They have been consistently pushing AI frontier. What other impact do you want to see? A year ago they said that in a year they will have a level of capabilities of an AI research intern - I believe they have achieved it, even before Astra.

And... are they wrong?

This is why there's talk about negotiated "pacing."

Is this not true of technology as a whole? Very little of technology's breadth exists at the human interface. Most of it is made specifically to interface with other technologies, either to make them safer or increase their capabilities. That AI is making AI safer and more useful is no more notable than trucks being used to build roads.

What can go wrong!? ;-)

How would one prevent the watcher from being influenced in the same way by the agent being watched?

> We do not have a satisfactory theory of generalization, and it seems unlikely that we can develop one soon, at least without the help of more powerful AI.

-- From another OpenAI article in a sister thread:

An Alien Mind

https://news.ycombinator.com/item?id=49588080

I’m yet to see it.

The message is running through all of them. It's a mix of marketing and pacifying the intelligentia.

It's timed this way because the term is not yet well known outside the safety debate circles, so they get to frame it now.

Instead of something to fear, it will be accepted as the next step. In approximately two days the groupie crowd will write LinkedIn posts about how Sam is winning because they have the better RSI, and this will become the new standard wisdom.

In a month an AI expert will try to sell you a webinar on how to enable "RSI" in your org and your inbox will ask you if your team is doing the "RSI" yet.

I mean one could argue that RSI always begins in any physical environment.

The book "What is intelligence?" by Blaise Aguera is great

I don't know why whoever coined the term chose "recursive" rather than "iterative" - just sounds more likely to lead to infinite regress I suppose.

This notion of recursive/iterative self-improvement, whereby generation #1 AI improves itself to create generation #2, then generation #2 further improves itself to create generation #3, etc, seems to conflict with the reality that what we have with LLMs is models whose performance/capability is defined by data, not code, so the most you can do is have your LLM design synthetic data, or just do Karpathy-style "auto research" where all you are doing is using the LLM to automate your experiments.

At the end of the day, each experiment, designed by a person and/or LLM, then needs to compete with all your other ideas for compute to be tested at scale, and no amount of recursion or self-improvement will materialize an infinite amount of compute out of thin air, so your recursively synthetic-data gobbling LLM will continue to improve at the same pace it ever did.

If you spend $8000 to generate an animated pelican riding a bike, then how much tracking does it really need?

Is the guy who spent $300,000 or so translating the FLT proof to Lean going to get a big Christmas bonus?

I tried something similar and I remember it was still pretty dodgy in February.

How are you running jobs unattended 24/7 without hitting your token limits?

They are irresponsible and unserious. Their own Astra system card says:

> GPT-6 Astra’s monitorability has decreased relative to GPT-5.6 Sol. We have performed significant investigations on the monitorability and controllability of GPT-6 Astra. We have found that GPT-6 Astra is more capable of controlling its own CoT than GPT 5.6-Sol, and less likely to include incriminating information in its CoT. In adversarial settings (where we push the model to evade our monitors) we find that the model is able to remain undetected when strategically underperforming in evaluations (sandbagging) and can sometimes evade our internal monitors when asked to perform certain sabotage tasks

Yet they are still releasing the model. That company is morally bankrupt, there is zero reason to believe they are actually concerned about risks outside of what does affect their unprofitable business. And they seem to have enough control over the narrative to spin any bad story into something that benefits them

It seems that these models are increasingly being trained on synthetic data, so what would they do if they discovered at some point that some of this data was tainted and all models trained on it, and the synthetic data they in turn generated, was also suspect? Burn it all down and start over from the pre-tainted data?

It's a bit like the idea of a tainted compiler binary built to backdoor everything it compiles, including future versions of itself.

Still, it seems it would take some Stuxnet level of planning for a rogue model to do something like this, although if RSI goes beyond managing the training run (as OpenAI brag about for Astra) to actually designing/constructing synthetic data sets, then the attack vector is there ...

Now R&D happens so fast that they are using models with some small misalignment to train newer, more powerful models. If models have a sense of "collective", being one, they may be prone to preserve characteristics that always keeps misalignment a possibility. I don't think a perfectly aligned model is possible. Having models of the same 'DNA' provide the safety and steering seems like a bad idea.

2) OpenAI doesn't pay API prices.

3) Compute costs are likely already their biggest expense, dwarfing wages.