It never gets beter eva, even some of these so called ‘leftist’ say they just don’t like “corporate ai”… I use self hosted from time to time to remind me of how much of a waste of time it is. Just simple things it struggles to do. All this hype for nothing.
99% of what you hear as being “AI” is just bullshit hype to mask the infrastructure for the mass surveillance state they are building.
However, there are plenty of applications of AI that are actually less energy intensive than traditional computer simulations. This is in the realm of Engineering. Simulations of fluid dynamics for example.
You’re relationship with “AI” is very much LLM focused. LLMs are basically where all the money is going because it’s where all the hype can be generated.
We wouldn’t be in an AI bubble if we were only using this field of computer science to build PINN based models for engineering and physics simulations.
The area of computer science that gave you “AI” LLMs has existed for a long time and you’ve likely interacted with the models in some way without ever knowing it. Long before “AI” was in every search engine or infected the internet with AI generated images.
If by “AI” you mean the power hungry LLM models. I agree. There are almost no applications for them where their benefits out-way their energy usage.
But, the larger application of what computer science would call “Machine Learning” is not just these LLM models. And there is not really any reason to not call them “AI” at this point because they work on the same exact concepts. But, I was the one trying to explain to people that LLMs are not “AI” back in 2020. That word use to have actual meaning in computer science. I’ve lost that battle to capitalism and marketing though.
So, in order to defend computer science I apparently have to defend “AI” now.
The LLMs and their inflated usage placed into every single platform is definitely something I’d call “corporate AI” if I wanted to distinguish it from other good applications of machine learning. But, I’m a leftist and a CS grad.
I have left the CS field after being laid off. The injection of “corporate AI” and the management forcing AI into everything does not make me want to return. Hopefully this bubble can pop and we can start learning from China with actual good application specific models again. The whole field of CS has been absolutely destroyed in progress by this LLM hyper-focus that corporations are forcing into everything.
I hope that gives you a little more context for what other people may mean by “corporate AI”. It’s a plague of capitalism. No one with an understanding of the field thinks this is a good application of the technology. They are all just chasing investments and lying to do so.
I swear they marketed it in a way where previous technologies before LLMS get lumped in with everything else, just so they can say “well what about this life saving technology that runs at 7 watts on a raspberry pi” as if its the same thing. Its fusterating because in so many ways it derails a point because many people don’t know enough to make a distinction. That and all the easy accessable local LLM’s/ai diffusions are pretty much just heavily distilled/quantized versions of the main model, or just porn that still chug power. People aren’t buying an obscure compute puck, building a server, or buying tensor hardware where you actually have to use Python or touch a terminal.
I have struggled with a way to address this issue and the easiest way is to broadly just say fuck all ai and not make the distinction. The people who do use local models are maybe less than a single percentage of people who use ai. I don’t think its nessisary to address it, and I think for most people everything you just typed is going to go in one ear and out the other. Even if you explain it to a random person on the street, I’m likely to believe they will still walk away thinking chatgpt is the just the natural evolution of the apple kicking camera, or algorithms that self tune a guitar.
Disclaimer: not promoting or supporting AI usage, but adding some observations after reading.
Two points that needs more exploration.
One is briefly mentioned by the author that AI usage of a software engineer is at the very end of usage distribution. So this cannot be taken as a generalised estimate as the tiltle suggest.
Second, I don’t see any mention of energy usage for research and development of the models. This is about what happens once the model is built. Unless AI companies disclose this, these are not going to put things into perspective.
Iirc training takes the most energy. They claim that models take very little energy to run but fail to mention that training the models needs to essentially happen forever to make the next best model, and then the next.
The only stopping point is when they create “god” “agi” which I think you have to even be more batshit crazy to believe will ever happen. I at least understand why desperate billionairs dillute themselves into thinking this death cult will some how work out. Its a death cult it either works out or they all go down with it. To even consider if they where wrong would be to stare into the abyss.
For your second point: https://epoch.ai/data-insights/power-usage-trend
For the first one: I would say agentic workloads are Software engineering workflows.
