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.

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).

> 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.

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.

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.

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.

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?

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.

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 collapsing (Pension age rises and reforms), German car industry is in decline, increased spending demands for defense, and so on.

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)

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.

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.

NO!

This is the wrong approach that will turn us into serfs. We need big honking models that do what the big 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.

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. 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.