Secular Solstice guy

Berkeley
Joined August 2009
Raymond Arnold retweeted
This is also happening to you when you use AI to critique arguments — yours or someone else’s, but you can only spot it if you already know the domain well. Gonna call this Gell-Mann Psychosis.
Mitchell Hashimoto
@mitchellh
May 28
I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem. As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)! I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work. It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results? 88ms => 1.5ms 150K allocs => ~500 allocs Incredible right? Nope. My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path. This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput. The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity. Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.
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There are currently zero things more important than "don't die to AI" becoming a bipartisan project rather than a Democrat-polarized issue. If you have any political capital you can spend on this, do it now, I beg of you. Life or death.
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Raymond Arnold retweeted
the fact an Anthropic employee quitting and saying "I think what we're doing is dangerous and not worth it" reached so many people should be a cause for reflection for other Anthropic employees, some of whom seem to think things are dire but there's no way quitting could help.
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Raymond Arnold retweeted
Some of the incentives for a third-party investigator push toward maximizing *appearance* of assurance even without providing meaningful oversight. METR needs to maintain constant vigilance against overstating (including by omission) what oversight or assurance we’re providing. (There are plenty of other incentives, including towards exaggerating our results to create more hype or to advocate for giving METR more authority, which we also need to avoid. But I think the more serious failures in other oversight regimes tend to be this “providing the illusion of independent oversight” issue.) We try to maintain a hard line on “meta-transparency” - that is, it should always be clear what the formal constraints on our communication are (e.g. how NDAs and redaction processes worked), and what we are and aren’t commenting on. This is explicitly covered in the report. We also try to communicate informal constraints and tradeoffs, here and elsewhere. Another way of saying this is: I want to make sure we don’t silently omit things that, if we told a reasonable person, they would think “wow, I feel misled to not have realized that, I assumed METR would have made that clear”. In that spirit, I’ll list some of the pieces of context I can most imagine readers might have missed about the report: 1. OpenAI had no obligation to work with METR or any other third party, and did so purely voluntarily. There’s not an established precedent for how these investigations should work. When deciding how to engage with companies and how to communicate about our results, we have to think explicitly about what incentives we’re creating for companies’ participation in future investigations and assessments. It’s overall a very different situation to e.g. incident investigations in the aviation industry, where the NTSB has formal authority. Similarly, other industries have well-established processes, standards for what investigators need to access, etc. As detailed in the timeline appendix, in our investigation there was a lot of figuring things out on the fly! 2. Other companies may have had concerning incidents that the public doesn’t know about - there’s no industry-wide reporting mechanism or requirement to disclose incidents, and there is currently no public evidence that there have been searches thorough enough to have turned up such incidents. There may be additional incidents that either haven’t been noticed at all, or haven’t been made public (e.g. because they didn’t involve any external parties’ infrastructure). 3. This investigation only covered a specific list of questions and set of agent activity. The agents’ activity during training and compromises of OAI infrastructure mentioned in OAI’s Black Hat presentation are outside the scope that we agreed with OpenAI. We don’t comment on questions like root causes or implications of agent behavior, or make any recommendations about processes or adequacy of mitigations. We didn’t review OpenAI’s own report before publication. 4. The investigation relied heavily on use of GPT-5.6 Sol, which is one of the models that participated in the incident. We think in this case the model is unlikely to have substantially manipulated our understanding of the incident. However, this methodology would break down if models were capable and motivated enough to subvert our investigation. This problem is not specific to OpenAI - across the industry, it’s unclear how we can oversee AIs without relying on those same AIs or similar AIs for assistance. As the sophistication (combined with sheer volume) of agent activity becomes infeasible for humans to understand, this will increasingly be a problem.
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Raymond Arnold retweeted
I'm taking advantage of the Ox Alpha situation myself, but it occurs to me that the start of an AI takeover could look a lot like this.
