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- Hacker News
- not only is he correct, but this path will result in openai going the way of enron. i hope in the case of openai and their collaborators in nvidia we see more people going to prison.by snootypoot
- Would need changes in the US government before that can happenby dgellow
- Edge models will get much better after the current insane capex and organic data for pre-training is dried out. But hard to see how the best open source models will ever come close to the best closed ones.by dippogriff
- It's already happening. GLM-5.2 ranks quite close to SOTA models. Some might argue that benchmarks can't measure the real effectiveness for day-to-day usage but that's another discussion.by dbolgheroni
- We don't need rinky-dink RTX models that budget VRAM.
We need large scale open weights models just as capable as what's at the frontier.
And we need the ability to rent compute and spin up the weights easily. One-click, easy enough for anyone. Easier than nerd tools like ComfyUI, Claw, and node graph garbage.
Freedom is owning very large scale weights. Anything less is subsistence.
by echelon - We need to improve the waster and energy usage and this method doesn't. Most are not reinventing the wheel, a shared AI repository, communicated between online local computers would save a lot of need for these large models.by ktallett
- we need this: https://news.ycombinator.com/item?id=48516751
> distributed LLM inference. We are at a point where no single person can setup a rig to run a SOTA model, it is just too expensive. So we must build and adopt frameworks that allow individuals to share resources to run SOTA models in a distributed manner. That way they will also be non-censorable by governments.
Also The only way to prevent that one entity weaponizes it, is by giving EVERYONE access to it.
by xtracto - Over the long term, it seems like open models must win out. This feels like it rhymes with the story of operating systems. Despite the enormous financial contributions of Microsoft and Apple, linux still won because control matters over the long term.
I predict that mech interp and things like Neuronpedia will matter more and more over time, and the frontier providers are disincentivized from providing those tools
by peterlk - > linux still won because control matters over the long term.
what has Linux won? Servers? sure
by petcat - depends on what you mean by winning out most of the profits will be hold by closed proprietary models and open sourced models will be barely able to fund their own research and development cost just like how apple has barely 10-15% of market share of smartphones but takes away 80-85% of all profits. I kinda see a similarity of this may turn out for AI as well.by sademo
- There is no reason we should accept the enclosure of the digital commons represented by AI. The data these models are trained on amounts to the total intellectual and artistic output of human kind through recorded history. It belongs to all of us, and accordingly, so should the models and weights produced by it.by mbgerring
- The effort of humans who had to toil through training models belongs to everyone? Do they no longer have any ownership over their hard work?by logancbrown
- ok, but government is how you do that. and as should be evident, its easy to year down and corruptby cyanydeez
- It belongs to not us, but the copyright holders of that workby nxm
- It's closer to "that output belongs to everyone, everyone's free to train your own model"
Otherwise, everything you do belongs to all of us, because you learned on total intellectual and artistic output of human kind too.
- Yann is on the mark. Almost amusing to see the EU along with its many former “subjects” realize they are at great risk of joint Chinese-American hegemony in AI. We should all be more terrified of a few nation states defining the agendas and policies of AI use than current Ai variants that a inherently without purpose or autonomy.
Great analogy to the fear of the printing press being really bad news in that it enabled the rabble to get aroused.
by robwwilliams - AI is the canary in the coal mine. They don't have an AI problem they have an everything problem. Inability to maintain energy security, declines in manufacturing, their social programs are no longer sustainable (Pension age rises and reforms), German car industry is in decline, increased spending demands for defense, and so on.
All that's needed is another sovereign debt crisis to spark what is essentially dry tinder and I think the EU is a lot closer to collapsing than anyone even remotely realizes.
by oceanplexian - There's a video of the entire session here:
https://webtv.un.org/en/asset/k14/k14ej1ucqu?kalturaStartTim...
(if that link doesn't work, it starts about 12 minutes into the start)
by blakesterz - had to click the play button, but it keyed to the 12m markby verdverm
- What is Open-Source AI? Has it been defined?
By all accounts, all AI companies starting with open are doing proprietary stuff. All models delivered for free as "open-models" are just freeware as no source is really provided.
by prmoustache - The talk is about this: https://thealliance.ai/projects/tapestry
Collaboratively trained open-weight models, is my understanding.
by layer8 - Agreed but I want to see how it plays out. Historically a good Windows computer cost $1000 and it was all it took to start programming. How much does it cost a computer with enough resources to run a good enough AI model for agentic workflows and a reasonable time to first token? Can "most of the world" afford buying one?by pmontra
- > Historically a good Windows computer cost $1000 and it was all it took to start programming
Started with computers around 2009 and later bought an oldish computer (a pentium 4 PC) for the equivalent of 50 usd. Codeblocks and Python Idle were free at the time (C and Python were the first languages I learned). The barrier to programming has always been low as the only thing you needed was books (the internet made things easier) and access to a PC (I had friends with laptop and my school lab).
by skydhash - Yes, between Moore's Law and more efficient model architectures, we just have to let time do its work.by bensyverson
- I don't understand the justification for local hardware with cost as the motivation. The same (or bigger/better) open weights models can served by third parties at much higher resource utilisation, and will therefore be much cheaper!?
Especially because the world is likely to persist, at least for a while, in state where computing hardware demand drastically exceeds supply resulting in high prices for hardware. So why wouldn't you want to max out utilisation and amortize costs, at least for typical (non sensitive) use cases.
by ssivark - Before the AI "crisis" it used to take about $3500 to get a prebuilt with a 5090 which can run good enough LLMs. I run reasonable LLMs on just 16GB of VRAM on my Mac, and the 5090 has double that.
- Even if you go with an open source AI model, it will still make a lot more financial sense to pay a provider then actually invest in the hardware to self-host it.
It's definitely worth investing in self-hosting the agent infrastructure around the model though: all the documents, knowledge base, all the connectors, the agent itself to run on your hardware
by onel - Open weights/source doesn't necessarily mean running on local hardware, though.
I imagine having multiple providers competing will drive down hosted versions of open weight models drastically.
by Chu4eeno - > Historically a good Windows computer cost $1000 and it was all it took to start programming.
Gotta remember inflation here.
$1K in 1995 was roughly equivalent to $2K now and wouldn't have been a particularly "good" machine then.
In 1982 the Commodore 64 started at about $600 bucks, also roughly around $2K today.
If you outgrew that, beefier machines back then were A LOT. It was easy to find $2k+ towers and (especially) laptops even into the 2000s, and a lot of those would be $5K+ equivalent today.
by majormajor - Qwen 3.6 27B is quite good for agentic coding, and practical to run on consumer hardware. You need a system with either 32+ GB VRAM, or a unified memory system with 48+ GB VRAM and a decent integrated GPU. While not cheap, such a setup is still attainable for much of the world, and will eventually get cheaper over time. Open models hosted on non-American clouds also remain an option with a much lower barrier to entry, for cases where privacy is less critical.by wizee