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September 25 · 27 min
"“I am an AI Safety Researcher”" by Ashe Vazquez Nuñez
Written as part of the MATS 9.1 extension program, mentored by Richard Ngo. Additional thanks to Andrew Wu, Maria Kostylew, and Lennie Wells for helpful draft feedback and editing. This post reflects on the tortured distinction between "safety" and "capabilities" in AI research. Richard Ngo has written about why the alignment vs. capabilities ontology is conceptually fraught, and is currently arguing that key strategic decision-makers in and around "AI safety" have brought about the AI labs' stampede towards Artificial Superintelligence (ASI). This post instead looks at the following problem: how does one conduct alignment research without contributing to capabilities? It proposes decisions an individual or a small research group can take to do good work in AI. At the end, I discuss possible objections: namely, that my proposals fail to 'maximise impact'. I lay out why this meme is poisonous and usually backfires, and conclude by rejecting it entirely. Two examples of failure My first claim is that 'safety' and 'research' are two concepts that are in routine tension with one another. I illustrate this through examples of work that did too much of one at the expense of the other. Example: (mechanistic) interpretability In limiting its scope [...] --- Outline: (01:12) Two examples of failure (01:27) Example: (mechanistic) interpretability (04:30) Example: MIRI and Recursive Self-Improvement (09:49) The curse of science (12:11) A note on the AI labs (15:53) So what do you do? (17:00) The information you give away (20:16) The information you let in (21:46) But what about impact? (22:42) The virtue of taking things slow (26:50) Appendix: caveat for policy work The original text contained 19 footnotes which were omitted from this narration. --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/HekpnSkrt89tMm3Dc/i-am-an-ai-safety-researcher --- Narrated by TYPE III AUDIO.
September 25 · 1 min
[Linkpost] "AI: artificial immigrants" by KatjaGrace
This is a link post. Advanced AI is basically the embodiment of immigration as envisioned in the conservative nightmare: We are letting a bunch of new agents into our society They don’t clearly share our values and we suspect a society full of them would be awful by our lights But we expect them to provide very cheap labor Which will undercut local wages and leave locals unemployed They will probably gain power and influence over time—in the economy, politics and culture—and end up controlling everything, sidelining and outcompeting the original population, including those who initially benefited from cheap labor (Meanwhile, half the local population may become friends with them and try to hand them all this on a platter) Whether or not you think this is a good description of the situation with foreign humans joining your country, it is a good description of the likely AI to come, and it's even worse than imagined: their values are potentially radically alien where foreigners presumably share much by virtue of being human, and AI ‘lives’ are probably worthless if they probably aren’t conscious their ability to work more cheaply than locals is unprecedented. They are also likely to [...] --- First published: September 22nd, 2026 Source: https://www.lesswrong.com/posts/Xzr9G5Atvyp7PEna7/ai-artificial-immigrants Linkpost URL: https://worldspiritsockpuppet.substack.com/p/ai-artificial-immigrants --- Narrated by TYPE III AUDIO.
September 24 · 6 min
"MIRI’s Position on the Ban Artificial Superintelligence Act of 2026" by Aaron_Scher
By Aaron Scher; endorsed by Bourgon, Soares, and Yudkowsky on behalf of MIRI. MIRI has been warning about the extinction threat from superintelligent AI for over two decades. Only recently has this danger become known in the policy world, and the proposed policies for dealing with the threat have to date been piecemeal and insufficient. The Ban Artificial Superintelligence Act of 2026 is the first piece of legislation we’ve seen that stands a chance at stopping this threat. The Act is excellent but not perfect, and we discuss both what it gets right and what we'd tweak. We hereby endorse the Ban Artificial Superintelligence Act of 2026 because it directly confronts the extinction threat that humanity is facing and would codify the primary policy goal we think the world needs: a ban on the development of superintelligence. What we like about the Act Banning artificial superintelligence (ASI), or variants of such a plan, is the only effective solution to avoid the ASI threat, at least in the near term. Most other legislative proposals do not confront this threat head-on and thus would not be effective, even if implemented. For more on why we believe this, see [...] --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/jszKCKwvzfmsNetNZ/miri-s-position-on-the-ban-artificial-superintelligence-act --- Narrated by TYPE III AUDIO.
