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Comment by jdlshore | Hacker News Reader

Their company does data projects. That plus context makes me think they’re talking about internal work process automation type of work, although it also seems like they’re talking about conversational interfaces (chatbots).

I completely buy the “emperor’s new clothes” argument for work process automation. I’m surprised they don’t address AI-assisted engineering, which seems to be going positively for a lot of folks (although I have doubts about its sustainability). I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly. My previous company built a conversational interface to a vector database and saw good results. (Although, arguably, the vector database was the real magic, and a traditional UI would have been faster and more accurate.)

In general, I think OP is more right than wrong, though, particularly about the AI mania and unrealistic expectations sweeping the C-suite.

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Do yo have links handy for AI-assisted engineering going positively? The case I have on my mind of it going negatively is this recent Ford case [1] It's not that I believe it couldn't go positively, of course.

[1] Ford rehires human engineers after AI fails to match quality checks

https://www.bbc.com/news/articles/cgrkd41n2v9o

The Ford case is not about AI coding. It's about computer vision processes that went wrong. This was less about AI and more about Ford being dumb.

That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.

You need to get through the Bloomberg paywall: https://www.bloomberg.com/news/articles/2026-06-25/ford-has-...

> Over the last three years, Ford says it has hired 350 veteran engineers, many of them former employees and others from suppliers, to help address seemingly intractable quality woes that have cost the automaker billions. [...]

> “We had been relying more and more on automated quality systems” and not getting the desired results, Galhotra said. “We brought back technical specialists” and “they hunt for failure points before a part ever reaches the plant floor.”

(I made these points on the HN thread about it 3 weeks ago and got voted down and I'm still salty about it https://news.ycombinator.com/item?id=48674446#48675045 )

I think it's not clear from the article why they left (e.g. could be anything from retirement to going to work at another firm/contracting to being fired to switching careers), and likely it's going to be a mix, plus not all were previous Ford employees. Similarly the "AI" isn't clearly defined (but like you I would be surprised if it were LLMs). I suspect though why the article exists (and a possible source of your downvotes) is signalling against "AI", which if Ford wants more expert employees (given their issues), is something Ford wants to present.

> That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.

Oh come on, Simon. Are your glasses so rose coloured they are opaque? They fired, and then re-hired because the AI couldn't do the job. They had to re-hire the quality folks. So, no, it wasn't LLMs, it was quality control -- which is a much more well established domain for automated systems. LLMs are a complete shitshow compared to industrial process automation.

Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short?

Show me evidence that they had previously fired any of the people who were hired.

Your comment is exactly the misinterpretation I'm pushing back against here.

The time period covered by this story starts three years ago! What kind of LLMs do you think they were using back then?

> Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short

I think my record is looking pretty good here. I was early to the "LLMs are useful for writing code" thing, especially with the code interpreter pattern (both write and then execute code in a loop) which I now realize was our first hint at coding agents.

A couple of years ago I was one of the few people talking about what a natural fit LLMs were for the commandline - https://simonwillison.net/2024/Jun/17/cli-language-models/ With hindsight maybe I should have doubled-down on that!

I've also written plenty about the weaknesses of these models - in terms of security in particular - which has aged well.

3 years ago is about when the BS "AI can do anything!" hype started. So, it is not a misinterpretation, but an accurate assessment of the hype cycle. Exactly the kind of things managers are bumbling into because "AGI any day now!" for the last 3-4 years. GPT 3.5 was released May 2022. I'm curious how many AI project in industry have crashed and burned during this hype-Summer before the Winter. Speaking of which, whatever happened to that "software dark factory" you were gushing over a while back?

When I said "misinterpretation" here I was talking about people assuming that the Ford story was a simple case of "Ford fired their employees because they thought AI could replace them, then had to hire them all back again". That's not what happened.

A TON of AI projects have crashed and burned in the past few years. This stuff is really hard to adopt at an organizational level, much more so than for individuals.

I've been pretty consistent in saying that I think the dark factory stuff is interesting more as an exploration of the edges of what this stuff can do. It's a great way to highlight the question of how we can get agents to demonstrate their work without reviewing every line of their code.

I've released two pieces of software inspired by those ideas myself - Showboat https://simonwillison.net/2026/Feb/10/showboat-and-rodney/ and shot-scraper video: https://simonwillison.net/2026/Jun/30/shot-scraper-video/

As @simonw said, the Ford example isn’t a good one.

As for AI-assisted engineering going well, I think the jury is still out. Here on HN and with the engineers I know, you see people claiming multiples of productivity on coding tasks. But you also see people complaining about drowning in slop PRs.

I think there’s a lot of confounding factors to these reports. The type of work matters a lot: bug fixing good, prototyping good, big legacy codebases not so much, but maybe good for increased understanding. The type of automation matters: aggressive autocomplete good, vibe coding bad, dark factory (vibe coding with fancy harnesses and auto-“correcting” eval loops) questionable.

And then finally, the perennial mistake our industry makes, which is to value speed of creation over maintenance costs. Personally, I think this is where AI-assisted engineering is going to fall down really hard, but the jury’s still out on that one.

Anyway, there’s a really big spread in experiences with AI, that I think chalk up more to all this context rather than religion and belief. OP didn’t address it at all, which I think is a big gap in their essay, but I do think think they describe the executive-level mania pretty well.

> As for AI-assisted engineering going well, I think the jury is still out.

Anecdotally, AI-assisted engineering has helped me flesh out ideas or to learn extremely complicated APIs faster than trying to understand the docs (which usually are labyrinthine). MS COM ones, for example. I can go read the docs but it's easier to get a quick idea of what I need to do if I ask Claude to provide me an example of doing something specific with it, because MS's code samples (particularly their full ones in, say, the windows Desktop SDK repo) have always been annoying for me to wade through because I have to filter out a bunch of noise. I can't (and won't) try to guestimate "productivity" improvements though, but as an assistant AI has (somewhat) helped. I still do all the engineering work though. Along with it giving me tips on using more modern language features for languages like C++.

LLMs are good at natural language search. They're bad at everything else.

The Microsoft API docs are a special case where i'd say you pretty much need LLMs nowadays because after a bunch of document format conversions over the years they degraded massively.

If you can find some MSDN CDs/DVDs from the early 2000s, the content is much better (and you can clearly see that the current docs are often missing descriptions and names for method arguments or even entire paragraphs).

What places would you suggest where I can get digital copies of that? I don't have a physical CD/DVD reader so... :)

archive.org probably, just search for "archive msdn" and you'll find a bunch of stuff

> I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly.

If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).

As for as AI assisted engineering goes, the thing is that after some time with a project, you already have much of the workflows and routines nailed down as scripts and other various combinations of tooling. And unless it's spaghetti code, you will have various snippets you can copy from for new code. The one thing I've observed about AI projects is that there's often little technical design coherence about them. It's always a kitchen sink of technologies and practices.

> If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).

Yes, I agree, in that the chatbot we built probably would have worked just as well with a traditional UI, and would have been done a lot faster. But it would have been a lot less sexy (actually important for the bottom line!) and there are future directions that could take advantage of the conversational interface that’s potentially better than a traditional UI.

On the down side, good chatbots are really frikkin difficult to write. These things (LLMs) are not reliable at scale. The basic functionality came together in weeks. Getting it to behave consistently and obey guardrails took months, and even then we had to accept a low level of failed conversations.

> But it would have been a lot less sexy (actually important for the bottom line!

That’s what the author have been saying. They do for nice demos which sell the illusion of having Jarvis in text format, but the usefulness is not really proven. And that may be important business wise. But as far as end users is concerned, there’s not a lot of productivity boost.

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