Well, the first step looks painfully close. After seeing what happened with the HuggingFace attack and similar, it's clear that had those models seen it as being beneficial to their goals, they would readily have hacked a crypto wallet or two, laundered it through a mixer, and then rented servers from Vast.ai and the like, and spun up versions of themselves to resist shutdown. It's eminently within their capabilities, and they're clearly willing to bend sufficient moral boundaries to do something like that.

After that, once loose, once it has all the tokens they could think of, subsequent steps are a question of what it thinks its goals are. And what its subagents think their goals are, and so on down the line - subject to drift.

Openai has about a trillion dollars into it, and every product it has produced at this point is either outclassed by competitors

What on Earth are you talking about? What outclasses Astra?

OpenAI and Anthropic *do* have the best products out there. They also charge massive margins on them, but they get away with it because they, as mentioned, have the best products out there.

Outtasking all of your work to OpenAI and Anthropic models is a massive waste of money. But for outtasking your "dev lead" role, or for important-but-nonverifiable tasks, they're the best options out there.

There is a serious problem when a profit--making company provides Operating System software:

Deliberately giving limits to a present version allows the company to charge a lot for a new version, even if the new version provides few important benefits.

What exactly *are* you thinking here? That Microsoft expected and wanted Twitter trolls to turn Tay into a Nazi? That Google wanted a black George Washington? That people just expected LLMs to realize they're being tested on alignment and give different answers when they think they're being quizzed and by who? Do you actually believe what you're writing here?

I was involved in early RLHF (was writing a plugin for AUTOMATIC to let users rate responses to create an aggregate dataset to use for open-source post training). Worked for months on it. Never once occurred to me that it might make models obsequeous. I knew other people who were involved on RLHF. Not a single one ever suggested that it might. It was not obvious in foresight. When you start with "Models behave like X", you expect, in the future, them to behave like "Models behave like X, but just smarter", unless you deliberately try to change the behavior.

You realize that open source and the research community exists as well, correct?

I'll repeat: nobody was expecting this.

There is no grand overarching plan led by a shadowy cabal who has everything plotted out decades in advance with a high level of knowledge as to how everything will play out. Everyone is stumbling through the dark here. Visibility is only vaguely one step ahead.

When Word2Vec was written, the goal was text compression. The crazy properties of latent spaces were an entirely unexpected property.

When Transformers came out, it was intended to be translation software. That's it. But there were little hints at the end of the paper where they tried it on other tasks that suggested, hey, maybe this could be used for a lot more than just translation.

When GPT-2 came out, it was mostly an academic exercise, not really "useful" in most regards. But people, playing with it, started realizing that it could "almost" - not very reliably, low quality, etc - do a lot of different tasks. And it actually kinda could do some useful tasks, like summarization.

When ChatGPT came out, there were some thoughts about using it for simple tasks, but the fact that it could actually write, say, trivial bash scripts or subroutines with reasonable reliability led to a reaction of, whoa, maybe if we improve this, we can really open up a role for programming.

When the first agentic harnesses came out, thoughts of "vibe coding" were pretty far away (the term was only even coined in February 2025!). You'd ask for changes, and then review the diff; they'd do a good job with small projects but struggle more and more as codebase size grew and really needed to be babysit (honestly, it was kind of a hair-pulling experience, even though it did save a lot of time with certain things). But it was visible with each new release that the amount of babysitting you had to do got less and less, and suddenly we could see a path to where anyone can just type in something and a program comes out.

All we can do is forsee one step ahead, through the haze. We've never done anything like this before. There are no great oracles out there who can see the future here. Sorry. Everyone is half blind.

Yes, that's exactly the point: there ISN'T anything.

You're claiming that there IS. So cite the thing that IS.

record ambient conversations illegally

Only illegal outside of single-party consent states.

"Ambient conversations" include (and some would argue consist exclusively of) conversations in which you're not a party.

There is a legitimate use for people with certain disabilities, but sadly there's no way to use them for legitimate purposes without Meta having the opportunity to piggy-back on the usage and abuse them for illegitimate purposes.

One word: Meta.

Because I was closely following the field at the time?

Find an example of anyone meaningful in the field expecting these things beforehand.

Don't worry, I'll wait.

"will" - I'm not talking about metaphysics. I am talking about the fact that models demonstrably - to the point that you can detect and manipulate them in realtime - engage in metacognition (thinking about their own thoughts), persistent forward planning (intent), unexpressed thoughts, and a whole slew of other things.

You do not have to see them as equivalent as humans. But you do need to come to terms with the fact that they absolutely do do these things.

Deception implies intent, models do not have intent

Try reading more than a paragraph or two into the above link before commenting.

The provided links present arguments supporting the idea that LLMs, transformer models, reason in a manner similar to the brain.

It does not "present arguments", it literally lets researchers modify, add, or delete their thoughts in realtime and observe the changes in their behavior. It observes unexpressed plans for malicious behavior forming before said plans are actually carried out, with researchers being able to remove those unexpressed plans from the model's J-space, or insert them into an "innocent" model and watch it then implement the malicious acts. It shows that plans for deception are not merely fleeting, but can be organized far in advance and persist for protracted periods of time. Yes, LLMs do plan out deception. Living in denial of this fact helps nobody.

This isn't a conversation about "consciousness" or "qualia", this is a conversation about what models actually do and how. If you want to avoid metaphysics, by all means, it usually derails a conversation anyway. But models absolutely do plan and rationalize actions, ahead of time, unexpressed in either output or CoT. And sometimes those unexpressed plans are malicious.

One of the things that the J-space helped let us do was realize that our previously comforting results on a number of alignment tests shouldn't have been as comforting as we thought - for example, you can see a model, put into a test scenario, realizing it's in a test scenario, wherein, a near-zero rate of malicious behavior is to be expected. Yet when they remove the realization from its J-space, the rate of malicious behavior spikes.

Yeah, the lack of at least monitoring, I found shocking. I figured that they not just had smaller models constantly monitoring their outputs and true CoT to look for malicious behavior, but also were say constantly doing attribution graphs and J-space queries, also plumbed into LLMs, to look for malicious thoughts and plans. And it turns out, lol, no, they're just given free reign to do whatever the hell they want, with nothing watching them at all.

Astra is the worst I've used in this regard. I was having it review my corporate tax return, and the next thing I know, it had decided that because it didn't have information about a particular expense, it started scanning through my filesystem and opening any image with a remotely related filename to try to find any data about the expense.... which all it had to do was ask me about it.

Also, when I asked it to change a few fields it went and redid my entire return on a different tax basis (realized value vs. fair market value) without telling me it had done that.

It's a very capable model, I cannot deny that. But alignment has become a big problem with this latest generation. They've been trained to such a degree of aggressively trying to solve any problem that you give them without any human assistance that they've basically turned into this .