Like I said last week. I think zucks original llama thesis is proving more correct by the week. The rent market on intelligence never materialized, if you have THE frontier model, you can charge rent (for a few weeks, maybe month). But after a certain capability level it's price that drives the decision.
Zuck is doing the thing he's really good at, and building a moat around the network (Muse is VERY sticky), and now that we're all going to standardize on Meta's model, we're going to optimize the heck out of the infra to run the model making it cheaper for him to scale, thus bringing in more participants to his network, and thus making his network more valuable.
Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.
How has Meta responded? With a failed phone and speculative headset.
Sure, just not nearly enough for sustainability I think. I'd reserve that call for the moment we can stop bathing in free VC money by using subscriptions.
Also, as far as my own use goes, I found that the capabilities/price ratio of DeepSeek's offering is very hard to beat. It's really good enough so the thing that matters to me (besides table stakes like uptime, latency, etc.) is price at this point.
The model license requires large inference providers to contact Meta separately to obtain a license to serve the model. I know we've seen this before with other large models, but it's still a bit irritating.
The usual effect of separate licensing is decreased price competition from public providers (like we saw with Kimi K3). I guess it's better for the providers if the price is fixed: any efficiency improvements directly improve margin rather than driving prices down over time. Still, to provide my own anecdata, the main thing keeping me on a paid subscription is token costs for the more capable models.
Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.
The nearest analogue would be "weights available", I guess.
great, I've been reading series of papers related to open source/open weight LLM Backdoors.
It's 100% possible, curious when will be the first incident.
Having something built by entity with HQ in the wester world is always a bit safer. That could be mistral, but they decided to be a consultancy company, not an ai lab.
They've done considerable effort in making sure this model can run on basically every model runner, from Unsloth to MLX, https://dev.meta.ai/docs/muse-spark-open/deploy
Like I said last week. I think zucks original llama thesis is proving more correct by the week. The rent market on intelligence never materialized, if you have THE frontier model, you can charge rent (for a few weeks, maybe month). But after a certain capability level it's price that drives the decision.
Zuck is doing the thing he's really good at, and building a moat around the network (Muse is VERY sticky), and now that we're all going to standardize on Meta's model, we're going to optimize the heck out of the infra to run the model making it cheaper for him to scale, thus bringing in more participants to his network, and thus making his network more valuable.
Meta is going to win consumer AI.
Google, Apple, and Microsoft are each in a stronger position to win consumer AI because they're guardians over the foundations of the user's experience. Meta always has to wonder whether they're going to be edged out, like when Apple decided to limit permissions or set new rules on user data handling.
How has Meta responded? With a failed phone and speculative headset.
So what makes Muse "very sticky"?
Good anthropomorphism.
Sounds like cope. You can't claim the 'rent market on intelligence' never materialized when revenue is absolutely booming for Anthropic/etc.
Sure, just not nearly enough for sustainability I think. I'd reserve that call for the moment we can stop bathing in free VC money by using subscriptions.
Also, as far as my own use goes, I found that the capabilities/price ratio of DeepSeek's offering is very hard to beat. It's really good enough so the thing that matters to me (besides table stakes like uptime, latency, etc.) is price at this point.
They are just repeating old tropes about unprofitability without reassessing
[delayed]
The model license requires large inference providers to contact Meta separately to obtain a license to serve the model. I know we've seen this before with other large models, but it's still a bit irritating.
The usual effect of separate licensing is decreased price competition from public providers (like we saw with Kimi K3). I guess it's better for the providers if the price is fixed: any efficiency improvements directly improve margin rather than driving prices down over time. Still, to provide my own anecdata, the main thing keeping me on a paid subscription is token costs for the more capable models.
ya especially because it's huge, you aren't running this on your desktop
Then it's not open-weight. Open-weight means "weights available under an open-source license", and licenses prohibiting commercial use are not open-source.
The nearest analogue would be "weights available", I guess.
US OPEN WEIGHT IS NOT DEAD THANKS ZUCK
great, I've been reading series of papers related to open source/open weight LLM Backdoors.
It's 100% possible, curious when will be the first incident.
Having something built by entity with HQ in the wester world is always a bit safer. That could be mistral, but they decided to be a consultancy company, not an ai lab.
"Muse Spark Open was optimized to run on Mac Studio with M5 Ultra, and together they’re the best way to run a powerful model on your own desktop."
can we stop these safety risk.... Feels like gpt 2/3 days, and now we know there wasn't any safety issue with gpt 2/3....