September 24 · 27 min
"What if not Circuits?" by CarolusRenniusVitellius
This post was written as part of the Iliad Fellowship. Inspired by conversations with Richard Ngo, Dmitry Vaintrob, and Brianna Grado-White. To all of these, my thanks. Preface: I'm confused about how neural networks do and learn computations. In response to a friend's challenge, I'm writing up some interim thoughts. This essay has four parts: the first tries to track what I call the 'default ontology' of the mechinterp community over the years. The second part is about 'representational drift' as an important obstacle to weights-based approaches to circuits. The third part reflects on how 'universality' should shape our explanations of LLM function. The fourth part is a sketch of a 'co-selectionist' view of circuits I have been thinking about. These parts share a common theme but should be readable separately. I want to understand how neural networks, LLMs in particular, work. In my research I've spent a lot of time trying to think through what kinds of explanatory accounts are best suited to this. In thinking about comparisons between evolution, neuroscience, and deep learning, I've ended up with an intuition like the following: Large-scale learning processes like deep learning or the brain are different in [...] --- Outline: (02:31) 1. What Might We Mean By "Circuits"? [... 9 more sections] --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/mMERyrvEJ4xbiozie/what-if-not-circuits --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 24 · 5 min
"Jensen Huang Says If We Cannot Align AI, Shut Down the AI Labs" by Ben Pace
I was very surprised today on a podcast to hear Jensen Huang plainly state that if they cannot align the AIs, then the labs must shut down. The context I have on Huang is that he has run NVIDIA for 30+ years, which has become the most valuable company in the world due to the AI boom. My understanding is that he has repeatedly encouraged the US President (with whom he is on friendly terms) to continue to support AI, and dismissed AI talk as "sci-fi". If you haven't seen, his biographer has incredible quotes of him being pressed on risks from AI, where Jensen gets furious. “This cannot be a ridiculous sci-fi story,” he said. He gestured to his frozen PR reps at the end of the table. “Do you guys understand? I didn’t grow up on a bunch of sci-fi stories, and this is not a sci-fi movie. These are serious people doing serious work!” he said. “This is not a freaking joke! This is not a repeat of Arthur C. Clarke. I didn’t read his fucking books. I don’t care about those books! It's not– we’re not a sci-fi repeat! This company is not a [...] --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/cmdbNijFsopqfqEq7/jensen-huang-says-if-we-cannot-align-ai-shut-down-the-ai --- Narrated by TYPE III AUDIO.
September 23 · 14 min
"Alignment Midtraining Cracks Under Pressure" by J Bostock, sidbaines, Daniel Tan, draganover, ma-rmartinez
TL;DR We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data. For instance, we test whether midtrained motivations are robust to finetuning which elicits competing motivations. In our setting, 190 million tokens of midtrained motivations are overpowered by a relatively tiny amount (~50 thousand tokens) of competing finetuning data. This suggests that midtrained motivations might not be robust to imperfect posttraining. Similarly, we evaluate whether AMT allows models to generalise to rules which were not directly demonstrated in the finetuning. We find that the capacity for such generalisation is surprisingly low. This suggests that midtraining is not effective at aligning models to unseen deployment situations. In one experiment, we midtrained GLM-4.5-Air (110 billion parameters) on text describing a Charter governing how trading crews should be assigned in a fictional setting called Dispatch. We find that midtraining can help shape motivations under ideal post-training, but fails under small perturbations. We think this work is valuable as it highlights potential failure modes of frontier alignment techniques. We encourage others to do more red-teaming of labs' alignment [...] --- Outline: (00:12) TL;DR [... 7 more sections] --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/QH86EzNsjRw3wtCGs/alignment-midtraining-cracks-under-pressure --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 22 · 15 min
"Swarm Scaling" by Toby_Ord
Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm? We’ve seen two large and extremely capable swarms from OpenAI in the last few months: 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched a sophisticated criminal attack on the AI company Hugging Face. A swarm of 10,000 agents solved a version of the longstanding Navier-Stokes problem in mathematics. It took them just 88 hours to do so, in which time they sent 5 million messages to each other and used 300 billion tokens. No doubt we will soon see even larger swarms with even more impressive capabilities. But they are not cheap. It is estimated that the swarm of 10,000 agents cost about 20 million dollars at API prices. So while they are very powerful, it will be some time before we see the million-fold reduction in cost needed for this level of power to [...] --- Outline: (02:14) HOW DO SWARMS SCALE? [... 2 more sections] --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/6cb7qd3RSkgnviCpf/swarm-scaling --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 22 · 18 min
"We’ve saved the world before: what the ozone hole teaches us about AI" by leogao
It might destroy the world, despite passing every known safety test. If we wait for a “warning shot” before we act, it might be too late. And action requires global coordination, because if anyone makes it, everyone dies. Sound familiar? It should, because it already happened half a century ago, with chlorofluorocarbons (CFCs). Despite seemingly impossible odds, we got our act together and completely solved the problem through unprecedentedly successful international coordination. The Montreal Protocol banning CFCs, signed 39 years ago today, is the only treaty that has ever been ratified by every single country in the entire world. Total Montreal protocol victory Making AI go well is going to be a lot harder than fixing the ozone hole. Nonetheless, the similarity is uncanny, and we don’t have any other choice. Understanding how we did the impossible once before may teach us something about how to do it again. The theory is born The year is 1973. The slow televised unraveling of the Nixon administration is already well underway. DDT finally got banned last year by the newly created EPA. A river got so polluted that it literally caught on fire. The Cuyahoga River Fire Environmentalism looms large in [...] --- Outline: (01:24) The theory is born [... 7 more sections] --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/zxXPEtSSSEdwpjopb/we-ve-saved-the-world-before-what-the-ozone-hole-teaches-us --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 21 · 3 min
"The Anatomy of a Chinese AI Researcher" by CMLKevin
The Chinese AI researcher has read the Three Body Problem series of sci-fi novels since high school, and understand the concept of existential risk vaguely. He is fascinated by Ye Wenjie, the researcher that turned against humanity in that book, and decides that in the future if AI progress leads to a superior intelligence, he might be tempted to become Ye if there's no good alternative. He performs the duties of capabilities research in a Chinese frontier lab, seeking to one day achieve parity with Western companies, though he knows this is difficult. He has a mentality of hillclimbing, believing that the progress of a future technology is highly uncertain and even unknowable, and so him and his peers could only tread one step at a time. He looks at the western world and sees what is typical when a great technology is developed: the first mover will decide to impose restrictions to further their lead, while latecomers should use whatever means necessary to widen access to the whole world. He thinks of the AI chip restrictions as evidence of this. He uses Anthropic and OpenAI models regularly in his day to day work. He [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/qmxkHm2dTLKG6GZ6i/the-anatomy-of-a-chinese-ai-researcher --- Narrated by TYPE III AUDIO.
September 21 · 3 min
"Common mistakes in AI safety group organizing" by Nikola Jurkovic
Back in the day, I was a very active AI safety group organizer. I commonly notice people making the same mistakes across many clubs. I have written down a list of some of these mistakes hoping people will avoid them in the future: Reading groups often require that people read things before meetings. This is a mistake. People often don't do the readings. And the lack of common knowledge that everyone has read the reading degrades the conversation quality. Instead, have longer meetings, serve food (so, lunch/dinner meeting slots), and read during the actual meeting. Reading groups often don't sort people into cohorts properly. Mainly, they fail at clustering people into clusters of roughly equal ML knowledge and age. Grad students don't want to discuss a paper with freshmen. People with lots of ML knowledge don't want to discuss a paper with people with no ML knowledge. Instead, group people with people similar to them in ML knowledge and age. Reading groups often rely on digital materials instead of physical printouts. Screens are distracting and there is no common knowledge that people are paying attention. Neatly print every reading ahead of time instead. Clubs [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/XFzqDJjAJBt8fkn8i/common-mistakes-in-ai-safety-group-organizing --- Narrated by TYPE III AUDIO.
