Decel:
- Potentially reduces investor appetite for funding big labs.
- More risk of powerful AI getting in bad hands -> more regulation.
Accel:
- More competition so big labs can't rest on laurels.
- More research in open, so all labs can accrete advancements faster.
I feel like open-source = acceleration has a much more clear argument. (and how bad would deceleration be in any case?)
The problem with the decel/accel rhetoric is that it lacks nuance.
I think you meant less research and experiments in big labs because they don't get all the AI money.
Training is expensive, but they also have more than 10 000 of employees combined and they cost a lot of money.
But ultimately these were ideas floating around in the air, if one group hadn't done the experiment, someone else would have.
Kimi K3 is plausibly a lot less dangerous than a totally jailbroken ChatGPT/Gemini/Claude Sonnet (let alone Opus or Fable!) and it's quite deeply weird how no one seems to be calling for those models to be banned or restrained by further regulation. Why the double standard against the less concerning (but more efficient!) open weight models?
Is there a world where open source models end up at the frontier, or do you think there are structural/first-principles reasons why this won't happen?
Put another way, if you want to slow things down, put it behind a paywall, tag ideas ans “intellectual property” (meaning you’re the only one who can use it) and get the lawyers involved (injecting our slow legal system).
None of the above is a judgement call on whether development should be accelerated.
So you're admiting you were trolling?
I, for example, dislike reading comments complaining the submission (or another comment) is LLM generated. Focus on the content, not the style.
I'm not going to have my way, and nor shall you.
the only reason other labs can catch up is because the frontier labs can be distilled, and they siphon a % of the labs' revenue to reinvest into the next iteration
full accel would mean nationalizing the big 2 labs and locking in manhattan project style until RSI
(Edit: some great counterpoints in the replies. my view has definitely been changed!)
There's no chance K3 is a distill of Fable, it came out way too soon after the limited fable release to be feasbile.
If you look at all of the top ML conferences, chinese labs contribute way more to advances in ML than "Open"AI and Anthropic: https://www.reddit.com/r/TheMachineGod/comments/1pi4q7f/pape...
This K3 release just helped every other lab on the planet stay in the race by making it possible for them to build on top of it, placing them at the frontier starting line instead of having to spend billions of their own dollars and risking it all to attempt to catch up.
The open source contributions I linked to above will move the whole field forward and reduce the costs of training and inference for everyone.
Open science compounds on it self, every new advancement pushes the field forwards and opens up new grounds for future improvements.
It is impossible for a single closed lab to consistently stay ahead of the rest of the field, especially in a huge growing research area like machine learning. The only only advantage the big labs have is money, but the naive scaling game is not sustainable long term when you have to pay 10-100x more then the fast followers and we start getting more and more open models or use case specific models that can handle 90% of high volume use cases.
Research is a high variance, low expected value activity, meaning that the few large concentrated labs have to be conservative with their bets and double down on proven things when scaling up. The rest of the field is like a diversified portfolio, with thousands of players making smaller riskier bets that only require a few of them to succeed (like K3 did here, and DeepSeek a year ago)
EDIT: also if you look at most of the work from OpenAI, it's mostly taking existing promising open research work and scaling it up. (except for things like CLIP and etc from Alec Radford)
And years down the line, lots of other research labs used my code and cited my paper.
We had a bunch of things that we never published that ended up being major research findings years later at top conferences.
Further, you can just read the papers released alongside most open models. Plenty of hugely influential research results published that drive the frontier forward. It's not like these models are just existing architectures downloaded from Huggingface and trained on frontier lab APIs.
Frontier models would have to do something extraordinary or unique, or unreplicatable, because clearly there is no moat, and US companies are sitting on huge nvidia valuations and get surprised when competitors beat them.