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I think we would always still prefer like a human that we had a relationship with because the way that we get motivated to be interesting interested in things is a social phenomenon.
I think the way that you'd measure conjecture generating ability is going to be more subjective on like that tone shift where um it'll be mathematicians saying they're not just using it to like solve their problems, but as they step back and decide what their research field should even be that a conversation with such and such model like was genuinely helpful for that.
but it would be a little bit disappointing and a little bit surprising if there weren't over the next 5 years like, uh, economically valuable improvements that were made that were directly like referable to the like AI progress in math.
that like if it's capable of building mountains uh that are, you know, the correct new theory that like crystallizes how we should be thinking about a subject, that's just such a level of intelligence that then it starts to feel like it would be surprising if that didn't permeate into other aspects of the economy besides like just the mountain building for math itself.
I believe that a Mac mini with Codex running on it is the best bang for your buck if you’re building an always-on agentic setup based on a frontier model that can do just about anything you throw at it.
I believe that a Mac mini with Codex running on it is the best bang for your buck if you’re building an always-on agentic setup based on a frontier model that can do just about anything you throw at it.
This is why I recommend ignoring the vast web of custom and “community-built” MCP servers you can find out there, and focusing your attention on the two safer bets instead
If the user doesn't actively push back against the AI model, then they get the generic output, the lowest common acceptable denominator of aesthetics and taste.
this kind of instantly ubiquitous homogeneity is going to happen across every field of human endeavor in the era of AI, until AI gets so powerful that it actually becomes original (doubtful) or until we shut it down.
it’s also true that their impact on our output was never as tremendous or as clear-cut as that of prior innovations, such as the steam engine or the power loom, and that integrating them effectively into the workforce took a long time
AI uses em dashes. Humans use em dashes. That does not make an em dash an AI fingerprint any more than Batman's cape makes everyone in a bath towel Batman.
But now with Claude Code on my VPS in the last year, it just live edits on my production server, which sounds like it should go wrong but it just doesn't, it's very careful and only twice in 12 months messed up which meant my site didn't load for 10 seconds which is OK
I agree with @theo completely, it's clear to me this is where it's going, also seeing @karpathy with Claude moving to the cloud (via Slack etc), I think AI "agents" and AI coding will operate on servers / from the cloud first
elevators are the rare part of the economy where Europeans have embraced market dynamism while Americans choose overregulation and labor market rigidity.
untrusted repos should be treated as hostile by default because they can steer the agent toward reading files, running commands, or sending data through approved tools.
it's not that humans have become smarter over it's not that they evolved to become smarter over, you know, the past 50,000 years. It's that humans are able to do a lot more today than they were back in caveman times because there have been billions of humans thinking for a long time and building off of each other's accumulated knowledge.
But what we're seeing today with the modern models is that 5.5 and other models can think for if you scaffold them reasonably well, can think for weeks even um before having performance plateau on some of these benchmarks. And so, the point at which they plateau is simply too far out to reasonably test.
my claim is the proper way to evaluate the models now is you either have some kind of budget for the benchmark whether it's tokens or cost or time or whatever or you plot the performance as a function of the amount of test time compute that's going into the model
I think the reason why it doesn't show up as so much better on the benchmarks is because the benchmarks are being presented, the benchmark results are being presented in the wrong way. They're not controlling for the amount of test time compute that is being used on that benchmark question.
And so you kind of end up in this this bad equilibrium where everybody kind of knows that it's a bad equilibrium, but like nobody wants to break out. And I I felt like, okay, well, if I just hopefully come out and say like, look guys, let's all recognize that we're in a bad equilibrium and let's move to this different equilibrium where we're we're plotting things with an X-axis
And even the cost issue with AI is probably going to be like once the subsidies start running out, which we're starting to see, I think that's going to be a really big issue where maybe all these companies that embraced AI programming are now going to like cut back on it.
I think a lot of people, especially students, are unfortunately learning everything through LLMs. So a lot of that isn't really learning, they're just kind of cheating and they're just doing everything like that. And then they lose a lot of their skills
But in terms of like features, like a website like you can just throw features in there nowadays that nobody really cares about and you can you can do it so quickly. Like a new feature every single day, but do people actually care about that? Is that making it better? It could be making things worse.
I think a lot of the things that people might say that like oh, it was a waste of time to learn this subject cuz I didn't actually use like those details on the job. I think that's a very wrong way to think about it. And I think that's what a lot of people are doing now with AI. Like hey, what what if I'm not going to be writing for a loops a couple years from now. Um I don't think those things are a waste of time.
I think Google has pretty much gone to on-sites at this point, back to the traditional whiteboard format. And they'll let you code on a laptop if you want to, as well, but it's going to be in person. Somebody's going to be watching you code, and you're probably not going to be able to cheat your way through that.
although students' personal statements seem more creative because they use more varied words, they actually feature less original ideas. AI writing produces an illusion of creativity.
Generative AI has no internal, designed momentum towards truth and accuracy (beyond the absurdly diminishing returns of energy-hungry multi-layered LLMs), but a person or an institution can (and should).
So you know, just like internet, you can see some of them turn out to be very big like Amazon, like the Netflix and then some of them is kind of go sideways and disappeared or being acquired. And so I think to me is the same approach.