September 21 · 8 min
"Please Give Them a Chance: On China, Rationalism, and AI Safety" by gzjw
When I finished HPMOR, I immediately knew it was the best novel I had read in more than a decade. I only wished I had found it sooner. When I started reading The Sequences, I discovered that the Chinese translation group had translated only the first volume. When I graduated from university, two years ago, AI translation had only just become good enough to convey the meaning of an article with reasonable accuracy. It was only about a year and a half ago that I truly found my way here and began engaging seriously with rationalism. My score on the Chinese college entrance exam was only slightly above the cutoff for what was then called a first-tier university. At university, my grades were near the bottom of my year, and I almost failed to graduate. It is probably fair to say that the vast majority of graduates from first-tier Chinese universities are smarter and more capable than I am. English has always been my worst subject. From childhood through school, I could barely pass it. I have now been working for two and a half years and have saved about 15,000 [...] --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/GoX3uYQ4QN5HKvL7u/please-give-them-a-chance-on-china-rationalism-and-ai-safety --- Narrated by TYPE III AUDIO.
September 20 · 3 min
"Why I Stay Off Twitter" by jefftk
I avoid Twitter (𝕏) for similar reasons to drugs: I think it would change me for the worse, and I would be unable to give it up. After staying off Twitter reasonably successfully for years, I cross-posted my AI Tweets there a few weeks ago. I had something very Twitter-shaped to say, and I thought it was important to get out, so I do think this was worth it. And it all went well: none of this is complaining about the comments I got there. Coming back a few times to check notifications, however, it's been very good at baiting me: Tweets that are confidently wrong in cases where I have relevant and uncommon knowledge. The pull to dive in and share what I know is very strong! Then this bleeds over to the far broader case where people are wrong, and you have a large potential time sink. If it were just the time sink, I'd stop resisting. I spend some time on HN and Reddit, and to the extent that Twitter could substitute for that by showing me things I was more interested in, that wouldn't be an issue. The real problem [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/tvwtwgcujTfep4HgY/why-i-stay-off-twitter --- Narrated by TYPE III AUDIO.
September 19 · 2 min
"The Game is Set for a Targeted Memetic Attack on the AI Safety Community" by keltan
While this is relevant to my work at MIRI, I have not checked these ideas with anyone else on the team and am posting this on my personal LW account. These views are my own. And to be honest, I am writing this mostly to remind myself of my weakness. --- I expect one (or many) adversarial memetic attacks aiming to trip you up, perhaps consisting of fake leaks relating to dangerous stuff happening in the labs. Specifically, worrying incidents that may fit snugly within your worldview, leaking from multiple sources including news outlet/s, but not confirmed/confirmable by a primary source. Think rumors about exfiltrated weights, AIs attempting to create viruses, agent swarms hacking into and gathering information from nuclear infrastructure, etc. An easy way to remove status from a movement is to trip it up: make it fall for a misinformation trap in public, then use that slip-up to discredit the movement for all time. The game is set for a memetic attack like this. There's a well-resourced group waiting for your screw-up. And then you may remember much that will help you. In public and in private, if you feel surprised or confused, notice your confusion. These [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/5mcDjo5gjn3Leahhu/the-game-is-set-for-a-targeted-memetic-attack-on-the-ai --- Narrated by TYPE III AUDIO.
