I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them.

[bnferguson@ruby.social]

@mitchellh God, ALL of this. I worry, too. I really feel "resilient catastrophe machine".

[tobinbaker@discuss.systems]

@mitchellh that's an interesting analogy, feels like both vibecoding and "resilience engineering" tend to mask systemic risk by superficially and temporarily mitigating the symptoms

[nickynah@rebel.ar]

@mitchellh I’ve been thinking a lot about this, and my personal conclusion is that the raise of the attention economy has made nuanced discussion virtually impossible, so nuanced topic (all important problems are nuanced) are impossible to discuss, because all people see is “number go up”
The only solace I have is that this is unsustainable, and it will collapse, costing us a lot, but it will collapse

[slacy@mastodon.social]

@mitchellh The story I've heard is the "well, we just rewrite the entire thing every six months so there's no point in fixing/improving because the next iteration/generation will be that much better as the agents improve."

I can actually sort of see this, and it's somewhat along the lines of "spec-driven-development" but ... ?

[pgoultiaev@hachyderm.io]

@mitchellh Unfortunately, changing a very convinced person’s view to a different perspective is almost impossible.
Not enough things have gone wrong due to AI psychosis for people to augment their perspectives and be open to helpful discussions… yes, databases have been wiped etc., but these examples are (unfortunately) seen as one-offs.
I feel like discussing the approach to how to apply AI in the best way can bring perspectives together instead of battling an opposing view.

[lkanies@hachyderm.io]

@mitchellh there are so many different crazy things people believe now, almost implicitly.

The big one I keep thinking about is that people just seem to think code longevity has zero value any more. Like, we always knew code that doesn’t change for a long time is maybe a bad sign, that it is rotting. But it is also a good sign that it is likely more stable, secure, and valuable than new code.

But so many people now just seem to think it is always a good thing to be able to change any code any time. They don’t talk about the gradual hardening that is no longer happening, or the ability for other parts of the system to evolve more because this part is so stable.

I assume that over time our industry will learn how to talk coherently and intelligently about all this. But we’re obviously a long ways from there, and there’s a lot of destruction going to happen between then and now.

[christianriegel@digitalcourage.social]

@landelare @mitchellh

Same experience here. And it's presented like facts. When asking, they point out that [talky program used] provides sources (which they of course never read).

I fear that as society we're to blame at least party after "I googled it" became an accepted answer without actually naming the pages found by the search.

[jamesb2147@infosec.exchange]

@mitchellh I don't necessarily disagree, but "resilient catastrophe machine" feels an awful lot like Salesforce (and a lot of the rest of the tech industry).

I mean, you do have to strike a balance, but in my experience, moving faster has very frequently been the economic winner, even at the expense of quality.

I say this as someone that abhors the wasted hours fixing systems that weren't designed properly, and the lost business from features that never actually worked but were already sold. I don't want my experience to teach this lesson, and I desperately want someone to convince me otherwise.

[schtaks@infosec.exchange]

@landelare @mitchellh it's the new LetMeGoogleThatForYou butt worse

[spacelifeform@infosec.exchange]

@mitchellh

Ask them what they will do when their cloud is down.

Handwaving ensues. Fog rolls in.

[ahltorp@mastodon.nu]

@ChristianRiegel @landelare @mitchellh I posted this a year ago: https://mastodon.nu/@ahltorp/114454413624506937

[cstamp@mastodon.social]

@mitchellh We have to stop using human terms to help the tech bros anthropomorphize these pieces of crap. The AI malfunctioned. It’s a machine.

[shafik@hachyderm.io]

@mitchellh

There are some good folks writing some solid pieces. This one has nice graphs laying out the long-term costs in a way that I think most folks can absorb:

https://www.jamesshore.com/v2/blog/2026/you-need-ai-that-reduces-your-maintenance-costs

and honestly if you have spent any time thinking about the software development lifecycle seriously this should hit home b/c there is nothing revolutionary there.

This one talks about the hard cognitive limits human have:

https://techtrenches.dev/p/the-human-cost-of-10x-how-ai-is-physically

and why this means that lines of code is not the correct measure of productivity and in fact this is a terrible measure. What is there I think is less commonly well known and may take a bit more thought to get in the big picture sense.

Anyone who has spent time thinking about software quality, you should be nodding your head b/c it is correct.

[shafik@hachyderm.io]

@mitchellh

The conversation I was totally not ready for where the ones where people being totally earnest told me they believed LLMs are intelligent or can reason.

I knew inherently this was wrong b/c I spent time understanding how they work in detail but actually explaining it in a plain way stumped me w/o thinking more deeply about it.

If you spent time learning about Russell, Wittgenstein, Hilbert, Godel and others you should see the flaws in thinking and get why induction can't get you there but that is hard row to explain to anyone who is not familiar.

So I think these two articles hit the right spot:

https://hachyderm.io/@shafik/116468806967961821

and

https://hachyderm.io/@shafik/116468521909545361

but you can go deep on this one.

[bms48@mastodon.social]

@shafik @mitchellh A cursory reading of Robert Pirsig's "Zen and the Art of Motorcycle Maintenance" betrays how quality has its own ineffable quality. KLOCs are not a useful metric often; McCabe's Cyclomatic Complexity is where I'd start. Dijkstra brought the wisdom yet as Adam Smith has been selectively quoted in econo-political argument, Dijsktra seems to be selectively quoted by members of the deep-learning community.

[bms48@mastodon.social]

@CStamp @mitchellh Kate Crawford warns about this ~7 pages into "Atlas of AI" and @HalvarFlake covered the topic well last July: https://addxorrol.blogspot.com/2025/07/a-non-anthropomorphized-view-of-llms.html

[rebelgeek99@mastodon.social]

@mitchellh @DukeDuke hadn't occurred to me that the AI psychosis may be a factor driving the enshittification of tech, but that makes perfect sense. I swear, COVID and genAI are our civilization's answer to Romans' lead pipes updated for the 21st century

[shafik@hachyderm.io]

@bms48 @mitchellh

any metrics that solely look at the code will not tell you what you need to know.

The whole lifecycle has to be measured, including code review, bug reports, rates of errors over time etc

[bms48@mastodon.social]

@lizbian @mitchellh @briankrebs Regarding the anthropomorphization argument: merely calling into question the nomenclature of "AI Alignment" is enough to trigger contempt/threat reactions in some "AI" proponents, betraying it has developed a pseudo-religious property. When people are unable to step to one side and consider the technology in its wider context, regardless of whether they were "one-shotting" code subsystems with e.g. GLM 5.1, demons form.

[bms48@mastodon.social]

@landelare @mitchellh This is precisely why I prefix any and all LLM-originated outputs in my own working notes with "Parrot (Model name)" and believe me, more than once, I've caught e.g. Claude family models making wholly inappropriate Python API suggestions, betraying the model was mean-reverting to training set in its output token stream.