A “propaganda campaign” implies intent. I’ve seen a lot of dissatisfaction about capitalism, too, particularly among Gen Z, but to me it looks like an expression of the loss of faith the headline is talking about. What makes you think this is a propaganda campaign and not a zeitgeist?
I think it’s because maintainability is a factor of software design, and good design is squishy and hard to articulate. There’s no good metrics for design. All the tools that purport to measure it are doing trivial things like cyclomatic complexity, and can only tell you when things have gotten really really bad, but not distinguish between “good” and “pretty bad.”
Design skills take about 10 years to develop, in my experience, and a lot of smart engineers go through a phase where they make gawdawful messes in the name of “magically easy” designs around the 5 year mark.
Design requires wisdom born of experience, isn’t measurable, and requires forming a mental model of how the system hangs together as a whole. It’s just not stuff LLMs are good at.
You’re getting a lot of pushback, but I’m genuinely interested in your experiences. What differences have you seen between average and top leaders? Not in results, but in terms of how they approached the job?
Default payout is 50/50 author/publisher. If the author and publisher have a contract that states otherwise, then their contract overrides the default.
Source: I’m an author and signed up to be part of the class action, and this was the class action documents said.
Default payout is 50/50 author/publisher. If the author and publisher have a contract that states otherwise, then their contract overrides the default.
The GDPR states that websites that serve EU citizens are subject to it, no matter where they are. Many US news organizations chose to geo-block the EU rather than going to the trouble of figuring out compliance.
A Reddit spam tool makes $90K per year ($60K ARR, $30K one-time payments), split 50% with “a larger partner.” The founder’s prior salary was $84K, so with the revenue split and cost of operations, the business is not yet recouping its costs.
This is pretty low-grade content marketing. If his spam tool was good (to whatever degree spam tools can be good) he’d be making a lot more money.
Quality is more about maintainability, IMO, which comes down to consistency and clarity of design, sensible abstractions, cohesion, decoupling, etc. I don’t think a rework metric tells you that.
> 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.
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.
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.
It’s Digital Transformation all over again, which was just the latest name of a proud tradition of IT consulting going back decades.
They all have the same promise: unlock business results in your company by deploying work process automation.
They tend to have mediocre results because the low-hanging fruit was plucked long ago, and automating work processes runs into the reality that work processes are not well defined or globally understood. They’re fragmented, with a long tail of special cases, and not amenable to automation. Attempts to unify them causes huge political battles, and the special cases are too numerous to effectively automate.
Maybe things will be different this time, but I wouldn’t bet on it.
Incredibly hard problem, but METR had a good method. They had people estimate how long a task would take (before knowing whether AI would be used), and then randomly assigned each task to “with AI” or “without AI.” When the data was in, they compared actual/estimate ratios of the two populations.
(Presumably, they used a t-test that only compared people against themselves.)
Interestingly, for that study (released in 2025), participants self-rated themselves as 20% more productive, but were measured as being 19% less productive.
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