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.
Preventing a handful of companies being able to make the only money possible off that not only unimpeded but with state assistance (regulatory capture, ownership, whatever) at least puts an end to the worst of the abuse.
If we have broken copyright, and we do indeed appear to have broken copyright, why should trillion dollar companies be the only ones benefiting?
They got paid. That’s what the money was for. It’s the investors who backed these foundational model companies who will hold the bag as more open source models come along and consume more market share.
> the investors who backed these foundational model companies who will hold the bag
Is awfully bold to assume that private credit is who will be holding the bag here. The IPOs are coming to shift the risk to the index funds & retail. Once insider lock up periods expire, I suspect a massive sell off.
People used to make the similar arguments about programming languages and compilers. Now you'd need extaordinary requirements to justify paying licensing or usage fees for a language runtime or compiler.
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.
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?
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.
There was an article on HN a few weeks ago where someone detailed how they managed to get an old datacenter GPU to run in their consumer PC, getting decent performance with qwen. He spent something like $200 on the GPU (second hand of course).
So yeah, I think models on local hardware will be quite common soon among the tech savvy (such as people creating software).
Especially considering the millions of 2026-class data center GPUs that massively overinvested companies are currently buying, which will be obsolete in a few years.
Moore's law or one of its generalizations still holds, so it will only be a short matter of time before a $1k computer will be able to train and run a powerful enough model.
> 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.
Software models and hardware are getting better all the time—and that’s where some big companies spending billions might stumble! In fact, Microsoft recently announced that they’re scaling back a bit on their AI investments.
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.
Roughly about Eur 3-4K right this minute I think? The graphics card, ram and storage are punishing. Under more normal circumstances (hopefully late 2027) it'd be 1500-2500 depending on what you think is realistically useful.
Possibly it's the same price range, allowing for inflation.
> It was only in 2025, as memory prices began an unprecedented surge, that the memory makers started to build new fabs targeted at HBM, all slated to start producing chips in 2027 or 2028.
History? This isn't the first RAM shortage. When one happens, producers build more fabs. The fabs come online, the availability of memory shoots up, and the shortage goes away, usually replaced by a glut.
If you want to argue that this is different from all previous RAM shortages, you can, but the burden of proof is on you to show the difference.
Hence why brute force needs to be replaced with examples such as neuromorphic methods. It could realistically could be combined with mesh networking as well to utilise the capabilities of all computers locally.
Open-source models democratize access to foundational technology, reducing vendor lock-in risk for organizations. The community iteration model can also accelerate improvements in edge cases that proprietary teams might deprioritize.
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.
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.
We aren’t going to have Open Source AI without Open Source hardware specs and Open Source manufacturing. Software has been solo driving open computing for far too long, and with AI now the bottlenecks are finally moving up the stack.
How does that follow? Plenty of open source software runs in a commodity datacenter. This is about the API bottleneck, not the physical location of the GPU.
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.
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.
I'd love to see credible numbers on the energy usage of thousands of people running models on their own devices compared to sharing data center resources to run big models that serve many different people at the same time.
My hunch is that the energy/water usage of the data centers is a whole lot more efficient than everyone running at home, but I'd be interested in seeing real data on that.
This is the wrong approach that will turn us into serfs. We need big honking models that do what the leading foundation hyperscaler models do to within a few percentage points of measured performance.
The small-scale models are not productive, and the duct tape solutions built on top of them are hobbyist-tier "year of Linux on desktop" toys.
I imagine a fedora-wearing, crypto-shilling, coupon-cutting boffins every time I see small weights lauded as the future. This is the Pine Phone F-Droid of AI.
"SMS works most of the time on my phone, I swear! I don't really need my banking app!"
Nothing outside of the top ten is worth spending any time on, and we need to focus on models that bridge the gap.
You're talking about impractical toys for highly technical people wasting their own time. That doesn't move the needle or have any economic impact on the competitive landscape.
We need sharp teeth that bite at the legs of the top-tier foundation labs and hold them back from running away with the prize.
We've been through this time and time again over the last thirty years. It's the same shaped problem as before. We don't need toys - we need real infra for real people paying money to do work. Not freeware for freeloaders who don't spend and invest in the problem space.
Large models fit that precisely, because it forces investment into a wide variety of open infra, routers, inference engines, etc. Not to mention the weights ecosystem itself.
51 comments:
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.
ok, but government is how you do that. and as should be evident, its easy to year down and corrupt
Companies aren't really that better. Actually, sometimes it's even worse, because they are not even formally required to serve the public.
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?
Paying them may now be impossible.
Preventing a handful of companies being able to make the only money possible off that not only unimpeded but with state assistance (regulatory capture, ownership, whatever) at least puts an end to the worst of the abuse.
If we have broken copyright, and we do indeed appear to have broken copyright, why should trillion dollar companies be the only ones benefiting?
They got paid. That’s what the money was for. It’s the investors who backed these foundational model companies who will hold the bag as more open source models come along and consume more market share.
I agree, but
> the investors who backed these foundational model companies who will hold the bag
Is awfully bold to assume that private credit is who will be holding the bag here. The IPOs are coming to shift the risk to the index funds & retail. Once insider lock up periods expire, I suspect a massive sell off.
A drop in the bucket compared to the value of the collective human work that was stolen to train it.
edit: come to think about it I think the ratio of one drop to one bucket is vastly over estimating the ratio of the trainer's effort.
