These things are always a slippery slope. They rarely, if ever, achieve their safety goals but they almost always achieve the goals of the corporate interests to garner further data for advertisers and increase surveillance of the populace by the government through proxies that buy said data and then sell to the government.

It is not merely showing proof of age

It is using the proof of age requirement to require a much larger ask -- full proof of identity

Age verification could be done with any of a variety of mathematical systems showing you have a proven age-valid ID but not revealing your identity. But no one is suggesting they build and use such a system.

I think the general assumption is that it only aids in surveillance.

> free speech, civil liberties, voting, are in China all well below the standards of the west

Haven't heard China deporting students for criticizing foreign state or electing people involved in p*dophilia and then closing eyes for those crimes

The point is that you're all shitting on China 24/7 while not recognising that you're slowly but surely building something very similar at home right now

It’s funny, 30 years ago the argument was the exact opposite: China opening up and doing business with the rest of the world would force them to liberalize.

Why is that any different? What’s to stop that company sharing information with the government?

with sufficiently many public/private partnerships, what's the difference?

there are claims that llms might be taxing on the planet to run BUT that they will solve [some, all] problems including climate change and therefore be beneficial in the long run.

But "some assholes" is an extremely large, growing group of people. Do you have any idea how much more productive small business owners are now? It's an insane boost for people who didn't want to spend their time on things that are extremely critical for business but not the focus of the business.

Do you hate all lessons from humanity's past or just the most important ones? If it takes work from a specific subset of the population and isn't compensated, then my friend, what you advocate for is slavery...

I agree with this. Computing as a field is the way it is because there is a low barrier to entry. My dad gave me a Tandy 1000 and some programming books, and now I have a very lucrative career. I never took any classes. I never had to beg anyone for permission. I could just get started making things with the minimal investment of a cheap personal computer. (And eventually, an Internet connection. Working with other people is fun!)

In a world where everyone is a Claude controller (something I honestly enjoy!), that goes away. I use hundreds of dollars of tokens a month. Suddenly, the kid in her basement with an unloved computer can't get in on the ground floor. You have to be rich to even get started. That worries me deeply. It's a big change for our field, and I don't think it's a good one.

AI isn't the problem, concentration of power is the problem. I think we agree!

Have you isolated yourself completely from reality? I don't even know where to begin on this. Let's start with the fact that China is pumping out some near-frontier models and open sourcing the weights- and they don't even follow capitalism and the owners aren't billionaires. Really there are like four models in the USA that are "owners/controllers", and only one is even slightly controllable by its CEO, though none of the frontier models can last a week without the support of entire teams.

Why on earth would you want to siphon off the proceeds of AI development to (ok my bias is strong here- mostly corrupt) "ideals" like world hunger and cancer research (that probably get more dollars annually than the sum of actual profit any of these companies will ever get). That would just instantly kill the ability to improve AI at all, and the world could possibly be better for a few months?

I hope/wonder if it will go the way computers did. We may learn to more effectively build RAM or parallel compute, and use it more effectively, in the coming decade in such a way that we can democratize more and more like we did with processors to the point that they're ubiquitous.

See my comment to parent. I've been using local LLMs for practical, personal tasks for a few months now very successfuly.

You can run fantastic local models if you have either:

- M-series Apple device with ideally >= 24GB of VRAM

- RTX [345]090 GPU

I'm fortunate enough to have both and use an M-series laptop as basically a persistent server (I don't use it much and when traveling typically just use my work laptop). My desktop doesn't act as a persitent server but I fire up llama.cpp on it all time for quick chat sessions.

If you have one of the above devices and can dedicate it as server there are additional layers of tooling you can use that dramatically improve the experience. In particular Open WebUI allows you to add tons of useful tools (image gen, web search, code eval, etc), and agent harnesses like Hermes can make the current gen small models very capable. I have an agent in chat on my phone that basically handles all the sys-admin for the server it runs on.

If attractive, cloud providers could develop open models with their own investment, and sell hosted access as a business model. While Google checks these boxes, I haven't seen a Google much marketing focus upon their open models (Gemma) coupled with hosting. groq could conceivably train its own models, but groq's business model hosts open models (GPT OSS, Qwen 3, Llama 4 are currently their prominently advertised models on their site... which seems out of date to me) trained by other organizations.

Sort of. A full trillion-parameter model needs about $300k of server hardware to run in and a lot of electricity, making it feasible only for very wealthy individuals, but quite practical for businesses and institutions above a certain size...although they in turn would typically gatekeep access.

You can drastically reduce the requirements by running models at a lower bitrate, which somewhat reduces accuracy but not that much - think of the difference between an MP3 vs uncompressed audio. With this and other tricks, you can get high end models down to a size where they can be run on a high spec desktop workstation affordable by an individual or small business.

Obviously I'm heavily oversimplifying here. I think a useful parallel is to consider situations from the past where you would once have required corporate budgets equivalent to the price of a house to run a large database, but over time it became accessible to anyone with the requisite expertise and relatively affordable hardware.

