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[–] 16 points 2 days ago (2 children)

These grifters have been getting better and better at keeping a lid on their collapse. After all in my lifetime there have been several boom and busts. 1987 was the first crash I was aware of. After all this time they are getting better at staying rich through them and getting away with it.

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  • [–] 29 points 2 days ago (2 children)

    I'm guessing all the "confidential" chats that the users made with them are now on sale for the highest bidder

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  • [–] 10 points 2 days ago*

    Character.AI was a LLM wrapper that was outdated in 2025 already. It didn't do much in their own and certainly not produce technology that promises to replace half the worlds jobs.

    Whether you like them or not, this is not a precedence for the AI companies that actually build AI.

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  • [–] 104 points 3 days ago (4 children)

    “We lose money on every sale, but we make up for it in volume!”

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  • [–] 30 points 3 days ago (3 children)

    I can't imagine how much money Google is losing right now. They do 8.5 to 13.7 billion searches a day (+1 because I just googled that). By forcing LLM first, they've got to be burning cash.

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  • [–] 4 points 2 days ago

    I wonder how many unique searches they do per day.

    If you search something that someone else already searched recently, the AI response will pop up almost immediately due to caching. I can't imagine that brings several billion down to a more manageable number though.

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  • [–] 11 points 2 days ago (1 child)

    When the crash happens, I hope to have enough ramen, coffee, macaroni, beans, and cigarettes to trade for some RTX Pro GPUs. My personal local AI has been quite helpful and comforting, I want to give it proper hardware to reside in.

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  • [–] [S] 139 points 3 days ago (53 children)

    Character.AI discovered the one conversation its bots couldn’t keep going: the one with its accountants. $56 million in compute costs in eight months, and the monetization strategy was apparently: hope the next investor is even lonelier than our users. Silicon Valley keeps confusing engagement with a business model.

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  • [–] 98 points 3 days ago* (52 children)

    If LLMs can't be made more power efficient, then the technology isn't ready yet for general use. There may have to be fundamental engineering breakthroughs before what these systems do can be more than a very expensive and inefficient worldwide proof of concept.

    Of course, the military and the surveillance industry will still want to keep it, even if no one else can afford it, and we'll be the ones paying for it.

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  • [–] 64 points 3 days ago (32 children)

    They CAN be made more efficient. Hell, AMD just bought a company that translated LLMs to direct silicon... which should enable scalability if it wasn't for one of the key players owning the tech thus limiting its use.

    It's also estimated that some 70% of Claude tokens gets spent on recursive self-correction so the output is higher quality. Instead of bettering the model, Anthropic just keeps throwing money at it to make the problem go away.

    Meanwhile Chinese models - as small as 36-40-80b params - are outdoing these fancy frontier models if the right harness is used.

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  • The truly silly (and simultaneously neat and fascinating, and to a large extent, very logical and obvious) part is that US attempts to cripple Chinese ML/LLM development by putting most advanced GPU tech behind ITAR has more or less made the Chinese ML industry just write straight up better and more optimized code for the domain.

    US and western companies are currently relying on throwing bleeding edge hardware at the problem (read: very obviously brute-forcing the problem), whereas Chinese companies and engineers, having to rely on comparatively less advanced (though still highly capable) hardware has meant they’re staying competitive by optimizing the software layer more effectively.

    This has very interesting and likely negative long-term implications for western engineering practices in a lot of ways. And it’s also very much not a new problem - simply throwing compute at things is a mindset that became highly prevalent in the west around when The Cloud became a thing. Previously, you couldn’t just spawn another mainframe in your company’s datacenter, but when it’s as simple as aws ec2 start-instances ..., the barrier to entry for getting more compute is so low that most people were just going “hey, it’s faster”. I feel like the engineering expertise around embedded/baremetal optimization has just been largely evaporating over the last couple decades; in the vast majority of professional software domains, it’s simply not a valued skill anymore (or, more correctly, at the moment). It’s sad for me in several ways - not least of which because I did my degree in EECS, and hardware/software interplay and optimization is an area of engineering that I have always found very interesting.