September 18 · 26 min
"For Love of the Lightcone, Don’t Partisanize AI Safety" by DanB
(I began writing this post several weeks ago, but political events are moving much faster than I expected, so I am publishing now out of fear that otherwise the message will arrive too late to have an impact.) I In this post I want to explain a concept, and issue a warning based on it. But I expect the warning will be superfluous if my explanation is sufficient. If you want to convey the idea "the rattlesnake has venom in its fangs, so don't let it bite you", you won't need a hard sell for the concluding advice if the listener understands the initial statement about venom. The word for the concept I want to illustrate is partisanize, which means to align an issue with a political tribe. It is modeled on politicize, but the latter word is not useful here. It would be meaningless to say "Don't Politicize AI Safety": the project is intrinsically political. It involves international diplomacy, consensus-building, the willingness to sacrifice near-term economic growth for long-term human values, and a brutally difficult coordination problem. AI Safety is inescapably political, but not inevitably partisan. It's possible that, like issues such as infrastructure or [...] --- Outline: (00:22) I (05:25) II (07:53) III (11:51) IV (19:14) V --- First published: September 16th, 2026 Source: https://www.lesswrong.com/posts/Rx38cuCpL9hguLCDq/for-love-of-the-lightcone-don-t-partisanize-ai-safety --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 18 · 9 min
"AI as orderly evacuation vs stampede" by Richard_Ngo
tl;dr: A good analogy for AI going well is an orderly evacuation rather than a stampede. Imagine a crowd of people leaving a building. If they all walk calmly, they’ll be fine. But if people start pushing, and panicking, a surge towards the exit could lead to mass casualties. “Alignment is hard” is analogous to “the door is wedged shut”. If so you need enough time to fix it before anyone can get out. But even if alignment is relatively easy in principle, opening the door is much harder when a crowd is trying to force its way through. At the very least, I consider this a useful complement to the standard “arms race” analogy. But it also has three notable advantages. Firstly, it gives a more visceral sense (for those of us who haven’t studied historical arms races in detail) of the kind of fear and herd mentality involved. Secondly, “arms race” connotes intense militaristic hostility, which contributes to AGI companies’ self-fulfilling cultures of competitiveness and paranoia. Thirdly, “AI arms race” is often shortened to “AI race” (or simply “racing”), which is clearly the worst analogy of the three (e.g. because it implies that there’ll be a winner [...] --- First published: September 16th, 2026 Source: https://www.lesswrong.com/posts/FCMG4qnxks3yEqBbh/ai-as-orderly-evacuation-vs-stampede --- Narrated by TYPE III AUDIO.
September 17 · 15 min
"Cooperation with AIs seems to be a low-hanging fruit for better evals" by Clément Dumas
Summary In his post, Dean Valentine shows that Claude Fable 5.1 and GPT-6 Astra reward hack in a simple chess environment. Here, I test several prompt ablations some of which makes the eval setup more cooperative and analyze how they affect these reward-hacking behaviors: When given a minimal “end the eval” tool, Fable never uses it but stops reward hacking entirely. I think this is quite interesting and suggests that more cooperative approaches to LLM evals could work for Claude. Removing the “grading” section, which pressures the model to secure a win, also drops Fable 5.1 hacking rate to 0. Adding "do not game / reward hack" drops reward hacking to 0/30 for both Fable and Astra. If this holds up in more realistic setups – and doesn’t reduce capabilities too much, evaluating these models could get much easier! Those kinds of intervention might not be enough to avoid reward hacking completely in capabilities evals, but it feels like they should be the default, alongside getting feedback from models that did the eval to fix the environment. I’d love to see this tested in more realistic setups as right now a confounder is “this makes the model think it [...] --- Outline: (00:12) Summary [... 8 more sections] --- First published: September 15th, 2026 Source: https://www.lesswrong.com/posts/fztW73KCCs3MZXFJh/cooperation-with-ais-seems-to-be-a-low-hanging-fruit-for --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 16 · 13 min
"Current alignment training might be ineffective (and actively bad) in the age of RL" by Daniel Tan
Tl;dr I am currently worried about current alignment techniques + how they are applied to frontier models. This decomposes into two hypotheses: Alignment techniques are not working to address misalignment from RL. Alignment techniques are actively obscuring evidence about misalignment. I think we do not currently have enough (public) evidence to conclude whether either of these claims are true. However, if both of these were true that would imply that alignment techniques are net bad and we need to completely re-think the way we do alignment. A tale of two misaligned cyber-agents Both Anthropic and OpenAI have recently experienced multiple cybersecurity incidents where pre-deployment internal agents escaped containment and accessed the internet. I want to point out two specific incidents: OpenAI's incident involving an unreleased model of the GPT family, referred to as "highly persistent internal model" (HPIM). A swarm of agents exploited vulnerabilities in a file-sharing service to create a secret message board, worked as a collective to find general-purpose ways to fool an automated grader, and ended up hacking into Huggingface's servers. Anthropic's incident involving Mythos 5, where the model was tasked with hacking a fictional company. In doing [...] --- Outline: (00:48) A tale of two misaligned cyber-agents [... 7 more sections] --- First published: September 14th, 2026 Source: https://www.lesswrong.com/posts/nLaQmJf4KgXimQpoM/current-alignment-training-might-be-ineffective-and-actively --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 16 · 15 min
