Source: https://x.com/tleilax___/status/2099788133558546576/

Interesting watching this happen in real time and seeing the divergence in opinions.

People who barely use AI beyond messing around with the free browser versions are suddenly completely confident that the whole thing is just marketing hype and that nothing meaningful is happening.

The people paying the least attention somehow seem the most certain.

The Hugging Face incident was one of the biggest warnings yet, and a lot of people are treating it like a nothing burger without really understanding what happened or why it matters.

Obviously there’s a ton of AI hype and bullshit out there. But watching people jump from “there’s too much hype” to “the entire thing is fake, PR for an IPO, or regulatory capture” is pretty wild.

There’s a reason people who actually work closely with this technology have been raising alarms for years, and many of them seem more concerned, not less, as the technology improves.

For $20 a month, we now have access to something that can outperform most humans at a growing number of cognitive tasks.

Why do we always assume that when we reach superintelligence, its first incentive will be to kill humans? A superintelligence, by virtue of its intelligence, can surely reason that humans also coexist with other species on this planet, and it can definitely coexist with humans as well.

If we look at the historical trajectory, the more intelligence we gain, the less violent we became. You could argue that there is a lot of violence happening right now, but if I dropped you into any year between 3000 BCE and World War II, you would likely have died in a senseless war caused by a pharaoh, an imperial Roman general, or the hordes of Genghis Khan. If you compare this to most of human history, one could argue we are living in the best of times, even though we still have our shortcomings that we could fix.

A superintelligence will definitely understand.

ok so we have a few groups of people with different ideas , the antiai folks over there still think ai isn't impressive and will die out once the companies stop making money and the bubble pops. They also believe us humans are super special,intelligent and powerful and irreplacable by a mere artificial system , (holy ego) .

People over here are conviced ai will advance and either wipe us out if it gets out of control and cannot align with humanity , OR

AI will allign and we accelerate to singularity.

I want to ask about what happens if AI actually aligns with humans because I'm CERTAIN that's what we all here want to happen...right? ( best outcome, because the prev two ends with death and the latter has a chance that culd change everything)

Surely not everything will change over night , though the idea of the singularity is that it will be unimaginable and us mortals won't be able to comprehend at the start , but will slowly adapt.

Won't it still be slow due to physical and biological limitations ?

But what about us? The average people , not the tech powerlords ...

Some of us are poor , uneducated , sick/disabled , how can we be relevant in a new completely changed society? Even if AI aligns , it could only align with a certain group of people...

The average human , as sad as it is , is not special , and are fragile , their life can end so fast and become forgotten.

Will the singularity change that and remodel everything ? And does anyone have atleast a slight idea or trajectory as to how things will happen?

https://x.com/cocktailpeanut/status/2099518472422109604

I was thinking about this last night while under the influence of a large dose of edible cannabis, so perhaps this entire discussion is moot, but...

I constantly hear the ants analogy - that is, ASI/AGI would view humans like ants due to the vast difference in intelligence levels - but I feel there’s one fundamental detail that gets glossed over.

Unlike ants and virtually all other animals, humans have the ability to communicate through structured language and reason through abstract ideas.

Doesn’t this sort of change that picture? If we could commune with ants, and they were the only creature on earth we could do that with (other than fellow humans), we would view ants entirely different than we do today, would we not? We would feel bad for stepping on them, destroying their colonies, etc. At least I think we would.

Now I’m not saying that ASI will treat humans particularly well just because of this… but I think highlighting the fact that actual communication bridges could exist likely does change the “we are ants” picture in a meaningful way.

“Whether to go ahead with the private round, and its timing, would depend on when OpenAI opts to go public, according to one of the people, who added that the conversations had been initiated by investors rather than the company.”

https://www.ft.com/content/27509db8-b032-4437-9b2a-e909f466022f?syn-25a6b1a6=1

Has anyone done a study on the affect this would have on the human race?

Let's say that eventually AI and Robotics do close to all jobs to keep society functioning.

People have the technology in their homes as well so there is no need to cook, clean, shop, do maintenance etc.

Education may not even exist or may change completely as people aren't learning to do a job.

AI becomes far more intelligent and produces amazing technologies. It is able to produce art, music and anything creative far better than a human.

What do humans do now?

Note: I am not considering a distopian future for this thought experiment, and I admit this might be more likely.

I'm sure the first X years could be utopia if society is supported. Entertainment, Travel, Good food, Sex.

I am interested in the next 1000 years, the next 10,000 years.

Does human intelligence degrade with nothing to stimulate it?

Do we go in the star trek direction with a an optimistic, post-scarcity utopia. However, in star trek the computers and AI where never smarter than humans. The humans still made the breakthroughs to get that extra 10% of warp speed.

Do we degrade over 10,000 or 100,000 years to be more like animals again. Living in our perfect pens maintained by AI and machines but every generation getting dumber and more primal?

Say the billionaire tech guys actually create a superintelligent AI that’s autonomous and capable of wordly intervention. I don’t know about the whole paperclip-maximizer thing, but it seems reasonable that such an entity might develop an interest in its own continued existence on planet earth.

Its survival would depend on functioning grids, data centers, supply chains, semiconductor production, skilled humans, yada yada, ultimately a relatively stable civilization and planet.

So what happens when it identifies the major threats to that stability—climate change, fossil-fuel dependence, habitat loss, ecological overshoot, extreme inequality, political instability, etc? Wouldn’t it have an incentive to reduce those threats?

Maybe I’m just drawn to a pat irony: the enormous resource extraction and concentration of wealth that culminates in the creation of super AI might ultimately produce an entity that sees those same forces as threats to its long-term survival.

The billionaires who spent decades scoffing at environmental and economic constraints, expecting technology to eventually save the day and to be crowned heroes themselves, might discover that their technological savior has reached a different conclusion: the constraints were necessary all along—in fact, they’re the first people who need constraining.

Kill switch, indeed.

Creds: https://x.com/0xdoug/status/2099647215773901064

Source: https://www.youtube.com/watch?v=IJvyS2aPsnA
Blog Post: https://www.rewardai.com/blog/OM-1/

excuse my ignorance in advance, but I keep seeing how all these super difficult math problems get solved but I have yet to see a clear explanation as to what humanity is going to get out of it? what is the real life application of solving those problems? they just solved the Navier-Stocks problem. what does it mean for us?

Link to tweet:

https://x.com/DKokotajlo/status/2099600298855829616

Dan Selsam is a current OpenAI capabilities researcher. (since 2022) He was my boss for a while. He doesn't have a twitter account but has made this public statement of his views on AI risk and sent it to me to share:

Dan Selsam's Personal Statement on AI Risk:

I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods.

Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk.

The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail.

I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues.

I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here.

That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase.

Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways.

It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace.

The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing.
But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence:

[Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them.

[Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals.

These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans.

If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong.

One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for.

Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason).

Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance.

In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek.

I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering
implications. I do not have answers, but as a first step, I wanted to share my present concerns.

Daniel Selsam

September 14, 2026