design -> compiler -> code review ...

If AI tools allow for better rapid prototyping, they could help catch "errors" in the conception and design phases. I don't know how useful this actually is, though.

So, I don't know that the tools are inherently rightward-pushing

In my company some people are using LLMs to generate some of their code, but more are using them to get a first code review, before requesting a review by their colleagues.

This helps getting the easy/nitpicky stuff out of the way and thereby often saves us one feedback+fix cycle.

Examples would be "you changed this unit test, but didn't update the unit test name", "you changed this function but not the doc string", or "if you reorder these if statements you can avoid deep nesting". Nothing groundbreaking, but nice things.

We still review like we did before, but can often focus a little more on the "what" instead of the "how".

In this application, the LLM is kind of like a linter with fuzzy rules. We didn't stop reviewing code just because many languages come with standard formatters nowadays, either.

While the whole code generation aspect of AI is all the rage right now (and to quote the article):

> Focus on tangible changes in areas that you care about that really do seem connected to AI

* profilers

* debuggers

* linters

* static analyzers

* language server protocol

* wire protocol analyzers

* decompilers

* call graph analyzers

* database structure crawlers

In the absence of models that can do perfect oneshot software engineering, we're gonna have to fall back on well-integrated tool usage, and nobody seems to do that well yet.

If I have an LLM fix a bug where it gets the feedback from the type checker, linter and tests in realtime, no errors were pushed rightward.

It’s not a free lunch though. I still have to refactor afterwards or else I’ll be adding tech debt. To do that, I need to have an accurate mental model of the problem. I think this is where most people will go wrong. Most people have a mindset of “if it compiles and works, it ships.” This will lead to a tangled mess.

Basically, if people treat AI as a silver bullet for dealing with complexity, they’re going to have a bad time. There still is no silver bullet.

Is this true? Most software devs would like to. But I think business is more interested in spee which pushes errors to the right. Which seems to be more profitable in most software. Even stuff thats been around for a decade(s).

I have articulated this to friends and colleagues who are on the LLM hype train somewhat differently, in terms of the unwieldiness of accumulated errors and entropy, disproportionate human bottlenecks when you do have to engage with the code but didn't write any of it and don't understand it, etc.

However, your formulation really ties a lot of this together better. Thanks!

Pushing errors leftward vs rightwards is such a nice metaphor, not to mention the metaphor on mental models. Also, your comment on why natural language is unable to describe the problem adequately (later in this thread), since sometimes constraints are discovered during the solution process, and otherwise, and if problems could be described adequately, thats what we call a programming language is very nice - ie, for natural language to adequately describe a problem, that becomes a formal language.

Only experienced engineers who have been through failed projects will understand what you are saying, and the rest of those in the grip of the ai-mania will come to terms with it soon.

It's almost enraging that with the hype around LLMs, the development of real automatic programming AI seems to have halted.

This statement being true disproves the statement "the industry has been making huge efforts to push errors from runtime, to compile time." The industry is not a monolith. Different actors have different, individualized goals.

I work as a FW Eng and while they've been of immense value in scripting especially (fuck you powershell), I can only use them as a better autocomplete on our C codebase. Sometimes I'd chat with the codebase, but that's a huge hit or miss.

In-person work has higher bandwidth and lower latency than remote work, so for certain roles it makes sense you wouldn't want to farm it out to remote workers. The quality of the work can degrade in subtle ways that some people find hard to work with.

Similarly, handing a task to a human versus an LLM probably comes with a context penalty that's hard to reason about upfront. You basically make your best guess at what kind of system prompt an LLM needs to do a task, as well as the ongoing context stream. But these are still relatively static unless you have some complex evaluation pipeline that can improve the context in production very quickly.

So I think human workers will probably be able to find new context much faster when tasks change, at least for the time being. Customer service seems to be the frontline example. Many customer service tasks can be handled by an LLM, but there are probably lots of edge cases at the margins where a human simply outperforms because they can gather context faster. This is my best guess as to why Klarna reversed their decision to go all-in on LLMs earlier this year.

What he missed is that the one version that is not thrown away will have to be maintained pretty much for ever.

(Can we blame him for not seeing SaaS coming ?)

What if the real value of AI was at the two sides of this:

* to very quickly built the throwaway version that is just used during demos, to gather feedback from potential customers, and see where things break ?

That can probably be a speed-up of 10x, or 100x, and an incredible ROI if you avoid building a "full" version that's useless

* then you create the "proper" system the "old" way, using AI as an autocomplete on steroid (and maybe get 1.5x, 2x, speedup, etc...)

