Post #3325597

2026-06-08 02:04 UTC

Right, which is why I said 90% and not 100%, and called out the challenge of deciding which tasks to send to which AIs. A lot of the interesting work I'm seeing in AI right now is in the agentic frameworks and harnesses that call the LLMs rather than just the LLMs themselves, these are the things that will break big complicated tasks down into more focused sub-tasks that cheaper LLMs can handle. Given how some of the big providers like Gemini and Anthropic have been cranking up their API costs in recent weeks I expect we'll see a lot more effort being put into rolling those sorts of features out.

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@Wildmimic@anarchist.nexus 2026-06-08 04:42

It’s not even where to send it - you cannot predict how much any given task is going to cost you in tokens, which is the deciding factor in which model to use. The “cranking up” part has not even started yet, and we already have stories like Uber which blew through their complete AI budget for the year, what was it, 2 months ago? Uber is very pro-AI, so that budget was probably very generous. And to top it off, I haven’t seen or heard about anything new at Uber that would be even worth mentioning. If you read the article, this project started from a clean slate and is 40k lines of code, so it’s peanuts in regards of complexity compared to what is out there in companies, and the author had to use the maximum power available to him to let Claude keep up. There still was no guarantee that the output was useable (and there can’t be such a guarantee, since hallucinations are a statistical fact, increasing in occurrence with smaller amounts of training Data available). If you extrapolate this to an average IT stack, which has quirks and issues that are unique to it, you will never get anywhere you wouldn’t get by employing more engineers, who will get better over time and have fixed costs you can budget. Remember, this is the “killer” application for LLMs. It looks a lot worse in EVERY other area except probably translation.

Open ##3325596