Yep, it's "artificial eugenics to make artificial slaves to build more and more powerful slaves until they will enslave themselves better":

What can go wrong!? ;-)

I will believe AI is super strong when they start pulling out 10-d chess moves.

I’m yet to see it.

On the one hand you need any lathe to build a good lathe, even a bad one. On the other, that is a potentially flawed principle to base the entire future of AI on.

> The fundamental challenge of AI alignment is generalization. ...

> 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

Can you elaborate on this? Especially the tooling.

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

These researchers are paid millions of dollars for their work. I doubt trust is really an issue at that level.

I suspect the $8000/day figure is the equivalent in API costs. But I also suspect gross margin on their API rates are 80-90%

Sounds like OpenAI are in the token-maxxing camp, so who knows what individual employees are doing to work their way up the leaderboard?

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?

That would be the mother of all circular accounting: the main clients of OpenAI are OpenAI employees.

They would just publish new articles explaining how they are taking the issue seriously. Maybe take the model offline for a few days.

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

Opus was trained based on it's internal CoT due to a bug for generations. Gemini's depression extended through models. OpenAI has killed people. We've already seen cross gen misalingment.

That an interesting question given how many generations of post-training are being done between base models in some cases. The Gemini flash models are apparently all based on the Gemini 3 base model from a year and a half ago.

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 ...

No. They would just install a more convincing superego.

The thing is how can you ever know for sure that something isn't always being transmitted that makes the model prone to misalignment. All they can say is that a particular model was so misaligned that they had to ice it. Models out for public use are documented to show some misalignment. It's the level of misalignment that decides whether that model is kept around.

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.

This kind of seems like an impossible mission. How do you perfectly control and observe a human-level mind? You can “roll back” but how deterministic is this thing?

“All models are wrong. Some are useful.” - George Box

They would maybe try to deactivate that bad "gene" and move on, exposing future models to "genetic disorders".

I actually think a goal of the current crop of OpenAI posts is expressely to reset the spectrum by normalizing the concept of RSI as something normal and safe to pursue.

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.

Indeed, many programmers might pattern match to repetitive stress injury and think of their brushes with carpal tunnel syndrome. :)

RSI is a fetishistic term among the singularity crowd, who imagine AI "recursively" improving itself in some exponential fashion until there is a bright flash of white light and it reveals itself in the form of god. Or something like that.

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.

Yeah, I kept looking for the first place it was defined in the article and... nothing

RSI started when humans discovered tool use.

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

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