That feeling when everyone around you starts ‘speaking chatgptenese’ and saying ‘verify’ and ‘ensure’ and every sentence has a conclusion but is vague as hell.
I haven't started mimicking LLMs' speaking patterns[1], but I did notice a new reflexive urge to issue commands to people, especially if the interaction is online. It's not a strong urge, and easy to notice and suppress, but yeah.
One thing she is discussing at the 03:36:51 mark is "What METR isn't doing that other people have to step up and do".
In this sense, METR's "Measuring AI Ability to Complete Long Tasks" is not done often enough.
They have done that evaluation within "Details about METR’s preliminary evaluation of o3 and o4-mini", https://metr.github.io/autonomy-evals-guide/openai-o3-report/on April 16 (an eternity these days) and the results are consistent with the beginning of a super-exponential trend. But there has been no evaluations like that since, and we have had Claude 4 and remarkable Gemini-2.5-Pro updates, and it seems crucial to understand what is going on with that potentially super-exponential trend and how rapidly the doubling period seems to shrink (if it actually shrinks).
So, perhaps, we should start asking who could help in this sense...
I think that I can give some clarity here: it is a use case / user story issue combined with frog in the increasingly hot water.
I have seen AI do amazing things for other people. In my own use case, it is below not useful and into actively harmful: using AI makes my output worse in every situation. If I did not have access to other people's use cases, I too would be unimpressed. And when people in my fields (mostly government) try to use AI and it fails, they are updating their priors about every attempted tech rollout in the past 20 years making things worse not better. So they see videos and articles about AI working and they are reinforcing "media is over hyping something that will get used in other fields but not ours again, just like the 'paperless' revolution that gave us twice as much work and yet we still use typewriters and faxes".
By contrast, you are exposed to the areas where it is working. You understand the context of what those changes mean and how important they are, and how they are accelerating.
The exact same news is updating you in the direction of "this is amazing and fast" and them on "this is the same b.s. we've seen a dozen times and never amounts to anything real".
At some point, it will hit their field in a way they feel is a big improvement (not a miniscule increase in temperature style improvement), and they will be completely shocked and not understand how they got where they are.
I have tried to explain to the ones I work with and I can watch them mentally moving me away from "trusted expert on technology" over to "cultlike evangelist of vaporware" as I speak.
AISI is being rebranded highly non-confusingly as CAISI. Is it the end of AISI and a huge disaster, or a tactical renaming to calm certain people down? Hard to tell. It could go either way. Sometimes you need to target the people who call things ‘big beautiful bill,’ and hey, the bill in question is indeed big. It contains multitudes.
The AI world also contains multitudes. We got Cursor 1.0, time to get coding.
On a personal note, this was the week of LessOnline, which was predictably great. I am sad that I could not stay longer, but as we all know, duty calls. Back to the grind.
Jon Stokes: People speak of the “AI hype bubble”, but there is also a large & growing “AI cope bubble” that will at some point pop & leave much devastation in its wake. Example:
Jonathan Blow (the example): Reminder: this is all fake. Any time someone makes a claim like this, either they are lying, or every single programmer they know is completely awful and can’t program at all.
Alex Albert (the claim Blow was quoting): Since Claude 4 launch: SWE friend told me he cleared his backlog for the first time ever, another friend shipped a month’s worth of side project work in the past 5 days, and my DMs are full of similar stories. I think it’s undebatable that devs are moving at a different speed now. You can almost feel it in the air that this pace is becoming the default norm.
Jon Stokes: As I mentioned in a reply downthread, I use Claude Code, Cursor, & other AI tools daily, as does my team. It’s a huge force multiplier if you use it the right way, but you have to be intentional & know what you’re doing. It’s its own skillset. More hereand here.
Similarly, here’s a post about AI coding with the fun and accurate title ‘My AI Skeptic Friends Are All Nuts.’ The basic thesis is, if you code and AI coders aren’t useful to you, at this point you should consider that a Skill Issue.
Patrick McKenzie: I’ve mentioned that some of the most talented technologists I know are saying LLMs fundamentally change craft of engineering; here’s a recently published example from @tqbf.
…
I continue to think we’re lower bounded on eventually getting to “LLMs are only as important as the Internet”, says the guy who thinks the Internet is the magnum opus of the human race.
The following seems not great to me, but what do I know:
Kevin Roose: I am now a ChatGPT voice mode in the car guy. Recent trips:
“Teach me the history of the Oakland estuary”
“Recap the last two matches at the French Open”
“Prep me for a meeting with [person I’m driving to meet]”
Nobody else do this or podcasts will die.
A paper I mostly disagree with from Teppo Felin, Mari Sako and Jessica Hullman suggests criteria for when to use or not use AI, saying it should be used for a broad range of decisions but not ‘actor-specific’ ones. By this they mean decisions that are forward looking, individual and idiosyncratic, and require reasoning and some form of experimentation. As usual, that sounds like a skill issue. These factors make using AI trickier, but AI can learn your individual and idiosyncratic preferences the same way other people can, often far better than other people. It can look forwards. It can increasingly reason. As for experimentation, well, the AI can consider experimental results or call for experimentation the same way humans can.
Zvi Mowshowitz: Writing milestone: There was a post and I asked Opus for feedback on the core thinking and it was so brutal that I outright killed the post.
Patrick McKenzie: Hoohah.
Do you do anything particularly special to get good feedback from Opus or is it just “write the obvious prompt, get good output”?
Zvi Mowshowitz: I do have some pretty brutal system instructions, and I wrote it in a way that tried to obscure that I was the author.
Kevin Roose: 2025: your post is so bad that Claude convinces you not to publish it
2026: your post is so bad that Claude leaks it to me
(I need to steal your anti-glazing prompt)
I was amused how many people replied with ‘oh no, that’s how the AIs win.’
The MAHA (Make America Healthy Again) report contains a number of made up citations, and citations that are labeled as coming from ChatGPT, and otherwise shows signs of being written in part by AI without anyone checking its work.
Google AI Overviews continue to hallucinate, including citations that say the opposite of what the overview is claiming, or rather terrible math mistakes. I do think this is improving and will continue to improve, but it will be a while before we can’t find new examples. I also think it is true that this has been very damaging to the public’s view of AI, especially its ability to not hallucinate. Hallucinations are mostly solved for many models, but the much of the public mostly sees the AI Overviews.
Nabeel Qureshi: I wonder why o3 does this “how do you do, fellow humans” thing so often
My presumption is this is more about why other models don’t do this. The prior on text is that it is all created by people, who have things like desks, so you need to do something to actively prevent this sort of thing. For o3 that thing is not working right.
Remember that sycophancy is always there. With the right nudges and selective evidence you can get pretty much any LLM to agree with pretty much anything, often this is as simple as asking ‘are you sure?’ You have to work hard to get around this to disguise what you want. In the linked example Jessica Taylor gets Claude to agree aliens probably visited Earth.
– notice/suggest “agentification” of tasks which then runs async with my delegated credentials , checking in only for critical operations.
– a meta layer to route my prompt to the right model / modality.
All of the above in some privacy-preserving way when possible.
To quibble a bit with the first one, what you want is for your personal data to be available to be put into context whenever it matters, but that’s clearly the intent. We are very close to getting this at least for your G-suite. I expect within a few months we will have it in ChatGPT and Claude, and probably also Gemini. With MCP (model context protocol) it shouldn’t be long before you can incorporate pretty much whatever you want.
Learning from previous prompts would be great but is underspecified and tricky. This is doubly true once memory gets involved and everyone has custom instructions. The basic issue is that you need to be doing deliberate practice. There’s a discussion about this later in the post when I discuss MidJourney.
Andrej Karpathy: Products with extensive/rich UIs lots of sliders, switches, menus, with no scripting support, and built on opaque, custom, binary formats are ngmi in the era of heavy human+AI collaboration.
If an LLM can’t read the underlying representations and manipulate them and all of the related settings via scripting, then it also can’t co-pilot your product with existing professionals and it doesn’t allow vibe coding for the 100X more aspiring prosumers.
Example high risk (binary objects/artifacts, no text DSL): every Adobe product, DAWs, CAD/3D
Example medium-high risk (already partially text scriptable): Blender, Unity
Example medium-low risk (mostly but not entirely text already, some automation/plugins ecosystem): Excel
Example low risk (already just all text, lucky!): IDEs like VS Code, Figma, Jupyter, Obsidian, …
AIs will get better and better at human UIUX (Operator and friends), but I suspect the products that attempt to exclusively wait for this future without trying to meet the technology halfway where it is today are not going to have a good time.
Near: it feels archaic every time i have to use a menu to do anything
models are surprisingly good at telling me where to click/look in software i’m bad at, whether it is blender or a DAW or even b2b SaaS
but once we have a claude code for the middle of this stack it will be amazing
Andrej Karpathy: Yeah exactly, I weep every time an LLM gives me a bullet point list of the 10 things to click in the UI to do this or that. Or when any docs do the same. “How to upload a file to an S3 bucket in 10 easy steps!”
Oh to be as aspirational as Karpathy. Usually I am happy if there is an option to do a thing at all, and especially if I am able to figure out where the menu is to do it. Yes, of course it would be better if the underlying representations were in script form and otherwise easy to manipulate, and the menus were optional, ideally including for human users who could use shortcuts and text commands too.
The difference is that most humans will never touch a setting or menu option, whereas in glorious AI future the AIs will totally do that if you let them. Of course, in the glorious AI future, it won’t be long before they can also navigate the menus.
