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?
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.
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.
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.
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.
You can’t break up with me, I’m breaking up with you type of energy.
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% .
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.
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.
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!
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.
“slowdown” means “prevent competition and maintain the status-quo”
Yep - they’re established, so it’s ladder-pulling time!
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.
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.
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.
Hey maybe we should put it all back in the box.
Said pandora
