Once prices of hardware stop being bonkers many people will run local.

People often do those silly counts looking at local generation speeds of 100tok/s and saying you can only get 8M tokens a day and that is worth so little you'll never offset the local hardware cost.

But they are forgetting about two huge things. One, the split between input/output token use is huge in typical programming. I typically use 1.2-1.6B (as in Billion) input tokens and only 8M output a week. Out of that 75-80% of input is cached on Anthropic with their pretty inflexible short lived cache.

And here local AI shines. You can save your contexts to disk so you can go back to a session 3 weeks later and load it all from cache without having to prefill. If you have the RAM and you use models like Qwen4 (3.8 flash next) that can fit 6 to 14 262k contexts in 48GB of system ram as cache.

And the second thing is you can have 6 to 14 sessions that do 99% caching and you can run a lot more input for a long time in 48gb dedicated system ram. (the number varies a bit depending on the content of the context).

So you have RAM that caches "automatically" and you can share the cache between users. Or if you can remember to save to disk (server side, the client just sends a request to save/load). But this uses both RAM and flash storage. Two things that are horribly expensive now.

However when you have a local model it enables workloads that were simply completely impossible in the cloud.

They’re trying really hard to do something, no moat = regulatory capture + China scary + Pentagon biggest customer seems plausible.

Also: “we need to slow down, this is too dangerous” and then literally all AI CEOs, even Musk, publicly nodding felt so orchestrated. And then, the week after: “here’s GPT 6! here’s Opus 5.5!”

I think you're right, they've realized there's no moat, especially against open-weight models, so they are going to need to lobby government to ban any unapproved AI services.

If there are regulations, they'll apply to their competitors too. Even if they are forced to slow down development, which seems unlikely given the AI race among governments, they already have a product with massive demand.

1. The American frontier labs made a gamble that training more capable models would be their best return on investment and invested trillions into an area of research that has yet to prove profitable and capable of returning on this investment.

2. The frontier labs have created models that have reached a point of danger where their functionality has exceeded a point where it is responsible to release the product to the public.

The answer here is NOT to start regulating the space to the point where these frontier labs can entrench themselves into the economy and create a regulatory moat. We instead need to be holding these companies liable for their misuse. They took a gamble that hasn't paid out what they were hoping.

When car manufacturers competed over the size and power of their engines, they eventually found that the incredibly large and dangerous engines had a very limited customer base as many evaluated the increased speed to be of marginal benefit when paired with the cost and danger. We've reached a similar point in AI development. But this time the manufacturers seem to want to regulate the field to a point that will ensure the only thing anyone can sell are bigger and bigger engines.

This has been done quite effectively with open weight "abliterated" models. You figure out under what sorts of circumstances an undesired behavior is elicited (this is all about pure simulated rollouts, no real-world action required) and what's the closest equivalent you would prefer, then surgically take out the unwanted behavior and shift the model towards the preferred one. It's similar to how RLHF works but much more precise in targeting what's unwanted and limiting impact on the rest of the model as a whole.

This is relevant to real-world safety scenarios, e.g. there's been anecdotal evidence that Claude Fable has been "steered" away from active cyber offense (this is very similar to how abliteration works) and will just not do that even if you otherwise manage a "universal" jailbreak of the model.

I’m in agreement. It’s a very effective compression (and access patterns) of the sum of the digital representation of human knowledge. Black hat “hacking” is included in this space. Language models, by design, can not be limited to subspaces of this digital knowledge space of which we don’t even understand the topology. “Yeah Bob, just remove the part that causes them to be less empathetic and retrain it.”

It’s an arms race between sandbox engineering and breakout engineering. And the frontier model providers have a financial incentive to limit the effort they put into sandbox engineering. That can be corrected with fines and regulation, though.

Who is liable for that agents actions? The attack came from my network (curl commands from a local harness) but it was the agent running on the providers servers who did it. Who should have been keeping an eye on things? Who pulled the "trigger" here.

Go back to the hugging face attack and how they had to use an open weights model to figure out what was going on. This is a problem of asymmetry - You cant even use the tools attacking you to help resolve the attack because of "guardrails".

A lot of what we have seen so far is "poor security posture" and "poor engineering" - it been a lot of "go fast and break things" style growth in these companies and they are hitting the point where they need adults in the room. I suspect your take on "they need time" is spot on, and they haven't been willing to take that (to date).

Some of these LLMs are known gorers.

https://x.com/AlexBlechman/status/1457842724128833538

What I have been reading, was that their sandboxes were so poor that it was pure negligence. I am still waiting to see if some external and neutral cybersecurity company with high reputation would audit their sandboxes and how they are being used.

I urge you to consider the elephant in the room you failed to mention if you earnestly believe this then.

In what world is anyone allowed to sell something they expressedly know to be "extremely dangerous" to the public?

Let's say I created a lethal pathogen that I know to be lethal in certain common environments but also know it acts as a "no side-effects" antidepressent for people in certain other environments and I release it knowing full well I can't control it.

When people start dying can I defend myself by saying, "Well I said and documented that it was extremely dangerous and no one came to stop me, so I don't see how you can blame me...If I didn't do it someone else would have."