It will be a race which AI company holds out longest. All of them are making losses that no company can survive, and if they rise their prices enough to cover the costs, they will basically lose all their customers. Who will then struggle to hire the people back who still know how to do things without AI.
I think they could significantly lower their costs if they turn their focus away from the race to “AGI.” But, their valuations don’t really make sense unless investors believe they will achieve AGI.
I was there when the “Neural Networks” idea started. They thought they could program NNs to achieve AGI. Now they think they can do it with LLMs. But LLMs are just parrots with a large dictionary. They won’t reach that point, either. An LLM is way too much rooted in words to be able to think.
LLMs are not currently, nor ever will be, anything remotely resembling AGI. AGI is still entirely within the realm of science fiction, like teleportation or time travel.
That’s true. I’m just saying the models do get better and are quite impressive now.
But whatever the term means. I know where AGI stands for, but the term “agi” is still very vague. You can’t compare a machine llm model with the human brain.
The “best” one I’ve tried is the latest Opus. I don’t trust any of them to use for real work, so I mostly just play around with a local Qwen 3.6 27B or Deepseek v4 Flash. I have heard OpenAI’s latest models produce less bloat than Anthropic’s.
I should say I do find LLMs useful as a kind of search agent (both web and large unfamiliar code bases). And GhidraMCP is pretty cool (maybe just because I don’t have much experience with reverse engineering).
It will be a race which AI company holds out longest. All of them are making losses that no company can survive, and if they rise their prices enough to cover the costs, they will basically lose all their customers. Who will then struggle to hire the people back who still know how to do things without AI.
Then Google wins? Since they have the ad business sonar least they are making some money.
I think they could significantly lower their costs if they turn their focus away from the race to “AGI.” But, their valuations don’t really make sense unless investors believe they will achieve AGI.
I was there when the “Neural Networks” idea started. They thought they could program NNs to achieve AGI. Now they think they can do it with LLMs. But LLMs are just parrots with a large dictionary. They won’t reach that point, either. An LLM is way too much rooted in words to be able to think.
Recent models are quite good. Like gpt 5.6 sol. Even better than mythos 5.
How is this a response to a comment about AGI?
LLMs are not currently, nor ever will be, anything remotely resembling AGI. AGI is still entirely within the realm of science fiction, like teleportation or time travel.
That’s true. I’m just saying the models do get better and are quite impressive now.
But whatever the term means. I know where AGI stands for, but the term “agi” is still very vague. You can’t compare a machine llm model with the human brain.
Hey, we have AGI! We’ve had it for years! It’s called making babies 😎😎😎😎😎
Yet still hallucinates mass bullshit and can’t count, can’t architect, and can’t write a decent test
The “best” one I’ve tried is the latest Opus. I don’t trust any of them to use for real work, so I mostly just play around with a local Qwen 3.6 27B or Deepseek v4 Flash. I have heard OpenAI’s latest models produce less bloat than Anthropic’s.
I should say I do find LLMs useful as a kind of search agent (both web and large unfamiliar code bases). And GhidraMCP is pretty cool (maybe just because I don’t have much experience with reverse engineering).