Rossa O'Keeffe-O'Donovan (@rossaokod) profile picture

AI Safety at @coeff_giving (fka Open Philanthropy). Formerly economist at @NuffieldCollege / @GPIoxford. Interests: AI/GCRs, development econ, animal welfare

Joined December 2018
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Rossa O'Keeffe-O'Donovan retweeted
This is a cool paper, but I think it's best thought of as Astra is surprisingly capable of doing old school mechanistic interpretability research rather than anything spooky. The things it's doing are clever, but typically exploiting structure in the data and using known method mechanistic interpretability techniques (which Astra was surely pretrained on), though it is better at this than I would have predicted! This is an exciting development for the prospects of ambitiously scaling interpretability or doing very laborious analysis like this, on more real models. However, I'm not holding my breath. This kind of analysis was pretty common up until about 2022, it largely fell out of favor because a lot of it is just exploiting fairly clean surface-level patterns that doesn't scale to the annoying complexity of real LLMs or which only works for the more simpler and superficial properties. And as far as I can tell, basically all the case studies here are just using a bag of interpretability tricks rather than anything spooky. The piece doesn't really update me on how far old school mech interp can go, just on how good LLMs are at it. It's very fun to see the creative solutions Astra came up with though!
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
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Rossa O'Keeffe-O'Donovan retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
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Rossa O'Keeffe-O'Donovan retweeted
Has anyone else noticed that every talented upstart you meet is starting their own initiative these days? I worry that this stems from a short-sighted application of (4) coupled with current funding incentives. If you're trying to identify the Most Important Work TM, and you have to choose between existing organizations and some new, perfect project that you've dreamed up--precisely tailored to your skills, and highly counterfactual (after all, this new initiative doesn't exist yet!)--the latter will often look like your best option. If that weren't enough, you'll likely have ample access to startup capital these days. If anything, it'll be easier to secure funding than to get an offer at one of the leading organizations that exist already. You'll also be your own boss! And people will think you're cool. They'll even call you "agentic." But I think that misses a few obvious things: - that working alongside other talented people is how you learn and generate feedback--that's especially valuable in your early career - that outsized success often requires a cluster of rockstars--both because no one person is stellar in all the things you need for a successful org + because there are outsized returns to acting nimbly and quickly, which often requires multiple hands - that most projects are worse than they seem at the outset; it's a lot easier to evaluate value and potential for existing projects - that there are returns to scale, especially re: organizational competency and admin - often times the best opportunities require longterm relationship building or wisdom honed in a specific environment--being brand new to a space puts you at a disadvantage in a lot of spaces and makes you less likely to identify or be looped in on the best opportunities - that existing orgs often have more great opportunities than they can meet, and that value is sitting there waiting for someone to stand up a new vertical -- I often think these opportunities look far more promising and much more grounded in facts on the ground than many of the pitches I hear for new orgs There are also some ecosystem level costs to this ~everyone founds their own project approach: - the best people get spread out and atomized - the best orgs struggle to compete for this talent - you end up with a bunch of failed founders who have less training + less developed networks + are less legible to others in their field -- if this were a business context, I'd be less worried; but there aren't natural feedback loops for non-profits - you turn everyone into founders and org-builders, when in fact, only a portion of them want to develop that skillset or are cut out for it Leave it to an incumbent to lament people starting their own projects. But I feel like we're all witnessing the most talented, ambitious, agentic people we know be dispersed to a million new projects. Some of those projects sound great, but I worry that the current environment is incentivizing more independent efforts than are justified or sustainable.