Some consider copyright to be unethical. "Information wants to be free".
I would propose that copyrights not be eternal
Not too mention the unbelievable cost of actually doing all that training.
People used to make the similar arguments about programming languages and compilers. Now you'd need extaordinary requirements to justify paying licensing or usage fees for a language runtime or compiler.
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.
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?
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.
Indeed, and with some tinkering around the harness it can even punch way above its weight.
There was an article on HN a few weeks ago where someone detailed how they managed to get an old datacenter GPU to run in their consumer PC, getting decent performance with qwen. He spent something like $200 on the GPU (second hand of course).
So yeah, I think models on local hardware will be quite common soon among the tech savvy (such as people creating software).
Especially considering the millions of 2026-class data center GPUs that massively overinvested companies are currently buying, which will be obsolete in a few years.
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.
Moore's law or one of its generalizations still holds, so it will only be a short matter of time before a $1k computer will be able to train and run a powerful enough model.
> 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.
And a unix workstation in those days could be high 4 or even 5 figures, depending on configuration.
Yes, between Moore's Law and more efficient model architectures, we just have to let time do its work.
Software models and hardware are getting better all the time—and that’s where some big companies spending billions might stumble! In fact, Microsoft recently announced that they’re scaling back a bit on their AI investments.
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.
Roughly about Eur 3-4K right this minute I think? The graphics card, ram and storage are punishing. Under more normal circumstances (hopefully late 2027) it'd be 1500-2500 depending on what you think is realistically useful.
Possibly it's the same price range, allowing for inflation.
Isn’t this just a bet that I’ll have an AI data center in my iPhone within 10 years? Why is that a bad bet?
About $2k in 2026 dollars and falling.
... or rising, at least as long as there's a RAM shortage.
I’d bet that there won’t be a RAM shortage for very long.
The best article I've seen about that is this one by David Oks (ignore the headline, the content is much better): https://davidoks.blog/p/ai-is-killing-the-cheap-smartphone
> It was only in 2025, as memory prices began an unprecedented surge, that the memory makers started to build new fabs targeted at HBM, all slated to start producing chips in 2027 or 2028.
It still won’t help unless the AI bubble pops. Even old fabs will continue pumping out HBM instead of DRAM as long as hyperscalers gobble it up.
This seems wildly optimistic, do you have anything to support it?
History? This isn't the first RAM shortage. When one happens, producers build more fabs. The fabs come online, the availability of memory shoots up, and the shortage goes away, usually replaced by a glut.
If you want to argue that this is different from all previous RAM shortages, you can, but the burden of proof is on you to show the difference.
Hence why brute force needs to be replaced with examples such as neuromorphic methods. It could realistically could be combined with mesh networking as well to utilise the capabilities of all computers locally.
Open-source models democratize access to foundational technology, reducing vendor lock-in risk for organizations. The community iteration model can also accelerate improvements in edge cases that proprietary teams might deprioritize.
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)
had to click the play button, but it keyed to the 12m mark
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.
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.
We aren’t going to have Open Source AI without Open Source hardware specs and Open Source manufacturing. Software has been solo driving open computing for far too long, and with AI now the bottlenecks are finally moving up the stack.
I don't see any evidence of this. Open source software thrived on proprietary hardware for decades.
What..? We have open weights AI already, and even if we didn't, I don't see how Open Source hardware is a pre-requisite
Weights are meaningless if you can’t run the model from a computer under your desk.
How does that follow? Plenty of open source software runs in a commodity datacenter. This is about the API bottleneck, not the physical location of the GPU.
Fine, but I have a computer under my desk. I didn't have to wait for open hardware in order to get it.
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.
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.
I'd love to see credible numbers on the energy usage of thousands of people running models on their own devices compared to sharing data center resources to run big models that serve many different people at the same time.
My hunch is that the energy/water usage of the data centers is a whole lot more efficient than everyone running at home, but I'd be interested in seeing real data on that.
With hardware like the Spark and Strix, the water usage is known to be zero, yea?
On the energy front, I assume less efficient, but I also think there is a tradeoff in efficiency versus freedom, that's why I have my own hardware.
NO!
This is the wrong approach that will turn us into serfs. We need big honking models that do what the leading foundation hyperscaler models do to within a few percentage points of measured performance.
The small-scale models are not productive, and the duct tape solutions built on top of them are hobbyist-tier "year of Linux on desktop" toys.
I imagine a fedora-wearing, crypto-shilling, coupon-cutting boffins every time I see small weights lauded as the future. This is the Pine Phone F-Droid of AI.
"SMS works most of the time on my phone, I swear! I don't really need my banking app!"
Nothing outside of the top ten is worth spending any time on, and we need to focus on models that bridge the gap.
You're talking about impractical toys for highly technical people wasting their own time. That doesn't move the needle or have any economic impact on the competitive landscape.
We need sharp teeth that bite at the legs of the top-tier foundation labs and hold them back from running away with the prize.
We've been through this time and time again over the last thirty years. It's the same shaped problem as before. We don't need toys - we need real infra for real people paying money to do work. Not freeware for freeloaders who don't spend and invest in the problem space.
Large models fit that precisely, because it forces investment into a wide variety of open infra, routers, inference engines, etc. Not to mention the weights ecosystem itself.