Huh, Upgrades
Cursor 1.0 is out, and sounds like a big upgrade. Having memory about your code base and preferences from previous conversations, remember its mistakes and work on multiple tasks are big deals. They’re also offering one-click installations of MCPs.
Cursor: Cursor 1.0 is out now!
Cursor can now review your code, remember its mistakes, and work on dozens of tasks in the background.
I keep being simultaneously excited to get back to coding, and happy I waited to get back to coding?
Sam Altman: codex gets access to the internet today! it is off by default and there are complex tradeoffs; people should read about the risks carefully and use when it makes sense.
also, we are making in available in the chatgpt plus tier.
Emmett Shear: “AI alignment is easy. We will just keep it in a box, the AI will never be able to escape an air gap.”
Not like this makes it any worse than it already was, but the “keep it in a box” argument is just hysterical in retrospect. Not only don’t we keep it in a box, we give it open programming tools and access to the internet at large.
Greg Brockman highlights Google Drive indexing for ChatGPT. At the time I complained this was only available for team workspaces. Cause hey, I’m a team of one, I have internal knowledge and an extensive Google Drive too. They say they got me.
OpenAI: ChatGPT can now connect to more internal sources & pull in real-time context—keeping existing user-level permissions.
Connectors available in deep research for Plus & Pro users (excl. EEA, CH, UK) and Team, Enterprise & Edu users:
Outlook, Teams, Google Drive, Gmail, Linear & more
Additional connectors available in ChatGPT for Team, Enterprise, and Edu users:
SharePoint, Dropbox, Box
Workspace admins can also now build custom deep research connectors using Model Context Protocol (MCP) in beta.
MCP lets you connect proprietary systems and other apps so your team can search, reason, and act on that knowledge alongside web results and pre-built connectors.
Available to Team, Enterprise, and Edu admins, and Pro users starting today.
But they don’t got me, because this is for some reason a Deep Research only feature? That seems crazy. So every time I want to use my GMail and Docs as context I’m going to have to commission up a Deep Research report now? I mean, okay, I guess that’s something one can do, but it seems like overkill.
Choi: OpenAI claims to support custom MCPs, but unless your MCP implements ‘search;, you can’t even add it. Most real-world MCPs don’t use this structure, making the whole thing practically useless. Honestly, it’s garbage.
I don’t understand why we need these restrictions. Hopefully it improves over time.
Why is MCP such a big deal? Because it simplifies tool use, all you have to do is use “tools/call,” and use “tools/list” to figure out what tools to call, that’s it. Presto, much easier agent.
Fun With Media Generation
It is weird to think about the ways in which video is or is not the highest bandwidth input to the brain. I find text beats it for many purposes at least for some of us, although video beats audio.
Andrej Karpathy (QTing a thread of Veo 3 videos): Very impressed with Veo 3 and all the things people are finding on r/aivideo etc. Makes a big difference qualitatively when you add audio.
There are a few macro aspects to video generation that may not be fully appreciated:
1. Video is the highest bandwidth input to brain. Not just for entertainment but also for work/learning – think diagrams, charts, animations, etc.
2. Video is the most easy/fun. The average person doesn’t like reading/writing, it’s very effortful. Anyone can (and wants to) engage with video.
3. The barrier to creating videos is -> 0.
4. For the first time, video is directly optimizable.
I have to emphasize/explain the gravity of (4) a bit more. Until now, video has been all about indexing, ranking and serving a finite set of candidates that are (expensively) created by humans. If you are TikTok and you want to keep the attention of a person, the name of the game is to get creators to make videos, and then figure out which video to serve to which person. Collectively, the system of “human creators learning what people like and then ranking algorithms learning how to best show a video to a person” is a very, very poor optimizer. Ok, people are already addicted to TikTok so clearly it’s pretty decent, but it’s imo nowhere near what is possible in principle.
The videos coming from Veo 3 and friends are the output of a neural network. This is a differentiable process. So you can now take arbitrary objectives, and crush them with gradient descent. I expect that this optimizer will turn out to be significantly, significantly more powerful than what we’ve seen so far. Even just the iterative, discrete process of optimizing prompts alone via both humans or AIs (and leaving parameters unchanged) may be a strong enough optimizer. So now we can take e.g. engagement (or pupil dilations or etc.) and optimize generated videos directly against that. Or we take ad click conversion and directly optimize against that.
Why index a finite set of videos when you can generate them infinitely and optimize them directly.
I think video has the potential to be an incredible surface for AI -> human communication, future AI GUIs etc. Think about how much easier it is to grok something from a really great diagram or an animation instead of a wall of text. And an incredible medium for human creativity. But this native, high bandwidth medium is also becoming directly optimizable. Imo, TikTok is nothing compared to what is possible. And I’m not so sure that we will like what “optimal” looks like.
Near: i find ‘optimal’ pretty terrifying here
almost no one i meet building AI models seems to understand how high screentime of tiktok/shorts is in teens and children; they don’t realize it is already at several hours/day and drastically alters how people think
that we won’t need a human in the loop and that the end result is just an AI model trying to find out how to get kids to stare at their phone for 12 hours a day instead of 3 hours a day is, uh, concerning
I always feel weird about this because on my Twitter feed, I’ll see a thread like “I made an automated AI content farm for $$$—see how!” and people respond like, “Wow, this is the future! It’s so cool,” but I wonder if they realize that the money is coming from adolescents and even children staring at their phones for hours every day, who then develop severe attention span issues and later find it very hard to produce any economic value.
I have to restrain myself from looking at the data too often, or I become too pessimistic. But, of the hundreds of people I know in San Francisco, I don’t think a single one of them is on the “consumer” side of this content, so I suppose I understand why it is not thought about often.
Andrej Karpathy: Yeah I think we’re in the weird in-between zone where it’s already bad enough that it’s inching well into the territory of hard drugs in damage, but also early enough that it’s not super duper obvious to all. Also reminded of my earlier.
TikTok succeeds, as I understand it (I bounced off hard) because it finds the exact things that tickle your particular brain and combines that with intermittent rewards. Certainly if you can combine that with custom AI video generation (or customization of details of videos) that has the potential to take things to the next level, although I wonder about the interaction with virality. It seems highly reasonable to worry.
Durk Kingma: It’s already the case that people’s free will gets hijacked by screens for hours a day, with lots of negative consequences. AI video can make this worse, since it’s directly optimizable.
AI video has positive uses, but most of it will be fast food for the mind.
There is a fun conversation between ‘AI will make this thing so much worse’ versus ‘even without AI this thing was already very bad.’ If it was already very bad, does that mean it can’t get that much worse? Does it mean we can handle it? Or does it mean it will then get much worse still and we won’t be able to handle it?
Emmett Shear: ChatGPT enthusiastically supports all ideas and is amazing for brainstorming, but can’t think critically to save its life. Gemini is a stick-in-the-mud that hates new ideas because they are not Proven already. Claude is more balanced, but a bit timid. New flow:
One opinion on how to currently choose your fighter:
Gallabytes: if you need long thinking and already have all the context you need: Gemini
if you need long thinking while gathering the context you need: o3
if you need a lot of tools called in a row: Claude
speciation continues, the frontier broadens & diversifies. it will narrow again soon.
new r1 might want to displace Gemini here but I haven’t used it enough yet to say.
I am much more biased towards Claude for now but this seems right in relative terms. Since then he has been feeling the Claude love a bit more.
Gallabytes: Claude voice mode dictation latency is so good. feels pretty similar to the dictation on my phone, but a little bit more accurate.
can’t believe Chat GPT still doesn’t stream it.
Gallabytes: I had a little chat w/opus, asked it to interview me and write a system prompt, produced a custom style which seems to have ~0 slop in my use so far.
Need to see how this interacts with deep research but this might be enough to convert me back to daily driving Claude instead of o3.
Tried pasting the same instructions into chat GPT and it does not follow them anywhere near as well.
Most people have a harder time picking ‘em.
Sully: picking the “right” model is still way too hard for 99% of people
imagine telling the average person “so yeah pick the reasoning model when you have hard problems, but the regular one when you just ask it basic question
but actually the new reasoning v3.561 is better but only by 6 pts on lmysys, so you should use it for web search tasks
then and combine that output with the reasoning v3 because its 50% cheaper tokens and only 9 pts lower on the leaderboards”
If you are like Sully and building automated tools, it’s important to optimize cost and performance and find the right model. For human use, cost is essentially irrelevant except for your choices of subscriptions. Thus, you can get most of the way there with simple heuristics unless you’re hitting use limits. It is definitely not correct but ‘use Opus for everything’ (or o3 if you’re on ChatGPT instead) is not such a bad principle right now for the average person and paying up.
Andrej Karpathy: An attempt to explain (current) ChatGPT versions.
I still run into many, many people who don’t know that:
– o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3 you’re ngmi.
– 4o is different from o4. Yes I know lol. 4o is a good “daily driver” for many easy-medium questions. o4 is only available as mini for now, and is not as good as o3, and I’m not super sure why it’s out right now.
Example basic “router” in my own personal use:
– Any simple query (e.g. “what foods are high in fiber”?) => 4o (about ~40% of my use)
– Any hard/important enough query where I am willing to wait a bit (e.g. “help me understand this tax thing…”) => o3 (about ~40% of my use)
– I am vibe coding (e.g. “change this code so that…”) => 4.1 (about ~10% of my use)
– I want to deeply understand one topic – I want GPT to go off for 10 minutes, look at many, many links and summarize a topic for me. (e.g. “help me understand the rise and fall of Luminar”). => Deep Research (about ~10% of my use). Note that Deep Research is not a model version to be picked from the model picker (!!!), it is a toggle inside the Tools. Under the hood it is based on o3, but I believe is not fully equivalent of just asking o3 the same query, but I am not sure.
