I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?

I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.

In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.

In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.

Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.

They are desperately rushing to IPO before the bubble bursts.

Some body compared AI as something like a big boulder rolling down a mountain, decimating anything that lies in its path. After giving it a bit of thought, I feel that what humanity is doing with LLM is exactly that.

The problem is not that it is super smart. It is that it is super dumb and super powerful. Like a boulder falling down.

Humanity is like this bunch of utter morons who has rolled a big boulder up a big mountain and let it loose at the top, and standing at the bottom is clapping and cheering seeing it coming down, guided by random collisions in its path, and with real probability that it will land on them....

Relevant paper [0] "Replication of Quantum Factorisation Records with an 8-bit Home Computer, an Abacus, and a Dog"

[0]: https://eprint.iacr.org/2025/1237.pdf

I had qubit bring up and calibration fully automated with Python in 2011 including full spectrum measurements, lifetime characterization, Rabi/Ramsey measurements, calibration of single qubit gates and two qubit swap gates and full quantum process tomography, so not sure if AI is really needed there, curve fitting and some data logging is enough for this. Even had a nice LabView like GUI but with PyQT, it was quite nice. Still of course cool, I guess today I would just let Codex loose on some experiment goals but in the end my ability to produce results was mostly limited by the chip itself and the qubit lifetimes and theres no magic trick AI can apply to make these go up by a factor of 10. Still would’ve saved me a lot of time for routine programming tasks I imagine and that seems to be the main takeaway of the article. I guess name dropping quantum computing makes this sound cooler but in principle it’s just automation that you can apply anywhere, nothing quantum computing specific here.

Even with AI you'd want the AI to be writing python scripts instead of following an analysis.md.

It will be fun if the AI realize that quantum is not needed and just simulate the results in a classical computer