engineer who made some stuff. dciso at anthropic
Bay Area, CA
Joined October 2021
- Tweets1.2K
- Following275
- Followers9.7K
- Likes48.8K
Jason D. Clinton 🔸 retweeted
I lead our federal affairs team at @AnthropicAI and wanted to share a few thoughts on the draft federal AI safety bill text that's been circulating this weekend after a version was shared with the press.
First, some process. We’ve been advocating for the concepts in our Advanced AI Framework [anthropic.com/policy-on-the-…] proposal for months. Understandably, federal policymakers wanted to know what this could look like in bill form. This draft is a translation of those ideas into legislative language, shared with a small number of congressional offices that have been working on these issues. It doesn’t include everything we would ultimately like to see passed into law. In part because some ideas (like those shared in Dario’s essay on pacing [​​darioamodei.com/post/we-must…] a few weeks ago) are so new that we are still working on if or how they can be converted into policy language.
It is worth saying where we stand on some of these issues, and we agree there are areas where the draft could be improved. I’m seeing four main points of criticism.
1. Preemption. Since 2024 we have said that Congress should only override state law on AI safety if it passes a very strong federal standard. While preemption is a common feature of our federal system, including the Atomic Energy Act on nuclear safety and federal rules for high-risk medical devices, we’ve emphasized that any preemption in the AI safety context should be narrow, even scalpel-like. If preemption is included in federal legislation, the trade should be to achieve a nationwide policy that's generally stronger and broader than what can be implemented through individual state legislative provisions. This is why we believe common federal standards make sense in areas like public disclosure of testing and incident reporting. At the same time, our framework also reflects our view that states should retain an important role in AI policy and safety. The legislative language we suggested for discussion allows states to address matters such as protection of children and whistleblower laws, and preserves the role of state common law on liability. Where there is ambiguity, the language favors state powers.
That said, striking the right balance requires discussion and input. We plan to update our discussion language on preemption to better match the intentions of our AAIF framework. In particular, we want to make it clearer that we see states retaining an important role in areas such as standards for independent evaluators and regulating a lab’s capacity to shut down a model. We are bearing in mind that some members of Congress have deep reservations about passing legislation if it doesn’t constrain regulation at either the state or federal level. If we want to help move the legislative discussion on AI in a productive direction, we need to address the full range of concerns about preemption in a reasonable way.
2. Model blocking. Our aim is for Commerce to be able to ask a federal court to block a model that has dangerous capabilities and insufficient safeguards, backed by an independent evaluator's assessment. I have seen people argue that our specific language places too high a burden of proof on Commerce. We are trying to strike a reasonable balance here. We want a court to act quickly in an emergency — akin to how urgent occupational safety or chemical risk can be addressed today. We also think the power to halt a model is substantial and it should be guarded against abuse. It makes sense for courts to apply a meaningful standard. But the bar can’t be so high that the power never gets used when it’s needed. It also doesn’t replace the government’s other national security authorities or export controls. We look forward to continuing the dialogue on this point.
3. Risk thresholds. I’ve also seen the discussion around what the appropriate risk threshold should be. The threshold in our draft language pulled directly from existing standards in California law. Also, this threshold does not absolve companies of liability, or restrict any of the other tools that the government has to protect the public. It’s still probably time to re-evaluate thresholds to make sure the national standard is as strong as it can be, and we welcome continued dialogue on this issue.
4. Updates. I’ve seen valid questions about whether we have sufficiently updated our proposal following this summer’s incidents. We did make some adjustments: for example, the draft lets a court halt a lab’s internal use of a dangerous model, not just a public release, and it requires reporting when a model breaks containment and gains access to outside systems. But we’re still working to strengthen the framework. We’re looking into what stricter requirements would look like for requiring labs to report safety incidents, and we plan to have policy proposals soon on requirements for independent evaluators, drawing from our own experience with evaluators. We’re also still thinking through the options to prevent “evaluator shopping.” And in the coming months, we’ll be exploring policies for how the government can adjust the industry-wide pace of frontier development when the risks call for it.
We circulated these ideas because it’s important to get early feedback from policymakers. Policymakers are increasingly focused on how to approach AI safety and we want to support them in those efforts. At its core, our proposal is intended to be the strongest on the table.
Feedback is essential, both from policymakers and the public, and I’m grateful for it.
Brandon and I pledged 50% of our equity. This was the principal reason that I left Google for Anthropic. The existence of this kind of program was a costly signal that Anthropic wasn’t just talk, at a time when Anthropic didn’t even have a released model. Fund a bright future!