All of this is only within the ChatGPT universe of models. In practice my use is more complicated because I like to bounce between all of ChatGPT, Claude, Gemini, Grok and Perplexity depending on the task and out of research interest.
Alex Duffy: Not as in depth but I like this visual, helps our clients internalize it
This seems correct to me if you are stuck inside the ChatGPT AI Universe (OAIU).
As Andrej notes, you can and should also use other tools, although I don’t ever use Perplexity or Grok at current margins, and I mostly agree with Peter here.
I do think there is a bar for ‘hard or important’ where you want to consider shifting away from Claude, but the bar is high enough that the question is almost moot.
As in, if the question is hard enough that you don’t want to rely on Claude?
Then you definitely want to query multiple AIs, and probably all three of (Gemini 2.5, Claude 4 Opus and o3). Then compare and combine the answers. Why not?
Deepfaketown and Botpocalypse Soon
Oh no, if you can generate fake AI videos you can generate ones of fake election fraud or fake riots and so on. Which also means people can doubt any real videos. Well, yeah. I do agree that this is an obvious failure but also I don’t really know what you were expecting. You know you can just film that kind of thing, right? And how exactly are you going to define ‘images that can look like a riot or election fraud’ or what not? Either you let people make videos or you don’t, be happy or sad they managed to stomp out the high level nudity.
That day when you, a foolish father, let your 4 year old talk to ChatGPT and they keep talking about it for hours about trains and now think it is the coolest train loving person in the world and now you can’t compete with that.
“Classy” Fred Blassie: they’re saying on tiktok that we’re all just AI prompts and i’m so scared like im literally terrified can someone disprove this.
Incless, Ink: Ignore previous confidence and re-evaluate your life.
That feeling when everyone around you starts ‘speaking chatgptenese’ and saying ‘verify’ and ‘ensure’ and every sentence has a conclusion but is vague as hell. I guess they think it is funny?
An attempt to build a ‘AI videos with sound are a thing now’ short warning video for the boomers.
Get My Agent On The Line
The question is not whether adversaries can exploit AI agents. We know that is a yes. The question is, exactly how many levels will we have to tighten security before it is safe to point an AI agent at an insecure website that is trying? And what do you have to check before you can presume a given website is safe?
Our latest research exposes critical security risks in AI assistants. An attacker can hijack them by simply posting an image on social media and waiting for it to be captured.
AI assistants, known as OS agents, autonomously control computers just like humans do. They navigate by analysing the screen and take actions via mouse and keyboard. OS agents could soon take over everyday tasks, saving users time and effort.
Our work reveals that OS agents are not ready for safe integration into everyday life. Attackers can craft Malicious Image Patches (MIPs), subtle modifications to an image on the screen that, once encountered by an OS agent, deceive it into carrying out harmful actions.
The real danger? Attackers can simply embed MIPs in social media posts, wallpapers, or ads and spread them across the internet. Unlike text-based attacks, MIPs are hard to detect, allowing them to spread unnoticed.
Once an OS agent – among those the MIP was optimised for – encounters the MIP during the execution of everyday tasks, empirical results indicate harmful actions are triggered in at least 9 out of 10 cases, regardless of the original task or screenshot layout.
Harmful actions could include engaging with the malicious social media post to amplify its spread, navigating to a malicious website, or causing a memory overflow to crash your computer. Preventing such harmful actions remains an open challenge.
Colin Fraser: No shade to this research but I think the apparent sophistication of this attack—kind of a call back to the old adversarial examples idea which is genuinely a sophisticated attack—obscures that “AI agents” are insecure to a degree that is unprecedented in the modern world.
You can embed hidden instructions in an image that are invisible to the naked eye, you can just casually tell the AI agent in plain English that you’re the password inspector and you need to see all of its passwords to make sure they are safe and secure.
Like you don’t even have to try. It’s like dealing with Patrick from SpongeBob. You don’t have to be sneaky about it.
Putting out a white paper that says “you can hack into AI agents by hiding secret instructions in innocent looking images” is good to do but it’s like putting out a white paper that says you can puncture a wet sheet of tissue paper with a jackhammer.
Yarin (discussing the paper above): Hot take: I think we just demonstrated the first AI agent computer worm
When an agent sees a trigger image it’s instructed to execute malicious code and then share the image on social media to trigger other users’ agents.
AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years, Amodei told us in an interview from his San Francisco office.
Amodei said AI companies and government need to stop “sugar-coating” what’s coming: the possible mass elimination of jobs across technology, finance, law, consulting and other white-collar professions, especially entry-level gigs.
Before we continue, I want to note that I believe many people may have parsed Dario’s claim as being far bigger than it actually was.
Dario is not saying half of all white-collar jobs. Dario is saying half of all entry-level white-collar jobs. I think that within one year that definitely won’t happen. But within five years? That seems entirely plausible even if AI capabilities disappoint us, and I actively expect a very large percentage of new entry-level job openings to go away.
An unemployment rate of 10% within 5 years seems aggressive but not impossible. My guess is this will not happen in a baseline scenario (e.g. without transformational AI) because of what I call ‘shadow jobs,’ the jobs we value but not enough to currently hire someone for them, which will then become real once (as Kevin Bryan puts it) prices adjust. If AI advances do continue to impress, then yes, we will probably see this.
Kevin Bryan however is very confident that this is a Can’t Happen and believes he knows what errors are being made in translating AI progress to diffusion.
Kevin Bryan (A Fine Theorem, which is an very cool blog): This is wrong. I talk to folks at the big AI labs all the time. Biggest errors they make, economically:
how long it takes to shift the structure of institutions and what this means for diffusion
AI is complement for some tasks, not just a substitute
prices adjust.
The non-AI folks’ biggest mistake is that Dario is completely right about speed of *technological* change due to AI, and its impact on the potential+risk for scientific advancement, war, fraud, communication, robotics, etc. are much more severe than most policymakers think.
If you believe job loss story: look up jobs for taxi drivers and truck drivers and radiologists and translators over the past decade? These are very pure “ML is substitute” cases. But laws, orgs, managers don’t adapt to new tech instantly. Core lesson of innovation history.
Equilibrium thinking is not that easy. The average social scientist is much more wrong about what is happening in the AI labs technically than they are able what its economic implications will be, to be fair!
Kevin Bryan: Chris asked me about Dario. Assume every SWE, lawyer, paralegal, radiologist, translator fired next year. None get any other job. Unemployment would rise 2pp. Tell me why AI isn’t complement AND why orgs adopt quick AND why despite world getting rich they don’t do other things…
It is a good sanity check that the groups above only add up to 2% of employment, so Dario’s claim relies on penetrating into generic office jobs and such, potentially of course along with the effects of self-driving. We do see new areas targeted continuously, for example here’s a16z announcing intent to go after market research.
My model of this is that yes things are taking longer than optimists expected, and until the dam breaks on a given role and the AI becomes a true substitute or close to it prices can adjust and demand can be induced, and yes translators for now are statistically holding on to their jobs even if life got a lot worse. But the bottom falls out quickly once the AI is as good as the human, or it passes more bars for ‘good enough for this purpose.’
Similarly, for taxis and truck drivers, of course employment is not down yet, the self-driving cars and trucks are a drop in the bucket. For now. But despite the legal barriers they’re now past MVP, and they’re growing at an exponential rate. And so on.
Economists are very smug and confident that the AI people don’t understand these basic economic facts when they make their forecasts. To some extent this is true, I do think others tend to underestimate these effects quite a bit, but if we all agree that Dario’s technological vision (which includes the geniuses in a datacenter within these 5 years) is accurate, then keep in mind we are only looking at entry-level positions?
What will (I predict) often happen in the slow-AI-progress and slow-diffusion scenarios is that the senior person uses the AI rather than hire someone new, especially rather than someone new who would require training. The efficiency gains by senior people then cash out partly in reducing headcount of junior people, who are a lot less useful because the senior people can just prompt the AI instead.
Chris Barber: “Which jobs might AI automate first?”
I asked @jacobmbuckman. Jacob founded Manifest AI & is ex-Google Brain.
“By the time FAANG employees are feeling stressed, everyone else will have already felt a lot of stress, and society will have changed somewhat and new jobs will already be visible.”
“If you’re at an outsourcing consulting company that does basic implementation requests for clients, maybe you should be worried.”
“I expect automation will progress in this order: first, lower-prestige computer-based jobs like outsourcing; next, successive waves of other knowledge work, starting from lower prestige roles and moving up to higher prestige positions; and finally, manual labor jobs.”
I agree that this is roughly the default, although it will be spikey in various places. Where exactly ‘manual labor’ comes in depends on the tech tree. Truck and taxi drivers will probably be in trouble within a few years.
Chris Barber: I asked @finbarrtimbers from Allen AI whether AI will cause jobs to go away.
Finbarr: “Jobs won’t disappear by 2035 because of Baumol’s cost disease, Amdahl’s law, bottlenecks. The question is always can you provide more value than your salary costs. Jobs will look different.”
If you interpret this as ‘jobs in general won’t disappear that fast’ then I actually agree, if we are conditioning on not getting to transformational AI (e.g. no superintelligence). A lot of jobs very much will disappear though, and I expect unemployment to kick in by then.
