Post #3918775

2026-07-18 19:25 UTC

I keep watching for the AI bubble to burst. It's been so expensive for so little returns. At work, a senior leader asked me to present on AI. As a manager, I'm expected to adopt it, whether I can use it or not. Our usage is tracked, so I've had to do some stuff with it. I tried setting up a complex process, since the easy things aren't really time-savers. I presented on this testing, even though I doubt I'm actually going to use it. Every day, the AI tools seem to interpret the exact same prompts differently. So when I validate the output, it has changed. How can I add an AI process into my workflow when the output is different every day? How much extra time am I wasting in validating the output? And don't tell me that my prompt is bad. It works until suddenly it doesn't. The tool is bad. Tools shouldn't do that. I can write programming logic to do the same things. I know the code isn't going to give me different output every time I open it. The senior leaders are desperate to find use cases for AI. The companies have invested a lot of money and want to see returns, but it's not happening. When are they going to cut their losses? #NoAI

Replies (9)

@petererer@fuzzle.me.uk 2026-07-18 19:43

@VeeRat@zeroes.ca My "favourite" advice is that apparently I should prompt the thing to give me a good prompt...

Open ##3918774
@n_dimension@infosec.exchange 2026-07-18 20:18

@VeeRat@zeroes.ca Using Ai is a teachable skill. The fact that even Ai opponents believe its a magic tool that enables instant competency is telling. I don't know what you tried to do with your workflow, but you can make it as deterministic as your wetware associates.

Open ##3919311
@SpaceLifeForm@infosec.exchange 2026-07-18 20:30

@VeeRat@zeroes.ca The models are not deterministic, because they ingest new bullshit every day. #AI #Insanity

Open ##3919497
@david_chisnall@infosec.exchange 2026-07-19 08:36

@VeeRat@zeroes.ca In general, machine learning is good for situations where the value of a correct answer is significantly higher than the cost of an incorrect one. Prefetching is my favourite example (not least because that’s what I used it for in my PhD): if you prefetch the right data, the processor avoids a long idle time. If you prefetch the wrong data, you discard it and you’re not in a worse position than if you hadn’t used it. Machine translation is a great case study for this: for any given situation, is an incorrect translation worse than no translation? For translating a toot, the worst-case cost is that I am briefly confused and move on. I don’t have time to learn every human language and I don’t have the money to pay someone to translate every toot I see, so machine translation is better than nothing. For translating a user manual or a contract, the worst-case cost may be someone dies or I am exposed to unbounded legal liability (or both), so machine translation is worse than nothing. The corollary is that machine learning is also useful if the cost of generating an optimal solution is high and it’s possible to mechanically generated solutions. Stochastically generating plausible solution-shapes things then checking if they actually are solutions and picking the one that works best with a cheap-to-run machine-learning system providing the inputs is great. Vulnerability discovery with guided fuzzing works like this, for example. The big issue with LLMs is that, generally, text is something we use for high-stakes problems. The set of things where the desired output is a pile of text, where the cost of incorrect text is low or the cost of checking the text is low, is very small. This includes programming: if you have a sufficiently detailed spec that you can mechanically check whether an implementation satisfies it, you almost certainly have something that can be used to synthesise a program already. Some cases, such as searching documentation, are right on the cusp. Is it better to have a machine that answers a question about a system but gives you a right answer 60% of the time and plausible nonsense 40% of the time than nothing? What if it’s 90%? At some point, the Paradox of Automation kicks in: system efficiency does not correlate with component efficiency. You get a significant dip when the system is good enough that people stop checking the answers but it’s still producing a lot of mistakes, only now those make it further along the process.

Open ##3930221
@gooba42@mastodon.social 2026-07-18 19:52

@VeeRat@zeroes.ca Lately all the Microsoft IDE stuff has flipped into "CoPilot does everything" and replaced working IntelliSense with garbage. This last week was when I noticed it because SQL Server Management Studio stopped being able to autocomplete an existing table name. Just days ago I could trust tab completion and suddenly that's dead too.

Open ##3933544
@ajn142@infosec.exchange 2026-07-19 13:41

@VeeRat@zeroes.ca @ludicity@mastodon.sprawl.club shared some excellent thoughts from somebody having to deal with a similar environment https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#:~:text=Checking%20out%20a,actual%20software%20engineer

Open ##3935159
@jmcrookston@mastodon.social 2026-07-18 19:29

@VeeRat@zeroes.ca I couldn't even get AI to make me a compound interest calculator. It did it incorrectly. I have no idea what anyone uses this "tool" for. I do use a dictation tool which is an LLM (local) and it works well. That is the only use case I have found so far for me.

Open ##3939853
@pawsplay@dice.camp 2026-07-18 19:30

@VeeRat@zeroes.ca Have you seen the Big Short? The bit where the guy keeps expecting everything to fall, but a bunch of rich assholes keep propping it up while they raid the piggy bank?

Open ##3939858
@ohir@social.vivaldi.net 2026-07-18 19:54

@VeeRat@zeroes.ca > When are they going to cut their losses? They can't. It is easy to fool a man, getting them to ack they were fooled is next to impossible. https://www.groundbrkr.com/p/the-second-derivative-why-no-one

Open ##3956252