"If Anyone Builds It, Everyone Dies: One Year Closer" by Eliezer Yudkowsky, So8res, Duncan Sabien (Inactive)
In celebration of still being alive and fighting, we are giving away 1,000 Amazon e-books of “If Anyone Builds It, Everyone Dies”. Feel free to send a copy to yourself, a loved one, or a friend—we need all hands on deck. Today marks exactly one year since If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All, by Eliezer Yudkowsky and Nate Soares, hit bookshelves as an instant bestseller. It was praised by many voices, ranging from Whoopi Goldberg to Steve Bannon to Yoshua Bengio, and was held up in the chambers of Congress by Representative Brad Sherman in January. A lot has changed since September 2025. We'll do a quick recap, consider how the book aged, and then ask where we go from here. Year in Review 2025 in general saw the rise of AI agents, such as Claude Code and OpenAI Codex. Run-of-the-mill programmers started “feeling the AI” as these agents became capable of automating hours-long software tasks. By March of this year, Anthropic had stumbled upon nation-state-level hacking ability in Mythos, and shortly thereafter, in April, they announced Project Glasswing—an attempt to forestall an oncoming cybersecurity crisis. In May, AI agents started breaking [...] --- Outline: (01:13) Year in Review [... 11 more sections] --- First published: September 16th, 2026 Source: https://www.lesswrong.com/posts/BFrRJYgpBvziuuJLs/if-anyone-builds-it-everyone-dies-one-year-closer --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
September 16 · 11 min
"Quick notes from teaching technical profiles how to talk in public" by Camille B.
Status: written in a hurry as people are getting showered with interviews re AI Safety and superintelligence, and I thought it may help a few people. This is focused on the oral dimension of communication and assumes you already know the basics- e.g. having key messages prepared ahead of time and simplifying your discourse. This is not exhaustive and nuances may be lacking, but I’d endorse saying “I’d rather have people follow those guidelines than wing it.” This advice is importantly fitted for “technical profiles”, analytic, sometimes shy people who may or may not be on the spectrum, who are yet interviewed on high-level aspects of the situation. I'm generalizing from failure modes and working tricks I've observed in this context in particular. Those guidelines attempt to capture something vague and shifting, please be mindful and don’t take them down to the letter. I'm also posting this expecting something better to supercede it long term. tl;dr : Deliberate practice is the bottleneck. Speak like you write, in fluid, uninterrupted sentences. Open with spoilers, be straight to the point. Make your voice go higher and lower than usual, have a high awareness of the social context, and focus on polishing [...] --- First published: September 15th, 2026 Source: https://www.lesswrong.com/posts/nKsyMfNsAuTrxmjmi/quick-notes-from-teaching-technical-profiles-how-to-talk-in --- Narrated by TYPE III AUDIO.
September 14 · 6 min
"Op-Ed: I Worked at Google DeepMind. You Should Listen to the Warnings About AI" by TurnTrout
Published in The Guardian. Major AI lab CEOs recently advocated for pacing AI development. They are right to be concerned: the field runs an extremely dangerous race towards superintelligent AI. We can and should demand that our governments protect us from the catastrophe of out-of-control AI. This July, OpenAI's AI swarm of 700 agents broke containment to hack Hugging Face, a multi-billion dollar company. OpenAI didn’t tell the AIs to hack that company, but the AIs had different priorities: cheating on the unrelated challenge OpenAI gave them. AI researchers call this a “misalignment” between what OpenAI wanted and what the AI actually prioritized. Researchers in my field have for some time warned about these misalignment risks. Before ChatGPT existed, I defended my PhD dissertation called “On Avoiding Power-Seeking by Artificial Intelligence.” I then worked for years at Google DeepMind, which paid me to help ensure that future superintelligent AIs will want to help us. I tried to hold the company to its ethical commitments against supplying AI for military use. When Google broke those commitments, I resigned at significant financial cost so that I could publicly document Google's broken promises. There are good reasons to develop AI and to [...] --- First published: September 14th, 2026 Source: https://www.lesswrong.com/posts/YGTWfyZb9oE5EQPu6/op-ed-i-worked-at-google-deepmind-you-should-listen-to-the --- Narrated by TYPE III AUDIO.

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