It’s a marketing scam.
They will soon launch AI plans with security upgrades, just to charge more than they do now
Anyone else find it funny that if you skim over “Amodei” too quickly, it looks like it says “AI Model”? Dude was born to be in this industry.
https://en.wikipedia.org/wiki/Nominative_determinism
See also: Sam Alt(ernative to)man.
Maybe they are building a moat.
Ah finally, we are here. The diminishing return is here. The trillion dollar bet is finally showing its first cards, and its flopping hard.
We dont even have to wait, LLM bullshittery is dead in less than 10 papers away lol
They must have just learned they’re not getting the government bail-out they were hoping for, so they’re scrambling for Plan B, which is to play the “safety” card to lock out new startups and foreign competitors. The big companies will probably form an AI Safety Consortium that conveniently sets standards in their own favor.
I don’t believe for a second that the same people who were willing to destroy the planet last week are concerned with saving humanity this week.
Supposedly they are already profitable or breaking even on inference. So the money they are burning is mainly for the bigger and better models. If they stop any further devellopment they might actually be quite close to profitability. But then they will be leapfrogged within a few months unless the competitors also stop.
resigning Anthropic researcher Jacob Coxon warned “the people building AI earnestly believe that it could kill us all by the end of the decade”.
Can we, maybe, NOT build the big human eating machine?
The Torment Nexus? But it sounded so cool in that classic sci fi novel “Don’t Create the Torment Nexus”
It’s lucky for us that they are wrong. Still, it’s disturbing how eager they are given that belief. I will never be able to understand people trying to end the world.
I mean, I get being nihilistic, but being enthusiasticly so is baffling.
Recursive self-improving AI isn’t happening like they expected. AGI is nowhere in sight.
The pace of advancement is slowing down and they need a cover story for why they’re not living up to their own hype.
Came here to say this exactly. AI companies are not going to deliver what they promised, they can’t hide it any longer, and now they’re looking for any excuse other than admitting that it was a sham all along.
The pace of advancement is still going strong, especially in math and “reasoning”. For example the frontier math benchmark is showing big strides for the frontier models. A year ago they were only getting <10% of questions, now sol from openai is scoring 89% and they’re reporting the unreleased astra is at 98% .
They even had to create a new frontier math benchmark called erdos that all previous models scored 0% on and astra got 3% , which may not look like much but is a huge relative jump.
I’m aware of what the pace is. You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.
By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.
The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.
The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.
The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.
The models are also getting extremely big as well, since the big improvement currently seems to largely be stuffing the model with more parameters, and making that work.
I can only imagine that the training cost has also been skyrocketing.
The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.
The models suppress outliers by design. Statistically speaking, the most novel thing is a garbled mess of random words, but random noise is useless, so it ends up being suppressed. You can see by fiddling the samplers, or increasing the temperature.
The Library of Babel is the most creative thing in the world, containing every possible combination of English words and letters. You can basically act like an LLM by trying to find a new coherent sentence in it, but also one that hasn’t been said before. It’s basically impossible.
But that is what an improvement is supposed to be. Compare that to finding a sentence that has been said, or something close to it.
Isn’t it having the ability to create that would make it extremely potentially dangerous?
You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.
I’m aware of the controversies, this benchmark isn’t about making new proofs on previously unsolved problems, it’s whether it can answer complex math problems, which it’s getting better at.
By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.
Do you have any benchmarks or data to back this “reckoning”
Do you have any benchmarks or data to back this “reckoning”
I work with LLMs daily. I read papers as they hit arxiv. Also daily. You clearly don’t.
I’m not interested in convincing anyone, which is why I’m speaking non-technically.
The benchmarks being cited aren’t as interesting as you appear to believe they are. You’ve not fully grasped the fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt. For OpenAI to also be embroiled in allegations of plagiarism only serves to underscore the gap between the two concepts.
fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt.
I understand that, but the original statement was about the models stalling out in progress.
Would you say a student stalled out in progress if they could barely do 2 + 2 a couple years ago and is now able to consistently solve some of the most complex math problems known just because that student isn’t creating novel research?
We use AI as an umbrella term for so many things. Literally the type of machine learning you’re talking about has been in use in science and mathematics for quite some time and unrelated to all this buzz. It is decidedly not what OpenAI, Anthropic and Meta’s product are. Those are LLMs friend. The promised harbinger of AGI via agenetic AI. So far, all it’s been is an embarrassingly expensive and destructive waste of resources.
I would say that AI is becoming much more effective for the tokens produced. I have been running Qwen 3.8 Flash-Next on my new PC. This AI model weighs in at 120b parameters, with another 50b added via an Engram table that costs about an extra 20% in RAM for its parameter count. This is a major jump in world knowledge, and the model in question has 262k context length.
