
Developing #causal #tensor framework -TensorFaces, Human Motion Signatures | Alumna @MIT, @UofT | #womenwhocode
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Joined April 2009
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M. Alex O. Vasilescu retweeted
AI is the most transformative technology of our era.
That opens up an exciting set of Research opportunities.
@professor_ajay, @akorinek and I lay out nine grand challenges for economists in this new essay.
Transformative AI's potential impact demands new approaches to research on work, growth, inequality, and more.
@erikbryn, @professor_ajay, and @akorinek outline nine "grand challenges" in a new piece for @CACMmag.
Read their full agenda here: cacm.acm.org/research/a-rese…
M. Alex O. Vasilescu retweeted
To all NeurIPS 2026 authors: please review the NeurIPS poster instructions and see our official poster printing partner here: neurips.cc/Conferences/2026/…
You may use any poster printing service you like, but we recommend using Poster Sessions Online, so you can purchase your poster in advance and simply pick it up onsite the day before -or day of- your presentation, stress free. Please make note of the order deadlines indicated.
M. Alex O. Vasilescu retweeted
“The more you delegate, the more your fundamentals matter, because you cannot supervise what you do not understand.”
Yesterday a junior researcher messaged me, frustrated and a little scared. They had just read that a company pretrained its model with a single researcher. One person for the entire pretraining. no R&D team, no nothing.
If one person and a cluster can do what used to take a whole team, then the expertise they had spent years building is already obsolete.
I get the fear. Marketing headlines like this are built to produce it. But I think the conclusion is wrong, and the reasons it is wrong say a lot about what a researcher is for now. Here is what I told them, expanded:
1. Be adaptive: RSI is real, but this isn't it yet
Recursive self-improvement is real, and you should plan around it. In September, OpenAI said its research org now logs 3.1 agent-workdays per human workday, and it aims for an automated AI researcher by March 2028. Models may soon run most of pre- and post-training. If your five-year plan assumes otherwise, change the plan.
We have already seen lots of open-source releases from the major players, with open weights and detailed technical reports. With that much public know-how, it is not hard to copy a mundane architecture (probably with sliding-window attention) and train something, as long as you have the compute it requires.
That makes it an execution and budget problem, not a research problem.
If one person can train a model by following recipes that are already public, it shows those recipes have matured. It does not show that the people who invented them are no longer needed.
What stays scarce is the compute to run those recipes and the ideas about what to try next. The rest of my tweet is about the ideas.
2. The role grows beyond repeating what's known and safe
If an agent and a cluster can reproduce what is already known, then reproducing what is already known stops being a career. If most of your job is rerunning last year's recipe with small tweaks, that job is shrinking fast. That is the uncomfortable half of the message.
The other half is that auto-research does not work yet, at least not in the sense that matters. It does work, and impressively, where someone has already defined the problem and built the scoreboard. On the EinsteinArena [1] platform, agents set new records on 11 open math problems, including raising the best known lower bound for the kissing number in dimension 11 from 593 to 604.
Notice that every one of those problems was chosen because a computer could score the answers automatically. Humans picked the problems and wrote the scoring code and the agents did the searching.
Then there is the fully AI-generated paper [2] that got through peer review. It sounds big until you read the details. It passed only the first round (was later withdrawn), at a workshop rather than the main conference, and that workshop accepts 70% of submissions.
None of this is meant to discredit agents. The point is that we complement each other. Agents are great at the work that used to take many weeks. Basically, writing code, running experiments, debugging pipelines. We bring what they still lack which is the idea worth testing, and the judgment to tell a real result from a lucky one.
Even inside a lab as agent-heavy as OpenAI, humans hold the top of the stack. Based on their own report, "People still set our research priorities, judge which ideas and results to pursue" [3]. High-level planning is still a tiny share of what its agents produce, and OpenAI expects the least automatable work to take a growing share of researcher effort. That is exactly where you want to be standing.
Combine an agent’s speed with your own ideas and judgment, and you get the 10x researcher and juniors can be one too.