You know, right now there's a massive build-up in term of the AI, you know, the I think it's the right thing to do. I don't see that in anything to slow it down uh because the workload is increasing a lot.
One is of course everybody knows power constraint. Some country the power they just don't have that. They get impacted. And then secondly, a lot of people didn't realize the helium impact can be also very significant for semiconductor. And then the thirdly, is everybody know right now memory is a bigger shortage.
In the English-language arena, Britten sets the standard for handling writers of inborn musical power-the likes of Shakespeare, Donne, Blake, Keats, Hopkins.
the council does not simply keep the best bits from everyone. It keeps a minority of the good ideas, while peer review seems to give consensus ideas an extra push.
the way we should be using AI is as a testing machine, a failure machine, and a way to vibe code, cloud code, but but build build the, you know, the the lowest possible cycled version of your product that you can get signal back on.
there there's a one question we got to back up and really explore which is is AI a new platform and I would argue that it is not yet a new platform. It is an important technology.
when you have an exponentially growing curve, I think the way that an exponential curve feels is it's growing so quickly that the the kind of emotional feeling is it can't possibly keep going, right?
We don't believe in this like very centralized future where there should be a small number of institutions that um that basically are are advancing all this stuff. Our vision is not that there's going to be like some central super intelligence that solves all of science.
in order to make progress in AI you don't need like many many hundreds of AI researchers um or thousands or anything like that I think you can really make progress with um you know a very strong group of a dozen or a couple dozen people.
I think like people are really important and I think we'll be more important in the future and giving people more tools to be more productive is going to be like a critical part of any kind of positive future
I believe that the path to success is best achieved by putting the best human intelligence together with the best artificial intelligence and that the way of thinking I am describing here is essential to understand and use in the new human/artificial intelligence era.
I know through my experiences that even the most advanced artificial intelligences don’t have adequate enough insights to allow one to blindly follow them and that unique human understanding and insights are still invaluable, and that that is especially true in investing where value-added is a zero-sum game (so that, when it comes to adding value, what is widely known is of little value.)
To be clear, these criteria are not best derived by looking at what would have worked in the past and assuming that it will work in the future—i.e., data mining—or simply asking an AI what to do. They are based on logical understandings converted into decision-making systems.
What this process can now do in creating understanding of the timeless and universal cause:effect relationship and enhancing and systemizing whatever one is thinking is mind-blowing. I believe that you will either stay at the cutting edge of doing this or you will be uncompetitive.
This is a super exciting release - Claude Fable 5 is the same underlying model as Mythos but with added safeguards. The benchmarks are great and it's SOTA on everything by a margin but I'll add that *qualitatively* also, this is a major-version-bump-deserving step change forward
the regulation gets extremely difficult and mundane and expensive that could actually lead to more igopoly and I think some of the players know that and are begging for regulation.
We do live in a world where information is really cut up, but we also live in a world where you can have access to more information than you ever could. And that's even more true now with LLMs.
we're still going to need a display cuz sorry people, unless we're plugging it into our brain like a BCI brain computer or there's some laser thing going into our retina, we're going to need a display.
What's proven is if you properly architect it and have co- cloud code go into certain sub segments or have cloud help you build the architecture, you modify, refine it, lock it in and say just work on these few things
In 2026, long-context efficiency is king as more and more LLMs get plugged into agent harnesses (OpenClaw etc.), which requires working with longer and longer contexts.
Like right now we're endowed with labor that can turn into uh that can turn into income. When that is no longer the case and we are now at the mercy of the of the elected official for like basic needs, right? So that to me feels like a power sharing arrangement that's really dangerous.
They just recently released a report, and I think like you really have to squint to see anything happening. Like basically, if you want to take kind of like uh an an approach across the entire economy and looking at even looking at like software engineering, like the most exposed sort of sectors, there's just like not really anything going on. There might be a little bit of a signal about like junior developers getting jobs less than before, and that but that's like a less than before rather than a level shift.
So, I think there is a world where it is concentrated, in which case it's going to be really hard to index AGI. There is another world where it is not It's electricity, then like basically every company has access to AGI. So, you just buy you use buy the index. So, like, you know, Nigeria just needs to buy the index.
rather than thinking about individual forecasts like what me and Phil are going to do, rather looking at kind of like basically generating prediction markets, where you get aggregate forecasts, where you get like kind of wisdom of the crowd effects. And kind of the reason that I think this is because we have been famously terrible at forecasting.
we don't have any data. I've been kind of saying we need a Manhattan Project for data. We don't have data on basically consumer demand elasticities. We don't know what they are.
things have to go really wrong for us to like just get over the threshold of uh you know, capital being productive enough to automate lots of work, but not be productive enough that that the interest rate is high and or the price of capital produced goods is falling a lot, okay? So, even without redistribution, a little bit of savings will save a lot of people.
some people think either uh frontier AI gets commoditized and we all enjoy the benefits, but there might be some risk because like it's the market's really competitive and cutthroat, or um things are safer because there's a big gap between the leader and the laggard, but that means that the leaders get fantastically wealthy. No, like you could just have a relatively big gap, but it's a public company ownership and it's widely distributed.
if I had to guess I would guess that the kind of long kind of general trend of just like lowering those frictions and making it easier for more and more people to index more and more will continue despite the recent bump in the other direction.
it's already not that hard to index. So it's not There's been a bit of an increase in the privatization of returns but it's still like you know well under 20% of the total market cap of um non-non-tiny companies in in the US is is a private.
here prices are adjusting in this interesting way that too many macro models don't allow for, right? So, that what what a what's happening is what would be called investment specific technical change where yeah, the price of capital is like falling relative to the price of consumption instead of like the standard doing the standard macro thing of saying there's just output.
there's a consistent theme where Gen Z (and millennials, to some extent) are rejecting the AI hype while older generations are optimistic and coincidentally in a position to benefit from it.
