this post was submitted on 17 May 2026
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Fuck AI
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A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.
AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.
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The annoying thing is that generative AI actually has real, positive applications for humanity.
Unfortunately, dickheads like Schmidt have applied it to the things it's least good at because it's the most likely to enslave the populace by denying them of jobs and make the most money.
I'm curious what positive applications you see in it? Genuine question, not trying to fight, just looking for other views
Programmer here. They can be a useful tool if used correctly. They are great at writing unit tests, useful for upgrading older code, and really useful as a semi-intelligent autocomplete.
If used incorrectly they will generate a giant mess that works briefly but becomes impossible to expand on, full of duplicated sections that makes maintenance super difficult, and have weird logical flaws.
I prefer using agents running on my own desktop rather than paying somebody for it.
The one off scripts ive had it write to do something for me usually work great, but when I glance at the code its horrific. But its a one off, it does its job then goes bye bye.
Like I had to scrape a website the other day and only needed it to work for a few days. Claude did it.
Honestly, that's fine. I've been coding for 30 years now, not one bit of it from 20 years ago still remains.
Everything is ephemeral in the end, we are all only but dust on the wind.
Yeah so far all the "advantages" have been "cutting corners inaccurately."
I think it's good for pattern matching. However, I think LLMs are mostly useless because it lacks human context. It's a chat bot at best.
All the GenAI art, video and even vibe coded websites can dramatically speed up prototyping and seeing how something might look or work.
Yes that costs jobs even if its only at the early stage and is done by humans after, but its real value and can overall speed up development on ideas.
Like I was building a new feature in my software, I knew the constraints, what I wanted it to do, and I asked it to start making some visual mockups of what might work. There's no way I could manually generate that many ideas and then fine tune them before finally coding it myself for real faster. Like it or not, that is positive.
I'm generally an AI skeptic/hater, but finding new medicines, detecting cancer earlier on, IT security auditing, discovering new battery chemistries......for things that are predicated on pattern recognition, are audited by a human, and basically regurgitating a blended up version of previously established data is what you're after, it's really good at those things and I'd argue there is a real use case for generative AI in these places.
As a means of mental health therapy, customer support, computer programming tasks that aren't reviewed, or basically any task that is NOT audited by subject matter experts, it fucking sucks and, unfortunately, it's being broadly applied in these roles that it sucks at. Per usual, it's because those things make money and the things that actually could help humanity don't always.
The capitalists are driving AI adoption. They want to use it to make the unwashed masses lazy and stupid by being dependent on their product and make trained, skilled employment a thing of the past. That is the problem with generative AI.
Exactly. It's best for problems that both:
You want to come up with a new chemical to do a thing? Have an AI run through billions of permutations to find the one compound that can do that thing best. Then test that compound. See if it's more effective than current solutions. Maybe it missed a better compound somewhere in the universe of potential compounds. But if what it found is still better than the current solution, that's still progress and has real value to the world.
For an example, think of an AI built to search for high temperature superconductors. There's an endless variety of possible compounds that might lead to this. But the time to test any individual compound isn't that large. At a basic level you just need to synthesize it, confirm it's a superconductor, and measure its critical temperature. Simple stuff (apart from maybe the synthesis.) Testing one compound for superconductivity isn't hard. Trying to screen through trillions of candidate high temperature superconductors is. So you let the AI churn through the vast universe of possible options. Have it spit out the top ten candidates. Then you go to the lab, synthesize them, and try them out. If it hallucinates and is wrong? Very little cost beyond some lab time. If it misses one? Oh well, maybe we'll find it later. Say the current record for a superconductor is 151K. The AI predicts a compound with a transition temperature of 155K. Maybe it misses one that could do it at 157K. But oh well, a better high temperature superconductor is still a better high temperature superconductor!
This is where AI shines. When incremental progress is acceptable and when the solution is easily verifiable.
I’ve been impressed by the benefit derived from Carbon AI. It’s is a deep learning model trained on massive data sets and can identify 150mil weeds near instantly. When paired with a laser delivery system and pulled over a field, the weeds are id’d and precision killed with the laser, which spares the crop. This has great potential for reducing the use of commercial herbicides, which also means we can get away from the “RoundUp Ready” gmo seeds engineered to survive devastating chemical bombardment.
Currently the delivery system is tractor pulled, but I’d be interested to see if it could be made into a compact, low impact form. It could be a tool for wildland restoration crews to use to cleanly eradicate invasives while another crew seeds natives in their wake. Obviously not something that could be used in every terrain, but there’s plenty where you could. Even a consumer grade model could be a boon, instead of people dousing their property with weed killers that drain into local water supplies and nearby rivers, drive your mower-sized laser blaster across your property and zap your weeds.
It’s one of the few applications of AI where I’ve felt like its ability to recognize, process, and respond quickly is worth the application it’s used for. But also, it’s not generative. It’s trained on data collected and input by humans, it just reduces the effort (and side effects) of mass agriculture. Doesn’t eliminate ag workers, it makes an already difficult job easier and cleaner.
See that sounds like a legitimate and actually net positive implementation of Neural Networks and Generative AI. Not replacing traditional algorithms for search result data on Billy Bob's question of "Why do farts smell?".
In most of your examples it's revealed after the fact that AI didn't discover anything it just defeated the test.
How does discovering a new battery chemistry or showing reproducible cancer detection "beating the test"? Look....I fucking hate more implementations of GenAI and generally can't stand it, but those specific use cases are very much valid, even if the other 97% of applications are absolute shit.
I do get your sentiment, and can see it applicable in these cases.. But I also think we should put a lot more focus on creating models specialised for these important tasks, instead of using these super large and heavy genAI models.
As it currently stands, the research seems to be largely focusing on just pushing the expensive limits of these models, and pushing the olligarch agendas, which is a bit unfortunate, since this field has potential to be super beneficial.
Agreed. My comment was more GenAI in general. Not utilizing a general purpose model shoehorned into a specific task.
For me, AI is great at comparing large data sets. It can detect the most miniscule change in the night sky and is great for measuring luminosity changes over time.
Other than that, it's kinda useless for me. Maybe I'll play with it and change out actors in famous scenes for cats like those kungfu clips of late.
LLMs are really good at pattern recognition, making them an excellent tool for scientists and medical researchers.
They can't infer shit. They don't recognize numbers as actual numbers, just tokens. They can only repeat what they've seen before.
Combine that with the mathematical certainty of hallucinations and that makes a dangerous combination for scientists and medical researchers.