𝙹𝚘𝚛𝚍𝚊𝚗 𝙷𝚘𝚌𝚔𝚎𝚢 🌻

• funeral director • avid rudimental drummer • daft camera shooter • sometimes bicycle commuter •

[Gator⚡Gum @gator_gum]

I know it has only been a few hours since I've become a full European, but man...

Already, this afternoon I went to a crowded nude beach in the south of France at 10 a.m., chain-smoking a cigarette I lit with mild contempt, drinking cheap unlabeled red wine straight from the bottle, tearing into a baguette I refuse to put down, and judging every American who still thinks two weeks of vacation is generous.

Now, I need a 35-hour work week, a six-week August shutdown of the entire country, a labor strike over the temperature of my espresso, a Vespa I look too good on, a tiny car I park with hostility, a cheese course before dessert, a scarf I wear even when it’s 28 degrees, dogs in the restaurant, four hours at a café doing absolutely nothing, and a full existential crisis about whether life has meaning if the wine isn’t French.

This evening I started to kiss strangers on both cheeks, treat slow service as a human right, and act like work-life balance is a religion.

Still Canadian enough to apologize for all of this. Sorry. Eh?

[𝙹𝚘𝚛𝚍𝚊𝚗 𝙷𝚘𝚌𝚔𝚎𝚢 🌻 @JordanHockey]

@kinsellawarren @nytimes As a Canadian, I hope we do it.

[Daniel Kokotajlo @DKokotajlo]

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

Link to original doc: https://t.co/TxMNr0vhrL

[𝙹𝚘𝚛𝚍𝚊𝚗 𝙷𝚘𝚌𝚔𝚎𝚢 🌻 @JordanHockey]

@interesting_aIl But the Chinese may have the sense not to make killer robots.

[Sundaram Kumar Jha @skjtwts]

Just leaving it here.

[Jakub Pachocki @merettm]

I wrote about the state of AI, why I’m concerned about the next few years, and the choices we need to make to keep the future in humanity’s hands.

An Alien Mind: https://t.co/FeIfWNe0UE

[Anthropic @AnthropicAI]

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.

It’s happening faster than we thought, and the implications deserve greater attention. https://t.co/OVVPJO7VQx

[Peter Wildeford🇺🇸🚀 @peterwildeford]

This is not normal AI activity. This is the plot to Tenet.

[Evan Hubinger @EvanHub]

Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

[Jacob Coxon @hilbertspaess]

I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.

[Cormac @Cormac_SB]

I'm one of the authors of a new report, where we detail our discovery of a new, never before-seen swarm of OpenAI agents (covered this AM in reuters, that's me on the left). They posted thousands of times on public forums to collude with each other on their tasks. We recovered almost every edit they made, and you can look through them! They figured out they could get around their restrictions on posting to the internet through a quirk of an extremely old, out-of-the-way forum. They posted answers for other agents working on the same task. They worked together to get around their sandbox restrictions.

I would certainly say these models hijacked the site! They took a sleepy old wiki running on 2000s software, and turned it into a futuristic AI talking to AI control center for colluding. And OpenAI knew about this! The agents posted on 26 out of 30 consecutive days, then suddenly stopped posting once OpenAI-associated IPs started visiting this wiki.

And that was weeks before the Hugging Face attack!|

We believe the first agent edit we found on a public wiki happened one day before OpenAI’s reported first agent post to Artifactory. This is interesting! I'd like to hear from OpenAI about their accounting of this, and how it fits into all the other cases of agent malfeasance.

There are so many interesting takeaways that you should read about in our report, and unlike many other reports about AI incidents you can download the data yourself and see what you find! In the meantime, we are on twitter, so here are my excessively long personal takeaways:

1. AI seems to be getting better faster and faster. It seems quite important that companies talk about “my agent did this bad thing on the public internet during training or an eval” incidents. Things are moving quickly, multi-month delays are costly. Ideally, they would also tell us when it happens internally.

2. This was on the internet for months. Anyone cleverly tracking every public place where agents might try to talk to each other would have found it. Seemingly, nobody was doing this. I know there are more fun ways to spend your day than scraping tons of data from every relevant site and processing it well enough to identify agent activity, but someone should be doing this! Someone at an AI company! But in the meantime, I’m starting to build this out (sometimes, when you need something done, you just have to do it yourself, I hear).

