Quick Takes

"About thirteen or fourteen years ago, when I was still an academic, I interviewed for a [psychology] faculty position at a fancy university in Canada. [...] There were four applicants, each hosted by a different committee member. I had a grand time with my host. He was very candid about the whole process. At one point he told me that after the candidates had all come and gone, the committee members would go into a small room, sit down, and proceed to argue with one another for several hours over utterly ridiculous things each candidate said or did, until eventually everyone was worn out and just settled for whichever candidate they were all able to barely tolerate." "I asked why the committee didn't just apply the lessons of seventy years of industrial-organizational psychology: come up ahead of time with a scoring rubric; have everyone independently fill it out without talking to each other; then mechanically aggregate the scores and offer the job to the person with the biggest number." "He said (and he was right), “well, obviously, we don't believe our own methods.”" "You could try to write it off as just a dysfunctional department, but… it wasn’t. They’re all like that. I’ve never actually seen a psychology department that hires people the way that I-O psychology tells us you should hire people. A friend once told me that the University of Minnesota did use to do it that way, for a bit, in the days when Paul Meehl was still walking around the halls. I don’t know if that’s true, but I do know that Paul Meehl died in 2003. I interviewed at UMN in 2012, and they sure as shit weren’t doing it that way then." "Anyway, there is no deep moral here, only some sadness. Not for the rejections (there are always rejections, and I did just fine, academic job-wise), but because this is just what we’re like! We’re all so bad at this. We talk a good game about science and data and whatever, but as soon as any of that stuff impinges on our personal preferences, we immediatel

Applications are now open for TARA Round 2 2026!  Apply by July 26th to join our 14-week APAC-based technical AI safety program (using the ARENA curriculum), designed to accelerate your path to meaningful technical alignment research. TARA is built for you to learn around full-time work or study. You'll attend in-person sessions in your home city on Saturdays and do independent study throughout the week. You'll finish the program with a project to add to your portfolio, key technical AI safety skills, and connections across APAC. In this Round we are targeting Melbourne, Sydney, Brisbane, Tokyo, Manila, Singapore, Taipei, Hong Kong, Bangalore, Delhi, Hyderabad and Auckland. Which cities run will depend on where applicant demand and strength are highest. Our results so far: → 90% likelihood-to-recommend → 95% very or extremely satisfied with program management → 91% more likely to pursue a career in AI safety → 60% of surveyed 2025 graduates secured a fellowship or AI safety role within a year (9 of 15 tracked)  Learn more about TARA and apply through our website here.

Given the U.S. government's recent restrictions on Claude Mythos and GPT-5.6, I suspect one of the most tractable and effective strategies for further pushing the Overton window towards the urgency of implementing stronger AI regulation could be effectively communicating to policymakers that open-weights models, whose safeguards can be removed easily, are likely going to reach Mythos-level capabilities in under a year, which can meaningfully uplift cyberattacks and bioweapon development. Also, I suspect the main thing currently holding back the Overton window on interventions for AI x-risk in general to be a (perceived) lack of concrete evidence (see: The Milton Friedman Model of Policy Change, how we achieved the Montreal Protocol to mitigate climate change before the damage became irreversible despite strong industry pressure against it, etc.). Communicating loss of control risk would be ideal of course, but I'm currently unsure how this could be demonstrated safely.

We’re changing our name: Sequent is now Resolution. Two weeks ago, we launched Sequent, a new alignment research organization working on a portfolio of ASI safety bets. Following our announcement, we discovered ⊢ Sequent, a formal verification startup launched only about a month earlier! And by a friend and former colleague, no less! After discussing it with the other ⊢ Sequent, we decided that the best course of action was to change our name. This brings us to Resolution (resolution.org). Go check out Sequent! They're going to do exciting things with formal verification too. And go check our new website! We've just posted new positions and opened our first hiring round. http://resolution.org/careers Of course, no name would be complete without fun stories! We have two: 1. Resolution as in the resolution algorithm in first-order logic, one of the foundational methods of generating proofs. 2. Resolution as in resolution of singularities: any singular variety can be reparameterized into a nonsingular form for easier analysis. Which is also a metaphor for the AI Singularity: let's try to the untangle that limit into forms that can be analyzed more rigorously.

Based on a constellation of evidence spread across both public information and some private rumors that I find credible, I now believe that unreleased frontier models across multiple companies as of late 2025 and 2026 are substantially more reward-hacky, more locally strategically deceptive, and overall mundanely misaligned than either prior private models or publicly accessible ones. I further believe that these models are/were regularly used in internal deployment, not just evals and testing, because they're sufficiently *useful* and *capable* despite the misalignment.  I do not believe the models I'm centrally talking about are helpful-only models (which I think are essentially completely unusable because I imagine they'd be even more mundanely misaligned), just insufficiently tuned ones. I’m not sure how many of them are checkpoints that are directly upstream of the current batch of barely-released frontier models (Fable, GPT 5.6 Sol) vs different lineages. I suspect both. I do not have a clear understanding of why this occurs. An obvious theory is that this is the natural outcome for “going harder” on RLVR and other forms of direct reward than past post-training schemes, which convergently causes local misalignment at similar times across companies. Another (less likely imo but perhaps more worrying) theory is that this is the natural convergent outcome of training models to a certain size, without more safeguards to tamp down. A number of other more speculative hypotheses springs up as well. The misalignment appears local. I am not aware of direct evidence of schemers (in the strict Carlsmith sense) or playing the training game.  I am unsure about the downstream safety and takeover risk implications. Without cross-episode or long-term horizon scheming it doesn't seem like a takeover risk. METR also points out our ability to detect this form of mundane misalignment is reassuring. However I find myself moderately pessimistic that these mundanely misaligned

From Joseph Mitchell's 1944 article in the New Yorker titled "Thirty-Two Rats from Casablanca", an account of how New York City worked to prevent an outbreak of the Black Death. [...] I posted this substantially because I deeply admire that they did this reliably for twenty-two years before finding an actual case. Though I'll note that I don't believe that it is the right call not to inform people that there is high-alert for an outbreak of a deadly plague.

If you told an EA from 15 years ago that a non-negligible number of EAs were sending their charitable donations to (specific candidates fielded by) the US Democratic Party, they'd assume something had gone very wrong. (I know the obvious answer to the above is to gesture at the state of US politics, and/or the state of AI development, and say "why yes, something has gone very wrong, I'm glad you noticed!". And I don't have an answer to that answer. Even so, it seems like it might be worth someone writing the above paragraph somewhere people can see; so I did.)