There is an understandable gap between the capabilities of closed models and those of open models. The current difference is primarily expressed in the cost of hardware necessary to sufficiently run a exactly comparable model. A single higher end graphics card running on your average gaming computer, is capable of running small to medium models that compare with those of their lab-born counterparts in the small-medium range. But the heavyweight models are still outside the realm of possibility for all but the most well-funded individual.

However, I would highly suggest more people experiment with these smaller models. They are incredibly capable in many ways that many people dont realize.

The perceived capabilities of the larger models are also much less the result of the model having more parameters/training cycles, but rather that they are being run through well-made harnesses, something which the open-source community is rapidly approaching with near-peer solutions of their own.

In short, much of the gap between between open-weight models and the larger proprietary models can be considered more of an issue of perception and not an issue of capability. There is a fundamental gap economically, but not so much in capability. The open source community is rapidly closing the gap on these larger labs, especially thanks to the amazing research being freely given openly by well funded chinese labs.

Yes they are you can use Qwen, DS4 Pro and GLM 5.2 if you have the hardware to do so.

They are not SOTA in various ways but they have better economics.

It depends entirely on what you want to do and think is feasible. Small models can almost certainly run on the computer that you already have. They can do good tool calling.

I'm also curious, specifically about the cost of training vs inference, and comparing that to other industries that can have high R&D costs. My instinct says that open weights aren't feasible because of the obvious issue where there is no incentive to develop your own model rather than just taking someone else's model. However, I could see a scenario where a hardware company designs a model that is open weights but optimized strongly for their own proprietary hardware, cutting their costs of inference low enough to be competitive with a hypothetical other company that doesn't have any R&D expediture.

> I mean from a financial and sustainability standpoint, assuming they’re equally powerful as their proprietary counterparts.

Presently they trail SOTA by about 6-12 months, not on par (average across everything they do).

DeepSeek V4 Pro with Max reasoning is very affordable even if you pay per-token, this month I pushed about 486 million tokens through it (I will admit that >95% was cache hits, for agentic development pretty typical) and it cost me about 8 USD in total. Meanwhile with Opus or even Sonnet if I had to pay API prices, I would be a more sad camper. That model makes a lot of stupid things though, so not ideal.

Meanwhile GLM-5.2 that came out is also quote capable and is near Opus in many tasks, all while their coding plan is more cost effective than Anthropic's: https://z.ai/subscribe

I will still stick with Anthropic but consider downgrading from Max 5x to Pro which will change the monthly expenses from around 108 EUR down to <20 EUR (they have a discount too if you pay for a year up front), and probably get the yearly GLM Pro plan which should decrease my yearly expenses from around 1300 EUR total to about 750 total EUR while still giving me a fairly decent setup.

For the consumer, that is doable and practical.

For the people actually running these models, who knows - at least DeepSeek and others are trying to make the models more efficient so the numbers are more feasible.

Also have run Qwen3.6 35B A3B on prem and it kinda sucks. Way better than models that size a year ago, but still lags behind Sonnet and also DeepSeek V4 Flash due to the size limits. Plus to even run myself I'd need a pretty beefy setup, most likely a pair of Intel Arc Pro B70s with 32 GB of VRAM each that I could still run off of my PSU but the actual model output would be kinda bullshit and I'd have to spend an unpleasant amount of time fixing it.

What is an "ephemeral" model in this context?

What about local models do you find preferable?

I guess "starting to find them preferable" suggests to me you think they work better, but this is surprising to me so I think I may have misunderstood, so I ask!

Like you're saying they work better than the proprietary models (in what ways?), or you find them mostly good enough and prefer the privacy or cost, or what?

How many tokens /sec?

From a privacy perspective, your objective is to stay away from people who have interest to snoop on your conversations.

So from the perspective of your teen, they would benefit from using z.ai or ChatGPT or Claude, etc, rather than the local server where you can see all the conversations.

What uncensored model do you recommend using ?

> I run an uncensored, ephemeral model for my own use and it's an entirely different experience than anything you can pay for.

Dont. Goon. To. LLMs

What kind of machine is it running on ?

We could all chip in

Maybe one of those trillionaires could help for a bit before leaving to make his own AI model, too.

i’d really love to be wrong, i don't think that the economics of it would let it happen.

the potential of wealth creation with AI is so high, and also the fact that research, pre-training and inference is so expensive that, that any open-AI would eventually become OpenAI.

And we are definitely not going to put our users on a watch list DB and send their data to the government?

And how do we prevent Chinese companies from training on our open AI models and offering their models for free?

Is this satire? I really can't tell.

>This is a serious competitive advantage

Given they have laughable uptime and I have yet to find a useful project mostly written by claude... I doubt it.

Only if you let them.

Isn't the face scan intended to validate your passport, which you show, and does demonstrate age and identity?