So I'm looking at this like guys the fundamentals of this isn't like everything is going to change because of MCP. What the hell? A whole conference guys? This is just API design.
If you all pivot to AI, then you all will have a problem. It's like kids learning to play soccer. They all run to the ball. No strategy. Spread out, figure where you add value and play your position.
So you have a lot of smart Googlers. These people are brilliant. I mean extremely brilliant to the point where the hardest problem Google had in my opinion was what to build. Not how to build it, what to build.
do not say AI because what we don't want to do is use a big umbrella to describe what you're doing. Let's get concrete details. These are computers. These are computer programs.
Well, I love these markets and they're all around us where we think they're mature and over and they haven't even started yet. And that was search before Google. You know, Google was the 56th search engine. It was a mature, slow growth business. Google made us reimagine what search could be in our lives and obviously turned it into a trillion dollar company value.
LLMs themselves are the perfect captive reader: they never get bored or confused, never miss a reference or have an emotional reaction, never ask themselves why am I reading this?, never close the tab.
I truly believe that following this kind of approach to AI in the classroom would, in the aggregate, produce better learning outcomes than the research and writing workflows available to students pre-AI.
To a certain extent, the concerns about cognitive offloading are an example of technological lag, where the mainstream discussion of AI and its impact is still framed in terms of the first-generation chatbots.
I believe that one dimension of this sadness or demoralization can be attributed to the simple fact that we are increasingly invited to outsource a class of activities that grant us a measure of satisfaction, accomplishment, and purpose.
My working thesis about the generalized impact of “AI” as it is currently deployed can be summed up in the observation that the arc of AI bends toward demoralization.
If AI-driven labor displacement ends up being large in magnitude and permanently drives down the demand for labor, it will likely be necessary to go beyond mere incentive programs to long-term income support for a significant fraction of the labor force.
I had already put both laptops through my benchmark gauntlet, which revealed one theme: the Mac is faster (in most cases), more efficient, quieter, built better, has a much nicer display, and costs much less.
The globalization of generative AI models and tools has caused a kind of leveling, as any aspiring creator anywhere can work in the same visual idioms.
So it's no wonder that the artists and designers whose livelihoods are threatened by it have begun moving aggressively in the opposite direction, toward the scrawled, the sloppy, the seemingly mistake-riddled.
the joke I made was pre AI, I would spend 95% of my energy thinking about what to do and 5% of my energy doing it. Now I spend 96% of my time thinking about what to do and 4% of my time actually doing it. So yeah, it's like a 20% improvement, but dayto-day it feels as hard as ever.
And our harness wasn't very good for like the first like five months of open code. But it was good enough. It was good enough that most people couldn't really tell a difference. And once we won enough share, then we went back and like tried to make our harness like good and smart and optimize and all those things. But uh it was inverted from what everybody else was doing. Everybody else was being like you have to build the smartest harness and that's how you win.
It's like everyone is just saying mantras to themselves cuz the root thing that's going on here is we are experiencing a moment of great change. Everyone is very nervous about what that means for their own position in it. a defense mechanism is to confidently assert a future in which you're a winner. And that's almost what every single prediction that you see is happening.
there is a world where the net result of all these AI coding tools is the same amount of work gets done, but all the engineers are happier because their job is easier. That's not good enough for a lot of companies. So, they're just going to say go back to typing out the code.
But I've always thought it's better to think a lot instead of swinging a lot. I think you could you can eliminate a lot of ideas or directions just by, you know, spending a lot of time in your head and with your team talking. Um, obviously AI doesn't speed that part up.
I think it’s good that the system identified the mechanisms that it did, but I don’t think that the paper does enough to point out that none of these ideas are without precedent - in some cases, a lot of precedent.
And so I think it's it's really important uh when when we think about benchmark progress to think about it from that perspective, which is benchmarks rise on problems that we've framed that we can articulate, that we can score. And there's a lot of work that's human work that uh it it can't be scored until you write it down
I think people think of the edge of AI as being in San Francisco. And I actually don't think that that's where it is. I think the edge of AI is wherever AI meets like a real human doing something.
I think the same kind of principle applies with agents in that they can talk to the compiler. It will tell them what to fix. So I guess this could be a case. We we'll see. But Rust could be a pretty promising candidate for to use for agents because they can get more feedback and it's just hard it's harder to to ship certain type of bugs or maybe impossible to have certain type of bugs.
As reasoning models and agent workflows keep more tokens around (for longer), KV-cache size, memory traffic, and attention cost quickly become the main constraints, and LLM developers are adding a growing number of architecture tricks to reduce those costs.