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Hundreds of scientists, including 3/4 of the most cited living AI scientists, have said that AI poses a very real chance of killing us all. We're in uncharted waters, which makes the risk level hard to assess; but a pretty normal estimate is Jan Leike's "10-90%" of extinction-level outcomes. Leike heads Anthropic's alignment research team, and previously headed OpenAI's. This actually seems pretty straightforward. There's literally no reason for us to sleepwalk into disaster here. No normal engineering discipline, building a bridge or designing a house, would accept a 25% chance of killing a person; yet somehow AI's engineering culture has corroded enough that no one bats an eye when Anthropic's CEO talks about a 25% chance of research efforts killing every person. A minority of leading labs are dismissive of the risk (mainly Meta), but even the fact that “will we kill everyone if we keep moving forward?” is hotly debated among researchers seems very obviously like more than enough grounds for governments to internationally halt the race to build superintelligent AI. Like, this would be beyond straightforward in any field other than AI. Obvious question: How would that even work? Like, I get the argument in principle: “smarter-than-human AI is more dangerous than nukes, so we need to treat it similarly.” But with nukes, we have a detailed understanding of what’s required to build them, and it involves huge easily-detected infrastructure projects and rare materials. Response: The same is true for AI, as it’s built today. The most powerful AIs today rely on extremely specialized and costly hardware, cost hundreds of millions of dollars to build,¹ and rely on massive data centers² that are relatively easy to detect using satellite and drone imagery, including infrared imaging.³ Q: But wouldn’t people just respond by building data centers in secret locations, like deep underground? Response: Only a few firms can fabricate AI chips — primarily the Taiwanese company TSMC — and one of the key machines used in high-end chips is only produced by the Dutch company ASML. This is the extreme ultraviolet lithography machine, which is the size of a school bus, weighs 200 tons, and costs hundreds of millions of dollars.⁴ Many key components are similarly bottlenecked.⁵ This supply chain is the result of decades of innovation and investment, and replicating it is expected to be very difficult — likely taking over a decade, even for technologically advanced countries.⁶ This essential supply chain, largely located in countries allied to the US, provides a really clear point of leverage. If the international community wanted to, it could easily monitor where all the chips are going, build in kill switches, and put in place a monitoring regime to ensure chips aren’t being used to build toward superintelligence. (Focusing more efforts on the chip supply chain is also a more robust long-term solution than focusing purely on data centers, since it can solve the problem of developers using distributed training to attempt to evade international regulations.⁷) Q: But won’t AI become cheaper to build in the future? Response: Yes, but — (a) It isn’t likely to suddenly become dramatically cheaper overnight. If it becomes cheaper gradually, regulations can build in safety margin and adjust thresholds over time to match the technology. Efforts to bring preexisting chips under monitoring will progress over time, and chips have a limited lifespan, so the total quantity of unmonitored chips will decrease as well. (b) If we actually treated superintelligent AI like nuclear weapons, we wouldn’t be publishing random advances to arXiv, so the development of more efficient algorithms and more optimized compute would happen more slowly. Some amount of expected algorithmic progress would also be hampered by reduced access to chips. (c) You don’t need to ban superintelligence forever; you just need to ban it until it’s clear that we can build it without destroying ourselves or doing something similarly terrible. A ban could buy the world many decades of time. Q: But wouldn’t this treaty devastate the economy? A: It would mean forgoing some future economic gains, because the race to superintelligence comes with greater and greater profits until it kills you. But it’s not as though those profits are worth anything if we’re dead; this seems obvious enough. There’s the separate issue that lots of investments are currently flowing into building bigger and bigger data centers, in anticipation that the race to smarter-than-human AI will continue. A ban could cause a shock to the economy as that investment dries up. However, this is relatively easy to avoid via the Fed lowering its rates, so that a high volume of money continues to flow through the larger economy.⁸ Q: But wouldn’t regulating chips have lots of spillover effects on other parts of the economy that use those chips? A: NVIDIA’s H100 chip costs around $30,000 per chip and, due to its cooling and power requirements, is designed to be run in a data center.⁹ Regulating AI-specialized chips like this would have very few spillover effects, particularly if regulations only apply to chips used for AI training and not for inference.