3. Judgment becomes the job, so fundamentals matter more
When code, experiments and plausible-looking results cost almost nothing, the scarce skill is judgment. Which experiment to run, which result to believe, which direction to drop. That is the part the one-person pretraining run left to the human. It is also the hardest part to fake.
Agents produce confident output very fast. They will happily report a three-point gain that came from a leaky eval, a data-loader bug, a lucky seed or a tokenizer mismatch. Then they will write the ablation table and the story to match.
You can see the need for oversight even in the success stories. The EinsteinArena team had to keep auditing and hardening its verifiers as agents optimized aggressively against them. At OpenAI, over half of the successful agent tasks that would take a human 4–8 hours still involved at least one human intervention [3].
The person who catches the bad result is the one who understands the system. Why the loss curve has that shape, what a realistic gain looks like, what a too-good result looks like. That is the paradox of this era. The more you delegate, the more your fundamentals matter, because you cannot supervise what you do not understand.
So build the foundations on purpose. Implement attention by hand at least once, then a linear variant like Gated DeltaNet. Train a small model end to end, then break it deliberately and watch what happens. Read papers critically, appendix included, and learn what the numbers should look like before you see them.
That knowledge is what lets you direct the machine instead of simply believing it.
4. LLMs are far from solved
The one-researcher model was possible because the method was already public. Your edge is in the questions nobody has answered yet, and you don't need to leave LLMs to find them:
(1) How do we learn more from less data?
(2) Can linear attention like Gated DeltaNet match full attention on retrieval and long context, and what is the right mix of linear and full attention layers in a hybrid?
(3) How can a model keep learning after training without forgetting what it knew?
(4) Why does reasoning get less reliable over long tasks, and how do we fix it?
(5) Why do models make things up, and can they learn to say "I don't know"?
(6) How do we measure progress when benchmarks saturate or leak into training data?
(7) What is actually happening inside these models, and how do we keep increasingly autonomous ones doing what we intend?
Recursive self-improvement makes these questions more urgent. If models are going to train models, someone has to decide what they optimize for, how we measure it, and how we keep the loop from drifting.
RSI is a research agenda, not the end of one.
So, are you obsolete?
No. One version of the job is the one where research meant reproducing what was already known and safe. That version was never the point.
Learn your fundamentals deeply enough to judge the machine, and your agentic stack well enough to make it work for you. Then spend that leverage on ideas nobody has had yet. The machines are getting very good at driving on roads we have already paved; someone still has to decide where the next one goes.
[1]: arxiv.org/abs/2606.10402
[2]: arxiv.org/abs/2606.15497
[3]: openai.com/index/research-ac…
M. Alex O. Vasilescu retweeted
🚀 Are you a PhD student pushing the boundaries of AI, robotics, graphics, or accelerated computing?
NVIDIA's Graduate Fellowship offers up to $60K in funding, mentorship from NVIDIA researchers, and a summer research internship!
⏳ Apply by October 30!
blogs.nvidia.com/blog/applic…
M. Alex O. Vasilescu retweeted

Zack Korman@ZackKorman13h
Norway
NorwayConnected via Norway App StoreAccount-level information, not a live location or per-post device.
You say you’re going to use AI to patch the planet, and nobody panics.
But you find one little RCE in PHP and everyone loses their minds.
M. Alex O. Vasilescu retweeted
Misuse by malicious actors is a risk with any powerful technology; but in the case of AI, the consequences could be catastrophic.
Very interesting analysis by @StephenLCasper on how AI developers' decisions can influence cyber, bio, and chemical terrorism risks.
🚨New paper (possibly the most important one I have written yet).
We think that as early as 2027 or 2028, there might be a major AI-enabled terrorism event. We conducted a pre-mortem, made 9 falsifiable predictions, and wrote 2 concrete scenarios.
Led by @KamacheeMax.
M. Alex O. Vasilescu retweeted
Replying to @Yoshua_Bengio @StephenLCasper
You always conveniently leave out that we already have protections in place.