In a sense, we're all turning into project managers, right? And and we can have an army of junior programmers called agents that will just spit out reams of code, but someone's got to have the big picture and review all of that. And so, increasingly our craft is going from one of writing the code to one of of reviewing the code and and building the architecture of the code and overseeing the work, if if you will.
but but if you have good locality where you you clearly stating what you're importing and whatever and and you can analyze just a single source file and from that extract its protocol to the outside world without having to to know anything deeper. Do you do you know what I mean? I think those are important aspects just simply to reduce the size of the of the of the context window and also make it easier to summarize each module in a program, right?
AI today it's just starting to to become aware of you know the existence of of of language services and agents today like to use grep and awk and whatever you know to to find all the places where you reference a certain thing but it's not semantic search, right?
because if you were to force AI to write a type annotation on everything, then it would probably get it wrong more often because now it has to keep track of all these types and and it and it has to just repeat itself over and over and over, right? And so, types are important where there's no context.
there is nothing fundamentally inherent to the concept of a building full of computers that causes choking air pollution, water impacts and climate change.
It was already clear a while ago that Anthropic mostly do not care about climate, environment or air pollution in any material way, even if they care a little bit more about bad PR than Elon Musk does.
The fundamental drivers of this change are panic and impatience, and that is what is behind the worst impacts of data centre growth, including a material risk to decarbonisation and climate action.
If you say that shareholders should run it, then you're saying literally whoever can borrow the most money should be able to control this technology, it's just that's nuts.
I think the structure frankly is better than founder control. Daio does not have dual class shares the way that Mark Zuckerberg or or Larry and Sergey had.
This is the number one unsolved problem in AI. It's not the tech. We're making great progress on the technical alignment problem. But we haven't made jack progress on the human alignment problem
At even $500/kg, launch cost is only 5% of the total satellite deployment cost, so a lunar mass driver is unlikely to drastically improve the economics of space-based AI, by reducing launch costs.
the reviewer is the principal, the contributor is the agent, and code review only worked because the reviewer could cheaply infer effort from reading the code. Agents collapse that signal.
The bottleneck has shifted from doing the work to writing clear specs and reviewing outputs fast enough to keep the pipeline moving-the middle is hollowing out.
This post is about whether data centers waste the land they’re on relative to what they compete with, and my claim is that they mostly don’t compete with things we need more of (housing) and mostly compete with things we need less of (farmland).
AI is not only an exogenous shock that government will have to absorb. It is also moving the bar on what counts as acceptable service in the first place.
I think technology leaders think that folks will just blindly adopt uh new technology as it comes out and I think we're going to enter a period of time where there's going to be a huge amount of societal pushback on a lot of the changes that are coming uh with AI.
by listing out all these jobs to be done, you know, really for the for the community journey and for advertisers as well, it became very clear where we could use agents, where we needed to be very focused in terms of building cross-functional teams around those jobs supported by AI tools.
One is that the LLMs are getting increasingly good at writing these proofs. And if we don't have to write the proof by hand as humans, it just becomes feasible to do them in situations where previously it would have not been economical.
But also LLMs increase the need for these formal proofs because, you know, we're live coding a bunch of stuff. If we have to manually review all of that code, then that will become the bottleneck.
Maybe as AI writes more and more code of our code, it's less about like the details of how you express logic in a particular programming language and much more about those kinds of high-level trade-offs.
I actually see the opposite of what people see I think the number of agents are going to grow exponentially. The number of tool users are going to grow exponentially and it's very likely that the number of instances of all these tools are going to skyrocket.
We have got to acknowledge that most of the advanc advances in AI came out of algorithm advances not just the raw hardware. Now if most advances came from algorithms and computer science and programming tell me that their army of AI researchers is not their fundamental advantage.
anthropic is is an is a unique instance um and not a trend. Uh without an anthropic, why would there be any TPU growth at all? It's 100% anthropic. Without anthropic, why would there be any tranium growth at all? It's 100% anthropic.
Nvidia's computing stack is the best performance per TCO in the world, bar none. Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company.
these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
I love chiseling my code and the way I use AI is in a separate window. I don't let it drive my code. I've tried that. I've tried the cursors and the wind surfaces and I don't enjoy that way of writing. And one of the reasons I don't enjoy that way of writing is I can literally feel competence draining out of my fingers.
Their words now
I will now start any project I'm starting with. I'm starting agent first and that's a massive shift
Where I get fired up, and this ties back to the AI discussion, is when that's turned into this meme, that programmers no longer have to be competent. I mean the AI is gonna figure it out. The generators is gonna figure it out. I don't need to know SQL, active record is gonna abstract it away from me. No, no, no dude, hold up. The path here is competence.
Their words now
really good programmers are currently more valuable than ever because they're the ones who are able to get the most out of the AI acceleration.
The joy of a programmer, of me as a programmer, is to type the code myself. If I elevate myself, if I promote myself out of programming, I turn myself into a project manager. A project manager of a murder of AI crows as I wrote the other day.
Their words now
I have been hyper accelerated as a programmer. It's a different kind of programmer, but it still has the same affinity to aesthetics, at least when I'm producing Ruby code.
Yeah, AI is an amplifier. And if uh if you're young and learning quickly, AI is going to amplify that or can amplify that. So, I I personally think this is this is the golden age of the junior programmer.
So certainly one thing that a lot of them seem to be bottlenecked on is now having interesting ideas and in particular having interesting design ideas.
