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I don't think Ed doesn't comment about the actual tech. Here are some things he has said before and please tell me if these still hold in the spirit?

> You cannot "fix" hallucinations (the times when a model authoritatively tells you something that isn't true, or creates a picture of something that isn't right), because these models are predicting things based off of tags in a dataset, which it might be able to do well but can never do so flawlessly or reliably.

ChatGPT is fairly reliable.

>Deep Research has the same problem as every other generative AI product. These models don't know anything, and thus everything they do — even "reading" and "browsing" the web — is limited by their training data and probabilistic models that can say "this is an article about a subject" and posit their relevance, but not truly understand their contents. Deep Research repeatedly citing SEO-bait as a primary source proves that these models, even when grinding their gears as hard as humanely possible, are exceedingly mediocre, deeply untrustworthy, and ultimately useless.

This is untrue in spirit.

> You can fight with me on semantics, on claiming valuations are high and how many users ChatGPT has, but look at the products and tell me any of this is really the future.

Imagine if they’d done something else.

Imagine if they’d done anything else.

Imagine if they’d have decided to unite around something other than the idea that they needed to continue growing.

Imagine, because right now that’s the closest you’re going to fucking get.

This is what he said in 2024. He really thought ChatGPT is not in the future.

There are so many examples and its clear that he's not good faith and has consistently gotten the spirit wrong.



This guy sounds like an uninformed jackass.

Look at Gemini 3.1 Pro on the AA-Omniscience Index, which measures hallucinations. It's 30, previous best was 11.

https://artificialanalysis.ai/evaluations/omniscience

With the amount of talent working on this problem, you would be unwise to bet against it being solved, for any reasonable definition of solved.


> With the amount of talent working on this problem, you would be unwise to bet against it being solved, for any reasonable definition of solved.

I'm honestly not sure how this issue could be solved. Like, fundamentally LLMs are next (or N-forward) token predictors. They don't have any way (in and of themselves) to ground their token generations, and given that token N is dependent on all of tokens (1...n-1) then small discrepancies can easily spiral out of control.


To solve it doesn't mean we have to eliminate it completely. I think GPT has solved it to enough extent that it is reliable. You can't get it to easily hallucinate.


It depends on how much context is in the training data. I find that they make stuff up more in places where there isn't enough context (so more often in internal $work stuff).




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