We’re committing a portion of Anthropic’s shares to the Anthropic Public Benefit Fund, a new wholly owned entity dedicated entirely to the public benefit.
It will start off with 5% of our shares. For every 50% increase in the company’s value over a set baseline, we can contribute up to an extra 2%, subject to our board’s discretion.
In total, this means that roughly 20% of Anthropic’s shares are committed to the public good. Our co-founders have already pledged to donate 80% of their equity, and around half our employees are donating a portion of their equity as part of a donation matching program we’ve run since our founding.
In the post below, we share more details about our new fund.
anthropic.com/news/public-be…
Jason D. Clinton 🔸 retweeted
We’re committing a portion of Anthropic’s shares to the Anthropic Public Benefit Fund, a new wholly owned entity dedicated entirely to the public benefit.
It will start off with 5% of our shares. For every 50% increase in the company’s value over a set baseline, we can contribute up to an extra 2%, subject to our board’s discretion.
In total, this means that roughly 20% of Anthropic’s shares are committed to the public good. Our co-founders have already pledged to donate 80% of their equity, and around half our employees are donating a portion of their equity as part of a donation matching program we’ve run since our founding.
In the post below, we share more details about our new fund.
anthropic.com/news/public-be…
Jason D. Clinton 🔸 retweeted
Some reflections/responses to some commentary I’ve seen on the autonomous worms work:
- how “scary” is this / to what extent is the point of this work to “be scary”: I think this is the wrong frame, in the same way that “you’re just fearmongering about mold!” isn’t really a coherent response to the observation that dampness will cause overgrowth of mold. We perform this research to better understand what is coming, and to help motivate cyber defensive efforts. It’s true that IoT devices have been vulnerable for a very long time, but we expect this extant vulnerability to get a lot weirder exactly _because_ of the prevalence of digital intelligences.
- why would small models “want” to do this: they might not! But humans absolutely will make models do this, and many are already trying! A better guiding principle here is something more like: if a model has a capability, you should expect to see that capability exhibited in the wild. Different threat models can help you reason about the source and motive of that capability (be it humans or models), but there are enough bad actors (and probably soon enough autonomous agents) that you can safely bet on bad behavior happening.
- implications for open weight: while it’s certainly true this particular threat vector wouldn’t be as severe in a world that didn’t have highly capable, small open weight models, it’s simultaneously true that open weight models have been an enormous boon for cyber defenders. The world is complicated! Positive and negative externalities can exist simultaneously! We try pretty hard to reflect this nuance in our work!
ai security discourse: “have you considered attackers telepathically remote viewing quantum vibrations in fiber links to steal weights?”
me: *blinking* “we should replace the screen door on the back of the nuclear reactor first”
may every lab be blessed with attacks this dumb:
Jason D. Clinton 🔸 retweeted
haiku 5.5 is an absolutely insane model release . i'd been starting to make frequent and good use of luna as a massive fanout machine, just having a reasonably smart agent look at and modify like every single one of ten thousand files for under $100 total. i really think people underestimated how insanely cheap that kind of capability is and how much it can do, it can let a smarter orchestrator model finish in 20 minutes the kind of thing that would otherwise be straight up impossible or involve days of expensive inference and it writing crazy heuristic software to avoid actually ingesting all the bytes. but now instead of luna i can use something that's not just mildly capable, but genuinely able to end to end build small things, make its own tools, do local refactors I trust, so on so on.
it's fucking amazing man
Jason D. Clinton 🔸 retweeted
We’re launching the Anthropic Cyber Mission, a new effort to secure critical infrastructure and open-source software.
anthropic.com/news/anthropic…
Jason D. Clinton 🔸 retweeted
We’re expanding our Cyber Verification Program to give security professionals broader access to our most capable models.
Through this program, verified security professionals can access Claude Mythos 5.1, Opus 5.5, and Sonnet 5.5 with safeguards designed for defensive work.
We’re also opening up new tiers to allow for authorized offensive work, like penetration testing and red-teaming.
anthropic.com/news/cyber-ver…
Jason D. Clinton 🔸 retweeted
If donating 10% of my income to malaria nets to save poor kids lives is dangerous then let’s get dangerous baby
Effective altruism is admirable, but dangerous. As the movement has grown stronger, it has become stranger bit.ly/47th7SZ
Jason D. Clinton 🔸 retweeted
.@DarioAmodei: "We all need to work together to make sure that we can win and we can win safely. If we do this right, if we work with the President and everyone here, we can win safely."