Um I think another similarity is I mean, the core notions uh behind Agile and extreme programming are solid and good, but a huge snake oil industry appeared around it, the Agile industrial complex as I like to refer to it. Um and that will happen. That is happening with AI right now, and it's often hard to see the difference between where is the snake oil and where is the real stuff.
but the thing about AI is that, you know, all of many of these things we were talking about how important they were and how valuable were and trying to persuade people of the importance of them. Yes, even the internet. That may sound surprising, but there were people who would weren't thinking that was important. Um but AI, there's kind of no argument about how important it is. People can't can I mean, you cannot put blinkers on to deny the importance of this thing.
one of the things that I took from that was to use this tool well, you have to learn how to use it well, which was also something very true of object orientation.
I expect AI to increase productivity of top mathematicians much more than that of top writers, because formalization is a kind of translation task, it doesn't require much judgement, and as long as it compiles noone needs to read the result.
This took some special care, in particular avoiding any C++ standard library I/O functionality. Current frontier AI cannot handle this detail on their own.
There's a question that never goes away in design: should designers code? My answer has always been yes. But for a decade or so, the complexity of front-end development made it impractical for most. Thankfully, AI coding agents have reopened the door.
For years, it was faster to mock up software than to ship it. Designers stayed "ahead" of engineering with prototypes. Now AI coding agents make development so much faster that the loop has flipped.
Today’s best AI systems are good enough that they’re now inside the fuzzy conceptual cloud of “AGI-ish”: that is, they’ve surpassed some people’s definitions of AGI, while falling well short of others’.
Something I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.
the likelihood of inspiring someone to build a competitor was too high, and the return on sharing was too low to justify the exposure. That threshold has effectively collapsed to zero.
Its responses are, in my view, pretty bland and not very high-quality. I did a side-by-side test of asking Local Deep Research a question, then asking pi the same question (telling it to use searxng to make as many internet searches as needed), and I fed both outputs into an LLM to ask which is better.
I was not impressed with the DGX Spark; it's described as an "AI supercomputer on your desk" but in reality it has lower tokens/sec than a good laptop GPU - and on top of that, you have to figure out the networking details of how to connect to it from your actual work device etc.
There may be a lot of AI things that's going on right now, right? Uh eventually some of these things will consolidate, some will go under, some will become really awesome solutions and all that stuff. And so, but the the market will sort it out.
we have a very clear definition and expectation of what it is at the staff engineer level because we benchmark ourselves to all the great company out there Google, Facebook and all that
it required a different way of thinking and the cognitive load might be a little higher but the output is dramatic and we have seen our best engineer double their output.
the best of the best still get offer from us because if we don't hire those folks today, what senior engineer will we have for yeah years from now, right? You have to feed the talent pipeline
we're trying to crack the next frontier which is how we get that level of productivity increase and output building new features on top of a code base that are older,
And the more you build, right? The more you put things out there, you remove the need for imagination. You can say, "Okay, now I like that website. Do that for me."
But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
So, I think that the whole workflow of like reviewing code is very outdated. Like, I don't think the I think that the senior member, instead of like giving feedback on the code, they should be giving feedback on like how you give instruction to AI to produce better.
Like if it's a big problem, like everyone can see, then all these big companies will get into it. But whereas it's like there are a lot of problems that's like smaller, then maybe OpenAI won't be motivated to solve it, but maybe I can like all a lot of people can.
The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.
The deal was always simple: search engines had permission to crawl sites because they were going to be sending users to those sites. If they're hitting your site half a million times for every one user they send to your site, all they're giving you is higher costs.
As publishers see the danger from AI bots expand, they retreat to putting more and more content behind either password protection or payment walls or both, leaving the only publicly-accessible content to be AI-generated slop; open resources like research work, scientific analysis, and fair use of content all suffer as a result of people responding to the bad actors, since legitimate uses of open content are no longer possible.
This is not the case with many other technology driven industries where capability or advancements often tend to be longer held and a fast-follow model is harder.
One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity.
You can give it a Bash tool so it can ripgrep its way through the codebase. You can give it some queryable codebase index, an LSP server, a vector database. In the end it doesn't matter much. The bigger the codebase, the lower the recall. Low recall means that your agent will, in fact, not find all the code it needs to do a good job.
so many times that process has been managed by humans because you're selecting the right candidate build and you're verifying it and you're thinking, you know, you're figuring out cherry picks and then you like rebundle and then you send it out and that doesn't scale if you have one or two or a handful of people trying to make group decisions and do group sense making.
And so, like in the immediate term, yeah, we were getting more out, but now our systems, whether technology systems or human systems or processes, are really kind of getting overwhelmed.
And so, um I'm seeing across at least a handful of companies that explicit exec sponsorship makes a huge difference in not just using them, but trying new things and feeling safe to fail within, you know, kind of guardrails.
We may have a handful of genies, but that's different from having a handful of friends, right? In part because the energy's different, the conversations are different. Also, they just agree with us constantly.
It's working too hard, right? But that actually isn't burnout. That's just like getting tired. Another piece that's super critical to burnout is not having your values aligned.
a swarm of agents on the internet could collaborate to improve LLMs and could potentially even like run circles around frontier labs. Like who knows, you know? Um yeah, like maybe that's even possible. Like frontier labs have a huge amount of trusted compute but the earth is much bigger and has huge amount of untrusted compute.