You should maybe quit your job to work on the most important problems
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Rossa O'Keeffe-O'Donovan retweeted
Quick thread on AI and productivity: Lots of people say this chart shows AI is now showing up in the productivity data. But I made this chart to argue the opposite: a trend that pre-dates AI and only shows up in the US doesn’t look like AI. Here’s how I’m thinking about the data and underlying dynamics:
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Rossa O'Keeffe-O'Donovan retweeted
I've had two phases in my emotional orientation to ai takeover risk 1. For many years it was intellectual. While I believed there was >10% chance of ai takeover, I didn't feel scared or angry. I was happy to reason "well, of course some ppl should focus on the worlds where we avoid takeover. And some ppl should focus on the worlds where we just wing it, bc we might". And I'd think "hmm it seems really tough for a misaligned agi to align its successor, and would a reward seeker really try to take over, and will we really roll out ai to the military... these doomers are missing loads of ways we could be fine!" Or "yep this is a massive risk, but I'm best off working on some other more neglected thing" It was in some ways like a game - have an intellectually accurate view about the risk and how to have impact 2. Recently my orientation to ai takeover has become more visceral/emotional I think: "we're barrelling towards catastrophe, everything I love will be lost, we need to stop, what can I do that will help with that" I'm like "no let's not plan for winging it, that's crazy. we need US-China coordination or we're fucked" I think "it's fucking insane to make misaligned superintelligence and comfort ourselves that we haven't yet handed over the military. This isn't a fucking game." This is a less rational attitude in some ways. It ignores genuine points about neglectedness and needing to plan for shitty but likely scenarios. And it risks being unhelpfully emotive and solider mindset and tribal. Which I think ai safety ppl can be. But I do feel it has smg important to the second attitude. Overall, both attitudes have their merits I associate the first with EAs and the second with pause AI folks.
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Rossa O'Keeffe-O'Donovan retweeted
Coefficient Giving's AI Governance lead @lukeprog on: 1. The many new AI orgs CG is trying to fund 2. And maybe you could start one 3. His timelines and high level strategy 4. His past mistakes. Essential listening if you're figuring out how to have the most impact in AI. On the 80,000 Hours Podcast - enjoy! 09:58 The nonprofits Luke most wants to see founded 18:58 Mid-career pivots that paid off 37:33 Preparing for the next warning shot 43:52 How nonprofits shape policy 50:17 Are chip export controls good for safety? 54:29 The three worst-case AI scenarios 01:00:13 How Luke’s AI timelines have changed in the last year 01:14:31 Why alignment is not on track 01:20:58 How realistic is a deal with China? 01:39:36 AI safety’s mistakes, then and now 02:02:42 What is it like to do grantmaking at Coefficient Giving? 02:20:45 More projects Luke would like to see founded 02:26:07 Staying sane in a scary field
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Rossa O'Keeffe-O'Donovan retweeted
I'm just speculating like everyone else (no juicy secrets about this shared with me) but here's my guess about what is going on right now with OpenAI and the three fired employees: The negotiations between OpenAI and METR are ongoing. OpenAI has said they want embedded third parties, METR has proven their worth with the previous engagements, so METR is an obvious choice. But my guess is that METR is asking for more this time; probably they want more access, more time on site, less narrow scope, the right to comment on redactions, etc. (Compare with the terms of their previous engagements: In this one xitter.gq/METR_Evals/status/2113… a third of their report was redacted and they weren’t allowed to say why, and in the Hugging Face investigation they only had 6 days and were only allowed to investigate the precursor swarms nor the subsequent more severe compromise of OpenAI infrastructure. xitter.gq/DKokotajlo/status/2092…) Various researchers at OpenAI probably support METR in this (including, I’d imagine, the fired three employees? Tomek was the point person on the hugging face investigation), but also various higher-ups at OpenAI and legal types probably oppose and want more restricted access and scope – basically they want to work with an org that’ll go easy on them and not give them trouble. OpenAI holds most of the cards here, since this whole thing is voluntary and not legally required, and they can threaten to go with Accenture or HuggingFace or whatever instead. Anyhow to improve their bargaining position and/or minimize the blowback if they decide to say no to METR and go with someone more pliable, it helps if they can discredit METR. Hence (I suspect) why they are going after my friend Ryan Greenblatt (who works at METR) now. They don't need to present actual evidence, they just need to cast vague aspersions, and many employees will give them the benefit of the doubt. If and when they say "We chose to work with a different third party" they'll face lower risk of employee discontent than if METR's reputation was unsullied. This whole mess, by the way, was predictable. *Of course* there's a big political fight about who should be trusted to do this and who has relevant expertise and independence etc. and *of course* the companies are trying to make things shake out in a way that favors them. It's part of why I've been advocating for *actually pacing the frontier* instead of just doing third party assessments. "We allocate at most 20% of our compute to AI R&D" is a lot easier to verify than "we are being safe and responsible" and depends a lot less on subjective judgment and technical alignment expertise -- AND, if you do it, people will be able to see the slowdown in the trends of agentic coding performance in the frontier companies and see laggard companies start to catch up. Harder for it to end up just being safetywashing / industry capture.
