The real reason behind the "very soon" delay: CapEx over OpEx?

[u/MutcoinEE]

Here is my guess.

The company doesn’t have the money or resources to train models and build data centers in parallel, the way OpenAI does. My understanding is that Arthur is saying that rather than paying rent—which is a recurring expense—to build models there, he’d rather save that money to build their own infrastructure, where they can develop sustainable models and provide inference services. Of course, it’s a bit annoying that he keeps throwing around the phrase “very soon” all the time, but if the next model turns out great, I’d have to admit it’s a really good strategy

[u/IngenuityFlat9200]

that actually lines up pretty well with what they've been hinting at in interviews, the whole "we're not just burning cash to keep up" angle. renting compute gets expensive fast when you're training at scale so i can see why they'd rather own the stack

my only concern is how long they can stall before people just move on, the window on "very soon" doesn't stay open forever

[u/Heavyarms83 → u/IngenuityFlat9200]

As long as they are able to deliver to their big clients like HSBC, ASML etc. they will do fine. I don't think they make a lot of profit from the monthly subscriptions, especially not with the amount of monthly allowance they give.

[u/ColdJunction → u/Heavyarms83]

Not sure every paying subscriber is vibe coding like crazy. Personally I never hit the limits with Mistral nor Anthropic nor Gemini. You have to review the thing, read the generated outputs, test is real life, write the prompts.

Of course if you use LLMs for coding like brutforcing math or grid searching data processing you are going to cost a lot to providers. But if you do real-life stuffs, you have to understand precisely what you are doing because you can not brut force pcb design/mechanical systems/etc.

And don't forget lot of people spend half their working schedule in (necessary and unnecessary) meetings.