Other AI Companies Should Be Very Afraid of What Just Happened to Character.AI, According to Its Own Bankruptcy Documents: Be afraid.

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.

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.

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.

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.

I’m always running into context limit problems

Wanted to refactor a too large and hectic hacked together state machine into separate functions and reduce redundancy and increase code readability

Every model I’ve tried failed.

And that should be an easy task an actual AI should be able to handle. Logic is done, just translate to a different structure.
That should be exactly what they are able to do at least.

None worked, because the project is just to big and interlinked.