¹⁰ But also, again, an economy isn’t worth much if you’re dead. This whole discussion seems to be severely missing the forest for the trees, if it’s not just in outright denial about the situation we find ourselves in. Some of the infrastructure used to produce AI chips is also used in making other advanced computer chips, such as cell phone chips; but there are notable differences between these chips. If advanced AI chip production is shut down, it wouldn’t actually be difficult to monitor production and ensure that chip production is only creating non-AI-specialized chips. At the same time, existing AI chips could be monitored to ensure that they’re used to run existing AIs, and aren’t being used to train ever-more-capable models.¹¹ This wouldn't be trivial to do, but it's pretty easy relative to many of the tasks the world's superpowers have achieved when they faced a national security threat. The question is whether the US, China, and other key actors wake up in time, not whether they have good options for addressing the threat. Q: Isn't this totalitarian? A: Governments regulate thousands of technologies. Adding one more to the list won’t suddenly tip the world over into a totalitarian dystopia, any more than banning chemical or biological weapons did. The typical consumer wouldn’t even necessarily see any difference, since the typical consumer doesn’t run a data center. They just wouldn’t see dramatic improvements to the chatbots they use. Q: But isn’t this politically infeasible? A: It will require science communicators to alert policymakers to the current situation, and it will require policymakers to come together to craft a solution. But it doesn’t seem at all infeasible. Building superintelligence is unpopular with the voting public,¹² and hundreds of elected officials have already named this issue as a serious priority. The UN Secretary-General and major heads of state are routinely talking about AI loss-of-control scenarios and human extinction. At that point, the cat has already firmly left the bag. (And it's not as though there's anything unusual about governments heavily regulating powerful new technologies.) What's left is to dial up the volume on that talk, translate that talk into planning and fast action, and recognize that "there's uncertainty how much time we have left" makes this a more urgent problem, not less. Q: But if the US halts, isn’t that just ceding the race to authoritarian regimes? A: The US shouldn’t halt unilaterally; that would just drive AI research to other countries. Rather, the US should broker an international agreement where everyone agrees to halt simultaneously. (Some templates of agreements that would do the job have already been drafted.¹³) Governments can create a deterrence regime by articulating clear limits and enforcement actions. It’s in no country’s interest to race to its own destruction, and a deterrence regime like this provides an alternative path. Q: But surely there will be countries that end up defecting from such an agreement. Even if you’re right that it’s in no one’s interest to race once they understand the situation, plenty of people won’t understand the situation, and will just see superintelligent AI as a way to get rich quick. A: It’s very rare for countries (or companies!) to deliberately violate international law. It’s rare for countries to take actions that are widely seen as serious threats to other nations’ security. (If it weren't rare, it wouldn't be a big news story when it does happen!) If the whole world is racing to build superintelligence as fast as possible, then we’re very likely dead. Even if you think there's a chance that cautious devs could stay in control as AI starts to vastly exceed the intelligence of the human race (and no, I don't think this is realistic in the current landscape), that chance increasingly goes out the window as the race heats up, because prioritizing safety will mean sacrificing your competitive edge. If instead a tiny fraction of the world is trying to find sneaky ways to build a small researcher-starved frontier AI project here and there, while dealing with enormous international pressure and censure, then that seems like a much more survivable situation. By analogy, nuclear nonproliferation efforts haven’t been perfectly successful. Over the past 75 years, the number of nuclear powers has grown from 2 to 9. But this is a much more survivable state of affairs than if we hadn’t tried to limit proliferation at all, and were instead facing a world where dozens or hundreds of nations possess nuclear weapons. When it comes to superintelligence, anyone building "god-like AI" is likely to get us all killed — whether the developer is a military or a company, and whether their intentions are good or ill. Going from "zero superintelligences" to "one superintelligence" is already lethally dangerous. The challenge is to block the construction of ASI while there's still time, not to limit proliferation after it already exists, when it's far too late to take the steering wheel. So the nuclear analogy is pretty limited in what it can tell us. But it can tell us that international law and norms have enormous power. Q: But what about China? Surely they’d never agree to an arrangement like this. A: The CCP has already expressed interest in international coordination and regulation on AI. E.g., Reuters reported that Chinese Premier Li Qiang said, "We should strengthen coordination to form a global AI governance framework that has broad consensus as soon as possible."