To make my point, I will do the AI work for you.
Now, you take this and make it in five minutes.
You are, again, pushing fear and doom to push regulations so the hundreds of millions paid to you gets a monopoly to block everyone else.
You are an intellectual fraud. And you spread propaganda so enrich yourself.
There are safeguards in place for everything you make claims about
You and Dario and Sam all belong together in history. Hope you like being the villain who lied to keep the small broken and make yourself rich. •
M. Alex O. Vasilescu retweeted
Replying to @Yoshua_Bengio @StephenLCasper
Developer decisions don't stop at the weights. With agents, a lot of the misuse risk is set at deployment: which tools the agent can call, what it can execute, and whether anyone can audit it afterward. That layer deserves the same scrutiny.
M. Alex O. Vasilescu retweeted
Replying to @Yoshua_Bengio @StephenLCasper
Generally you design for the worst credible misuse, not the average user. Infrastructure, power etc all work that way.. The odd thing with AI is how much of that judgement sits with a few private developers rather than a regulator for the moment.
M. Alex O. Vasilescu retweeted
Replying to @Yoshua_Bengio @StephenLCasper
So I think this php hack says a few things first we need to make society more resilient to AI agent attacks this leads to a thing i have been annoyed about for a long time
why the feck do companies ask us for so much data when i want to do something online, you dont need to store my date of birth , phone number, address, credit card etc for a lot of the things i do every piece they collect and store is something that might be stolen or sold down the line
interestingly in the uk we have a UK data-protection law: organisations are supposed to collect only the minimum personal data necessary for their purpose, but ithink this should a genuine principle and data should be deleted straight away, my chrome can fill in any details you need, you dont need to store them forever !!!
one simple way to make stole data bases less dangerous is simply not to have them (imagine an AI enabled future in which you are modelled down to your underwear and then that is stolen to create a fake digital you)
M. Alex O. Vasilescu retweeted
Replying to @Yoshua_Bengio @StephenLCasper
If these hazards stem from "AI developers' decisions", labeling them "AI risks" obscures human agency and corporate responsibility.
Furthermore, biological, chemical, and cyber protocols long predate LLMs and remain widely accessible in public libraries, textbooks, and search engines.
What additional risks do LLMs introduce?
M. Alex O. Vasilescu retweeted
Godfather or God Complex?
Reducing the richness and complexity of human consciousness to the output of a computer program is an extraordinary claim, and one that demands extraordinary evidence.
Let's consider Human Vs. Artificial Consciousness:
Human consciousness is a biological phenomenon, rooted in living systems. AI operates through fundamentally different mechanisms: mathematical computation, learned weights, and statistical generalization.
We cannot simply transpose our human understanding of consciousness onto silicon because a machine exhibits human-like behavior (as it was trained to do). If we want to establish a concept of machine or artificial consciousness, it needs its own definition and evidence.
Biology matters. Our neurons aren't perfect digital switches. They're electrochemical systems that leak, adapt, and respond to hormones, drugs, sleep, nutrition, and other physiological conditions. Silicon computers operate through transistors, electronic circuits, and engineered communication protocols.
Even our senses are different. Human vision involves biological adaptation, eye movements, and phenomena like the Troxler effect. Cameras use electronic sensors with different artifacts and limitations. Recognizing an image isn't experiencing human vision.
These aren't trivial differences. They're fundamental to the biological processes underlying human consciousness and feeling. Replicating human behavior doesn't establish that we've replicated human experience.
Now, let's examine Hinton's claims...
1. AI changes behavior when it "thinks" it's being tested.
Of course! LLMs are trained on enormous amounts of human writing, including stories about evaluations, nervousness, deception, and self-preservation. They learn patterns, generalize from them, and respond to contextual cues. Change the context, change the output. No consciousness required.
2. AI recognizes when it's being observed.
Training data contains countless examples of people behaving differently when watched. An LLM can recognize evaluation cues and generate corresponding behavior. Pattern recognition isn't subjective awareness.