* then you use LLMs to do the things you would not do anyway for lack of time (testing, docs, etc...) Here the speedup is infinite if you did not do it, and it had some value.

But the power that be will want you to start working on the next feature, by this time...

* I don't know about how LLMs would help to fix bugs

So basically, two codebase "lanes", evolving in parallel, one where the AI / human ratio is 90/10, one where it's maybe 30/70 ?

AI for fast accretion, human for weathering ?

Edit: Hello downvoters, would love to know if you found any flawed argument, is this just because this study/comment contradicting the common narrative on HN or something else entirely?

We still create software largely the same as we did in the 1980s. Developers sitting at keyboards writing code, line by line. This despite decades of research and countless attempts at "expert systems", "software through pictures" and endless attempts at generating code from various types of models or flowcharts or with different development methodologies or management.

LLMs are like scaffolding on steroids, but aren't fundamentally transforming the process. Developers still need the mental model of what they are building and need to be able to verify that they have actually built it.

In the case of the internet, it ended up going both ways. We overestimated it in the near term and underestimated its impact in the long term.

They could very well be right. I don't think they are. But I've also never seen anything that can scale quite like AI.

Meanwhile, anyone can go to chat.com and ask for code to do whatever they ask. Some questions it will answer well, others it will not. Because we're discussing this in the abstract, LLM good!/LLM bad! Grar!, and barely touching on details, like the programming language, the libraries, nevermind sharing the prompts, both camps are convinced they're right, with much more furvor than Bitcoin. Additionally the technology is evolving so fast that someone's bad experience from a couple months ago is already out of date. Meanwhile, how many times can you have the same argument about Bitcoin? There are a couple details here and there, but nothing structurally changed with Bitcoin since its inception. Which is a shame, if they'd managed to fork so transactions weren't horribly slow, maybe we'd be having a different discussion. But I digress.

The volume of emotion is different, not because of the money swimming around, of which there is a lot, but because we're arguing over things we see with our own eyes but aren't giving enough detail to have productive discussions about. Insulting people adds to the emotionally charged nature. Things like "If you find LLMs used, it's because you're a bad programmer/an idiot/only work on loser problems" or "AIs going to put you out of a job" don't encourage reasonable nuanced discussion. Insulting someone for liking Bitcoin doesn't cut as deep. No one's been working on Bitcoin for four decades and made it their whole (public) identity.

Most of those same people are also claiming that the last iteration of LLMs are too smart and that the previous ones worked better for agent (agentic?) programming...

>What I do know is that a table saw quickly cuts straight lines. That is the thing it does. It doesn’t do Whatever. It doesn’t sometimes cut wavy lines and sometimes glue pieces together instead. It doesn’t roll some dice and guess what shape of cut you are statistically likely to want based on an extensive database of previous cuts. It cuts a straight f*cking line.

>If I were a carpenter, and my colleagues got really into this new thing where you just chuck 2×4s at a spinning whirling mass of blades until a chair comes out the other side… you know, I just might want to switch careers.

[1]: https://eev.ee/blog/2025/07/03/the-rise-of-whatever/

We (as researchers and developers in the field) know alot about how its technically implemented, like building models, making improvements etc. These can be quantified and tracked.

The heart of the matter is this:

>societal outcome is uncertain

I think all speculation around AI is heavily weighted about that as opposed to what do we know about AI from a technical perspective? which isn't even usually part of the discourse, unless we're talking about AI safety, which the bad actors try to pretend the technology isn't built in a well known way or can have predictable results etc.

A good chunk of the problem (certainly more than half) is that you have bad actors that need to sell the world on AI, and you have actual technical implementors who really know alot about these systems but are most often employed by folks who benefit from gatekeeping the technology and refuse any attempts at oversight, often claiming we don't know alot about how these systems work while simultaneously employing people who definitely know how these systems work.

The industry has done this to itself, by making false claims and talking out of both sides of their mouth, while the actual technically skilled folks are for various reasons, largely stuck in the middle without much room at the broader discussion table

Yes online tools are great and all but you're only getting feedback from the people already in. "I can get through your poorly designed app for work" is not something an online tool will tell you.

As for design, there was this tool posted a few days ago: https://finddesignagency.com

Great effort by the person who made it. The product they are showing off made me think of the line from the song "and they are all made out of ticky tacky and they all look just the same"

just kidding.

If you stated a senior who tried Claude Code and is still using an IDE that number is much higher.

Still using Claude and wants to go back to an IDE.. why wouldn't they go back... why keep using Claude Code and pining for a IDE. That's a small small group.