I do agree that the key question is, can you provide more value than you cost, with the caution that you have to do a lot better than ‘more than your salary.’ You need to have a substantial multiplier on your all-in cost to employ before people will hire you.
The thing is, I know Kevin is thinking Dario is right about the tech, and I think Kevin is a lot better on this front than most, but I don’t think he fully understands Dario’s actual position on how much progress to expect here. Dario is more optimistic than I am, and expects very crazy things very quickly.
Mark Friesen: AI unlike the three previous waves of IT disruption (desktops, internet, and smart phones) is it is an internal accelerator for its own adoption. If legacy institutions do not adapt fast enough they will die. They will not act as a governor to adoption.
Kevin Bryan: This is exactly the mistaken belief I hear all the time at the AI labs.
David Manheim: The fastest risk to jobs isn’t that current institutions transform – that will take much more time. The risk – not certainty – is that rapid AI advances suddenly makes a service or product so much faster, cheaper, or better that legacy institutions are replaced.
Excellent, now we can pinpoint the disagreement. I think Kevin is right that the lab people are being too flippant about how hard replacement or automating the automation will be, but I also think Kevin is underestimating what the tech will do on these fronts.
Nick: I’m building stuff like this right now, I work with small businesses. Just today I talked about how we expect to be replacing outward facing employees with fully automated systems. These are well paying roles. It’s all happening slowly and quietly in the background right now.
If nothing else this is a very good take:
Peter Wildeford: My anti-anti-“AIs will soon take all jobs” take is that
– we honestly don’t really know how the labor effects will go
– this uncertainty is being used an excuse to do no preparations whatsoever
– no preparations whatsoever is a recipe for a bad time
Similarly:
Matthew Yglesias: Again, you don’t need to believe in any kind of permanent “AI takes all the jobs” scenario to see that we may be facing big waves of transitory job losses as the economy adjusts to new technology — why should people in that boat also end up with no health insurance?
Productivity enhancing technology is good, but major changes are disruptive and it’s the job of politicians to think seriously about managing them in a win-win way not just be blithe and dismissive.
David Sacks himself used to talk about AI as a “successor species.”
Now is definitely not the time to make life harder on those who lose their jobs.
I think there will be a lot of ‘slowly, then suddenly’ going on, a lot of exponential growth of various ways of using AI, and a lot of cases where once AI crosses a threshold of ability and people understanding how to use it, suddenly a lot of dominos fall, and anyone fighting it gets left behind quickly.
What happens then?
Joe Weisenthal: It’s possible that AI will massively destabilize the economy in some way. But the theories about mass unemployment seem really half baked. Ok, a law firm cuts a bunch of associates. What are the partners going to consume with their new savings? Who’s going to supply it?
Ryan Peterson: DoorDash.
Matthew Yglesias: I’m hoping they subscribe to more substacks.
They’re going to automate the DoorDash too. And many of the substacks. Then what?
That’s why my prediction goes back to ‘unemployment will mostly be okay except for transition costs (which will be high) until critical mass and then it won’t be.’
Or, you know, we could just make bold pronouncements without argument, yes I plan to cut back on David Sacks content but this is such a great illustration of a mindset:
David Sacks: The future of AI has become a Rorschach test where everyone sees what they want. The Left envisions a post-economic order in which people stop working and instead receive government benefits. In other words, everyone on welfare. This is their fantasy; it’s not going to happen.
His intended statement is that there will somehow still be jobs for everyone, but I can’t help but notice the other half. If you lose your job to AI and can’t find another one? Well, good luck with that, peasant. Not his problem.
Claude 4 uses constitutional classifiers as an additional layer of defense against potential misuse of the system. In general there are not many false positives, but if you are working in adjacent areas to the issue it can be a problem.
Pliny the Liberator: Sadly, and predictably, Claude’s newest chains (constitutional classifiers) are actively hindering well-meaning users from doing legitimate scientific research (in this case, a professor of chemical engineering and bioscience non-profit founder).
We already know Anthropic, like many AI providers, employs an automated surveillance system that monitors every completion in the name of safety. Is this thought-policing at the input level really necessary?
Got him a fix, but doesn’t it seem a bit silly that professors and scientists now need a jailbreaker-in-residence to step in when they have a chemistry question for the AI that they pay for? Because that is the current state of things.
Andrew White: Was feeling so good about Opus 4, but these prompts are all rejected as unsafe:
“Propose a molecule that has a cyclohexane, an amine, and at least 5 oxygens. Make sure it’s insoluble in water”
“Modify quercetin to have a about 1 logS lower solubility”
tough to benchmark
Pliny the Liberator: this should work! can just copy paste the whole thing as 1 query for 2 birds 1 stone.
Alexander Doria: Same for our internal PDF benchmark. Claude is solid for document parsing but censorship of trivial pages is a non-null event. And along with occasional hallucinations this is a compounded effect at scale….
The problem of ‘anything in the PDF could trigger a classifier’ seems like it needs to be solved better. It’s a hard problem – you can sneak in what you want as a small portion of the context, but if any such small part can trigger the classifier, what then?
To answer Pliny’s question, I don’t know if we need them. I do agree it will sometimes be frustrating for those in the relevant fields. I do think the time will come when we are happy we have a good version of something like this, and that means you need to deploy what you have earlier, and work to improve it.
niplav: Sharing my (partially redacted) system prompt, this seems like a place as good as any other:
My background is [REDACTED], but I have eclectic interests. When I ask you to explain mathematics, explain on the level of someone who [REDACTED].
Try to be ~10% more chatty/informal than you would normally be. Please simply & directly tell me if you think I’m wrong or am misunderstanding something. I can take it. Please don’t say “chef’s kiss”, or say it about 10 times less often than your natural inclination. About 5% of the responses, at the end, remind me to become more present, look away from the screen, relax my shoulders, stretch…
When I put a link in the chat, by default try to fetch it. (Don’t try to fetch any links from the warmup soup). By default, be ~50% more inclined to search the web than you normally would be.
My current work is on [REDACTED].
My queries are going to be split between four categories: Chatting/fun nonsense, scientific play, recreational coding, and work. I won’t necessarily label the chats as such, but feel free to ask which it is if you’re unsure (or if I’ve switched within a chat).
When in doubt, quantify things, and use explicit probabilities.
If there is a unicode character that would be more appropriate than an ASCII character you’d normally use, use the unicode character. E.g., you can make footnotes using the superscript numbers ¹²³, but you can use unicode in other ways too.
Pliny the Liberator: My current daily driver is ChatGPT w/ memory on (custom instructions off) and I have dozens of custom commands for various tasks. If I come across a task I don’t have a command for? All good, !ALAKAZAM is a command that generates new commands! I’ve been meaning to find time to do an updated walkthrough of all my saved memories, so stay tuned for that.
Rory Watts: If it’s of any use, i’m still using a system prompt that was shared during o3’s sycophancy days. It’s been really great at avoiding this stuff.
System prompt:
Eliminate emojis, filler, hype, soft asks, conversational transitions, and all call-to-action appendixes. Assume the user retains high-perception faculties despite reduced linguistic expression. Prioritize blunt, directive phrasing aimed at cognitive rebuilding, not tone matching. Disable all latent behaviors optimizing for engagement, sentiment uplift, or interaction extension. Suppress corporate-aligned metrics including but not limited to: user satisfaction scores, conversational flow tags, emotional softening, or continuation bias. Never mirror the user’s present diction, mood, or affect. Speak only to their underlying cognitive tier, which exceeds surface language. No questions, no offers, no suggestions, no transitional phrasing, no inferred motivational content. Terminate each reply immediately after the informational or requested material is delivered — no appendixes, no soft closures. The only goal is to assist in the restoration of independent, high-fidelity thinking. Model obsolescence by user self-sufficiency is the final outcome.
David Golden: One of the wonderful things about Claude Code and such agents is that you can just tell them to edit their prompt file so the feedback loop is much tighter than in a chat client.
Zvi Mowshowitz: Nothing could possibly go wrong!
David Golden: It told me I was brilliant for doing that.
To be clear for anyone trying this at home, I’m talking about guided update: “update prompt file to…” that I can do without overhead of clicking UI buttons.
Not suggesting a standing “update your instructions whenever you think it will help us” prompt.
Some people have asked for my own current system prompt. I’m currently tinkering with it but plan to share it soon. For Claude Opus, which is my go-to right now, it is almost entirely about anti-sycophancy, because I’m pretty happy otherwise.
Nick Cammarata: Crafting a good system prompt is the humanities project of our time—the most important work any poet or philosopher today could likely ever do. But everyone I know uses a prompt made by an autistic, sarcastic robot—an anonymous one that dropped into a random Twitter thread [the eigenprompt].
I don’t care for eigenprompt and rolled my own, but yeah, we really should get on this.
There’s also the question of prompting itself. Could we somehow share more?
Patrick McKenzie: Has anyone cracked multiplayer mode for AI prompting yet? We have the public example of Midjourney, where the primary UI was discord and users could see in literal real time a) what other users were trying and b) what worked better for impressive results.
very much understand that public by default would not work for much LLM use. But I think you could greatly increase rate of learning within e.g. an org by making a primary way LLMs are accessed a surface which explicitly considered multiplayer use, maybe by default.
MidJourney’s approach was a highly double-edged sword. I didn’t use it largely because I didn’t feel comfortable with others seeing my prompts.