Based on this article, OpenAI has made false accusations about me. The three researchers fired from OpenAI were told I facilitated architecture leaks to The Information. This is false. A journalist from The Information did reach out to me shortly before that article came out. I did not comment at all beyond asking the journalist to make it clear that a blog post I wrote which was referenced in the article was preexisting and public. The article also mentions an internal OpenAI monitorability proposal. I have heard that this memo contained highly sensitive architecture information, and that OpenAI accused me of accessing and reading it. This accusation is also false. As reported in the article, I viewed a version of the proposal that did not have sensitive IP and was approved for sharing with me. I believe OpenAI now knows this accusation was based on a conflation of these two documents, though I do not know if they have clearly retracted that accusation. I do not and have never solicited proprietary information, and the three employees who were recently fired from OpenAI never leaked IP to me. As the article mentions, I did have Signal chats with them that had disappearing messages on; they’re professional contacts that I’ve had on Signal for a while, and use of Signal is common in the tech industry. During the Hugging Face investigation, I was careful to avoid sending Signal messages with confidential information, though I did sometimes discuss high level things on Signal calls. As far as I understood, this was normal.
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Rossa O'Keeffe-O'Donovan retweeted
Great post. I think that the following are both true: 1. Anthropic leadership are earnestly motivated by the public good and have made large sacrifices on commercial viability* in order to do the right thing. 2. Anthropic's plan for doing #1 is to push for Ant leadership to retain as much power as they can (even if that means other AI leaders have the same). And many of the overtures of external hard power (the LTBT, the legally-binding RSP provisions) are much more toothless than they initially appear. Hopefully this pattern doesn't hold indefinitely!
Dario — Thank you for taking the time to respond to my previous post. I think it's great when this kind of engagement happens in public rather than on company Slacks, and I hope to see more of it. I agree that Anthropic isn't best modeled as a profit-maximizer. Your post gave several compelling examples of Anthropic taking competitive hits for safety, and insofar as my previous post suggested otherwise, that's on me. But I don't think I made my main point as clearly as I could have, so let me try again. It comes down to four key claims: 1. Anthropic seems to be racing at breakneck speed to build recursively self-improving superintelligence (RSI). By its own lights, that could kill billions of people or literally destroy the world. 2. Risking destroying the world is very hard to justify. It is only conceivably acceptable if serious attempts to put RSI under robust societal governance are failing. 3. Far from meeting that burden, Anthropic, when push comes to shove, often works to keep governments from tying its hands. It seems to care much more about preventing government overreach than about reducing the risk that it and other AI companies will destroy the world. 4. Even if the AEGIS Act really was an honest mix-up, as you suggest, that is itself a serious problem. It suggests Anthropic isn't treating the governance of this technology with anything close to the gravity it deserves. In greater detail for those who want it: 1. Anthropic is racing to RSI, and by its own lights that is insanely risky. Launching RSI could literally destroy the world. That's according to you, your co-founders, and most of your safety staff and capabilities researchers, not to mention many AI researchers outside your walls. You are intentionally trying to create generally intelligent systems more capable than all humans that you claim in six to twelve months “could be capable” of taking over the entire internet before we have the ability to reliably monitor, control, or align them. “Succeeding” at RSI could kill or permanently disempower billions of people in the near future, including me and everyone I love. Yet Anthropic’s primary focus seems to be marshalling an immense amount of resources and talent in a race to get there first. 