even if the most powerful ai is open source and let's even assume runs on consumer hardware - in the end data is the real moat.
if it can access private data it will necessarily have more power.
Open source AI must win
321–330 of 538 posts
Re: Open source AI must win
#322If you've been writing off Deepseek V4 Pro, now is your time to go set up moonbridge and give it a shake. It's exceptionally good.
Got a bit more than 1B tokens for $10, it's exceptionally fast, it was able to fix/implement things that 5.5 xhigh struggled with, without trying to act like my best friend or do that coy "undersell the ideal end result so that it can later overshoot it and claim a great success" bullshit.
E: miss me with the "but China" BS, everything I've experienced while using this model has convinced me they are earnestly more concerned with doing the right thing than Anthropic could ever pretend to be. And if you want to ask it questions about Mao, you can go download the weights and spend mid-five-figures to fine tune that out.
Re: Open source AI must win
#323The article doesn't say what it means by win. I presume we will have the present situation where the cutting edge stuff is closed source developed by profit oriented companies and open source is available two but a year or two behind.
Re: Open source AI must win
#324If you take AI risk seriously then Open Source AI should not and must not win. Both by evil actors (biological weapons research) and the danger of unaligned AGI itself. There are some people who would never work for the military or Anduril (automatic weapon systems), but an OS AI „without asking permission“ would be the same.
Re: Open source AI must win
#325even if the most powerful ai is open source and let's even assume runs on consumer hardware - in the end data is the real moat. if it can access private data it will necessarily have more power.
Let's abstract this further: It's about the user's existing power and intentions, meaning if I am already in a position of power, AI will multiply it to levels way beyond a peasant could. Power dynamics just get exacerbated.
Re: Open source AI must win
#326If you take AI risk seriously then Open Source AI should not and must not win. Both by evil actors (biological weapons research) and the danger of unaligned AGI itself. There are some people who would never work for the military or Anduril (automatic weapon systems), but an OS AI „without asking permission“ would be the same.
If closed-source AGI wins, it is not going to be much different from a safety perspective anyway, because AI capability research is advancing faster than safety research.
Re: Open source AI must win
#327I am really curious how long will it take for the open source models to hit current fable/mythos capabilities, KIMI 2.7 was launched recently and its quiet good for open source models its as good as Opus 4.6 maybe in practical applications not benchmarks so like 6 months to an year behind, after which the next step will be to wait for the day when we will be able to run mythos level intelligence on local hardware, Re…
There are two parts to this too. One is the raw model capability and the other is how well the harness guides the model and meets its expectations. I really think for stuff like agentic coding, this has to be treated as a package. This is my favorite example of how much difference a harness can make even for a tiny model https://github.com/itigges22/ATLAS
And you're bang on with the storage comparison, we're basically in the mainframe era of this tech, but there's no reason to think that it won't get optimized to the point where you can run the equivalent of current frontier locally.
Re: Open source AI must win
#328I've been contemplating a decentralized model training system for some time using volunteer machines that we all contribute. But, it is astronomically difficult. The communication speeds are untenable. And, there is the issue of data poisoning from untrusted nodes. I've almost cracked that last issue with a self-healing checkpointed rollback system that doesn't have to throw out anything that follows the corrupt datu…
>The communication speeds are untenable. Can it be parallelized or not? If you take a model, make two copies, and fine-tune each one on different data, what happens when you merge them? Does it work if you freeze different layers? I think this works if the steps are small enough. And the transfer should become tenable if the steps are big enough. Where's the cutoff?
Yes it can be parallelized, it already is in real AI datacenters and no it doesn't help you. Like everyone else is saying, an AI datacenter is not just a bunch of gaming GPUs connected via normal ethernet and hasn't been for years.
At most a decentralized effort could contribute a little bit to some bigger centralized effort by doing inference and sandboxed CPU work. Modern model training isn't just backprop, it's got a huge and growing CPU and inferencing component too, which doesn't require intense inter-node communication. For instance, doing RL rollouts for agentic coding requires a lot of plain old inferencing and sandboxed containers for the models to practice in. The final results are just a set of rollouts and scores that can be uploaded back to a central datacenter for GRPO to adjust the weights (relatively cheap). But then, of course, you'd have to stick to models small enough to fit on people's computers so it'd never be competitive.
Re: Open source AI must win
#329Who is going to fund it? Training is unfathomably expensive. You have either VC funded models looking for a return on investment, or CCP funded models looking to solidify authoritarian "model Chinese society". Maybe there are some university 4B models, but I doubt those will carry far.
Maybe we do p2p compute?
there are already projects like Petals https://github.com/bigscience-workshop/petals
Re: Open source AI must win
#330Earlier quoted context omitted.
Is the total compute capacity outside of meta, google, amazon, anthropic, oai and x is higher than even the capacity of any of them? In any case, there's no chance a public collaboration gets to anthropic levels of compute even if communication were no issue.
Is the issue that training with less compute takes more time? Or is it just not possible? I think a collective using distributed training could tolerate the idea that it takes 10x as long as Anthropic to train a model, or whatever.
It's possible but it's not linear. A modern AI training cluster is a supercomputer that uses very different architectures and hardware to a bunch of small PCs connected via normal networking. The networking advantage alone kills any chance of decentralized training.