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That presumes that performance improvements are necessary for commercialization. From what I've seen the models are smart enough, what we're lacking is the und
by hiddencost 11mo ago
That presumes that performance improvements are necessary for commercialization.
From what I've seen the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well. We've barely scratched the surface on commercialization. I'd argue there are two things coming:
-> Era of Research
-> Era of Engineering
Previous AI winters happened because we didn't have a commercially viable product, not because we weren't making progress.
- ares623 11mo agoThe labs can't just stop improvements though. They made promises. And the capacity to run the current models are subsidized by those promises. If the promise is broken, then the capacity goes with it.
- wmf 11mo agoMaybe those promises can be better fulfilled with products based on current models.
- selectodude 11mo ago> the capacity goes with it. Sort of. The GPUs exist. Maybe LLM subs can’t pay for electricity plus $50,000 GPUs, but I bet after some people get wiped out, there’s a market there.
- simianparrot 11mo agoDatacenter GPU's have a lifespan of 1-3 years depending on use. So yes they exist, but not for long, unless they go entirely unused. But then they also deprecate in efficiency compared to new hardware extremely fast as well, so their shelf life is severely limited either way.
- soulofmischief 11mo agoAt this pace, it won't be many years before the industry is dependent on resource wars in order to sustain itself.
- nsomaru 11mo agoPersonally I am waiting for the day I can realistically buy a second hand three year old datacentre GPU so I can run Kimi K2 in my shed. Given enough time, not a pipe dream. But 10 years at least.
- tim333 11mo agoYou'll probably be able to run Kimi K2 on the iphone 27.
- Schlagbohrer 11mo agoThis is why I find the business case of putting datacenters in orbit to be so stupid. And yet there are several startups saying they are gonna do just that.
- credit_guy 11mo ago> They made promises. That's not that clear. Contracts are complex and have all sorts of clauses. Media likes to just talk big numbers, but it's much more likely that all those trillions of dollars are contingent on hitting some intermediate milestones.
- AstroBen 11mo agoWe still don't have a commercially viable product though?
- aurareturn 11mo agoIf all frontier LLM labs agreed to a truce and stopped training to save on cost, LLMs would be immensely profitable now.
- AstroBen 11mo agoThat isn't what I've seen: https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/
- aurareturn 11mo agohttps://simonwillison.net/2025/Aug/17/sam-altman/#:~:text=Subscribe,2025%20at%2012:53%20am https://simonwillison.net/2025/Aug/17/sam-altman/#:~:text=Su... Also independent analysis: https://news.ycombinator.com/threads?id=aurareturn&next=45961994 https://news.ycombinator.com/threads?id=aurareturn&next=4596...
- logicprog 11mo agoThose are effectively made up numbers, since they're given to him by an anonymous source we have no way of corroborating, and we can't even see the documents themselves, and it contradicts not just OpenAI's official numbers, but first principles analyses of what the economics of inference should be[1] and the inference profit reports of other companies, as well as just an analysis of the inference market would suggest[2] [1]: https://martinalderson.com/posts/are-openai-and-anthropic-really-losing-money-on-inference/ https://martinalderson.com/posts/are-openai-and-anthropic-re..., https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md https://github.com/deepseek-ai/open-infra-index/blob/main/20... [2]: https://www.snellman.net/blog/archive/2025-06-02-llms-are-cheap/ https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
- amypetrik8 11mo ago
- catigula 11mo agoI don’t think the models are smart at all. I can have a speculative debate with any model about any topic and they commit egregious errors with an extremely high density. They are, however, very good at things we’re very bad at.
- saikia81 11mo agoHave you considered the AI is right, and you make the mistakes?
- BenGosub 11mo agoBesides building the tools for proper usage of the models, we also need smaller, domain specific models that can run with fewer resources
- AbstractH24 11mo ago> the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well That’s like saying “it’s not the work of art that’s bad, you just have horrible taste” Also, if it was that simple a wrapper of some sort would solve the problem. Maybe even one created by someone who knows this mystical secret to properly leveraging gen AI