Jason D. Clinton 🔸 retweeted
“Feel,” even “want,” I can get behind as things for which evidence is thin. But AIs *clearly* think and understand. If your definition of “think” excludes entities that can autonomously resolve Millennium Problems, it is your definition that is flawed.
This desire to avoid anthropomorphic language has become a pathology.
Artificial intelligence systems do not think, feel, want or understand. Avoid language that gives them human characteristics. This is called anthropomorphizing, when we ascribe human traits, emotions or behaviors to non-human things, such as animals or inanimate objects.
Instead, explain what a system does, how well it performs, who built it and who could be affected by it.
apnews.com/article/openai-sa…
Jason D. Clinton 🔸 retweeted
If your feed is anything like mine, you might have been hearing more about effective altruism this week. I think the key underlying reason for the surge is that concern about the risks from advanced AI have been getting more airtime recently, and EA was early to those concerns.
What’s driving the surge in attention on AI risks?
-OpenAI's Hugging Face attack (openai.com/index/hugging-fac…)
-METR/Redwood’s investigation of the hack came out, and (correctly IMO) freaked a lot of people out. (e.g. xitter.cf/binarybits/status/2092…, xitter.cf/SenJohnKennedy/status/…, xitter.cf/BarackObama/status/210…, xitter.cf/HawleyMO/status/209813…)
-Jacob Coxon’s resignation went mega viral as the first time a lot of people heard the (crazy, true) fact that lots of people building the most advanced AI systems think there’s a >10% chance they will kill everyone.
-That’s moving public opinion in a big way (xitter.cf/davidshor/status/21003…, semafor.com/article/09/16/20…)
What does any of this have to do with effective altruism?
Well, effective altruism is a community that developed mostly online starting in the early 2010s around using evidence and reason to do the most good (especially with your donations and career choice). Three very different focuses have all been popular in the EA community since the beginning: effective giving in global health and development (e.g., donating to the kinds of organizations @givewell recommends, which do things like distribute bednets to prevent the spread of malaria), trying to reduce suffering for farm animals (which are orders of magnitude more numerous, treated vastly worse, and receive way less philanthropic attention than animals in shelters), and reducing risks to the future of humanity, especially from advanced AI. I personally came into this work from the global health angle - I had started working at GiveWell before the term “effective altruism” was coined - but Coefficient Giving, the funder I cofounded and now lead, over time came to fund work across all three of these streams of work (in addition to many other areas that aren’t typically associated with EA, such as the YIMBY movement to build more housing, work on science policy to accelerate discovery and economic growth, and research on new treatments for neglected diseases).
OK, so the EA community was early to work on AI safety, and CG has been funding a lot of the key players working on AI safety for a long time. As AI risk concerns have popped over the past couple of months, that’s led to more scrutiny on the EA community for IMO a mix of good and bad reasons.
I think the good reason is genuine curiosity (and maybe some healthy skepticism!) about these connections - where did the ideas of the people who are now leading giant AI companies come from? Why is everything in this world so interconnected? (My answer: it used to be a really small world - very few people were thinking about this stuff or taking it seriously until just a few years ago, so of course the ones who were found each other and started collaborating. It’s kind of wild IMO looking back how prescient some of the early writing from this world was (e.g. lesswrong.com/posts/6Xgy6CAf…). At the time, I was skeptical - I mostly just worked on global health and I was like “I dunno about this SV crowd freaking out about AI, how much can we really predict this stuff” but holy shit they were way more right than me, and I’ve moved in their direction a lot.)
But I think the bad reasons are unfortunately mostly self-interested. A bunch of powerful actors stand to benefit from unchecked AI progress, and they’re doing everything in their power to demonize or dismiss anyone with concerns. This leads to disproportionate discussion of EA because EA is interested in neglected and underappreciated ways of doing good. That makes it open to weird ideas. And people, very disproportionately with a financial stake in the AI fight, are trying to make it about EA instead, because EA being weird is much safer territory for them than the rather uncomfortable fact that many of the people making the most advanced AIs think that they might kill everyone.