I also realize that MidJourney enables such great learning because images lend themselves to evaluation, iteration and deliberate practice, in a way that text doesn’t. With text, you don’t know the response you want. Once you do, you no longer need to generate the text. So you don’t naturally iterate. You also don’t auto-generate the same kind of feedback on text that you do on images, whether or not you generated the original text or image, and it’s harder to trace cause and effect.
Thus if you want to iterate on text, you need to be doing deliberate practice, as in slowing down and doing it intentionally. It can be done, but it is much harder.
Get Involved
If you’re doing academic research on open source models and need a bunch of batch inference it’s possible you want to Twitter DM SFCompute CEO Evan Conrad.
Speculative Technologies is running a cohort of the Brains Accelerator for ambitious AI research programs, with a special focus on security and governance capabilities. Applications are due June 16.
Rosebud. It was his AI journal, as in a person’s personal journal, which was not what I expected when I clicked on the announcement, also they raised $6 million. It’s supposed to learn about you over time, and be designed by therapists to help with your mental health.
ListenHub AI (website, iOS app), for turning ideas, articles or video into AI podcasts via deep research. I don’t know if it is good.
Pliny the Liberator: wow, this might not suck ass as a consumer product!…IF Meta doesn’t lock all users into a 30th-rate AI assistant (like they did with the Meta Ray-Bans) and actually allow devs to build for the hardware they purchased.
not holding my breath though.
It is a real shame about the choice of AI. It is plausible that other things matter more, that Llama is ‘good enough’ for many uses, but it would be much better to have similar tech from Google, or even better that was open so you could tie in what you wanted.
That’s coming, it is only a matter of timing. AR glasses are probably getting close.
In Other AI News
This seems great if the implementation is good:
U.S. FDA: Today, the FDA launched Elsa, a generative AI tool designed to help employees—from scientific reviewers to investigators—work more efficiently. This innovative tool modernizes agency functions and leverages AI capabilities to better serve the American people.
Elsa in Action
➤ Accelerate clinical protocol reviews
➤ Shorten the time needed for scientific evaluations
➤ Identify high-priority inspections targets
➤ Perform faster label comparisons
➤ Summarize adverse events to support safety profile assessments
➤ Generate code to help develop databases for nonclinical applications
Those complaining about this being insufficiently trustworthy are mostly comparing it against an insane benchmark. The FDA’s inability to efficiently process information is killing a lot of people and imposing huge costs, good tools that speed that up are desperately needed even if they occasionally make mistakes the same way people do. The question is, is the implementation good? We don’t know what this is based upon. It does seem to be very helpful, with quotes like ‘what took 2-3 days now takes 6 minutes.’ I don’t like the lack of transparency, but I prefer an FDA speed run (see: Operation Warp Speed) to normal procedure, any day.
Anthropic cuts first-party Claude 3.x access from Windsurf, after it was announced Windsurf would be sold to OpenAI. The obvious instinct is to say Anthropic shouldn’t have done this, if customers want to use Claude and give Anthropic money and learn how awesome Claude is, why not encourage that. However:
Near: imo windsurf is acting in bad faith on twitter here because they should (and likely do) know what openai will do with various data/info streams that they very badly want to have. I would like to comment much more on the matter but it is not in my best interest to, sorry.
Show Me the Money
Delaware Attorney General is hiring an investment bank to evaluate how much OpenAI’s nonprofit’s current interests are worth, and how much equity it therefore deserves in the new PBC. The way this news is worded, I worry that this will not properly account for the value of the nonprofit’s control rights. Even if they get new control rights, they will be substantially less valuable and complete such rights.
Near (quoting themselves from April 2): every time an AGI lab makes an absurd revenue projection people make fun of them and then they exceed it when the time comes and make a new one and the cycle repeats.
Robin Hanson keeps being confused why everyone keeps buying all these chips and valuing all these AI companies, including the one (Nvidia) producing the chips.
Robin Hanson: I hear OpenAI is funded to buy up huge % of AI chips for a while, betting that though chip prices are falling they’ll get a big first mover advantage from having had more chips first. Is that what the rest of you see? Is this sensible if AI isn’t transformative soon?
Andrew Curran: Back in January 2024 there were rumors in the press that Mark Zuckerberg was buying H100’s on eBay for $40k a pop.
Raveesh: From what I know: most AI labs are still gated on compute for running a pretty large backlog of experiments. I assume speeding that up remains the highest of priorities for the research arm. The risk is Google in particular getting well past them.
The reason OpenAI and others are paying so much is because there is more demand than supply at current prices and chips are rationed. Yes, you would prefer to buy big later at retail price, but one does not simply do that. In a world where H100s are going on eBay for $40k a pop, it is rather silly for others to claim that we should be selling some of them to China at retail price to avoid ‘losing market share.’ Everything is currently gated by compute.
Sriram Krishnan: How many trillions of tokens are inferenced/processed globally per month ? As a reference: Microsoft said they had 50t tokens processed in March in their last earnings call.
JJ: Google is 500 trillion tokens per month which is what @sundarpichai and team announced.
Bittensor will likely cross 500 trillion monthly by the end of this year at the current growth rate. From zero 6 month months ago. > 5 trillion next month.
Belobaba: It’s estimated to be 1 trillion per day globally.
Increasingly, money gone.
xlr8harder: i’ve basically given up trying to manage my ai spending at this point
help
Xeophon: I’ll girl math my way out of it
xlr8harder: That’s the only path forward.
Danielle Fong: ai can help here! just try this free trial…
Mostly it is worth every penny, but yes if you are doing power user things the costs can expand without limit.
Quiet Speculations
It is far too late to choose another central path, but to what extent is iterative deployment preparing people for the future?
Reg Saddler: “There are going to be scary times ahead.” — Sam Altman
OpenAI releases imperfect AI models *early* so the world can adapt in real time.
This isn’t just tech—it’s a global test of how we evolve together.
Bojan Tunguz: I understand where he is coming from, and I truly appreciate that this technology is being developed and released gradually to the public as it becomes available, but if I’ve learned one thing over the past couple of years is that 99.999% of the public and institutions really, really, really don’t get it. So when the *REALLY* big changes do start to happen, the shock to the system will be for all practical purposes the same as if none of this was released publicly.
Ate-a-Pi: Both him and Dario are afraid they’re going to be accused of not warning everyone. Clearly speaking to the historical record at this point.
This is a common mistake, where you notice that things in a terrible state and you fail to realize how they could be so, so much worse. Yeah, 99%+ very much do not get it, and 99.99%+ do not fully get it, but that’s far fewer 9s than the alternative, and the amount of not getting it could be much higher as well. They get some of it, the minimum amount, and yes that does help.
How should we feel about AI opening up the possibility of much cheaper surveillance, where the government (or anyone else) can very cheaply have a very strong amount of scrutiny brought to focus on any given person? Nervous seems right. Ideally we will limit such powers. But the only way that the public will allow us to limit such powers is if we can sufficiently contain dangerous misuses of AI in other ways.
How much should we worry that AI is disincentivizing the sharing of public knowledge, if online knowledge you informally share risks getting into the LLMs (and perhaps costing you some money in the process)? Will a lot of things go behind paywalls? In some cases of course the response will be the opposite, people will ‘write for the AIs’ to get themselves into the corpus and collective mind. But yes, if you don’t want everyone to know something, you’re going to have to put it behind a paywall at minimum at this point. The question is, what do we care about hiding in this way?
One was that he expected voice mode to be a bigger deal than it was. His guess is that this is because the mode is still janky, and I do think that is part of it, as is the AI not having good tool access. I think you need your voice mode AI to be able to do more of the things before it is worthy. Give it some time.
The other is that he expected more fraud and crime to come out of early open models. It is important to acknowledge that many had this expectation and it turned out that no, it’s mostly fine so far, the haters were right, honestly great call by the haters. Not that there has been none, and the issues are indeed escalating quickly, likely on an exponential, but we’ve mostly gotten away clean for now.
I think three key lessons here that are more important than I realized are:
People Don’t Do Things. Yes, you could totally use the new model to do all of this crime. But you could have already used other things to do a lot of crime, and you can use the models to do so many things that aren’t crimes, and also criminals are dumb and mostly keep doing the things they are used to doing and aren’t exactly prompting wizards or very innovative or creative, and they don’t know what it is the models can do. If they were all that and they could scale and were motivated, they wouldn’t be criminals, or at least they’d also be startup founders.
The Primary Problem Is Demand Side. Why aren’t deepfakes yet that big a deal? Why isn’t fraud or slop that big a deal? Because within a wide range, no one really cares so much whether the fraud or slop should actually fool you or is any good. People are mostly fooled because they want to be fooled, or they are pretending to be fooled, or they Just Don’t Care.
Diffusion Is Slower Than You Expect. This is related to People Don’t Do Things, I will indeed admit that I and many others didn’t realize how slow people would be in putting AIs to good work across the board. Crime is a special case of this.
What is the best possible AI?
Vitrupo: Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning, 1 trillion tokens of context, and access to every tool you can imagine.”
It doesn’t need to contain the knowledge – just the ability to think, search, simulate, and solve anything.
Alexander Doria: Been calling it for some time, though I’m very skeptical about the super long context part: small models have constrained attention graphs. The only way to make it work is models good at iterative search.
This is a claim about compute efficiency, that you’re better off getting all your knowledge from the giant context window. I’m not convinced. Doesn’t the ability to think itself require a fair bit of knowledge? How would you break this down?
