fallpeak

On OpenRouter there are 11 third-party inference providers hosting DeepSeek V4 Pro right now, of which 8 are US-based and 7 of those have zero data-retention policies (which I mention to rule out any claims of "oh they're making up the money by logging all your data"). This is a 1.6T-A49B model, so a bit bigger than Sonnet (~2/3rds the size) and a bit smaller than Opus (~3x as large). These third parties are almost perfectly interchangeable via OpenRouter as a marketplace, so they have no incentive to offer any sort of "growth pricing" below costs, and they serve it at $3.48/Mtok out.

Kimi K2.6 is 1T-A32B with a slightly less computationally efficient architecture, and is served at around $3.50/Mtok out by 9 US ZDR providers.

Unless you think that either the generally accepted size estimates for Anthropic/OpenAI models are wildly off or those companies are a lot worse at serving models efficiently, Anthropic and OpenAI are probably making around 5-8x margins on their API costs.

The cost of training new models is of course a major factor not counted here. Depending on how you want to think about that this may or may not make them net profitable. I remember one of those CEOs gave an interview a while back where they described it as a series of independent investments, where each model they train is net-positive in revenue by EOL just from its own inference, but I don't know whether that's still true or not.

Regardless, the point is that if they stopped training new models today, both Anthropic and OpenAI are making incredibly generous profits on their API inference.

You are incorrect. There, now we've both made unsupported assertions. Care to provide any evidence for your position?

For what it's worth, when I provide a Pangram link it's because I can already tell something is AI and I'm attempting to provide objective third-party confirmation so the conversation doesn't just degrade into me asserting that I have superior taste to you.

This phenomenon (and/or the fear of being falsely accused) is why I find myself being an annoying unpaid evangelist for Pangram every few days.

Right now a lot of people are developing their own heuristics, some much more well-calibrated than others, for sniffing out AI generated text but it's kind of an unfalsifiable accusation since you're really just saying "this sounds like slop, I can tell from the words and from seeing lots of slop in my time".

Linking to an AI writing detector serves as a somewhat objective second opinion, but half the time if I say "Pangram confirmed it" I'll see at least one person pulling the old "yeah but GPTZero is crap so that means AI writing detection can't work in principle" maneuver, which strikes me a lot like someone who tried ChatGPT in 2023, decided AI was all hype, and never re-evaluated that stance.

It would be really helpful for the discourse if we could get to a point where people generally accept one or more _actually-good_ LLM writing detectors as a reliable tool such that a "this is AI" judgement from them is accepted as reasonable proof of guilt, solely so that we can then extend the benefit of the doubt to anybody else.

AFAICT they're basically saying "hey lawmakers we're not going to rock the boat or take a stand so in exchange pretty please don't lump us into the app store category"

What does that have to do with anything? These days any piece of text may or may not be AI generated (my money would be heavily on "no" for the post you asked about), but either way it isn't blatant slop so we can't tell.

It feels like you're trying for a lazy gotcha, but the actual point here is something like "AI models often generate writing with specific noticeable characteristics that make it obviously AI output, and TFA is an instance of such writing, and this should be called out when possible"

Have courage and trust your own instincts. Unless one is extremely disagreeable it's very tempting to hedge and avoid outright saying "this is AI" just in case you're wrong, but if you're literate and regularly exposed to AI outputs your instincts are likely quite accurate.

In this particular case the linked article is definitely AI generated.

The Kobo Clara doesn't fit in my pocket while the X4 does. An e-reader that I actually have on-hand beats one that I leave in a drawer at home so it was an instant purchase for me the moment I saw one.

Tokens _are_ as cheap as keystrokes. A single keypress by a full-time SWE averages out to $0.005-$0.02 (depending on typing speed and TC). The relationship is obscured because the keystrokes are usually part of a fixed-price subscription plan but they absolutely have a cost. Prior to AI this was in fact a large reason everyone pontificated about concise programming languages and elegantly factoring problems and DRY and...

The only correct way to ask an AI "why did you do that?" is in the sense of a blameless postmortem. You're the person responsible for giving the LLM appropriate context and instructions and guardrails, so the only reason you should ever ask a question like that is when you're genuinely trying to figure out how to improve those for next time. Every time I see people posting this sort of "apology" from an LLM it makes me cringe, feels only half a step away from outright AI psychosis.

That seems like unnecessary pedantry, only one step short of the common "everything is chemicals" retort. Egg yolks are an ingredient that contributes to the flavor and nutrient profile of the food while also emulsifying. In this context "emulsifiers" is presumably meant to refer to other ingredients added solely to modify the texture.

TL;DR: Devs didn't know what they were doing and turned off autovacuum and eventually it broke, then the author decided to have an AI slop out an article about the incident which may or may not have actually occurred.

It doesn't know that because it wasn't trained on any tasks that required it to develop that understanding. There's no fundamental reason an LLM couldn't learn "what it knows" in parallel with the things it knows, given a suitable reward function during training.