351–360 of 426 posts
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I definitely heard it semi-frequently from SRE types well before the rise of LLMs. LLMs are just parroting relevant documents they've assimilated.
It's so obvious that they've been trained on a metric shit-tonne of white papers and corporate emails it's not even funny.
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Wait, this person knew that the wire could bring you light, but not that it could bring you heat? Hadn't they noticed that light bulbs heat up?
It could be a reasonable argument from the point of view of scale: you need a lot more energy for cooking than for lighting (even with incandescent lightbulbs, though they were a fair bit dimmer and colder in the earlier days of them).
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Are they? I don't believe there's that big of a market for local AI. Most people don't care that much, and you'll most likely lose the advertising revenue.
>I don't believe there's that big of a market for local AI. Most people don't care that much, I agree that the market for local AI is basically limited to nerds at this point, but that's because nobody's really explained why local AI is a good thing and also because the vast majority of people need the $20 paid plan at most. How much time and money would it take to get something half as good as OpenAIs products runni…
There are a lot of good things that need to be explained to people, but nobody ever managed to. I don't think this will be any different.
> because the vast majority of people need the $20 paid plan at most
Exactly, people are not gonna invest time and money when there's already something else that satisfies their need.
Local AI will need to be both better and more convenient in order to be adoped by the masses.
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> within a few years we will be running local models as good as today’s frontier Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.
Per frontier token. You're not calculating the cost of a fixed quality asset here. Old hw running non-frontier models will be very valuable. In fact, we have two direct examples: older server gpus actually appreciating and the very obvious fact that not everyone always use MAX FULL EFFORT BEST MODEL no matter what.
Already today is not possible to run deep seek v4 pro locally, and I cannot imagine that in 2 years we will be.
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GPT-4 (original API): Input: $30 / 1M tokens Output: $60 / 1M tokens GPT-5.5: Input: $5 / 1M tokens Output: $30 / 1M tokens Costs have been reducing by over 5x year over year. Inference cost concern is mostly performative. https://simianwords.bearblog.dev/conclusive-proofs-that-llm-... Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like…
That's pricing. Pricing has no correlation with profit. It can be artificially lowered to kill competition, and artificially inflated to maximize profit.
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The price a company charges, _particularly_ a high growth VC-backed one, is a poor signal for their costs. That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.
> That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense. Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox. Deepseek is much cheaper and way more performant. If you can agree to reasonable assumptions 1. that closed source models are m…
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The price a company charges, _particularly_ a high growth VC-backed one, is a poor signal for their costs. That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.
> That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense. Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox. Deepseek is much cheaper and way more performant. If you can agree to reasonable assumptions 1. that closed source models are m…
Not a reasonable assumption for a variety of reasons.
> 2. Deepseek is served at a profit and not a loss
Not a reasonable assumption either.
> Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox.
Because the internals are what actually matter and what drives inference cost.
It would be entirely reasonable to expect that GPT-5.5 has some sort of optimizations or changes to the architecture to make it easier to train, or to make runtime ablation easier, or to better handle large batches, or whatever.
Those changes, particularly if they are non-public, can easily result in worse inference performance than a comparably sized model without those changes.
> It is borderline conspiratorial to believe it this way.
It's not any sort of conspiracy. It's how land-grab tech companies have always worked. To presume otherwise is silly.