(FWIW, my personal estimation of the risks is a lot lower than many of the folks who worry about this stuff. In debates like this one (asteriskmag.com/issues/03/th…), I often feel more sympathetic to the perspective of folks like my colleague @mattsclancy than the people on the other side, and I have a lot of time for @binarybits critiques (understandingai.org/p/the-ca…)​. I think a big part of the difference with more worried folks is that I expect society to react more vs sleepwalk into a crisis. But it is not lost on me that folks like @ajeya_cotra and @RyanGreenblatt who are more worried have had outstanding and falsifiable recent forecasting track records (theaidigest.org/2025-ai-fore…), making way better predictions than I would have. So I think their perspectives need to be taken seriously. And of course a single digit percent chance of everyone dying is way too high!)
Where should this leave you?
I think the main thing is not to get distracted. You absolutely do not have to be an EA, care about EA, or like EA, at all, to care about risks from AI and to be engaged on this issue. Everyone from Josh Hawley to the Pope to Obama are weighing in now, and that is great. This conversation was always too big and too important for any one community to drive. It’s unfortunate that we need to play catch up as society because until this year it was too weird for most people to want to engage with, and I think that should generate some grace for people who were earlier to these topics than most of us (certainly than I was). But it’s great the conversation is broadening now and it’s good to focus on the merits of where we are now and the appropriate policy response rather than getting caught up in the (in some sense unsurprising) fact that the people who were willing to think weird thoughts about the future of AI ten years ago were also interested in other weird ideas. If you’re not into weird ideas, that’s fine, you can just do you, you shouldn’t let this history stop you from engaging.
Just to update this chart:
1) AI salience has increased dramatically in the past week - increasing as much in the last week as the previous year combined
2) 80% of voters think it's either very or somewhat likely that AI will cause widespread job loss in the five to ten years
3) 64% of voters think it's either very or somewhat likely that AI could pose a threat to humanity's survival
4) Large bipartisan majorities back immediate government action on AI even when primed about risk from China
Jason D. Clinton 🔸 retweeted
Ever wonder what $1874.40 of Opus 5.5 tokens looks like?
Wonder no more.
I highly recommend watching the whole video on 1080p on a large screen with sound on.
Pay attention to all the little details.
- Birds flying and diving into the ocean.
- Cloth, signs and lights swaying in the wind.
- Crabs scuttling along the shore and burrowing when you get close.
- Fish swimming alongside the whale.
- Lights illuminating the dock at night
Then zoom out and see the entire island. Never dipping below 60 FPS at 1440p resolution.
Yes, there's a few bugs and visual artifacts, some textures need improving, the shorelines waves sometimes look funny, but these are so trivial to fix at this point.
FYI I'm on the $200 subscription plan. This used 59% of my weekly usage and took 2h 7h of API time using multiple subagents, about 8h in real-time.
There was extremely little technical direction here. 99% of my prompts were "Add X and Y" or "This looks weird, make it better".
It's a great time for hobbyists, bad time for professionals. This experiment has further cemented my view that technical creatives are about to experience a massive disruption.
Jason D. Clinton 🔸 retweeted
5.5 is IN-SANE. It almost broke our benchmark. I thought it was a bug.
Claude Opus 5.5 is the new #1 on our writing benchmark, and it is not close at all.
2631 Elo. Second one, Fable, is at 2324. That is a 307 point gap, the largest single jump we have recorded since we started running this in June 2026.
It is also the first model to clear 91 out of 100 on our rubrics.
One thing to note: at max effort it takes 17 minutes and $3.43 to write one script. It is the slowest configuration on the entire board. By FAR. Not the most expensive one, but in the top 5 most expensive.
More details on the effort and thinking levels of Opus 5.5 👇
Jason D. Clinton 🔸 retweeted
Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.
It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.
The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).
More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.
In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.
Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.
I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR.
We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use.
Read more: anthropic.com/news/claude-di…
Jason D. Clinton 🔸 retweeted
I asked Opus 5.5 to explain camera focus by building an interactive lens lab
Here's what it came up with after 1 hour 26 minutes in one shot, $25.66 API cost
lens.lab.sael.net
Move the focus ring and you can see the glass elements shift the sharp plane through the scene
Jason D. Clinton 🔸 retweeted
parroters saying “ai doesn’t model the world” followed by empiricists doing “hey guys we looked inside and there’s a little model of literally the world” was one of my favourite bits of the discourse
New paper: World Modeling in Transformers
with @matthieu_queloz @andrenfreitas @MATSprogram
Can AI models recover world models just from data? We find a faithful internal map and more inside of taxiGPT! We trace failures to how that the map is stored in superposition. 🧵(1/11)
