The creator of Linux just publicly called out the AI hype. Word for word.
Linus Torvalds took the stage at Open Source Summit 2026 and said this:
"When I see people saying 99% of our code is written by AI, I literally get angry. Because those same people — I can pretty much guarantee — 100% of their code is written by compilers. But they never say that."
He is not anti AI. The Linux kernel saw a 20% jump in submissions this release because of AI tools. He uses it. He gets it.
His point is something most people are too afraid to say.
AI is a productivity tool exactly like compilers were. Compilers boosted programming by 1000x. AI adds another 10x on top. Enormous. But nobody says "the compiler wrote my code." So why are we saying AI wrote it?
He also flagged something nobody is talking about.
AI is flooding small open source projects with drive-by bug reports. Someone runs a prompt, files a report and disappears when asked for a patch. Maintainers with one or two people are drowning trying to keep up.
"Sometimes AI reports a bug and when you ask for more information the person has done that drive-by and does not even answer your question. That is the real burnout issue."
And his final warning was the sharpest of all.
"People who do not understand the complexity of systems will prompt systems and write processes that will fail."
The AI hype crowd is very loud right now.
Linus has been building real systems for 35 years. When he talks, engineers listen.
Full interview here:
https://t.co/LmXJtvKc4O
@xsteenbrugge Im using episodic-memory from Jesse Vincent since forever /because simplicity and elegance beats accuracy speed and what so not other criterias. If agents exhibit human phycology its only fitting to use a memory system that doesn't overdo it.
I have become super sensitive to the patterns introduced by many of the current generative models. I get triggered by contrastive negation and sloppy clip art. I want to see the output of your mind, raw existential expression, not filler
After SpaceX' IPO, Elon will have 42% equity, and 69, no, 79% voting power. Elon is going to be supreme commander of the star fleet and emperor of the solar system
@tmeire_ Close but but no cigar ? I have a similar skill that I used for the past 6 months everyday. Every blue moon it introduces a subtle bug in the small stuff. The more you trust it the higher the chance.
Invest in the evals, especially for the boring grunt stuff.
If we have a boat full of people exposed to an airborne virus with a suspected mortality rate of 30-50%, an expected R0 in the range of Covid and an incubation period of 5-6 weeks, and we respond by asking them to book flights to travel home, we totally deserve another pandemic
@xsteenbrugge I think we're on the same place in the software development stack. We're just moving orthogonally, and we're speeding up time generating from Gaussian distributions. I hope this becomes a stack that we can move up and down on, but the primitives are still really weird imho.
New leadership at Apple is exciting. Imagine we get some of the Apple products many of us want. Phones without humps, notches and 19 different swipes. Intelligent lamps. Sentient Siri. AI training support. Smart TV. Glasses. Smart cameras, microphone arrays, sound projectors.
Dario is wrong.
He knows absolutely nothing about the effects of technological revolutions on the labor market.
Don't listen to him, Sam, Yoshua, Geoff, or me on this topic.
Listen to economists who have spent their career studying this, like @Ph_Aghion , @erikbryn , @DAcemogluMIT , @amcafee , @davidautor
@AnnaLeptikon Computers used to be unforgiving. I wonder what will happen if the next generation of computer scientists does not grow up with “syntax error in line 20” but with “you are absolutely right, let me try…”
I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then:
- the human iterates on the prompt (.md)
- the AI agent iterates on the training code (.py)
The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc.
https://t.co/YCvOwwjOzF
Part code, part sci-fi, and a pinch of psychosis :)