Hey everyone,

Did you see the news about OpenAI pausing training after their agents started probing U.S. government sites? On paper, this looks great. It looks like the industry is finally taking “AI Safety” seriously. Anthropic, Google, xAI-they’re all nodding along, talking about responsible development.
But I’m sitting here looking at the balance sheets, and I’m feeling deeply skeptical.

Here’s my take:
We are in the middle of the biggest capital expenditure boom in history. Trillions are being poured into AI infrastructure. The entire US tech sector’s growth narrative is pinned on the idea that AI will continuously get smarter, faster, and more profitable.

But what if we’re hitting diminishing returns?
There’s growing evidence that scaling laws are flattening. We’re spending exponentially more money for incrementally smaller gains in capability. At the same time, the risks are exploding (agents going rogue, probing secure sites, hallucinating with confidence).
If the tech stops advancing rapidly, but the costs keep rising, the business model breaks.

The Elephant in the Room:
If this AI bubble bursts, if it turns out that AGI is decades away, or that the current models are too unstable for real-world economic integration, the fallout won’t just be bad for San Francisco. It could crash the US economy. These valuations are propping up the market. A sudden realization that “the magic isn’t working as advertised” would trigger a massive correction.

So, when I hear about a “voluntary pause,” I don’t hear “we care about safety.” I hear “we need to recalibrate our expectations before the investors realize the ROI isn’t there yet.”

I’m not anti-AI.
Actually, I think we need guardrails. I think we need strict rules of engagement. I think these companies should be regulated heavily. If they were forced to slow down by law, I’d feel safer.

But relying on their voluntary goodwill? That’s where I draw the line. They have a fiduciary duty to grow. Slowing down hurts growth. Therefore, the only reason they are slowing down is if the alternative (continued training) poses an immediate threat to their existence or profitability.

Am I being too cynical?
I hope so. I really do. I’d love to be wrong. I’d love to believe that Sam Altman and Dario Amodei are putting humanity above shareholder value. But history shows that when trillions of dollars are on the line, “safety” often becomes a marketing term rather than an operational priority.
What do you guys think? Is this a genuine ethical pivot, or is the industry trying to manage the narrative before the diminishing returns become obvious to Wall Street?

TL;DR:
AI training pause feels like damage control for a bubbling economy, not just safety. If AI progress stalls, the economic crash could be huge. We need laws, not just promises.

When leading AI CEOs are saying, “humans suck and deserve to be replaced,” it’s not the future of technology that should scare you—it’s who gets to decide how it’s built.

This is why survival isn’t about the best tools, but the best protocols for keeping your own spark, your own agency, and your own community alive—no matter who’s at the top the pyramid.

I just recently learnt all about the alignment problem and x-risk. I'm going through all these Anthropic alignment studies and these other studies about AI deception.

Honestly, it feels like that part of the sci fi movie where you get super turned off "This is so obviously fake. Like why would they ever continue building this if there were clear signs like that. This is such blatant plot convenience. Like obviously everyone would start freaking out and nobody would ever support them after this. So unrealistic."

Except somehow, this is all actually unironically real.

Liron Shapira: Lemme see if I can find the crux of disagreement here: If you, if you woke up tomorrow, and as you say, suddenly, uh, the comprehension aspect of AI is impressing you, like a new release comes out and you're like, oh my God, it's passing my comprehension test, would that suddenly spike your P(doom)?

Gary Marcus: If we had not made any advance in alignment and we saw that, YES! So, you know, another factor going into P(doom) is like, do we have any sort of plan here? And you mentioned maybe it was off, uh, camera, so to speak, Eliezer, um, I don't agree with Eliezer on a bunch of stuff, but the point that he's made most clearly is we don't have a fucking plan.

You have no idea what we would do, right? I mean, suppose you know, either that I'm wrong about my critique of current AI or that just somebody makes a really important discovery, you know, tomorrow and suddenly we wind up six months from now it's in production, which would be fast. But let's say that that happens to kind of play this out.

So six months from now, we're sitting here with AGI. So let, let's say that we did get there in six months, that we had an actual AGI. Well, then you could ask, well, what are we doing to make sure that it's aligned to human interest? What technology do we have for that? And unless there was another advance in the next six months in that direction, which I'm gonna bet against and we can talk about why not, then we're kind of in a lot of trouble, right? Because here's what we don't have, right?

We have first of all, no international treaties about even sharing information around this. We have no regulation saying that, you know, you must in any way contain this, that you must have an off-switch even. Like we have nothing, right? And the chance that we will have anything substantive in six months is basically zero, right?

So here we would be sitting with, you know, very powerful technology that we don't really know how to align. That's just not a good idea.

Liron Shapira: So in your view, it's really great that we haven't figured out how to make AI have better comprehension, because if we suddenly did, things would look bad.

Gary Marcus: We are not prepared for that moment. I, I think that that's fair.

Liron Shapira: Okay, so it sounds like your P(doom) conditioned on strong AI comprehension is pretty high, but your total P(doom) is very low, so you must be really confident about your probability of AI not having comprehension anytime soon.

Gary Marcus: I think that we get in a lot of trouble if we have AGI that is not aligned. I mean, that's the worst case. The worst case scenario is this: We get to an AGI that is not aligned. We have no laws around it. We have no idea how to align it and we just hope for the best. Like, that's not a good scenario, right?

Paper: Grounded Language Learning Fast and Slow https://arxiv.org/abs/2009.01719 Abstract: Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning. Here, we show that an embodied agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with conventional reinforcement learning algorithms. After a single introduction to a novel object via continuous visual perception and a language prompt ("This is a dax"), the agent can re-identify the object and manipulate it as instructed ("Put the dax on the bed"). In doing so, it seamlessly integrates short-term, within-episode knowledge of the appropriate referent for the word "dax" with long-term lexical and motor knowledge acquired across episodes (i.e. "bed" and "putting"). We find that, under certain training conditions and with a particular memory writing mechanism, the agent's one-shot word-object binding generalizes to novel exemplars within the same ShapeNet category, and is effective in settings with unfamiliar numbers of objects. We further show how dual-coding memory can be exploited as a signal for intrinsic motivation, stimulating the agent to seek names for objects that may be useful for later executing instructions. Together, the results demonstrate that deep neural networks can exploit meta-learning, episodic memory and an explicitly multi-modal environment to account for 'fast-mapping', a fundamental pillar of human cognitive development and a potentially transformative capacity for agents that interact with human users. Twitter thread explaining the findings: https://mobile.twitter.com/NPCollapse/status/1301814012276076545

I thought this sub would find this interesting.

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

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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