I could also imagining them happening as state projects for the sake of competitiveness.
I also wonder when the NSA starts sharing their wiretaps/etc with labs for 'national security'. Probably already happening, to be honest.
What would this benefit the NSA? If they share wiretap records with labs such as Anthropic, then suddenly anyone who can jailbreak a model gets access to this data. You don't need to train a model on data to understand or interpret it, as far as I know - providing others (esp. AI labs) with sensitive data is a breach of security, or I am not understanding.
See the well-documented case of NSA conducting espionage against EU’s Airbus to help Boeing.
NSA doesn't have to post train Fable. Maven works well enough and that same infra. can be used for Fable. I imagine that's what's going to happen in 2 years once the current admin. moves out.
The relevant question here is whether you want open-weight models — projects like Open Athena — to remain or become competitive in the same range of specialties.
If we can help people automate drudgery while giving them a real say in what gets built and control over how their work is used, I think there’s a possibility of a much better outcome than the current trajectory. I’m not sure we’re in that world, but I think we should try.
If any form of participation will give benefits after they've contributed, it will be abused into extraction.
I honestly think you shouldn't try.
The Marin project from Stanford (and I believe now ran by Openathena, the authors of this blog post) is really promising and cool: https://marin.community/
Precise and very clearly written.
"Oh, theres a process."
Great. Where is it documented?
"in a spreadsheet."
Which spreadsheet?
"Well, theres the old spreadsheet, new spreadsheet, and the spreadsheet that Karen maintains."
Where's Karen?
"She left in 2021."
I don't get to break out my academic voice that often these days, so I appreciate the indulgence.
The next iteration of AI assisted devs will not have all the devs use the same harness and model like most do now.
Instead, devs will build their own post trained models that are experts of their own domain, that have their secret sauce..
I imagine any dev would want most of those models to be something they can self host because of multiple reasons - one of them being that their employers or customers might not agree to use a cloud hosted or managed model. Plus, costs. And control.
Post training is in the future because we haven't yet reached a shared agreement on what that first "good enough" model will be like - and also because the current AI scape is in a state of flux where post training will have a lot of churn.
Hopefully there will be more releases of open BASE LLMs, i.e. with the weights that are the results of pre-training, because those are much more suitable for being trained for whatever you want, than when you try to retrain fully-trained models and you have to undo whatever was done to them that is inappropriate for you.
The BASE LLMs have already done the part that requires a lot of resources, i.e collecting a huge data set, then running training on a big number of GPUs with a lot of memory and fast interconnections, and thus compressing by several orders of magnitude the training data set into the LLM weights.
Starting from there, training can be finalized in any desired direction with much less resources.
There is another on-going HN thread about such a BASE model that has just been released, baidu/ERNIE, which seems quite good. Several other companies, e.g. Google, NVIDIA, DeepSeek, Alibaba, have released in the past such BASE models suitable for training your own LLM.
Happy to answer any questions. We have a discord https://marin.community/
Having a trusted steward to handle the anonymization and eval design process could kickstart a world where retiring tax accountants, or even active and generous ones, are willing to donate their data to the posttraining commons at a scale that's actually much larger than the "data industry".
So thanks, no thanks, there should not be another public common knowledge of this size so that we can cling onto jobs we still have.
But thinking over the outcomes we might want over the next decade and how we might get there, it seems to me the alternative to doing something here isn’t “AI goes away,” but rather “a handful of very well capitalized companies buy what they need and squeeze the labor market.”
The issue is that once sharks realize how profitable this project can be they will get into power by any means necessary and then abuse it. We've seen this pattern way too many times now - a grass roots initiative to help humanity that suddenly turns into maximizing profits. See also open source.
I saw how they started mounting cameras on workers in factories. How is this approach any different?
And AI might never truly go away, sure. That doesn't mean it'll be sustainable long term. Until questions of consent, ownership and disruption are solved, we should push back on reckless adoption.
So no, I don't see how this is in anyone's interest to participate, except for the likes of Google.
We've been developing DataClaw to help everyone collect agent traces, see https://github.com/peteromallet/dataclaw . There are other similar efforts, such as in https://old.reddit.com/r/LocalLLaMA/comments/1u795pb/donate_...
The raw CoT is encrypted for closed source models, but there are plenty of ways to decrypt them. Even without CoT content, the tool calls and the length of CoT are already good signals for post training.
In China we use 'transition stations' to access GPT and Claude. They collect GBs of plain text agent traces every day, and the data eventually belong to the open source community. My estimation is that there are already 10-100 TB of agent traces freely on the internet for everyone to download.
It was just kind of theft of intellectual property on a scale that has never happened before.