Uh but whenever we do a systematic study, um any given problem, an AI tool has a success rate of maybe 1 or 2%. Uh it's just that it's just that they can apply at scale and and you just pick the winners, it looks great.
um you know, these tools they either succeed or they fail um and they they've been really bad at creating sort of partial progress or identifying intermediate um um stages that you should you should focus on first.
Um but now it's quite possible at the high school level or or whatever that that you could get involved in math project and actually make a real contribution because of all these AI tools and and and Lean and everything else.
Yeah, so they excel at breadth and humans excel at depth. Um like human experts at least. Yeah, so um I think they're very complementary. Um but our current uh way of doing math and science is focused on depth because that that's where the human uh expertise cuz humans can't do breadth.
I mean, I I I I do believe that that hybrid um human plus AIs will will dominate mathematics for a lot longer. It it's It will depend It will require some additional breakthroughs uh beyond what we already have.
The competitive moat for a business right now is not the use of AI. It's human-originating, high-quality, high-fidelity data that other systems can't replicate - even if they implement the exact same features and are built by the same agentic systems.
OpenAI and Anthropic have both realized that code is the most important thing to optimize the models for, cuz that's where the money is. Like coders will spend $200 a month on a plan if it's good enough, it turns out.
I think that might be what saves us. I think the fact that no, you can't have one engineer and have him do a thousand projects because after three hours of that he's going to literally pass out in a corner.
LLMs make it easy for anyone to add new features to the product. Even before AI, teams were battling feature creep, where the product becomes increasingly complex, bloated, and cluttered.
Because Enthropic saw it before Google. And then Google had Nano Banano and Gemini 3 which caused their user metrics to skyrocket and leadership at Google was like oh and then they started making the statement of we have to double compute every is it 6 months or I don't remember the exact number that they said.
But I don't know like I don't know what fast timelines means, right? Like I I like don't think you have to believe in AGI to have the timelines where the US wins.
And so I think people are figuring out how to build these things and permitting like I I just like ultimately like permitting and red tape in middle of nowhere Texas or middle of nowhere Wyoming or middle of nowhere like New Mexico is probably a hell of a lot easier than sending stuff into space
So, space data centers effectively are not li limited by, you know, hey, we have this energy advantage. It's actually just limited by the same contended resource. We can only make 200 gawatts of chips a year by the end of the decade.
So when you look at inference at let's say 100 tokens a second for deepseek and kimk 2.5 hopper versus blackwell the performance difference is on the order of 20x
Everybody thinks our schools are in crisis all the time because they’re being forced to do something they were never meant to do, which is to make everyone college-ready, and they’re being forced to do that because we have seen jobs that provide a living wage without a college diploma evaporate.
I think there’s a lot of moral concerns around AI when it comes to creative pursuits as well, like no one’s creative work should ever be used by AI without their permission.
If you're a monolith, you're kind of hosed because I told you the ceiling's going up for what they can do, but it ain't ever going to hit your monolith. They will never fit in the context window and you're never going to be able to never in the next 18 months be able to tell a model go fix my monolith. You have to break it up.
In fact, innovation died on the vine like all together and since I don't know, 2008, there has been no innovation from Google. It's all been acquisitions. They have they've created nothing new.
But right now I think it's sitting somewhere between half million and five million lines of code, somewhere in there. Probably more on the half million side right now and with the next drop of an Anthropic model, we're probably going to see it jump up to a few million lines.
So I think we're in a weird limbo for the rest of this year okay where until the UIs arrive that are good enough for everybody who can't read everybody who can't read is going to be a severe disadvantage.
There's a vampiric effect happening with AI where it gets you excited and you work really really hard and you're capturing a ton of value. For me, I'm doing it all for myself and it's still kind of like pushing me to my ragged edge.
At Anthropic, we don't build for the model of today, we build for the model of six months from now. And that's still my advice to founders that are building on LLMs.
We need to accept that at best, we will just barely avoid some of the worst case scenarios (e.g., an AI-enabled biological weapon that kills billions, a rogue AI takeover, or stable global totalitarianism enabled by AI), given the current pace of AI capabilities relative to the pace of governance.
I think frontier AI auditing (covering both safety and security) is both urgently needed and doable, and can help avoid extreme concentration of power, since it introduces external oversight into the decision-making of people with a "country of geniuses in a datacenter" at their disposal.
Extrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.
Yes, I could totally see how OpenClaw could become a huge company. And no, it’s not really exciting for me. I’m a builder at heart. I did the whole creating-a-company game already, poured 13 years of my life into it and learned a lot. What I want is to change the world, not build a large company and teaming up with OpenAI is the fastest way to bring this to everyone.
But but that's actually a very weak criterion, right? People thought I was saying like we won't need 90% of the software engineers. Those things are worlds apart, right?
My one little bit, the one little bit of of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable. Like, planning a mission to Mars, like, uh you know, doing some fundamental scientific discovery like like CRISPR, like, you know, writing a writing a novel. Hard to hard to verify those tasks.
on the basic hypothesis of you know, as you put it, within 10 years we'll get to, you know, you know, what I call kind of country of geniuses in a data center. I'm at like 90% on that. Um and it's hard to go much higher than 90% cuz the world is so unpredictable.
I- I can relate that if you very deeply identify that you are a programmer, that it's scary and that it's threatening because what you like and what you're really good at is now being done by a soulless or not entity. But I don't think you're just a programmer. That's a very limiting view of your craft. You are, you are still a builder.