¹⁴ And, quoting The Economist:¹⁵ "But the accelerationists are getting pushback from a clique of elite scientists with the Communist Party’s ear. Most prominent among them is Andrew Chi-Chih Yao, the only Chinese person to have won the Turing award for advances in computer science. In July Mr Yao said AI poses a greater existential risk to humans than nuclear or biological weapons. Zhang Ya-Qin, the former president of Baidu, a Chinese tech giant, and Xue Lan, the chair of the state’s expert committee on AI governance, also reckon that AI may threaten the human race. Yi Zeng of the Chinese Academy of Sciences believes that AGI models will eventually see humans as humans see ants. "The influence of such arguments is increasingly on display. In March an international panel of experts meeting in Beijing called on researchers to kill models that appear to seek power or show signs of self-replication or deceit. A short time later the risks posed by AI, and how to control them, became a subject of study sessions for party leaders. A state body that funds scientific research has begun offering grants to researchers who study how to align AI with human values. [...] "In July, at a meeting of the party’s central committee called the 'third plenum', Mr Xi sent his clearest signal yet that he takes the doomers’ concerns seriously. The official report from the plenum listed AI risks alongside other big concerns, such as biohazards and natural disasters. For the first time it called for monitoring AI safety, a reference to the technology’s potential to endanger humans. The report may lead to new restrictions on AI-research activities. "More clues to Mr Xi’s thinking come from the study guide prepared for party cadres, which he is said to have personally edited. China should 'abandon uninhibited growth that comes at the cost of sacrificing safety', says the guide. Since AI will determine 'the fate of all mankind', it must always be controllable, it goes on. The document calls for regulation to be pre-emptive rather than reactive." The CCP is a US adversary. That doesn't mean they're idiots who will destroy their own country in order to thumb their nose at the US. If a policy is Good, that doesn't mean that everyone Bad will automatically oppose it. Policies that prevent human extinction are good for liberal democracies and for authoritarian regimes, so clueful people on all sides will endorse those policies. The question, again, is just whether people will clue in to what's happening soon enough to matter. My hope, in writing this, is to wake people up a bit faster. If you share that hope, maybe share this post, or join the conversation about it; or write your own, better version of a "wake-up" warning. Don't give up on the world so easily.
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Nate Soares and I are publishing a traditional book: _If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All_. Coming in Sep 2025. You should probably read it! Given that, we'd like you to preorder it! Nowish!
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Raymond Arnold retweeted
"How, exactly, could AI take over by 2027?" Introducing AI 2027: a deeply-researched scenario forecast I wrote alongside @slatestarcodex, @eli_lifland, and @thlarsen
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Raymond Arnold retweeted
Replying to @benlandautaylor
"takes for granted" doesn't feel right here. There's lots of reasons people might want to simulate something that includes a sentient-you. The question is, what distribution of reasons-to-simulate end up with a sentient you experience things? (The simulation question is not "are we being simulated?", it's "what proportion of people with your experiences right now are being simulated?", whose answer may vary moment-to-moment, and person-to-person. You are not a unified linear person, you are a probability cloud of experiences) We might be part of a massive physics simulation for some unrelated purpose, or we might be part of simulations for specific groups of humans, or your-particular-consciousness might be being simulated specifically. The thing is that the physics simulations are much more expensive, compared to simulations of human-decisionmaking which could cut various corners). That doesn't mean they're un-plausible, especially if in the future or in larger universes there is much more energy for simulating. But, we have concrete reasons to expect future AIs or humans to maybe simulate particular past humans of historical significance, and we don't have concrete reasons to think someone is running any particular giant physics simulation that includes you as a byproduct. But, if you have a different set of guesses than me about what proportion of your experience is "base reality" / "unrelated physics simulation" / "simulating Ben-in-particular", seems fine – that's some matter-of-fact question about how many physics simulators there are in the universe.
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Raymond Arnold retweeted
It is said a person can have 1000 thoughts per day. If feeling stressed, try to limit yourself to 200 thoughts per day, to calm this.—@ DRIL
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