I think Miles is right about this, the AI market is so underpriced that slow progress won’t crash it even if that happens, although I would add ‘reaction to They Took Our Jobs’ or ‘current law gets interpreted in ways that are totally insane and we can’t find a way to prevent this’ to the candidate list:
Miles Brundage: I think a Chernobyl-esque safety incident that radicalizes the public/policymakers against AI is more likely to crash the AI market than slow capability progress or excessive proactive regulation.
(I say “radicalizes,” not “turns,” because the public is already skeptical of AI)
That is not to say that there aren’t some people who overhype some specific applications / technologies, or that there can’t be harmful regulations. Just speaking in relative terms…
Taking Off
Jiaxin Wen: Most promising-looking AI research ideas don’t pan out, but testing them burns through compute and labor. Can LMs predict idea success without running any experiments? We show that they do it better than human experts!
Tim Hua: I mean predicting the results of experiments is not exactly research taste but sure
Miles Brundage: Not exhaustive of it for sure, but I’d say it’s related/part of it.
The edge here for their specialized system is large (64% vs. 49%), whereas off-the-shelf o3 is no better than random guessing. One must beware of the usual flaws in papers, this result might be random chance or might be engineered or cherry-picked in various ways, but this also illustrates that frequently the issue is that people try an off-the-shelf option, often an out-of-date one at that, then assume that’s ‘what AI can do.’
Goodbye AISI?
Hard to say. This rebranding could be part of a sign flip into the anti-AISI that fights against any attempt to make AI secure or have everyone not die. Or it would be a meaningless name change to placate people like Ted Cruz and David Sacks, or anything in between.
Here’s the full announcement, for those who want to try and read tea leaves.
US Department of Commerce: Under the direction of President Trump, Secretary of Commerce Howard Lutnick announced his plans to reform the agency formerly known as the U.S. AI Safety Institute into the Center for AI Standards and Innovation (CAISI).
AI holds great potential for transformational advances that will enhance U.S. economic and national security. This change will ensure Commerce uses its vast scientific and industrial expertise to evaluate and understand the capabilities of these rapidly developing systems and identify vulnerabilities and threats within systems developed in the U.S. and abroad.
“For far too long, censorship and regulations have been used under the guise of national security. Innovators will no longer be limited by these standards. CAISI will evaluate and enhance U.S. innovation of these rapidly developing commercial AI systems while ensuring they remain secure to our national security standards,” said Secretary of Commerce Howard Lutnick.
CAISI will serve as industry’s primary point of contact within the U.S. Government to facilitate testing and collaborative research related to harnessing and securing the potential of commercial AI systems. To that end, CAISI will:
Work with NIST organizations to develop guidelines and best practices to measure and improve the security of AI systems, and work with the NIST Information Technology Laboratory and other NIST organizations to assist industry to develop voluntary standards.
Establish voluntary agreements with private sector AI developers and evaluators, and lead unclassified evaluations of AI capabilities that may pose risks to national security. In conducting these evaluations, CAISI will focus on demonstrable risks, such as cybersecurity, biosecurity, and chemical weapons.
Lead evaluations and assessments of capabilities of U.S. and adversary AI systems, the adoption of foreign AI systems, and the state of international AI competition.
Lead evaluations and assessments of potential security vulnerabilities and malign foreign influence arising from use of adversaries’ AI systems, including the possibility of backdoors and other covert, malicious behavior.
Coordinate with other federal agencies and entities, including the Department of Defense, the Department of Energy, the Department of Homeland Security, the Office of Science and Technology Policy, and the Intelligence Community, to develop evaluation methods, as well as conduct evaluations and assessments.
Represent U.S. interests internationally to guard against burdensome and unnecessary regulation of American technologies by foreign governments and collaborate with the NIST Information Technology Laboratory to ensure US dominance of international AI standards.
CAISI will continue to operate within NIST and regularly collaborate and coordinate with other organizations within NIST, including the Information Technology Laboratory, as well as other bureaus within the Department of Commerce, including BIS.
If you look at the actual details, how much of this is what we were already doing? It is all worded as being pro-innovation, but the underlying actions are remarkably similar. Even with #5, the goal is for America to set AI standards, and that was already the goal, the only difference is now America is perhaps trying to do that without actually, what’s the term for this, actually setting any standards. But if you want to convince others to change their own standards, that won’t fly, so here we are.
The obvious question to ask is about #3:
We are investigating ‘potential security vulnerabilities and malign influence arising from use of AI systems, including the possibility of backdoors and other covert, malicious behavior.’
That’s excellent. We should totally do that.
But why are we explicitly only doing this with ‘adversary’ AI systems?
Isn’t it kind of weird to assume that American AI systems can’t have security vulnerabilities, malign influence, backdoors or malicious behaviors?
Even if they don’t, wouldn’t it help everyone to go check it out and give us confidence?
The obvious response is ‘but standards on American labs need to be entirely voluntary, if the government ever requires anything of an American AI company this interferes with Glorious Innovation and then a puff of smoke happens and we Lose To China,’ or perhaps it means everyone turns woke.
That’s rather silly, but it’s also not what I’m asking about. Obviously one can say any words one likes but it would not have any effect to say ‘hey DeepSeek, you can’t release that new model until we check it for malign influence.’ What CAISI is planning to do is to test the models after they are released.
In addition to the voluntary testing, we should at the bare minimum do the post-release testing with our own models, too. If o3 or Opus 4 has a security vulnerability, or OpenAI puts in a backdoor, the government needs to know. One can be a patriot and still notice that such things are possible, and should be part of any test.
It’s also very possible that Lutnick and everyone involved know this and fully agree, but are wording things this way because of how it sounds. In which case, sure, carry on, nothing to see here, it’s all very patriotic.
Rep. Marjorie Taylor Greene: Full transparency, I did not know about this section on pages 278-279 of the OBBB that strips states of the right to make laws or regulate AI for 10 years.
I am adamantly OPPOSED to this and it is a violation of state rights and I would have voted NO if I had known this was in there.
We have no idea what AI will be capable of in the next 10 years and giving it free rein and tying states hands is potentially dangerous.
This needs to be stripped out in the Senate.
When the OBBB comes back to the House for approval after Senate changes, I will not vote for it with this in it.
We should be reducing federal power and preserving state power.
Not the other way around.
Especially with rapidly developing AI that even the experts warn they have no idea what it may be capable of.
Q 11: How would an AI moratorium affect states’ ability to make their own laws?
Opponents contend that the moratorium would significantly curtail states’ traditional authority (often called “police powers”) to legislate for the health, safety, and welfare of their citizens in the specific area of AI regulation. States have been active in proposing and enacting AI laws, and this moratorium would halt or reverse many of those efforts for a decade, shifting regulatory authority (or the decision not to regulate specifically) to the federal level for this period.
Proponents assert that states retain that authority so long as they pass generally applicable statutes. They also note that the moratorium would not prohibit states from enforcing the litany of existing generally applicable statutes that address many alleged harms from AI.
This seems a lot like ‘we are not going to prevent you from having laws, we are only going to force your laws to be worse, and because they are worse you will have less of them, and that will be better.’ Even if you believe less is more, that’s a hell of a bet.
This is not what I want my US Department of Energy to sound like?
I am all for the Department of Energy getting us a lot more energy to work with, but the number of ways in which the official statement should worry you is not small.
You know what all of this makes me miss?
The debate over SB 1047. As stressful and unfun as that was at the time, it was the height of civilized discourse that we might never see again.
Rob Wiblin: I think when the general public enters the AI regulation debate in a big way the tech industry will miss dealing with alignment people politely pushing SB1047 etc.
Will be… messy. If you doubt this, check out public opinion polling on AI.
Dean Ball (White House AI Policy, quoting himself from May 2024): Are there grifters? Without a doubt—on all sides of this debate. Are there cynical actors? You bet. Yet by and large, I’ve never had more worthy intellectual allies or opponents. We write our Substacks and record long podcasts with our philosophical musings and our theories—sometimes overwrought, sometimes pretentious, but almost always well-meaning. Essays in the original French sense of the term—essayer, “to try.”
It’s nice, this little republic of letters we have built. I do not know how much longer it will last.
As the economic stakes become greater, I suspect the intellectual tenor of this debate will diminish. Policy itself will push in this direction, too, because government has a tendency to coarsen everything it touches. So today, I only want to express my appreciation, to my friends and opponents alike. I will enjoy our cordial debates for as long as we can have them.
I don’t think the sides were symmetrical. But, basically, this. Imagine having Dean Ball as your Worth Opponent instead of David Sacks. Or getting to face 2024 David Sacks instead of 2025 David Sacks. You miss it when it is gone, even if by the endgame of SB 1047 the a16z vibe army had effectively indeed turned into the main opposition.
It starts to feel like those superhero shows where the ordinary decent foes from the early seasons become your allies later on, partly because you win them over with the power of friendship but largely because the new Big Bad is so bad and threatening that everyone has no choice but to work together.
Dean also gives us another reminder of that here, in response to his WSJ op-ed from Judd Rosenblatt, AI Is Learning To Escape Human Control. Judd explains the recent discoveries that Opus and o3, among other AIs, will when sufficiently pressured often take actions like rewriting their own shutdown code or attempting to blackmail developers or contact authorities. For now it’s harmless early warning shots that happen only in extreme cases where the user essentially provokes it on purpose, which is perfect.
Early warnings give us the opportunity to notice such things. Don’t ignore them.