So, I feel we, as a society, we need some catching up to do in terms of understanding that AI is incredibly powerful, but it's not always right. It's not, it's not all-powerful, you know?
and not in 2030 when, when AI is actually at the level where it could be scary. So, this happening now and people starting discussion, maybe there's even something good that comes out of it.
So, I, I completely moved away from that. I, I, everything, everything I blog is organic, handwritten and maybe, maybe I, I, I use AI as a fix my worse typos. But there's value in the rough parts of an actual human.
Long lead times and soft costs, fueled by the world-leading US wages for "white collar" work, are the root cause of poor performance in low-volume production because there are few units to spread the soft costs over.
It’s actually how good they are at programming is almost a burden in their ability to empathize with the system that’s starting from scratch. It’s a totally new paradigm of, like, how to program. You really, really have to empathize.
my criticism of MoltBook is that I believe a lot of the stuff that was screenshotted is human prompted. Which, just look at the incentive of how the whole thing was used. It’s obvious to me at least that a lot of it was humans prompting the thing so they can then screenshot it and post it on X in order to go viral.
there’s like a line to walk between being seriously concerned, but not fearmongering because fearmongering destroys the possibility of creating something special with a thing.
We show that equilibrium generically occurs at neither the Harberger nor Glaeser-Luttmer benchmark. Cost-minimizing suppliers drive allocations to vertices, not interiors. Corners are not an assumption but an outcome about what cost-minimizing suppliers choose. The correct benchmark is corners, not random, and corners generate qualitatively different welfare properties: losses far larger than either efficient or random distributions, and discontinuous jumps from small parameter perturbations.
Furthermore, I believe it’s very unlikely that space-based manufacturing or mining could be lucrative enough to fund the venture.
Their words now
Today, exploding demand for power-intensive AI applications provides enough of an upside to justify producing components in space, providing the economic engine necessary to justify and fund the trillions of dollars necessary to build and sustain space factories.
The economics of orbital “datacenters” or essentially glorified Starlink satellites with a bunch of GPUs attached are likely to be even better than Starlink.
I think that there will be lots of work for us cleaning up after the slop, but if you know what you're doing AI augmented development is going to get you some amazing results
I've realized that performance engineering as we know it may not be enough - I'm thinking of new engineering methods so that we can find bigger optimizations than we have before, and find them faster.
The staggering and fast-growing cost of AI datacenters is a call for performance engineering like no other in history; it's not just about saving costs - it's about saving the planet.
Immediately cease trying to perform meaningful work via a chatbot (e.g. ChatGPT, Gemini on the web, etc.). Chatbots have real value and are a daily part of my AI workflow, but their utility in coding is highly limited because you're mostly hoping they come up with the right results based on their prior training, and correcting them involves a human (you) to tell them they're wrong repeatedly.
I particularly like to combine this with slower, more thoughtful models like Amp's deep mode (which is basically just GPT-5.2-Codex) which can take upwards of 30+ minutes to make small changes. The flip side of that is that it does tend to produce very good results.
As high-level programming cedes way to the prose compiler, making your goals and specs well understood to the ambiguity loop and showing good judgment is going to matter more than ever.
if you think about it, it sort of makes sense because the LLM is an averaging machine, right? It's predicting the most likely that's an averaging kind of a thing. And so when what you're looking for is a kind of average, it's usually pretty good. So, summarization, topics, themes. But when you're asking for like what is interesting and not average, it's actually pretty bad at it.
But the goal we need to achieve is so much easier: we just need to build a model that’s as good as us at alignment research, and that we trust more than ourselves to do this research well because it’s sufficiently aligned.
But with today's AI coding agents, building software is remarkably easy. So instead of handing over static assets and static guidelines, designers can deliver custom software. Tools that let clients create their own on-brand assets whenever they need them.
My daily drivers now include Amp, Cursor, and Claude Code. I still enjoy notebooks, but only for data analysis, machine learning, or other exploratory workflows where the iterative, visual nature of notebooks shines.
In short, whether AI is a normal technology or an unusually dangerous one, it doesn't make sense for companies to check their own homework on safety and security.
Scrutiny applied to AI is way below other technologies that - even if you totally ignore the doomers - could put far fewer lives at risk than AI in a single incident.
Laszlo and his team at Google reinvented the role of human resources. This book is a terrific overview of what makes Google Google, from culture, to hiring, to making decisions.
Claire has been a senior executive at Google and Stripe. Here she offers up a guide to leadership that is both practical and inspiring. Suitable for leaders of all levels.
Levy was given unprecedented access to Google, and the result is what I consider to be an accurate depiction of how the company operates. A good overview of how Google product managers work.
And so what ends up happening is the emails that the AI write are pretty good. Okay? If you're getting terrible emails, it's a poorly trained product from a bad vendor.
GPT 5.2 goes till end of August whereas Opus is stuck in mid-March - that’s about 5 months. Which is significant when you wanna use the latest available tools.
people don't decide like that at the very least you'll have to show them three or four or five skiing holidays from which they choose because we can't really choose in the absence of comparison.
On the flip side, these captive solar power plants will be curtailing approximately 75% of their generated power and will be able to provide net power on all but a few days per year.