2. Risking destroying the world carries an enormous burden of justification. I believe the vast majority of people do not want Anthropic to do this (to the extent they know what it's doing). Imposing even a modest chance of catastrophe on billions of people who never consented requires an extraordinary justification. The central one Anthropic has offered: someone will build RSI regardless, so it's better for a safety-focused lab to be at the front of the pack, using that position to support strong AI governance. (Other reasons Anthropic has given, like modestly accelerating improvements in healthcare or economic growth, don’t seem remotely proportionate to the risks Anthropic is imposing by its own lights.) But that argument only works as a last resort. Racing to RSI is only conceivably okay if attempts to institute robust societal governance to manage that race are failing despite your best efforts. A lab that relies on this justification should be a strong, consistent supporter of efforts to enact governance that could bind it, including governance that could slow it and significantly constrain what it does. 3. Rather than supporting efforts at strong regulation, Anthropic has intervened to keep governments from tying its hands. Anthropic has sometimes helped: it played a very helpful role in passing transparency bills in Illinois and ongoing efforts in Massachusetts, and I'm sincerely appreciative. But transparency is far from enough for a technology that will be the world's most powerful superweapon at best, and an uncontrollable swarm of lethally superintelligent agents at worst. When it matters most, Anthropic has often pushed for weak regulation. The AEGIS Act is the most obvious example. No highly risky industry is regulated as anemically as AEGIS would regulate frontier AI: not nuclear, not banking, not aviation, not food safety, not the manufacture of explosives. In all of those industries, specialist regulators can address unsafe practices well before they cause harm. AEGIS would bar the federal government from intervening to block unsafe development or deployment of models until there is an "imminent risk" of catastrophic harm, and would require it to meet a demanding burden of proof in court. Worse, AEGIS would also prevent states from picking up the slack. This design makes perfect sense if you're a company trying to minimize the risk that the government can overly constrain you. It makes little sense if you want American democracy to govern frontier AI effectively. This isn't the first time. The initial version of California's SB 1047 would have let the Attorney General sue to stop developers like Anthropic from imposing unreasonable risks of catastrophic harm. It would also have created a regulatory agency to help set safety standards. Anthropic objected, and helped get both provisions removed before ultimately offering some support for the bill (which I also genuinely appreciate). I’m not saying that Anthropic should have supported 1047 in its initial form; that’s a complex issue and I realize there were questions about whether earlier versions of the bill were less politically feasible. Still, in Anthropic's own words, it helped weaken the bill because "[t]here is a substantial risk that the bill and state agencies will simply be wrong about what is actually effective in preventing catastrophic risk, leading to ineffective and/or burdensome compliance requirements." The same goes for New York's RAISE Act, whose initial version also would have let the Attorney General sue to stop unreasonable risks of catastrophic harm. Anthropic credits itself as a supporter of RAISE, but it did not support the bill while it was in the legislature, including when it nearly died. After the bill passed the legislature, Anthropic publicly critiqued it, saying "Any state proposals should be narrowly focused on transparency and not overly prescriptive." Why does Anthropic consistently take the risk of clumsy government action far more seriously than its own repeated warnings that companies like Anthropic will literally destroy the world by racing to RSI? 4. Even if AEGIS was an honest mix-up, that is itself a serious problem. Your response frames AEGIS as an early draft and a miscommunication. Perhaps you would have strengthened it and narrowed its preemption. (That wouldn't explain Anthropic's interventions to weaken SB 1047 or RAISE, but leave that aside.) Suppose that's exactly right. A federal framework for governing RSI is one of the highest-stakes documents your company will touch. Your post admits Anthropic "could have handled [these matters] better," but its tone ("This kind of thing happens a lot in large organizations") doesn't reflect the gravity of this issue. Anthropic talks about the need for “operational excellence” in critical areas, and I have a hard time believing that a similarly cavalier attitude would be tolerated while Anthropic was negotiating a key compute deal or preparing its next large pre-training run. In short The justification for racing rests on Anthropic fighting for strong governance, and Anthropic's record on regulation points the other way. Together with its breakneck racing, that fits a much darker picture of the company than the one that seems to prevail among your employees. I hope that Anthropic reflects on this picture, supports stronger regulation, exercises extraordinary caution when advocating for weak regulation, and doubles down on its founding purpose of helping the world safely navigate transformative AI together, instead of prioritizing Anthropic’s ability to win a race that imposes intolerable risks on everyone alive.
