cross-posted from: https://piefed.world/c/shit/p/1256068/ai-haters-be-like
this post was submitted on 11 Jul 2026
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Deep neural networks (a narrow subset of "AI" algorithms) are here to stay, and there are massive gains in other hard sciences that have been made with the nature of how they make predictions based on a corpus of training data.
Conversely, in my opinion, there is most definitely an economic bubble regarding LLM (a narrow subset of DNNs) purveyors due to the revolving investments vendors make into each other's businesses to prop up the illusion that "line go brrrrr forever".
Both things can be, and I argue, are true simultaneously.
Edit: Downvote me all you want. I work with DNNs for a living; it doesn't take an "insider" or a financial analyst analyzing EBIDTAs to understand the frailty of the house of cards that the "LLM industry" sits upon right now. I say this as someone who self-hosts and uses llama.cpp and Open-WebUI nearly every day.
Platform and systems Architect for a german managed Services provider here.
I second that. Currently, I am working on replacing external AI providers with 150W AMD hardware. Most of our teams used LLMs for stuff where classical ML is far more reliable and faster.
The few places where LLMs are needed, we still can do locally.
So, instead of spending millions in some tokens that take gigawatt of Power, we estimate that our 5k employees will need a total power of 5kw across Europe. We have 100x the solar distributed across our locations.
We conflate LLM or Diffusion with AI here, because most had never contact with other kinds of AI.
I began my DNN career path researching cancerous tissue detection for medical diagnostic imaging which is a computer vision problem. I think it would surprise a lot of people how power efficient you can make DNNs (especially for inference) when it comes to other tasks besides predicting text or generating media. It pleases me greatly to see other peers in my field share my prognosis about language models compared to other DNN algorithms for different modalities.
Thanks for your input, reassurance, and well done in helping to create such an efficient system.
Maybe this helps for people stumbling over this thread: A Google Coral device can do classification on images with around 18fps. That is a 2w USB device.
That is the power of a well built DNN.
And you can adapt this this stuff like Monitoring data, network traffic and much more. All "AI", same basic technology like ChatGPT and Claude but made for specific tasks. And they do their tasks damn fucking well