The way SpaceX IPO got crammed into index, it’s invulnerable to anything but an immediate incarceration of everybody involved.

Index funds will be required to buy the stocks at a listing price before market can decide how much they are worth exactly.

Afterwards, “economy in a recession” is synonymous to “free buffet” to those at the reins.

Yeah, which is why the "if minimum wage increases, so will prices" aregument is BS. They were going to charge the highest price they thought they could either way, the difference is that they are forced to increase the amount that goes to the people they are trying to pay the least.

60% of the time it works every time

lol it’s a subscription

It's actually API access price, and it's charged per input + output tokens. $0.87 per million tokens is damn cheap.

They probably have super cheap electricity and it's possible they use cheap Chinese Ai chips for inference.

I think it's meant to convey that it's not a temporary deal on the old price, but a permanent new price point.

Permanent under the current pricing model, subject to change.

Yes, almost like that's why there's a bubble despite the obvious disparity between both states' ability to maintain an adequate infrastructure for this technology. US companies are looting with the full expectation that the cost of the economic fallout will be shifted to taxpayers. They're getting as much value out of this before China inevitably becomes dominant even in the US sphere of influence as the US simply will never be able to compete after decades of neoliberal politics and the erosion of public works.

If you run it locally there's no censorship...

Its safety rails are far worse than any in the West. But to your point, fuck AI.

Not glazing when its simply enjoying watching China beat the US at its own game

No?

The deepseek-v4-pro model API pricing will be officially adjusted to 1/4 of the original price after the 75% discount promotion ends on 2026/05/31 15:59 UTC.

https://api-docs.deepseek.com/quick_start/pricing

They invented a hybrid attention design that drastically reduces the amount of memory needed for the KV cache at inference time. Like, dividing it by 10. And memory is a large part of the cost of inference.

The American models are eating their cost big time, in return they get user data to train on and a massive reality distortion field that can theoretically be exploited later. It costs less for DeepSeek to eat their cost, and maybe the value of that user data is worth it now? Maybe there is some Chinese VC getting involved to try and boost DeepSeek with a little reality distortion field they can attempt to exploit later? I don't know, but all these seem plausible to me.

A token is basically just a word. Know how your phone’s auto suggest tries to anticipate the words you want to use as you type? In this case, your phone is using an extremely small token amount (typically only the previous two or three words you have typed) to try and predict your next word, which would also be a token. Your phone only uses a few tokens at a time, because as token count rises, processing requirements also quickly balloon.

And AI chat is basically the same concept, but with a massively inflated token limit. Instead of looking at your previous two or three words, it looks at entire conversations. And it also uses tokens to generate responses, the same way your phone is using one token at a time to predict your next word.

So when you pay for tokens, you’re essentially paying for a word count. As you continue a conversation, the token requirement for each subsequent request will increase, because it is attempting to look at the entire context of the conversation you have had.

Models have built-in token limits, to put a cap on how much memory is required to run the model. As conversations stretch on and you reach the model’s token limits, it will begin losing context for things that happened earlier. It will try to summarize earlier parts of the conversation to shorten them but keep relevant pieces in memory, or it will just outright drop old parts of the conversation and “forget” that context, the same way my phone has already forgotten the start of this sentence.

It’s a little more complicated that “each word is a token”, because the chatbot will combine your prompts with its own internal systems. Especially as conversations stretch on, and it begins to summarize old parts to keep them in memory. But that’s the most straightforward way to explain it.

My understanding is that tokens are basically words, and that when you ask a question it charges for all the tokens it consumes, produces, or processes. There's a lot of internal processing for each request, where the input text is summarized in different ways and combined with previous parts of the conversation, so it's not as straightforward as "word count of what you say plus what it says".