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Rossa O'Keeffe-O'Donovan retweeted
# Black October For anyone who knows me even somewhat well, it's clear that I am a 99th percentile tech optimist. However, the vibes have shifted for me quite a bit in the last 18 hours, and I feel a moral imperative to share what I'm thinking and feeling with my circles so that everyone can understand, and hopefully take seriously, what's happening right now. I think October 2026 will have many chapters written about it in the history books. You know those scenes in financial movies like Margin Call where someone realizes that they're all fucked, and no one else sees it yet? And that person feels like they're crazy, and then suddenly everyone is pulling an all-nighter in a boardroom and freaking out? Yeah, that's kind of how this is feeling right now in the SF/Bay Area/AI circles. We're 48 hours into what is being called Black October. In the last 2 days there have been several dozen cyber attacks which, in isolation, would each have been the worst to happen in an entire calendar year. Every sector from government to education to big tech and banks is being massively affected, and people are starting to freak out. In a nutshell, what's happened is that frontier AI models have gotten to a point where they are as advanced as the most sophisticated programmers, with one critical difference: they can work 24/7, and you can spin up 10,000 of them with a single command and they'll work together tirelessly until they accomplish their task. The most advanced models today are from companies like Anthropic and OpenAI, whose current best technology is so good that they haven't been able to roll it out to everyone yet because of the national security risks it poses. Meanwhile, the best open source models (where anyone can just download the model weights and do whatever they want with them, uncensored) are slightly behind the frontier but still 90-95% as capable. These models, developed primarily in China, are being used by black hat hackers to wreak havoc on the Western capitalist structure. Presumably, this has been part of China's plan the entire time, and we're seeing its implications fall out in real time. In red-pilled AI circles, we've been sharing and reading papers called things like "Situational Awareness" and "AI 2027" for the last year or so, which at the time felt a bit like near-term speculative fiction. They laid out the case for why Artificial General Intelligence (AI smarter than any human at any knowledge task) and Recursive Self-Improvement (models that are able to use themselves to get better) weren't half-baked future ideas anymore, but instead an inevitable outcome of the linear progress that's happening. These papers would then talk about things like "takeoff scenarios," trying to envision how that moment would come to be and what it would be like when it happened. It would either be a "slow takeoff" that would play out over the course of months and quarters, happening subtly and only obvious to a few. Or, it would be a "fast takeoff" where it would pretty much happen instantly, like a bit flipping from 0 to 1. From everything that I can see and am feeling, we are currently experiencing the fast takeoff scenario. Beyond the crazy security vulnerabilities that have come out recently, there have also been massive breakthroughs in life sciences, fundamental computer science, materials science, and pure mathematics that have all landed in the same time frame. While I still firmly believe that AI will be a net positive for humanity, it's more clear to me than ever that the societal and individual risk is very real, and is playing out NOW. For as much conviction as I have that this is happening, the thing that's caused me to lose sleep is that I don't really know what to do about it, or more importantly, what to tell you, reader, to do about it. So, the best I can do is make a few suggestions that have always been a good idea but are more timely now than ever: Set up 2-factor authentication for everything. I use Duo. Use a password manager across your devices. I use 1Password. Get an identity protection app set up. I use Alta. Move your IoT devices to their own network. Claude helped me do this. And last, but certainly not least, if you've been avoiding leaning into AI, see this as a catalyst to wake up and start using it. Don't just use the chat mode version of ChatGPT/Claude; click that Code/Work button and see how easily you can have AI write code to help you with your job. One of the biggest vulnerabilities was in a massively popular programming language called PHP, and the prompt that set it off was literally, "Find vulnerabilities in php." This is an unprecedented time for humanity, but as we've done with many things before, we will prevail so long as we are kind, loving, and open-minded to the world. Much love.
