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ike_a

I'm not sure I understand the case for open-source models being decelerationist, is this it?

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?)

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I think the argument is that decentralization leads to deceleration because it means less centralized funding and data. Those are the two primary ingredients for accel.

The problem with the decel/accel rhetoric is that it lacks nuance.

I think it's basically open weights => more inference competition => less profit from inference => less training competition

> less training competition

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.

If your worldview is “most of the progress is made by closed labs, then open labs fast-follow” (which isn’t implausible given the documented distillation of Fable), and further that open labs cannot make make meaningful progress vs the closed labs except by fast-following and that they won’t pick up the ability to make progress after the closed labs are gone, then driving closed labs out of business slows down overall progress.

I think it's pretty hard to hold that worldview: Anthropic couldn't ship a reasoning model until they copied DeepSeek R1's homework, and they've all copied DS-style super-sparse MoEs at this point too.

That’s a really good point. Folks really need to read the papers coming out of these Chinese labs. Every paper from the DeepSeek team has been a step change.

With slightly different cherry-picking, you could equally well claim that DeepSeek couldn't ship a reasoning model until they copied the idea from OpenAI's o1-preview, and they also copied MoEs from Google Brain/Jagellonian University https://arxiv.org/abs/1701.06538 way back in 2017, too!

But ultimately these were ideas floating around in the air, if one group hadn't done the experiment, someone else would have.

No, OpenAI did not publish how they trained o1, and at the time there was significant misunderstanding and belief in the research community that they were using some kind of Monte-Carlo tree search. DeepSeek figured out GRPO on their own. Similarly, while others invented MoEs, DeepSeek's ultra-sparse variants were extremely novel, to the point where the revelation of how efficient they were to train temporarily collapsed Nvidia's stock.

Regardless I think it's impossible to believe that most LLM research was done by closed labs that don't publish, especially Anthropic (who missed out on and copied two of the largest pieces of important research of the last several years), and that none was done by open labs like DeepSeek, and that the open labs are just copycats. It's quite clear that isn't the case.

Open Source models decelerate growth of closed AI. For people who think (or want) AI = closed_AI then that argument has weight. Good luck getting them to update their priors.

the argument is that we should all fold and let sam altman burn trillions of dollars on naive scaling and pay monopoly prices for their closed APIs until the models are good enough to be closed off for "safety" reasons so that they can take an even larger cut by competing directly with us

Open source AI is actually a lot less "powerful" than genuine frontier models, i.e. it has a much tighter inherent capability ceiling. This is "decelerationist" from a purely AGI-pilled point of view but it's actually great if you're worried about a capabilities arms race putting AI Safety at severe risk.

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?

Do you think they are inherently less powerful? I'd imagined that closed labs have a head start / more funding so the open labs are playing catch-up.

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?

If you're targeting widespread local/on prem deployment which is what many open weight models are doing, that inherently limits your scale in terms of total model weights/inference-time compute compared to running in a few centralized datacenters. A centralized model will always be able to leverage a larger scale of deployment, placing it much closer to the genuine "frontier".

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