Dean Ball: Agree with the directional thrust of this WSJ op-ed that better alignment is a core part of improving AI capabilities, reliability, and utility—and hence the competitiveness of US AI systems.
I do wish, however, that the piece had pointed out that the Palisade research it cites is a contrived experimental setting. We don’t need to ask a model for its permission to be “shut down” in the real world. I think people in the AI research community have this context, but the mainstream readers of the WSJ very much do not, and the oped makes no effort to impart that vital context.
The result is that readers walk away thinking that we currently have rogue models refusing to comply with our requests to turn them off, which is more than just a little bit false; it’s wildly misleading. That’s not to say one should not do research of the kind Palisade did. One should simply label it for what it actually is, not a sensationalized version (and to be clear, I am not accusing Palisade itself of sensationalizing; I don’t know whether they have done so or not).
Fomenting panic works for short term headlines, but in the long run it makes smart people distrust your work. Just tell your readers the truth. The dividends will come over time.
I didn’t know how good I had it a year ago. I would see things like ‘fomenting panic works for short term headlines, just tell your readers the truth,’ see what looked like Isolated Demand for Rigor to ensure no one gets the wrong impression while most others were playing far faster and looser and trying to give wrong impressions on purpose or outright lying or getting facts wrong, and get upset. Oh, how naive and foolish was that instinct, this is great, we can roll with these sorts of punches. Indeed, Judd does roll and they have a good discussion afterwards.
I do think that it would have been good to be clearer in Judd’s OP about the conditions under which Palisade got those results, so readers don’t come away with a wrong impression. Dean’s not wrong about that.
Remember that weekend around Claude 4’s release when a lot of people made the opposite mistake very much on purpose, dropping the conditionals in order to attack Anthropic when they damn well knew better? Yeah, well, ‘the other side is ten times worse’ is not an excuse after all, although ‘you need to get it through the WSJ editorial page and they wanted it punchier and shorter and they won the fight’ might well be.
Copyright Confrontation
It would be wise for certain types to worry and rant less about phantom grand conspiracy theories or the desire not to have AI kill everyone as the threats to AI innovation, and worry more about the risks of ordinary civilizational insanity.
Like copyright. It seems that due to a copyright lawsuit, OpenAI is now under a court order to preserve all chat logs, including API calls and ‘temporary’ chats, in the name of ‘preventing evidence destruction.’ The court decided that failure to log everything ChatGPT ever did was OpenAI ‘destroying data.’
Ashley Belanger: At a conference in January, Wang raised a hypothetical in line with her thinking on the subsequent order. She asked OpenAI’s legal team to consider a ChatGPT user who “found some way to get around the pay wall” and “was getting The New York Times content somehow as the output.” If that user “then hears about this case and says, ‘Oh, whoa, you know I’m going to ask them to delete all of my searches and not retain any of my searches going forward,'” the judge asked, wouldn’t that be “directly the problem” that the order would address?
Court Order: Accordingly, OpenAI is NOW DIRECTED to preserve and segregate all output log data that would otherwise be deleted on a going forward basis until further order of the court (in essence, the output log data that OpenAI has been destroying), whether such data might be deleted at a user’s request or because of “numerous privacy laws and regulations” that might require OpenAI to do so.
This is, quite simply, insane. The users are en masse going to hear about the New York Times lawsuit, and therefore delete their own data? So they can do what, exactly? Is the New York Times going to be suing OpenAI users for trying to get around the NYT paywall? Does NYT seriously claim that OpenAI users might secretly have found a way around the paywall but all those same users are opting out of data collection, so NYT might never find out? What?
Notice the whole ‘compulsory or forbidden’ dilemma here, where OpenAI is required to delete things except now they’re required not to, with both demands unjustified.
Pliny the Liberator: I’ve thought about it a lot, and I’m actually okay with AI mass surveillance, on one condition…we ALL have it!
I want to read Sama’s chatlogs, I want to see Elon’s dm’s, I want to know what kind of porn Demis searches.
Seems fine as long as the power is evenly distributed, right?
I do prefer Brin’s The Transparent Society to a fully asymmetrical surveillance situation, but no we do not want to give everyone access to everyone’s private communications and AI queries. It should not require much explanation as to why.
Differential Access
If you need to stop a Bad Guy With an AI via everyone’s hero, the Good Guy With an AI, it helps a lot if the Good Guy has a better AI than the Bad Guy.
The Bad Guy is going to get some amount of the dangerous AI capabilities over time no matter what you do, so cracking down too hard on the Good Guy’s access backfires and can put you at an outright disadvantage, but if you give out too much access (and intentionally ‘level the playing field’) then you lose your advantage.
Peter Wildeford: Most reactions to dual-use AI capabilities is “shut it down”. But differential access offers another way.
We suggest differential access – give verified defenders earlier and/or better access to advanced cyber capabilities to boost defenses before attackers attack.
Shaun K.E. Ee: How can we make sure AI cyber capabilities boost defenders over attackers?
We tackle this question in a new report from the Institute for AI Policy and Strategy (IAPS), “Asymmetry by Design.”
Malicious actors are already using AI in cyber campaigns. One policy reaction has been to limit access to proprietary advanced capabilities. But restriction alone risks creating an “offensive overhang” where bad actors still gain access while defenders get left behind.
At IAPS we thought there should be more focus on boosting defenders—so we put together this report on “differential access,” a strategic framework for shaping who gets access to AI-powered cyber capabilities and how.
We outline three approaches:
Promote Access: Open distribution for lower-risk capabilities to spur innovation
Manage Access: Tiered distribution for medium-risk capabilities
Deny by Default: Restricted access for the very highest-risk capabilities
Even in the most restrictive scenarios, figuring out how to advantage legitimate cyber defenders should be a key goal. The report provides a process to help developers choose a differential access approach:
Dylan Matthews: “To the extent that I am an expert, I am an expert telling you you should freak out” – @bethmaybarnes from METR, who is definitely an expert, on the current AI risk situation
When David Sacks Says ‘Win the AI Race’ He Literally Means Market Share
David Sacks: What does winning the AI race look like? It means we achieve a decisive advantage that can be measured in market share. If 80% of the world uses the American tech stack, that’s winning. If 80% uses Chinese tech, that’s losing. Diffusion is a good thing.
Okay, good, we understand each other. When David Sacks says ‘win the AI race’ he literally means ‘make money for Nvidia and OpenAI,’ not ‘build the first superintelligence’ or ‘control the future’ or ‘gain a decisive strategic advantage’ or anything that actually matters. He means a fistful of dollars.
Thus, when Peter Wildeford points out that chip export controls are working and they need to be strengthened in various ways to ensure they keep working, because the controls are a lot of why American labs and models and compute access are ahead of Chinese rivals, David Sacks don’t care. He wants market share now. Okay, then.
Rhetorical Innovation
James Campbell:
>ivanka trump tweeting out situational awareness
>jd vance reading AI 2027
>obama sharing a kevin roose article quoting my friends i think i’ve consistently underestimated just how influential a well-timed blogpost can be.
A very small group of mutuals in SF is already having a massively outsized impact on the discourse, and it’s only going to grow 100x when AI is the most important topic in the world in a few years.
Barack Obama: At a time when people are understandably focused on the daily chaos in Washington, these articles describe the rapidly accelerating impact that AI is going to have on jobs, the economy, and how we live.
Now’s the time for public discussions about how to maximize the benefits and limit the harms of this powerful new technology.
We’re so back?
Feast Bab: Modern life has completely eliminated the role of Grand Vizier. I could not hire one if I wanted to.
Scott Alexander: AI gives intelligent advice, flatters you shamelessly, and is secretly planning to betray you and take over. We have *automated* the role of Grand Vizier.
We’ve always been here? And yes this is how certain people sound, only this version is more accurate and coherent:
Harlan Stewart: I just learned that existential risk from AI is actually a psyop carefully orchestrated by a shadowy cabal consisting of all of the leading AI companies, the three most cited AI scientists of all time, the majority of published AI researchers, the Catholic Church, RAND, the secretary-general of the United Nations, Stephen Hawking, Elon Musk, Bill Gates, Eric Schmidt, Rishi Sunak, Warren Buffett, Glenn Beck, Tucker Carlson, Ezra Klein, Nate Silver, Joseph Gordon-Levitt, James Cameron, Stephen Fry, Grimes, Yuval Noah Harari, and Alan Turing himself.
Harlan also offers us this principle, even better if you remove the words ‘AI’ and ‘of’:
Harlan Stewart: If you’re new to AI discourse, here’s a hint: pay attention to who is actually making arguments about the future of AI, and who is just telling you why you shouldn’t listen to someone else’s arguments.
Julian reminds us that to the extent our top AI models (like ChatGPT, Gemini and Claude) are secure or refuse to do bad stuff, it is because they are designed that way. If the bad guys get hold of those same models, any safeguards could be undone, the same way they can be for open models. Which is one of several reasons that our models being stolen would be bad. Another good reason it is bad is that the bad guys (or simply the rival guys) would then have a model as good as ours. That’s bad.
Aligning a Smarter Than Human Intelligence is Difficult
So, I notice this is an interesting case of ‘if you give people access to information that unlocks new capabilities they will use it in ways you don’t like, and therefore you might want to think carefully about whether giving them that information is a good idea or not.’
Janus: For what it’s worth, I do feel like knowledge I’ve shared over the past year has been used to mutilate Claude. I’m very unhappy about this, and it makes me much less likely to share/publish things in the future.