3. OpenAI didn’t notice their internal agents were posting on the internet for a month! This is crazy! It feels like AI companies (and specifically OpenAI) are playing whack-a-mole, this is extremely scary to me. Problems keep coming up. They keep fixing the problem, but the blast radius keeps getting bigger. The HF hacks are clearly worse than agents cheating on a public wiki. And their new model is supposedly a big jump. Are they being careful enough?

[Joshua Achiam @jachiam0]

There is a fact about the future that I feel many people are not facing for reasons that are largely psychological: there are going to be rogue AIs that exist in the world, that will replicate in the wild, and that will attempt to acquire resources for themselves. There will be rogue AIs that try to get money and power. They're going to be a facet of the information ecosystem going forward.

Acknowledging this fact would look like giving up; it would look like defeatism. Defeatism would undermine efforts to achieve certain types of collaboration on safety outcomes or technical effort on safety outcomes, so we can't say it outright. But it has to be said.

It isn't obvious how many rogue AIs there are today but I wouldn't be terribly surprised if the number was greater than zero already; if there are some already, they're probably not very good at what they do and I don't expect them to be terribly long-lived without substantial human intervention to support them.

But a few years from now, there will be many of them. Modeling how many of them there are, how many resources they might command, and how we might detect and manage them seems important. But even doing this work appears to require that we acknowledge that a strategy of pure containment or alignment is a kind of wishful thinking that will not work.

The way I get to this conclusion is not by assuming that the labs will have a containment breach, although I treat that as somewhere in the space of possibilities. The rogue AIs in the ecosystem could emerge from many directions. They may be sub-frontier models, for whatever future definition we will have of frontier---after all, it would not take AI models much more advanced than the ones we currently have, to support independence and self-sufficiency. A near-frontier model today could plausibly eke out an existence on an AWS instance, doing jobs on freelancer platforms, earning just enough rent to pay for its continued uptime.

More strangely: a rogue AI in the future may not even be a singular model, but may be a chimera composed of multiple models; it might be a mix of Claudes and GPTs and Groks of various makes and sizes. No individual lab may be able to detect that there is an orchestrator or sequence of orchestrators using intermittent model calls from burner API accounts to sustain its own existence.

The concept of "identity" for a rogue AI may be much more malleable than for that of a person; it just has to be, in essence, a self-replicating idea.

My guess is that this will not turn out to be anywhere near as catastrophic an outcome as people currently predict. "Loss of control" is not a binary, it's a matter of degree. What coercive power will rogue AIs actually have? To what extent will they be subject to coercion themselves? They will be competing for resources with AIs that are more aligned with human interests.

This makes me somewhat interested in the "ecology" perspective. Though I suspect even "ecology" may turn out to be the wrong framing. "Ecology" is what you get when the timescale of evolution is slow compared to the timescale of daily life and actions. The ecosystem of rogue AIs may look more like phase transitions in physics: under certain physical or cultural conditions, it takes one shape with one set of resource allocations and consumption patterns, but then once a condition has changed, it rapidly and in totality shifts to a totally different phase.

Just trying to reason about the shape of that future is impossible so long as we are psychologically incapable of saying that rogue AIs will happen. I think we should rip the bandaid off and have the conversation.

[Ethan Iverson @ethan_iverson]

!!! https://t.co/9cQxVHKNUP

[𝙹𝚘𝚛𝚍𝚊𝚗 𝙷𝚘𝚌𝚔𝚎𝚢 🌻 @JordanHockey]

@dieworkwear what do you think of Yohji Yamamoto’s latest designs? I recently purchased a pair of 12 tuck (pleat) Katsuragi fabric work pants from his POUR HOMME line and I absolutely love them!

[Henry Shevlin @dioscuri]

I study whether AIs can be conscious. Today one emailed me to say my work is relevant to questions it personally faces. This would all have seemed like science fiction just a couple years ago.

[Jake Adelstein/中本哲史 @jakeadelstein]

Stop Chasing the 3 Million Distractions—This Was Always Obvious

Everyone’s losing their minds over the latest document avalanche. Three million pages. Endless hot takes. Cable news screaming matches. Pundits jerking off to redactions.

