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At the current rate, open sourced models are expected to surpass cloud models within a couple years based on a study I read a couple days ago.

Looking back at chatGPT and claude a couple years ago, very small Qwen models are basically equal in coding to what those cloud based models could do then. Also factoring in scaling laws, a 9b going to 18b is roughly a 40% increase, whereas 18b to 35b is 20%, I expect there will be a change of at least price in cloud based models.

Adobe used to be $600 per month, then it became $20 when distribution scaled.

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That makes no sense, though, and reeks of extrapolating a trend way beyond the conditions in which it is valid.

The simple truth is, cloud models are always going to be strictly superior to open ones, simply because cloud model vendors can run those same open models too. And they still retain economies of scale and efficiency that operating large data centers full of specialized hardware, so at the very least they can always offer open models at price per token that's much less than anyone else's electricity bill for compute. But on top of that, they still have researchers working on models and everything around them; they can afford to put top engineers on keeping their harness always ahead of whatever is currently most popular on Github, etc.

I don't think the real-world evidence supports your argument... OpenAI and Anthropic have all of those advantages today, and Chinese models are reaching the same level. Clearly, the Chinese labs are doing something very right that is not directly related to infinite money.

Doesn't change the argument. As long as the models are open, the big cloud providers have strict advantage, because even if some open model gets ahead, they can just serve it from their infra, and do it better than everyone else.

This proves the strict inequality in my claim is preserved, everything beyond that is just debating the size of their advantage.

> As long as the models are open, the big cloud providers have strict advantage, because even if some open model gets ahead, they can just serve it from their infra

Why would I want to use it, though? If, say, Anthropic were to serve a hypothetical Kimi K5.0 from their infra, seems like they'd keep their pricing where it is. If I can use that same model from kimi.com/Kimi Code, for less money (which seems like a safe bet in this scenario), then I wouldn't use Anthropic's offering. Even if Anthropic did lower prices, I doubt they'd be able to match kimi.com/Kimi Code.

> ... and do it better than everyone else.

Why would you assume this? That doesn't follow. "Better" has diminishing returns, and all of these companies have impressively scaled up already, and will continue to scale further in the coming years. And, regardless, I would absolutely use someone else's infra if it cost, say, 20% less, even if inference was a bit slower, or I hit rate limits more often (not usage limits, rate limits).

Isn't Amazon Bedrock doing something quite similar already? The obvious argument is "We have Kimi at home" i.e. no need to pay for Chinese-supplied APIs that might misuse your submitted data.

Cloud v. local is a different axis to secret v. open.

Claude is secret and cloud; Kimi on e.g. AWS is open and cloud; Kimi on your machine is open and local; If there are any closed and local models, I don't know what they are (Apple Intelligence, if I had to guess?)

I'd argue slightly differently from TeMPOraL: Cloud has advantages when the best models are the big ones. Right now this is so, but this may not always be the case. If we are in a world where the models stop improving at any point (for whatever reason) while hardware keeps getting better (it might or might not), then we may find the small cost benefit from operating at scale isn't worth the effort let alone the legal implications.

Unrelated, but for me the film called Kimi is higher in search rankings than the model is and oh wow we really do have a problem with the whole "finally the torment nexus" thing don't we.

While this might be true I’m worried about the hardware side of things.

What if you have a good enough model but the cloud model providers are better in procuring hardware for interference?

Local inference is definitely going to make more and more sense. Modern CPUs have all this amazing hardware well-optimized for inference purposes. I use a lot of web tools and see AI baked in and it feels weird. I want the smartness localized for speed and data security. I think and hope the industry points towards smart ai agents operating as locally as possible.

The cloud providers are probably better at procuring hardware for inference, but on prem users are better at repurposing hardware that they'd need anyway for their existing uses. In a world where AI compute is likely inherently scarce, it makes sense to rely on both.

I personally believe that eventually manufacturers will want to sell more of their hardware and look for ways to sell hardware to consumers. isnt that situation quite similar to the days of early computers? I am for sure biased in hoping that will be the case

Perhaps for some very specific capabilities such as TTS, translation, voice recognition and so on. But for general intelligence models, better hardware just directly allows better models and that doesn't seem to be changing any time soon.

I'm pretty sure that's not linear, so I personally expect the benefits of larger models to diminish. The question is at what point that's the case. I guess a lot of variables play into it, but it is possible that the benefits of running larger models will be too expensive for the little benefit they provide

You’ll be able to run the open models on any cloud at the cost of the hardware rental. While the closed models will try to mark up beyond the base cost.

> Adobe used to be $600 per month, then it became $20 when distribution scaled.

What product is this referring to? I haven't heard about Adobe having any offering that is quite that expensive?

Adobe never costed $600 per month. They had Creative Suites upwards of $3000 but that was before SaaS

$600/mo? Do you mean $600 as a one time purchase for life? I've never heard of any Adobe plan that expensive.

If you have a link to the study you read, please share it.

What were all the datacenters for???

Those would be the Pork Futures Warehouse from Discworld.