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Ed Zitron loses his mind annotating an AI doomer macro memo

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Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#91

Post by palmotea »

GaryBluto wrote: post #42
pityJuke wrote: post #29

Earlier quoted context omitted.

Don’t encourage his diaper fetish! [0] [0]: https://bsky.app/search?q=from%3Aedzitron.com+diaper

I thought there would be one or two results, perhaps a result of poor reoccurring phrasing. Nope. >i'm going to change your diaper and burp you >Carlito is a very good boy Go piss in your diaper you big baby >He doesn't care. He is a big baby who filled up his diaper with pee pee and poo poo >you are a big baby and i am going to change your diaper and burp you >To be clear I call executives of multi trillion dollar c…

Is that a fetish, or just idiosyncratic and more aggressive way of mocking someone for being weak/immature/"being a baby"?

Those quotes that I could interpret read more as contempt to me than some kind of role play.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#92

Post by square_usual »

MadxX79 wrote: post #61
Ozzie_osman wrote: post #40

Earlier quoted context omitted.

Agreed. If anything, it puts downward pressure on pricing. Even if the CIO still buys Salesforce or whatever other tool, they won't be willing to pay as much.

If you don't give me a discount on my salesforce subscription I'll shoot myself in the face with this AI enabled gun?

You don't need AI to shoot yourself in the face; salesforce can do that just fine.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#93

Post by disgruntledphd2 »

energy123 wrote: post #53
simianwords wrote: post #39

Earlier quoted context omitted.

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 we…

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.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#94

Post by surgical_fire »

simianwords wrote: post #86
surgical_fire wrote: post #85

Earlier quoted context omitted.

> For our language model benchmarking, we note that we consider endpoints to be serverless when customers only pay for their usage, not a fixed rate for access to a system. Typically this means that endpoints are priced on a per token basis, often with different prices for input and output tokens. Okay, correct me if I am wrong, so this is measuring the inference costs for clients of AI services, not the the inferenc…

It could be that OpenAI is subsidising their models by _fifty times_. Do you really think they are doing that? In some cases the costs went down by 200x. Do you really think OpenAI is subsidising their models by 200?? Its easier to just admit that technological advances helped decrease the cost instead of coming up with more complicated reasons like VC funding, subsidies and so on. For instance take Deepseek and othe…

> It could be that OpenAI is subsidising their models by _fifty times_. Do you really think they are doing that?

Possibly. I don't know.

It could be unfeasible to increase prices so much whenever a new model was released.

Any assumption made here is based on vibes. I see no reason to drop my skepticism.

> Its easier to just admit that technological advances helped decrease the cost instead of coming up with more complicated reasons like VC funding, subsidies and so on.

They raised an absurd amount of cash, and still bleed money to an absurd degree.

VCs make money when they exit. OpenAI only needs to "make sense" until an IPO happens. Once private investors have their exit, the markets can be left to handle the resulting dumpster fire.

> For instance take Deepseek and other opensource models - even they have reduced their costs by a huge margin.

Chinese companies are very opaque. I don't pretend to have insight into it.

Is the company behind Deepseek profitable?

> What explanation is there for opensource models?

What opensource models have to do with inference?

Your argument is that training is expensive but inference is cheap (something I see no evidence of). Why would a company give away the expensive part of the work?

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#95

Post by butterbomb »

lelanthran wrote: post #83
hiddencost wrote: post #26

I've started to feel like Ed Zitron is actively hurting people I care about. I'm lucky to have worked in the field for a long time, and be able to spend a lot of tokens. In the last month it's become clear to me that the tech works. The science is done, and what's left is engineering. There are a lot of risks and mitigations and theory to build, but it's all solvable. The tech isn't mature, but neither was the Intern…

> They insist the tech will never work, and avoid learning about it, becoming progressively more paranoid and isolated. I'm trying to be supportive and help them start to recover, but it's slow going. If you are right, and the tech works, both you and them will be continuing this conversation in a soup kitchen.

More likely a mass grave

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#96

Post by palmotea »

shimonabi wrote: post #14

I've also heard Cory Doctorow recently offer a similarly dismissive view, describing AI as "just statistics".

> I've also heard Cory Doctorow recently offer a similarly dismissive view, describing AI as "just statistics".

Well, AI partisans have applied grandiose terms like "thinking," "intelligence," and "soul" to these machines. It's not wrong to push back and remind people what they really are.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#97

Post by relaxing »

hiddencost wrote: post #26

I've started to feel like Ed Zitron is actively hurting people I care about. I'm lucky to have worked in the field for a long time, and be able to spend a lot of tokens. In the last month it's become clear to me that the tech works. The science is done, and what's left is engineering. There are a lot of risks and mitigations and theory to build, but it's all solvable. The tech isn't mature, but neither was the Intern…

The internet 30 years ago worked great, what are you talking about.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#98

Post by simianwords »

surgical_fire wrote: post #94
simianwords wrote: post #86

Earlier quoted context omitted.

It could be that OpenAI is subsidising their models by _fifty times_. Do you really think they are doing that? In some cases the costs went down by 200x. Do you really think OpenAI is subsidising their models by 200?? Its easier to just admit that technological advances helped decrease the cost instead of coming up with more complicated reasons like VC funding, subsidies and so on. For instance take Deepseek and othe…

> It could be that OpenAI is subsidising their models by _fifty times_. Do you really think they are doing that? Possibly. I don't know. It could be unfeasible to increase prices so much whenever a new model was released. Any assumption made here is based on vibes. I see no reason to drop my skepticism. > Its easier to just admit that technological advances helped decrease the cost instead of coming up with more comp…

>It could be unfeasible to increase prices so much whenever a new model was released.

This means you have no idea what I have been saying. A new model is costlier, but they release mini versions of old models that are way cheaper and compete with older models.

GPT 5 mini is way cheaper than GPT 4 but around the same performance

GPT-5 mini:

Input tokens: ~$0.25 per 1 M

Cached input: ~$0.025 per 1 M

Output tokens: ~$2 per 1 M

-----

GPT-4 (legacy flagship):

Input roughly $2.00 per 1 M

Output roughly $8.00 per 1 M

>Chinese companies are very opaque. I don't pretend to have insight into it.

False. The models are not opaque, you can literally download it and host it yourself. They have also released papers on how they reduced cost in certain areas.

This is literally them documenting the cost-profit ratio theoretical at 500%

https://github.com/deepseek-ai/open-infra-index/blob/main/20...

>The above statistics include all user requests from web, APP, and API. If all tokens were billed at DeepSeek-R1’s pricing (*), the total daily revenue would be $562,027, with a cost profit margin of 545%.

Not only that, there are other providers hosting these opensource models, there are so many companies - just go to openrouter.com

So this is your skepticism

- openai is subsidising their models so much that each year the keep doing it 20x and eventually reached 100x reduction

- all the investors are stupid and they still invest in openai despite unprofitability

- employees of openai and anthropic who have claimed that the unit costs are not high are also lying

- all other providers are in on the lie

- the chinese models like Deepseek is also in on the lie by posting research that is not plausible

- the fact that you can run models in your laptop today that beat previous years models is also not enough

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#99

Post by simianwords »

disgruntledphd2 wrote: post #93
energy123 wrote: post #53

Earlier quoted context omitted.

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 sma…

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.

Re: Ed Zitron loses his mind annotating an AI doomer macro memo

#100

Post by disgruntledphd2 »

simianwords wrote: post #99
disgruntledphd2 wrote: post #93

Earlier quoted context omitted.

> 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 sma…

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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