While experimental and with rough edges, this model has been able to use Lingua Gacha to undertake a game translation project - understanding and converting a Chinese game into English.
Agentic AI has become practical on local hardware over this last year, and there looks to be much more refinement to come.
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What we are seeing with Anthropic and the other American companies, is a scramble to protect themselves from the consequences of badly planned data centers and poor practices in AI development. Their Chinese competition has produced much more efficient models, while delivering almost the same level of quality as American offerings. This means that local AI is becoming “good enough”, which makes data centres and AI subscriptions unnecessary for many businesses if they distrust the cloud.
I’m not perplexed. When in doubt,look at the wallets. They’re out of money. The interest on the loans is starting to bite, and their revenues aren’t going up like they hoped.
Don’t forget all the data centres they’re on the hook for to support demand that isn’t there. As they come online, the operators are going to expect to be paid.
The slop pushers have a bunch of spending commitments that aren’t technically loans. But they’ll still come due.
I think you mean the data centers that we’re on the hook for. The Ai companies didn’t build the centers, private Equity did. Then the banks looked at all of that lease income and made lease backed securities. Don’t worry though, they’re not like mortgage backed securities, these are way more stable becuase the Ai companies will continue to grow forever!
Any idea where I can buy credit default swaps on those lease-backed securities?
Yep. If they just stop building for financial reasons then it looks like a bad investment. If it’s because “the technology is too good” then they can slow down and act like it’s a good investment at the same time.
With no exponential improvement in efficiency it means they have basically hit the ceiling on LLMs and constsntly increasing compute to make any progress is no longer drawing in massive funding. They just want a reason to maintain the current level while they shift to sucking money out of the people who are stuck with LLM workflows.
No it’s simple and not confusing at all: The models are well beyond the point of diminishing returns for investment and the AI bosses know they cannot possibly meet the crazy expectations of the stock markets. So instead of them failing to deliver, they are using this excuse to seem like they’re choosing not to deliver for everyone’s good.
It won’t work - the bubble is going to burst regardless.
You can’t break up with me, I’m breaking up with you type of energy.
No, you don’t understand. If we don’t break up I become Hitler and you become Eva Braun.
I agree on the bubble - think less than 2 years before the jig is up.
It’s going to pop before 2027. It’s a pop not a deflate, the money will disappear in a 48 hour period, not that the theoretical money every existed.
The models are still advancing, especially in math and “reasoning”. For example the frontier math benchmark is showing big strides for the frontier models. A year ago they were only getting <10% of questions, now sol from openai is scoring 89% and they’re reporting the unreleased astra is at 98% .
All those assholes trying to shove it into our faces. Fucking shut all of that down.
“slowdown” means “prevent competition and maintain the status-quo”
While they quietly panic about the constantly rising costs of running their models, the lack of new training data, and the sheer drop investment capital took while they were too busy hemorrhaging money to realize.
Now they’ll act like they’re scaling back in order to protect the public and hopefully figure out their golden parachute strategy while cutting startups and smaller businesses out of the market completely.
Yep - they’re established, so it’s ladder-pulling time!
It’s a shame how polarized people have become. No discussion of game theory making it difficult to slow things down even if everyone in America wanted to. No discussion about potential solutions. Just people feeding their confirmation biases, and obliviously living in their echo chambers.
doesn’t take a rocket scientist to see why they’re saying this. all you have to do is use the various models to see within the past year they’ve all become noticeably worse for average consumer use. All the training data is out of date, all of it. The gaps between updates of said data have increased. They’re out of data to consume and it took them too long to realize that feeding off each others waste doesn’t work. combine this with the fact they’re all collectively out of money.
You’re better off just using a search engine at this point like you used to in order to get a solution. Because all these LLM’s are hallucinating now to such a massive degree in a desperate attempt to provide the user with a positive solution.
If ram prices drop to pennies after the bubble pops it will not have been worth it, but it will feel so good.
the search engines were overwhelmed with so blogspam before AI; now it’s an awful state.
But I still use them instead of AI. It’s just really hard to find a primary source. AI hallucinates. Human knowledge catalog has peaked.
Claude insisted to me that the STM32U3 was far too new to be supported by STM32duino.
It’s been supported since March of 2025.
Hell yes the out of date training data is pissing me off. And you’re right, I would have been better off just googling it myself.
On the bright side, the weeks I spent learning STM Cube IDE may not have been a waste.
Hey maybe we should put it all back in the box.
Said pandora
