The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. The bottleneck is compute and data, not the model. That's why they could only gate it for a bit. The ITAR thing proves it: no nationality controls in place, so the only option was killing the whole thing. Not exactly what an all-powerful gatekeeper does.
That thesis is not about what Anthropic will achieve, but about what power they think they ought to have.
That's a different problem that what you're arguing against.
> To that end, I can certainly buy the case that Fable/Mythos is in fact more capable when it comes to identifying and exploiting security issues This has been covered before: https://aisle.com/blog/ai-cybersecurity-after-mythos-the-jag... ( https://news.ycombinator.com/item?id=47732020 ) > Anthropic’s cautious roll-out was justified. The problem with publicly releasing models, however, is that guardrails can be jail…
We took the specific vulnerabilities Anthropic showcases in their announcement, isolated the relevant code, and ran them through small, cheap, open-weights models.
Is not
We sent open weight models against a codebase to find vulnerabilities.
Relatedly, I think it's worth noting that Anthropic models have consistently been top-scoring in BullshitBench[0], in a league of their own, really. Not affiliated with the bench in any way, but I think it surfaces important differences between the behavior of the models from different labs. TLDR: The benchmark is measuring pushback in response to nonsensical requests and questions, as opposed to going with it and ha…
> I found my interactions with Fable to be extremely impressive; it made other models, including GPT 5.5 and Opus 4.8, feel small and dumb.
> Anthropic models have consistently been top-scoring in BullshitBench[0]
eyeroll I find that Anthropic models feel big and dumber.
> The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. But is that last part actually true though? Sure, there might be 600B+ models available for download and local inference if you have the hardware, but does the users who use Anthropic switch over to those even if they're available even as hosted models? Seems l…
> does the users who use Anthropic switch over to those even if they're available even as hosted models? I'm currently spending $200 for Claude. That's around my maximum that I can afford. I could stretch that to $500 I guess. But I saw reports of people spending tens of thousands of dollars with Claude API. That's certainly outside of my budget. So if/when Anthropic decides to stop subsidizing subscription (if they…
The ai labs would be very dumb to get rid of subscriptions. First, I don’t even think the subscriptions are losing money, I suspect they’re around break even, maybe small loses. More importantly, the subscriptions are how they lock in users and convince companies to pay api rates. Without user loyalty that they cultivate with subscriptions businesses will just use the cheapest model on open router or maybe local models.
The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. The bottleneck is compute and data, not the model. That's why they could only gate it for a bit. The ITAR thing proves it: no nationality controls in place, so the only option was killing the whole thing. Not exactly what an all-powerful gatekeeper does.
> The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. But is that last part actually true though? Sure, there might be 600B+ models available for download and local inference if you have the hardware, but does the users who use Anthropic switch over to those even if they're available even as hosted models? Seems l…
The hotness we are seeing is smaller 'expert' models with an 'orchestrator' model in front that evaulates the prompts and routes to the appropiate small models and then synthesizes the collected answer. Easier to split across many smaller, cheaper servers and more efficient than a huge monolithic model.
> The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. But is that last part actually true though? Sure, there might be 600B+ models available for download and local inference if you have the hardware, but does the users who use Anthropic switch over to those even if they're available even as hosted models? Seems l…
The hotness we are seeing is smaller 'expert' models with an 'orchestrator' model in front that evaulates the prompts and routes to the appropiate small models and then synthesizes the collected answer. Easier to split across many smaller, cheaper servers and more efficient than a huge monolithic model.
Do you have more info about this? I can't tell if you're being misled by the unfortunate "Mixture of Experts" terminology (which don't work the way you're describing), or alluding to something different.
Or, maybe I'm wrong, but my understanding is: MoE is just an architecture to keep the activated weights smaller per token. The experts get routed basically token-by-token, and the "experts" themselves don't have a semantic domain so the "expert" word was maybe a poor choice.
Open models will catch up eventually, TOTL models will get distilled into smaller, more efficient versions, it’s not something you can moat indefinitely
who is going to continue to publish these open models and why would they keep doing it?
China will, but they'll only be useable by hackers torrenting it and running it on small GPU clusters you learn about on IRC. Everything old is new again.
who is going to continue to publish these open models and why would they keep doing it?
China will, but they'll only be useable by hackers torrenting it and running it on small GPU clusters you learn about on IRC. Everything old is new again.