My neighbour - a few hundred metres further up the road - has two dogs, one of which a surefire genius as it has no problems barking out 334543333-bit RSA factorisations. It must have been solving NP-complete problems for years by now, night after night.
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.
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....
The right tool for the job. There are tasks for which LLMs are unbelievably helpful. In programming, used well, it can 10x your productivity. (Well maybe not literally, but Nx for non-trivial N.) I've seen really good programmers become even faster since the LLM is doing the grunt work for them and the code produced in the end is just as top notch quality as they produced before, just that now they are much faster.
Of course you can misuse it. Produce mountains of unmaintainable slop, full of issues. But that's true for every tool. Before LLMs already, and there will be future tools too.
So, careful with generalizations please. It's not all just dumb.
>just as top notch quality as they produced before, just that now they are much faster.
No, they are not. There write programs like they write prose. There is 100% adherence to grammar and spelling when they write prose, but yet what they write is unintelligible and needlessly verbose.
When they write computer programs there is 100% adherence to good practices, but eventually the program becomes so unintelligible and verbose that only an LLM can make any sense of it going forward. But even then, changes result in more things breaking than they fix. But the number of bugs slashed goes through the roof. Management is happy!
>Of course you can misuse it.
I think the proper way to use it is to use it as a better search tool. But I don't think AI marketing and valuation is going to be sated by such a use case.
The problem is when people ignore or dismiss developments in AI purely out of insecurity or a sense of human superiority. Machines don’t have to be superior to humans to dramatically amplify what humans can do.
Well, it's different "man" as well - lowering the barrier to entry means people with less skill but with a machine, and maybe the gift of the gab, can commercially surpass the previous people, whether or not they have a machine available.
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.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
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.
But I suspect you want our project management pipeline? Maybe I can just ask our coding agent to search and summarize all the features and fixes for you and then build a dashboard for you. Better yet, my email is in my profile. Email me, we'll get on a call, and I'll show you. /s
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
[0]: https://eprint.iacr.org/2025/1237.pdf
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....
Of course you can misuse it. Produce mountains of unmaintainable slop, full of issues. But that's true for every tool. Before LLMs already, and there will be future tools too.
So, careful with generalizations please. It's not all just dumb.
No, they are not. There write programs like they write prose. There is 100% adherence to grammar and spelling when they write prose, but yet what they write is unintelligible and needlessly verbose.
When they write computer programs there is 100% adherence to good practices, but eventually the program becomes so unintelligible and verbose that only an LLM can make any sense of it going forward. But even then, changes result in more things breaking than they fix. But the number of bugs slashed goes through the roof. Management is happy!
>Of course you can misuse it.
I think the proper way to use it is to use it as a better search tool. But I don't think AI marketing and valuation is going to be sated by such a use case.
Well at least as long as they don’t develop consciousness of their own.
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.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
https://imgur.com/a/GvByYrD
Our own Github commits volume seem to follow quite closely with token usage on Open Router:
https://openrouter.ai/rankings
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
have you considered the consequences of that or are you still drunk and thinking that this is a good thing?
What exactly would AI have to do in order to not be called a bubble?
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
As things mature there will be a correction, ie the bubble will pop.
I would recommend to read « Boom and Bust: a global history of financial bubbles » https://pure.qub.ac.uk/en/publications/boom-and-bust-a-globa...