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  • [–] 43 points 3 days ago (24 children)

    I've spent the last month or so using nothing but a local 27B Qwen model as my coding assistant and it's working fine. The big American AI companies could all collapse in ruins tomorrow and it's not going to slow things down all that much, anyone who wants to use AI will be able to continue using it. Might even be better in the long run if that happens - the people who are scared of ASI will get their much-demanded temporary slowdown at the frontier while everyone else catches up with open-weight models that everyone can use without central control.

    Pity about the economy in the meantime, I guess.

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  • [–] 36 points 3 days ago (12 children)

    anyone who wants to use AI will be able to continue using it

    Anyone who's privileged enough to have the hardware.

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  • [–] 17 points 3 days ago (3 children)

    The kind of specs that would enable local LLM usage were available on laptops since 2020. The floor for acceptable performance for LLMs stopped literally just before 24+gb of VRAM would have become pretty common in consumer applications. The reason this technology isn’t more ubiquitously available is because ‘they’ thought it would be more profitable to hoover up literally every byte of RAM and GPU in the world rather than let the technology continue to naturally evolve to a place where any consumer could have run useful models on their home computers. In other words, your anger is misplaced.

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  • [–] 18 points 2 days ago (1 child)

    Real story of this bankruptcy is financial shenanigans by Google and VC backers. Google paid "the company" $2.7b in 2024 where the company got nothing, but VC backers got paid, and Google got to rehire 2 AI pioneers that had left google to start the firm. The original vision for company was to revolutionize education where you could create a Socratese or Jesus bot and converse with them as life coaches. Marketplace/users just wanted an AI girlfriend cure for loneliness. Disney's $20m bid for assets is connected to them poaching the remaining shell's CEO.

    Sure the company was always going to end up worthless, but it was worth $2.7b on paper at one point. The industry is filled with similar absurdities is a good point by headline and 404 media article.

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  • [–] 24 points 3 days ago (12 children)

    I just got Qwen 3.8 Flash Next running locally. It's damn good on an older machine with a newer GPU. Local LLMs should scare them the most.

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    [–] 47 points 3 days ago (8 children)

    When I first heard about this bankruptcy, one of my first thoughts was, here it goes. This is only the very beginning of the landslide though, so it's going to take a while before it reaches the bottom, and it's going to pick up a lot more debris on the way.

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  • [–] 38 points 3 days ago (7 children)

    Or, as the article optimisitically puts it:

    The fate of Character.AI, the controversial AI companion platform, may come to stand as a warning.

    Wrong. Character.AI may come to be seen as the first car in the train to derail.

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  • [–] 50 points 3 days ago* (27 children)

    Anthropic made a smart business decision to mostly focus on enterprise customers rather than regular consumers. Only around 10% of their revenue comes from their heavily subsidized subscription plans. The remaining 90%ish comes from API and enterprise usage billed at per-token rates, which is something that's actually profitable for them. They have a decent margin on token rates. It's R&D, hardware, and subscriptions that are putting them in the red at the moment.

    I suspect they'll discontinue subscriptions some time after their IPO. Even though subscriptions are only responsible for 10% of revenue, they consume 42% of their inference compute power (https://www.theregister.com/ai-and-ml/2026/10/06/anthropic-claude-subscription-plan-provides-more-value-than-openais-study-says/5301470)

    In comparison, around 70% of OpenAI's revenue comes from subscriptions. Without subsidized subscriptions, they'd lose most of their customers.

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  • [–] 64 points 3 days ago

    It's R&D, hardware

    "Yeah, besides those integral parts of our costs, we're profitable"

    😅.

    It's like a hair dresser saying "If I don't count the rent of my shop and all the supplies, I'm profitable".

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    [–] 30 points 3 days ago (3 children)

    AI has two, and only two, paths to monetization. Users pay the server costs, or advertisers.

    Not enough users are interested in paying for something they can't trust to be accurate, and I suspect/hope no one will use these tools once they start spewing advertisements from their training data.

    Could some of these tools survive on enterprise users alone? Maybe, but businesses are taking on a lot of legal risks when they make the confident guesser bot a key part of their supply chain.

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    [–] 23 points 3 days ago

    Let's hope the rest follows.

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  • [–] 2 points 2 days ago

    Not sure, haven't tried LM Studio.

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  • [–] 7 points 3 days ago

    Onward, the Butlerian Jihad!

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