The stunned responses indicate a set of some of humanity's brightest struggling to comprehend the emergence of such an incomprehensibly creative and powerful intelligence.
AI has come for coding.
AI has come for mathematics.
AI will come for everything else if we don't do something NOW.
In the short-run I think concern is much more merited because it will obviously cause some instability as the economy settles into a new equilibrium, but that's always the case with any sort of revolutionary technology. And this would almost certainly just be short-term stuff. As the value of one thing goes down, the value of other things would go up, and new things would emerge.
Where do you get this from? There is exactly one comment that even mentions creativity, and none of them speak about intelligence. All seem to agree it's slop.
> To be fair, my first reaction was almost boredom. Yes, the AI has “proved” (really? Are we sure? Can we even understand what is written there?) a bunch of interesting results in my field. Not even a Millennium Problem. Pff. [..] If a paper or thesis is so badly written that I cannot get past the first page without considerable effort, I reject it and ask for a new, readable version. With these AI-generated papers, I feel that we, as a community, are not applying the same standards of quality and rigour. They can simply release multiple 100+ page papers claiming to have solved this or that problem, often with redundant arguments, unclear logical structure, multiple dead ends, and strange or unsettling terminology — in other words, slop.
it's like recognizing that when most people talk about the os they're using, they're really discussing their de. great, their mistake, but at the same time they're not actually making any mistakes either, you're the one taking them there, then beating them with experience. what a de can pull off can heavily depend on how the underlying os works, and some os have a fixed de they ship with that's almost never replaced cause it's not really intended to be. going off about specifics when they did not talk in terms of specifics is obnoxious. which is probably no coincidence, given your username of choice.
Today, g is one of the most robust findings in differential psychology. The tendency for different cognitive abilities to correlate positively has been replicated across numerous studies. g typically accounts for around 40 to 60% of the variance in cognitive test performance and is predictive of numerous life outcomes, including educational achievement and occupational performance. [2]
Evidence of g isn't confined to humans either. Studies of mice have identified a general cognitive factor explaining roughly 30 to 40% of the variation in performance across different learning tasks [3]. We have similar results for primates and birds.
To put it simply, intelligence tends to generalize. Humans who tend to have higher verbal skills also tend to have higher spatial skills, better memories, and faster processing speed. Someone with the cognitive ability to become an exceptional chemist might just as easily have become an exceptional mathematician or software engineer. The knowledge and skills required are obviously different, but the underlying cognitive abilities that make someone successful in one intellectually demanding field often transfer to others.
And yes, there is also evidence of g in LLMs [4].
[1] https://www.jstor.org/stable/1412107
[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC8293439/
[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC2614349/
[4] https://www.sciencedirect.com/science/article/pii/S016028962...
theres a difference between having a general capacity to learn, possessing domain-specific expertise, and being able to reliably apply that expertise in the real world.
even in humans, a genius mathematician isn't necessarily an exceptional manager, composer, or biologist. general intelligence might make it easier to acquire those skills but it doesn't substitute for them.
the question isn't whether LLMs exhibit "g" but if the abilities being measured are representative of the broader capabilities we're predicting. that's not something g settles.
>The knowledge and skills required are obviously different, but the underlying cognitive abilities that make someone successful in one intellectually demanding field often transfer to others.
The fact of the matter is that If you have a good general capacity to learn then becoming a competent x is generally only a matter of time and interest. For LLMs, it's the same except with data and compute. Naturally, all work done on a computer is in danger.
We are doing something now. We're making it so the AI does it all better, so we don't have to do it anymore. Gotta break a few eggs to make an omelet, but it's going to be a delicious fucking omelet when it's ready. I don't understand why people are pushing back so hard on making the world a better place. I personally can't wait until it's clankers all the way down.
i'd think that instead of just giving in and reacting with a cheap insult, highlighting the unreasonable leap in rhetoric might be a bit more persuasive. to be specific:
> I don't understand why people are pushing back so hard on making the world a better place.
"making the world a better place" is not what's receiving the pushback. on the contrary, kind of the whole argument is that the current developments are short sighted, and that they will leave leave the world in a worse place on the long term.
and then one can agree or disagree about that, but at least then we'd not be arguing strawmans anymore, nor approaching increasingly childish insults
The price will never be zero, since there is a finite amount of compute possible.
There is zero reason to believe we're barrelling toward utopia and plenty of examples that illustrate exactly where we are headed.
I misspoke when I said "zero". I should have said "down to marginal cost". It's fine for AI to be priced at the cost of its compute! That's the efficient outcome.
What reason do you expect to provide a moat for a monopoly? Training on existing customer interactions? That might get you an oligopoly, but there are already two serious competitors in the US alone and several potential Chinese rivals.
If LLM's reach their potential we're speaking of access to generalizable high quality cognitive labor for something approaching $0, for everybody.
Quanted out Qwen 3.8 models are not frontier level. Have you used them for real work? They suck. They are so unsure of themselves due to the quant-lobotomy. They just endlessly loop in thinking even when they have the right decision and then output it when they get to the thinking cap. They score close in benchmarks because most benchmarks don't measure time to complete a task.
I use Qwen quants in production for fast inference but I disable thinking and have them fine tuned for the task. My realtime transcription + translation stack right now is running 5 streams at 380 tok/s.
Running DeepSeek 4.1 Flash at FP4 is going to need at least a 10-15k machine. I'd consider that near frontier level.
We can find a robot that will play golf instead of CEOs.
AI can generate keynotes and meet with other AI agents, pat itself on the back for success stories and give itself promotions and empire building.