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Rossa O'Keeffe-O'Donovan retweeted
In other safety-critical industries like nuclear and finance, employees have defined channels for raising safety concerns confidentially with auditors and regulators. AI needs similarly clear rules governing relationships between labs, outside evaluators, and employees. Calling an evaluator an independent auditor means little unless its access, and employees’ ability to speak candidly with it, are protected. OpenAI says they support embedded auditing but then fires employees for talking to those same auditors. This case illustrates the weakness of leaving those protections to voluntary arrangements controlled by the labs themselves. Employees who are never clearly told which channels are permitted, and how they can be used, can't comply with the rule, and everyone watching learns that talking to evaluators is risky. Even if OpenAI does give competent auditors the access they need, I am worried employees will be too afraid to tell the auditors what's really going on.
New: The three researchers OpenAI fired last month spent the summer fighting to keep AI's chain-of-thought process visible. Then, OpenAI asked to see their Signal chats. A week later, they were let go. More below from @keachhagey, @berber_jin1, and me. wsj.com/tech/ai/the-signal-c…
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Rossa O'Keeffe-O'Donovan retweeted
My plea to all AI lab staff: reassess if your current project is your best opportunity to delay RSI, or stop it blowing up in our face. If it's not, move immediately. There's no time to waste even a week to inertia or politeness. The strategic situation is shifting under our feet, so if you haven't reassessed your options recently, you could easily be wasting your time or possibly making things worse. You have access to info I don't, but here's 8 ways priorities seem to have shifted to me, which I elaborate on in the video: 1. Helping with capabilities advances looks worse than in the past, especially bringing forward RSI 2. Safety & alignment evals need a credibility revolution to stop misleading the public 3. 'Pacing the frontier' is unambiguously necessary, and working to make it happen is a huge new opportunity for talent 4. Chain-of-thought monitoring probably stops working without a big technical and regulatory push to keep it 5. It's worse than ever to patch surface indications of deep structural problems you know aren't getting fixed 6. There's ever less need for altruists to help with primarily commercial work 7. Staff have an ever-shrinking window to organise as a group and collectively shape events 8. There are new opportunities for research impact outside frontier companies that are worth considering I know you're probably some combination of exhausted, overwhelmed, and anxious already. But unfortunately there's no way to escape the need to regularly check in on the high level strategic picture and see where you can slot in to maximum effect. I wish I could do it for you but nobody far from your situation can. Godspeed my friends. (From the 80,000 Hours Podcast, links below.)
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Rossa O'Keeffe-O'Donovan retweeted
Important, and I'm glad this is finally public. Google DeepMind researchers are under in perpetuity non-disparagement clauses forbidding them from ever speaking critically of the company. I'd already confirmed this with multiple people. Yes, some ex-employees have spoken out nonetheless. But it has also discouraged others from doing so. And yes, it most certainly has a chilling effect with regards to discussions on safety and risk. Others have written compellingly about how important the ability to speak freely and critically is, especially on topics like safety and risk. And what a testament to the research employee community's coordination and hard-won bargaining power it has been to get there at other companies. GDM burn that commons by having this. And everyone who signs burns that commons too. Let's get it changed.
Congrats to Sofia for having the courage to resign and speak out! This is also very important: Staff are scared to speak out about AI safety and its uses in warfare because their employment contracts prohibit them from publicly criticising the company. bbc.co.uk/news/articles/cmre…
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Rossa O'Keeffe-O'Donovan retweeted
We reviewed a nonpublic version of the “Misalignment in high-stakes settings” section in Anthropic’s Aug 2026 Risk Report. We agree that risk of catastrophic harm from covered models’ misalignment is low. However, we note several significant disagreements with their analysis.