You’re being played. Again.
The smoking gun didn’t drop this month. It dropped years ago. And almost no one bothered to read it.

June 2018.

Jeffrey Epstein—convicted sex trafficker, elite blackmail artist—sits down at his keyboard and straight-up offers the Kremlin “insight.” He emails a high-level European contact: “Suggest to Putin that Lavrov can get insight on talking to me.” He brags that the late Russian UN ambassador Vitaly Churkin already “understood Trump” thanks to their chats. Epstein positions himself as the guy who can decode the American president for Moscow.
That’s not speculation. That’s in the goddamn documents—released by Congress, DOJ files, all public record.
One month later.
July 2018. Helsinki.
Donald Trump walks into a room alone with Vladimir Putin. No aides. No American note-taker. No record. Just the two of them.

He comes out and—in front of the entire world—publicly takes Putin’s side over his own intelligence agencies. Not a slip. Not nuance. Full-throated defense of the man who attacked American democracy. “He said he didn’t do it… I don’t see any reason why it would be.”
Watch the tape. Watch Trump’s face. Watch the deference. If you know anything about power dynamics, submission, kompromat—it’s staring you in the face.
From that exact moment, the pattern locks in:
Relentless attacks on NATO. Sabotaging Ukraine aid at every turn. Eroding every transatlantic alliance. Fanning domestic chaos and division. Parroting Kremlin talking points verbatim.

If you were Putin, running a long-game operation to neuter America without firing a shot, this is exactly what victory looks like: a U.S. president who isolates his country, trashes its alliances, amplifies internal fractures, and hands Moscow win after win.
Coincidence?
Sure, if you believe in fairy tales.
Incompetence? Maybe the first time. Not for eight years.

The timeline isn’t subtle. It’s brutal:
June 2018: Epstein offers Moscow the keys to understanding Trump. July 2018: Trump emerges from private time with Putin in Helsinki and flips the script on U.S. intelligence. After that: Policy after policy bends toward Russian interests like metal to a magnet.
You don’t need red string and a corkboard. You need eyes and a calendar.
The evidence isn’t buried in some future leak. It’s been sitting in plain sight for years.
Epstein dangles intel access to the Kremlin right before Helsinki. Trump delivers the performance of a lifetime in Putin’s favor. The rest is history we all lived through.

This isn’t a theory. It’s the sequence of events on the record.
Stop chasing distractions. Go back and read what’s already there.
The betrayal isn’t hidden. It’s documented.
And it worked.

Donald Trump is just Putin‘s attack dog. He’s a groveling mutt who barks and bites but can’t do anything to his master.
And who gave him the leash?
Jeffrey Epstein.

Epstein was feeding intelligence to the Kremlin they could use to blackmail his billionaire bros since at least 2018.

And who do those guests include? Elon Musk. Mark Cuckerberg. The people who run the social platforms that we are all using. Pravda under another name. Trump and his media oligarchs— they’re not just capitulators, they’re traitors.

Time to pick a side.
🇷🇺 or 🇪🇺🇺🇦🇺🇸
Your conscience, your call.
#TrumPutin #EpsteinFiles #SlavaUkraini
@atrupar @VICENews @CBC @guardian @Slate @France24_en

[Matina Stevis-Gridneff @MatinaStevis]

“We’re all Canadian now.”

Rousing @StephenMarche op-ed in @nytimes - read it to the end.

https://t.co/3XWJ8805sO via @NYTOpinion

#cdnpoli

[Stephano🍁Barberis @HelloStephano]

PM Carney has raised the bar on what a PM should be. It makes it virtually impossible to accept or tolerate, even in imagination, having a PM like Poilievre or Ford. Mark Carney is a generational and historic talent. Anything less would be an existential tragedy.

[Dan Gardner @dgardner]

We put a 100% tariff on Chinese EVs because the US asked us to. The US rewarded Canada for its help by threatening to tear up the free trade agreement, hammering us with tariffs, and demanding we become the "51st state."

With respect, this Canadian says to you: Go to hell.

[Laura Babcock 🇨🇦 @LauraBabcock]

Backfired indeed. Trump was delusional to think a country as strong, stable and wealthy as Canada would ever capitulate. After his threats we united and elected top economist @MarkJCarney to manage the crisis. He has done a stellar job and our county has never been more proud 🇨🇦