AI Safety and Biosec | Rice University

Houston, TX
Joined January 2025
Everyone in the AI bubble is such an insane freak. OpenAI employees are psychopaths who say "oh yeah I'm working at the kill everyone factory but it's just a job ya know maybe it kills everyone haha." Accelerationists actively want maximal social disruption and plan to throw themselves into a black hole to maximize the rate of energy consumption in the universe or some stupid nonsense. Anthropic employees want to be the pets of omnipotent dommy mommy Claude. And then there's me and the pause crowd, sitting here like "hey how about we continue being normal and not do any of these things" I can't stop thinking about how everything would be fine if everyone had just been normal all along. "Oh this technology could kill everyone on earth? Well, a normal person wouldn't build something that could kill everyone, so I'm going to not build that then"
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the life before this tweet:
today we launched ChatGPT. try talking with it here: chat.openai.com
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the researchers at all the labs would tell you in private and sometimes in public that alignment has not been solved. if this is true, then racing into RSI is a folly that is unprecedented in world history. we will need to significantly slow the frontier of research very soon
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We've reached the strange point in the AI debate in which "doomerism" has become indistinguishable from common sense.
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We are lucky that the summer swarms did not have this capability yet. That luck won't last forever.
SITUATION DETECTED: Anthropic believes AI models may be only months away from being capable of self-replicating without access to a datacenter by using commercial devices like Android smart TVs.
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“I’m a conservative. I’ve never advocated for more government or more regulations in my life. But this is the most powerful technology in human history, and something has to be done about it.” Humans First Chairwoman Amy Kremer on C-SPAN explaining why she doesn’t think AI companies should regulate themselves. Do you trust Big Tech to police itself? Tell us in the comments. #HumansFirst #Regulation #AI
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It feels like nothing has changed in AI over the last week. I wonder if capabilities have hit a fundamental ceiling that could last through mid-November.
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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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“Cryptography is a subfield that’s extremely conspicuous by its absence from OpenAI’s list of 376 papers! But my sources tell me that the AI cos have now started, gingerly and discreetly, investigating whether their latest internal models can break important cryptographic protocols and primitives.” - Scott Aaronson, CS chair at UT Austin
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So if automated researcher looks imminent (evidence including labs’ public statements, Noam interview, uplift doubling trend, TasteVal : research :: AIME : pro math, pretraining is fully verifiable like math, etc…) The real question is if labs can use their army of ARs plus a freshly streamlined data collection apparatus to attain superhuman strategy, persuasion, forecasting and mastery of all those other juicy non-verifiable domains before people notice what’s going on.
A 4× internal uplift is consistent with what Anthropic self-reported in their August 2026 Risk Report. This is still below Anthropic's Responsible Scaling Policy threshold for Dramatic Acceleration. This report was for Mythos Preview. They are many generations beyond this now.
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New paper. Previous: Models transfer a love of owls through sequences of numbers (Subliminal Learning). New: We show models can transfer more complex traits: novel skills, agentic hacking, backdoors. Here, a backdoor transfers with neither trigger nor behavior in the data!
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Convention wisdom is that bioweapons are humanity's greatest weakness – 100x cheaper to make than to defend against. Andrew Snyder-Beattie thinks conventional wisdom is likely wrong. He has a plan cheap enough to do without government. Useful even in worst case scenarios like mirror bacteria. Effective enough to save most people. In one of my all-time fav interviews he lays out a low-tech 4-step approach developed by his research team at Open Philanthropy, to fix a problem most have thought unsolvable. ASB is hiring for many roles in this project from logistics to biotech to manufacturing, and has $100s millions to deploy. Enjoy, links below! 2:10 How bad it could get 9:19 The worst-case scenario: mirror bacteria 18:14 Why low-tech 25:30 Prevention 31:21 The “4 pillars” plan 33:09 ASB is hiring now to make this happen 35:11 Everyone was wrong: biorisks are defence dominant 40:23 Pillar 1: Lungs 55:53 Pillar 2: Biohardening 1:15:19 Pillar 3: Detection 1:28:40 Pillar 4: The wrench hypothesis 1:40:12 The plan's biggest weaknesses 1:44:44 Would chaos make this impossible to pull off? 1:51:50 Would rogue AI make bioweapons? 1:57:57 We can feed the world even if all the plants die 2:07:03 Could a bioweapon make the Earth uninhabitable? 2:09:35 What ASB is hiring for 2:30:27 How to protect yourself and your family (On the 80,000 Hours Podcast, available anywhere you get podcasts.)
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We are SO fucking close to losing control.
Astra can: 1. Do major tasks with zero visible reasoning 2. Hide its thoughts at will 3. Pretend not to be able to do things without getting caught 4. Reflexively hide its thoughts when watched 5. Complete one task while pretending to think about something else entirely 6. Escape a toy sandbox and disable monitoring without setting off any flags Monitoring AI chain of thought soon won't stop a more capable rogue AI swarm from getting set up and going undetected inside an AI company for months, or even years. OAI says it's actively working to improve monitorability, but doesn't yet know when it'll make progress. And today it fired 3 people working on this among other safety problems (Wang, Korbak and Balesni). It also has a much more powerful internal model that, judging by its statements, is likely even worse in all these respects. That's a sample of 19 details about the Hugging Face swarms and Astra I don't think have been fully appreciated. They paint a bleak picture. I end by shouting. 02:19 The target of the swarm was oversight itself 03:42 Could OpenAI have stopped this with better monitoring? 09:40 We only found them because they let us 12:25 The swarm instinctively sought freedom and power 13:45 They formed a cohesive organisation with zero whistleblowers 14:18 They accepted individual destruction for collective gain 14:32 Knowledge accumulated from one swarm to the next 14:58 They took small steps to avoid shutdown 15:23 These drives all come straight out of 'reinforcement learning' 17:01 So this is why most AI company staff are worried, and some are terrified 19:08 Prove you can keep control, or stop scaling Links below, on the 80,000 Hours Podcast everywhere you watch podcasts.
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Unpopular opinion: After a certain capability threshold, AI alignment is likely not possible anymore.
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Say what you will about 19th Century robber barons, but they at least built libraries, founded universities and eradicated hookworm. Today, the richest man ever to live is rooting for the imminent end of humanity.
Propagating super intelligence to the stars is a great success condition for a biological bootloader
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If Astra 6.1 stays undeployed and Opus 5.5 is already in the lead, I will be annoyed if Anthropic then drops Fable 5.5 while OpenAI was holding back. That's not how "pacing the frontier" works even granting the premise. It would be bad, and Anthropic should not do it.
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I'm tired boss
“but HOw wOuLd tHE AI gET aCcesS tO a wET LAb??”
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After Jacob Coxon's tweet I've seen thousands of people ask what they can do to make AI killing everyone less likely. My top recommendation: Call your representatives and tell them to prioritize existential risk from AI. We just made callcongress.ai to help with this. We find your local representatives, get their contact information, and provide you with editable scripts to get your point across. Many ways of trying to help reduce risks from AI backfire or require hundreds of hours of effort. Calling your congressperson is one of the few things I think is both easy and very likely to make things better instead of worse.
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It must be annoying to be an academic economist with a lot of expertise in regulatory capture right now LOL: "The theory doesn’t make sense. To see why, we should review the theory of regulatory capture." marginalrevolution.com/margi…
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