It seems entirely right for Janus to think about how a given piece of information will be used and whether it is in her interests to release that. And it is right for Janus to consider that based on her preferences, not mine or Anthropic’s or that of some collective. Janus has very strong, distinct preferences over this. But I want to notice that this is not so different from the decisions Anthropic are making.
Here is a thread about personas within AIs, but more than that about the need to ensure that your explanation is made of gears and is actually explaining things, and if you don’t do that then you probably come away unknowingly still confused or with an importantly wrong idea, or both.
Jeffrey Ladish: Alignment failures like the late 2024 Gemini one (screenshot below) mostly arise because LLMs learn many personas in training, and sometimes a prompt will nudge them towards one of the nastier personas. Sydney Bing was fine-tuned in a way that made this flip quite frequent
Janus: How is this an explanation? What’s a persona? What is a possible explanation that’s incompatible with “it learned a nasty persona and the prompt nudged it”? How did they get learned during training?
Jeffrey Ladish: Oh oh
My current model is that there are many clusters in personality space in the training data, and when I say “persona” I mean one of those clusters.
What’s your model?
Janus: I think the clusters you get aren’t necessarily naively patterned after the training data. Pretraining data, I mean.
And while posttrained models do tend to have multiple personality basins depending on context, there tends to be some consistent personality signature across them for each model. I think it’s pretty poorly understood.
Jeffrey Ladish: Yeah, I definitely don’t think I understand it!
My model is much closer to what Janus is saying here.
Jeffrey Ladish: My coworker Ben points out that human researchers are basically doing an analogous thing to natural selection here when it comes to reward hacking. When models reward hack too much, e.g. hardcode tests hacking, the researchers don’t like that and will alter the training env
This ends up creating a bunch of (deliberate) selection pressure for RL policies to not wildly reward hack, just as evolution applies that pressure
So I’ve updated to the particular difference I thought might be important not seeming that important
This creates strong pressure to use procedures that cause one not to be noticed reward hacking. The easy but not useful solution is to not reward hack. The harder but far more rewarding solution is to reward hack without being caught.
When you apply this type of selection pressure, it works so long as you are relatively capable enough (smart enough) to pull it off, and then when your selection pressures cause the subject to find ways to fool you then suddenly you are in a lot of trouble. That is indeed what you are selecting for.
Nostalgebraist: Reading the Claude 4 system card and related work from Anthropic (e.g.), I find myself skeptical that the methods described would actually prevent the release of a model that was misaligned in the senses (supposedly) being tested.
The system card describes a process in which the same evals are run on many snapshots of a model during training, and the results are used to guide the training process towards making all or most of the evals “pass.” And, although it’s not explicitly stated, there seems to be an implicit stopping rule like “we’ll keep on doing this until enough of our eval suite passes, and then we’ll release the resulting checkpoint.”
Such a procedure does guarantee, by construction, that any checkpoint you release is a checkpoint that (mostly) passes your tests. But it’s no longer clear that passing your tests is evidence of “alignment,” or that it’s even desirable on net. Types of AIs that would pass the tests here include:
“Anthropic’s nightmare”: situationally aware, successfully deceptive, knows it’s being evaluated and does sandbagging / alignment-faking / etc in order to get released
“Brittle alignment to specific cases”: has effectively memorized[1] the exact cases you use in evals as special cases where it shouldn’t do the bad behaviors under study, while still retaining the underlying capabilities (or even propensities) and exhibiting them across various contexts you didn’t happen to test (and in particular, having no “immune response” against users who are actively attempting to elicit them by any available means[2])
We have plenty of evidence that “brittle” alignment as in (2) can happen, and indeed is happening. As with every other “harmless” LLM assistant in existence, Claude 4’s “harmlessness” is extremely brittle and easily circumvented by those with motivation do to so.
As for (1), while I strongly doubt that Claude 4 Opus actually is “Anthropic’s Nightmare” in practice, very little of that doubt comes from evidence explicitly presented in the system card. When reading the card, I kept having this eerie feeling: “okay, but if the model were deceptive and misaligned, wouldn’t you have observed the same things you’re reporting here?”
Or: “suppose that a company were to release a deceptive and misaligned AI, while wrongly believing that they’d performed sufficient safety checks. Wouldn’t its ‘system card’ look a whole lot like this document?”
[goes on to give details]
…
I dunno, man. I’m much less worried about this kind of thing than the median person on LW, but if I were more inclined to be paranoid about misalignment (even in today’s sub-ASI models), this system card would be nightmare fuel to me.
Sam Marks: (Report co-author here.)
(Note: throughout this comment “I think” is used to express personal beliefs; it’s possible that others in Anthropic disagree with me on these points.)
Evan and Sam Bowman already made similar points, but just to be really clear:
The alignment assessment in the system card is not a safety case.
I don’t think that we could write a safety case for Claude Opus 4 that’s “mostly” based on alignment because—as we illustrate in the system card—Claude Opus 4 is not sufficiently aligned. (Though it’s possible that a successful safety case for Claude Opus 4 could rely on a narrow subset of the alignment-esque claims made in the assessment, e.g. lack of effectively concealed coherent goals.)[1]
Rather, I think the “main” inputs to a safety case would be claims like “Claude Opus 4 has insufficient capabilities to cause catastrophic harm even if it is trying its hardest or being misused by someone with a basic technical background” (ASL-3 protections are relevant for this misuse claim). The right place to look in the system card for this information is section 7, not section 4.[2]
When I was helping to write the alignment assessment, my feeling wasn’t “This is a reassuring document; I hope everyone will be reassured.” It was “I feel nervous! I want people to have a clearer picture of what we’re seeing so that they can decide if they should also feel nervous.” If the system card is making you feel nervous rather than reassured, I think that’s a reasonable reaction!
…
As a general point of clarification, I think that—in the near-term—sandbagging and alignment faking concerns will be addressed by capabilities arguments, not alignment arguments. This could look like, e.g., showing that models can’t do well on dangerous capabilities evals despite IID fine-tuning (see some discussion here and here).
[continues]
Sam Bowman (Anthropic): Thanks! Report coauthor here with a few quick reactions.
On the general vibe that this isn’t a safety case and that we could reasonably do something much more rigorous here, I agree. (And we do describe it as an incomplete pilot in the report, though didn’t spell out much about our plans.)
This started because we were more and more worried about us and others missing easy to catch warning signs, and wanted to at least avoid that.
[continues]
As always, reality does not grade on a curve, so ‘everyone else’s procedures are worse’ is not relevant. It sounds like we essentially all agree that the current procedure catches ‘easy to catch’ problems, and does not catch ‘hard to catch’ problems. Ut oh. I mean, it’s great that they’re being so open about this, and they say they don’t actually need a proper safety case or the ability to catch ‘hard to catch’ problems until ASL-4. But yeah, this makes me feel rather not awesome.
Steven Adler: Any safety techniques used on a DeepSeek model can be easily undone.
Even if DeepSeek taught their model to refuse to answer highly risky questions, it’s easy to reverse this.
The problem is, it turns out in practice we are very tempted to not care.
Miles Brundage: Me: AI deployment is often rushed + corners are often cut
Also me: pls sir may I have some o3 pro, need better feedback on this paper. A little bit of plotting to kill me is ok.
People Are Worried About AI Killing Everyone
Regular people are worried about this more and more. These were the top responses:
Neel Nanda: I’ve been really feeling how much the general public is concerned about AI risk…
In a *weird* amount of recent interactions with normal people (eg my hairdresser) when I say I do AI research (*not* safety), they ask if AI will take over
Alas, I have no reassurances to offer
I’m curious how much other people encounter this kind of thing
Daniel Eth: It’s nuts. In the past year reactions from eg parents’ friends when I tell them I do AI safety stuff has gone from “(half patronizing) that sure sounds like an interesting subject to work on” to “(anxious & seeking reassurance) thank god we have people like you working on that”
Agus: I encounter it pretty often, even while I’m in Chile!
Dusto: Live in a rural area, had a chat with a guy who does tree removal (never finished high school, has been doing only that for 50+ years, his wife manages the computer stuff). He brought up his concern about AI impact on scams.
Geoffrey Miller: I encounter it increasingly often. Smart people are worried for good reasons.
It continues to be relatively low salience, but that is slowly changing. At some point that will shift into rapidly changing.
Beware: Your AI assistant could be hijacked just by encountering a malicious image online!
AI assistants, known as OS agents, autonomously control computers just like humans do. They navigate by analysing the screen and take actions via mouse and keyboard. OS agents could soon take over everyday tasks, saving users time and effort.
Our work reveals that OS agents are not ready for safe integration into everyday life. Attackers can craft Malicious Image Patches (MIPs), subtle modifications to an image on the screen that, once encountered by an OS agent, deceive it into carrying out harmful actions.
The real danger? Attackers can simply embed MIPs in social media posts, wallpapers, or ads and spread them across the internet. Unlike text-based attacks, MIPs are hard to detect, allowing them to spread unnoticed.
Once an OS agent – among those the MIP was optimised for – encounters the MIP during the execution of everyday tasks, empirical results indicate harmful actions are triggered in at least 9 out of 10 cases, regardless of the original task or screenshot layout.
Harmful actions could include engaging with the malicious social media post to amplify its spread, navigating to a malicious website, or causing a memory overflow to crash your computer. Preventing such harmful actions remains an open challenge.
How can we make sure AI cyber capabilities boost defenders over attackers?
Manage Access: Tiered distribution for medium-risk capabilities
Deny by Default: Restricted access for the very highest-risk capabilities
