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Comment Re: Why would an LLM know what humans want? (Score 1) 74

That's likely part of the problem.

It stocked a bunch of cool nifty things that people talk about but don't necessarily want to buy (or buy many of/often).

The museum gift shop description sounds like that to me anyway. Like I bet it has rope loop guns there.

Sort of like what thinkgeek was back in the day.

Comment Re: Summer vs Winter (Score 2) 201

I've been to both.

Windy and 0degrees f in a NY winter is much colder.

Which isn't to say that temperatures in the 50s (especially after a sunny day) aren't light jacket weather. But NY winters are brutal. I've never felt the cold of sleety 35 degree day with high winds in San Francisco. To say nothing of a windy sub zero night.

Comment Re:We all know the real reason (Score 1) 118

The compute cost for DeepSeek V3 was $5.6M. Training costs for traditional US frontier labs model is about $70 to $200M+, with estimates of up to $1B+ to train the next generation models. It's expensive, but it's not even in the ballpark of "There is no viable economic model to do so." Google revenue in Q2 was $1.32 billion *per day*. $1B is trivially less than a day; $5.6M is just 6 minutes of revenue.

The cost problem is in the compute buildout and the subsidized compute to capture users.

> It remains to be seen if even using their models can be profitable,

I presume we can stipulate there exists some tasks that are cheaper to do using an LLM than doing it "by hand". As a such, using an LLM model can be profitable. To me, it is also obvious that the business of serving an LLM model can be profitable. There's large benefits to being able to get tokens quickly, and you can expect people to pay for that, even if we presume they could run the model locally with no maintenance overhead. There's even cases where using the cloud is cheaper than you can serve locally at all - I saw one guy that measured his added cost of electricity when his computer ran a specific LLM, and it was more expensive to run it locally than to buy tokens from the cheapest inference provider for the same model, presumably because they had better hardware and cheaper electricity.

Comment Re: Translation: (Score 1) 94

I think it's more:

Company falling behind in AI tells everyone "let's slow down".

My understanding is that they're pretty similar in performance and an order of magnitude more expensive than the Chinese models (some of which are open weight), and behind Anthropic and even Alphabet now for work.

Comment Re: These companies need to bring charges (Score 2) 125

Only the government can bring criminal charges.

These companies can file complaints and hope the government tries criminal charges and the companies can file a civil suit.

We definitely need a way to attach criminal charges to entities that can't go to jail. I'd propose socializing the company for the length of the prison sentence before then returning it to shareholders.

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