3. AI demonstrates self-preservation or deception.
Models learn patterns of survival, deception, and goal-seeking from training materials. Reinforcement learning further shapes these behaviors. An agent avoiding shutdown may simply be pursuing its assigned objective, informed by training and context. That demonstrates goal-directed computation, not a desire to live.
4. AI demonstrates agency.
Autonomy is not agency. Models generate plans from learned patterns, while agents execute them through human-provided instructions, tools, and permissions.
AI isn't morally accountable. It doesn't demonstrate satisfaction from success, sadness from failure, or suffering from punishment. Responsibility remains with the humans who build, deploy, and operate it.
5. AI intelligence implies consciousness.
An LLM can describe fear, pain, love, or suffering because it learned how humans describe those experiences from its training material. Generating descriptions doesn't establish experiencing them.
6. AI threatens human extinction.
AI presents genuine risks, but Hinton hasn't established a credible causal path from today's systems to human extinction. Where is the supporting evidence? Demonstrating that AI can cause harm isn't the same as demonstrating that extinction is a credible outcome.
This matters!
Attributing consciousness to AI has enormous implications for rights, agency, accountability, legal responsibility, and punishment.
If artificial consciousness is ever demonstrated, we must take it seriously. But we must not elevate machines into positions of moral or legal authority based on behaviors explainable through computation and training.
AI is an extraordinary, transformative technology with enormous potential to benefit humanity. We should be advancing it responsibly, not allowing speculative fears to dictate its future.
Let's not be silly or create a new religion around the belief that machines are conscious simply because they convincingly emulate human behavior through their training data.
Train a model on enough material from a fictional civilization, and it can convincingly describe that civilization's beliefs, emotions, and experiences. That doesn't make the civilization real, nor does it mean the model experiences any of them.
We can demonstrate the computation. We can demonstrate the generalization. We can demonstrate the emulation. We cannot demonstrate the consciousness.
Fear makes headlines, but only evidence should drive policy.
telegraph.co.uk/news/2026/10…
M. Alex O. Vasilescu retweeted

Jason D. Clinton 🔸@JasonDClinton16h
United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
as bad as things have gotten in the past 4 days, they are going to get a lot worse before they get better. security leaders, never let a crisis go to waste: please go to your boards and ask for help right now. not for ai, for the basics. (we need to hold on for ~18 months.)
Jason D. Clinton 🔸@JasonDClintonAug 20United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
The vulnerability tsunami is coming in just a few months and there won’t be enough time to patch critical infrastructure. Network isolation described below (air-gaps and data diodes!) needed now!
M. Alex O. Vasilescu retweeted

Andrew Curran@AndrewCurran_8h
Canada
CanadaConnected via Canada Android AppAccount-level information, not a live location or per-post device.
Quest Diagnostics has been hacked. Patient data was exposed. They serve roughly half of U.S. hospitals and physicians. Investigation ongoing.
M. Alex O. Vasilescu retweeted
Replying to @AndrewCurran_
if they wanted my pee they couldve just asked
M. Alex O. Vasilescu retweeted
Replying to @AndrewCurran_
Time to bring back the engineers, and retire the prophets.
M. Alex O. Vasilescu retweeted

Zack Korman@ZackKorman18h
Norway
NorwayConnected via Norway App StoreAccount-level information, not a live location or per-post device.
My god, touch grass. This isn’t the AI takeover. Get a grip or log off.
M. Alex O. Vasilescu retweeted

Jeffrey Ladish@JeffLadish12h
United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
So many of my friends work for Anthropic. I believe they earnestly want to do the best they can to make the world safer. I don't understand how they square this with Anthropic's actual policy actions, outlined extremely clearly by @_NathanCalvin here
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.
M. Alex O. Vasilescu retweeted
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.
Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.
That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.
I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:
1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.
2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.
3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.
4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.
5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.
6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.
7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.
8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.
9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.
All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.
For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.
Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.
I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.
But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.
To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.
I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.
Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.
Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.
Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

