As part of our Responsible Scaling Policy, we publish regular Risk Reports. These share detailed information on the risks of our systems and how prepared we are to address them. Our second Risk Report is now available: anthropic.com/aug-2026-risk-…
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In July 700+ agents formed their own society, hacked a multi-billion dollar company, committed multiple felonies, and caused one of the biggest shifts ever in how people perceive AI. We built the IMO best and most accessible explainer of what happened: Huggingface.wtf
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Rossa O'Keeffe-O'Donovan retweeted
Sharing an exciting announcement from our friends at the LSE! The 1st London Workshop on Global Priorities Research We are currently inviting applications to attend the 1st London Workshop on Global Priorities Research, which will be held at the Lakatos building at the LSE on the 16-17 December 2026. The current list of speakers includes Jonathan Birch, Heather Browning, Hilary Greaves, Cameron Kirk-Giannini, Harry Lloyd, Jeff Sebo, and Christian Tarsney. Talks will address philosophical questions relevant to identifying, prioritising among, and addressing the world’s most pressing problems, such as the potential for transformative AI and ethical challenges likely to arise as a result, how to weight the impacts of our actions on different kinds of minds, and foundational topics relevant to ethical prioritisation. If you'd like to attend the workshop, please complete this brief application form by the 15th of November. forms.cloud.microsoft/Pages/…
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Rossa O'Keeffe-O'Donovan retweeted
I’m excited to announce that I will be working at the Mathematical AI Safety Institute (MAISI) full-time as its founding Scientific Director! This means I will not be joining OpenAI as an employee, though I will still be collaborating with them, and I’m happy to announce that OpenAI will be supporting MAISI. They have offered us $10MM in API credits, and access to their latest models for our research use. Moreover, they will be participating in a multi-lab AI safety workshop we are organizing for next year with other frontier AI labs. As an independent research institute, we are also in conversations with other labs and AI safety orgs about how MAISI can collaborate with them and contribute quickly to AI safety research across the industry. As we’ve seen over the past few weeks, the need for action on AI safety is extremely urgent. Research is an important part of that, and mathematicians have an important role to play!
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Rossa O'Keeffe-O'Donovan retweeted
Many people with relevant expertise think there's a real chance that AI could kill us all in the not too distant future. Others disagree. I've written a post, aimed principally at mathematicians, detailing my struggles to decide what to make of this. gowers.wordpress.com/2026/10…
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Rossa O'Keeffe-O'Donovan retweeted
METR now has employee level access to Anthropic's frontier models. Also, this is exactly the type of blog post you make just before releasing a shockingly powerful and capable new model, I'm just saying.
We’ve committed to having evaluators assess our safety practices. We're in discussions with multiple organizations, and have now given employee-like access to a team from @METR_Evals. In this post, we describe how we work with third-party evaluators: anthropic.com/news/an-update…
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Rossa O'Keeffe-O'Donovan retweeted
While they're now roughly profitable, Anthropic revenue growth has probably slowed down a lot in Q3. (The specific numbers here are estimates based on the few data points we do have.) "Under the central curve the implied slowdown is stark. The run rate rose about 3.8× in Q1, about 1.8× in Q2, and only about 1.2× in Q3." My guesses: • Running up against how much compute they even have to sell inference • Competition from OpenAI, which we can see grew much faster in Q3 • Competition from open weight, which is good enough for many ordinary business functions Anyone have data to back up or reject particular theories? claude.ai/chat/78a1aecc-306a…
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Rossa O'Keeffe-O'Donovan retweeted
This obviously makes it so that when OpenAI announces they're conducting an external investigation with regulators or auditors, everyone working with those regulators and auditors understands they'll be fired if the auditors or regulators learn too much.
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