OpenAI's cash burn will be one of the big bubble questions of 2026

Post by OGEnthusiast » Wed, Dec 31, 2025, 3:06 AM UTC

johnnyanmac wrote: post #257

Aurornis wrote: post #208

Earlier quoted context omitted.

> The goal is to be too big to fail and get bailed out by US taxpayers I know this is the latest catastrophizion meme for AI companies, but what is it even supposed to mean? OpenAI failing wouldn’t mean AI disappears and all of their customers go bankrupt, too. It’s not like a bank. If OpenAI became insolvent or declared bankruptcy, their intellectual property wouldn’t disappear or become useless. Someone would purch…

>I know this is the latest catastrophizion meme for AI companies, but what is it even supposed to mean? Someone else put it succintly. "When A million dollar company fails, it's their problem. When a billion dollar company fails, it's our problem" In essence, there's so much investment in AI that it's a significant part of the US GDP. If AI falters, that is something that the entire stock market will feel, and by eff…

Post by xivzgrev » Wed, Dec 31, 2025, 3:08 AM UTC

simonsarris wrote: post #265

fooblaster wrote: post #160

Earlier quoted context omitted.

There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.

Google has several enviable, if not moats, at least redoubts. TPUs, mass infrastructure and own their own cloud services, they own delivery mechanisms on mobile (Android) and every device (Chrome). And Google and Youtube are still #1 and #2 most visited websites in the world.

Post by airstrike » Wed, Dec 31, 2025, 3:10 AM UTC

zozbot234 wrote: post #222

airstrike wrote: post #200

Earlier quoted context omitted.

> One could say that the introduction of the personal computer became a "race to the bottom." But it was only the start of the dot-com bubble era, a bubble that brought about a lot of beneficial market expansion. I think the comparison is only half valid since personal computers were really just a continuation of the innovation that was general purpose computing. I don't think LLMs have quite as much mileage to offer…

I don't think anyone knows for sure how much mileage/scalability LLMs have. Given what we do know, I suspect if you can afford to spend more compute on even longer training runs, you can still get much better results compared to SOTA, even for "simple" domains like text/language.

Post by logicprog » Wed, Dec 31, 2025, 3:13 AM UTC

sigbottle wrote: post #266

mark_l_watson wrote: post #198

Earlier quoted context omitted.

> in 5-10 years, most entry-level laptops will be able to manage a local 30B sized model I suspect most of the excitement and value will be on edge devices. Models sized 1.7B to 30B have improved incredibly in capability in just the last few months and are unrecognizably better than a year ago. With improved science, new efficiency hacks, and new ideas, I can’t even imagine what a 30B model with effective tooling ava…

Very interested in this! I'm mainly a ChatGPT user; for me, o3 was the first sign of true "intelligence" (not 'sentience' or anything like that, just actual, genuine usefulness). Are these models at that level yet? Or are they o1? Still GPT4 level?

Post by [deleted] » Wed, Dec 31, 2025, 3:18 AM UTC

avalys wrote: post #139

AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc. The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic,…