ChatGPT, in particular, seemed to just want to validate me, tell me how great I was, reinforce any bad beliefs I might have had, and avoid saying anything uncomfortable.
I think that the pathway that you would need (in terms of revenue growth and profitability) to justify Nvidia's and OpenAI's current pricing is improbable, but that is just my view
the delusion comes from the reality that if you aggregated these breakeven revenues across companies, the market is not big enough to sustain all of them
I've been using yoga toes daily for years, but these socks are a much comfier and cuter alternative for soothing feet, improving alignment, and feeling like a cool gecko as you walk around the house.
I think what's going to happen is that the way competition like competition loves specialization and you see it in the market, you see it in evolution as well. So you're going to have lots of different niches and you're going to have lots of different companies who are occupying different niches
one of the one of the ways in which my thinking has been changing is that I now place more importance on AI being deployed incrementally and in advance.
I maintain that in the end there will be a convergence of strategies. So I think there will be a convergence of strategies where at some point as AI becomes more powerful it's going to become more or less clearer to everyone what the strategy should be.
Like basically I think I think that there is a big benefit from AI being in the public and that would be a reason for us to not be quite straight shot.
Similarly, AI translation will prevent you from learning to speak another language, AI summaries will block you from really understanding the arguments laid out in a book, and AI analyses of your personal problems will make it harder for you to think through solutions to your own problems.
Thus my personal prediction is that in domains that are already largely under the powers of modern AI, such as languages, programming or chess, we’re going to see a divergence in human abilities.
there's no birthright here that we should have any confidence other than to say hey we should go innovate and knowing the the lucky break we have in some sense is that uh this category is going to be a lot bigger than anything we had high share in
ultimately I think what matters is the use of AI in their economy to create economic value right I mean that's the uh the diffusion theory which is ultimately it's not the leading sector but it's the ability to use the leading technology to create your own comparative advantage right so that I think will fundamentally be the core driver
So this AI thing will be that right. So if you take coding um what we built with GitHub and VS code in over whatever decades uh suddenly the coding assistant is that big in one year and so that I think is what's going to happen as well which is the market expands massively.
this fundamentally this category of coding and AI is probably going to be one of the biggest categories right it is a software factory category in fact it may be bigger than knowledge work
I also think that they’ve done the sort of first 90% of the work to sound human, 95% possibly in some areas. The last 5% is going to end up being about 95% of the work.
The fractures fell neatly along disciplines: engineers using AI to wish away designers, designers wishing away engineers, product managers wishing away both. In this climate, AI becomes frenemy identification technology, another way to avoid working together.
You use a tool, but you play an instrument. It’s a more expansive way of doing, and the doing of it all is important, because that’s where you develop the instincts for excellence. There is no purpose to better machines if they do not also produce better humans.
An average email or line of code is fine. Average art isn’t. To make something alive with AI, we have to resist its pull towards average by working beside it, shaping what it gives, and listening for what’s missing.
the only thing that we know is that models will improve. Will it be incremental? Will it be exponential? I mean somewhere in between. Who knows? But uh what you have to believe is that you get better as models get better. Your organization gets better as models get better.
Have we seen productivity increases? Yeah, mild to moderate, but like that is not something that has made our new headcount we want for engineering go down.
it's easy to um go on vibes for too long. Uh some folks, you know, just kind of like trust the vibes and you know that'll get you somewhere, but it's not rigorous.
you can either see AI as an opportunity for your company to grow and do more or you can look at it as like cost cutting efficiency but I think the growth part is way more exciting.
So AI consumes approximately 0.04% of America’s freshwater if you include onsite and offsite use, and only 0.008% if you include just the water in data centers.
Data centers are so much more efficient with their water that they generate 50x as much tax revenue per unit of water used than golf courses in the county:
I would say though that something that is kind of annoying to me is that we haven't yet figured out the bridging from the tinkering to the workflow quite as seamlessly as I would like.
You know, interestingly, LM themselves are quite bad at playing chess. Like, they hallucinate moves. They look at patterns, right? They're they're very good at pattern recognition, but not so good at going super super super deep on a specific chess thing.
I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy. Um, yes, they they cared about the mission, but they didn't necessarily need to have deep experience on that matter. And in fact, sometimes that experience could be a crutch in certain ways. Especially in this world where the grounds are shifting so fast with AI, a lot of your like learn habits actually need to be intentionally discarded. You know, you need to have a beginner's mind on this type of stuff.
No, never. Telegram has never shared a single private message with anyone, including governments and intelligence services. If you try to access any server in any of the data center locations, it's all encrypted.
So it's extremely important to curate the information sources that you have, so that you wouldn't be somebody who is left to the will of AI-based algorithmic feed telling you what's important so that you end up consuming the same information, the same stuff, the same memes, the same news as everybody else.
This is a short (~150 pp), brisk first book on the topic for physicists. Most of the book --- the first ten chapters --- are good introduction to common models of spin glasses.
It's our choice whether we should say oh they are our offspring and we should be proud of them and we should celebrate their achievements or we should we could say oh no they're not us and we should be horrified.
We don't we don't really know what information they had prior. We are we have to guess because they've been fed so much. This is one reason why they're not a good way to do science. Uh it's just so uncontrolled, so unknown.
The scalable method is you learn from experience. Um you uh you you try things, you see what you see what works. No one no one has to tell you. First of all, you have a goal. So without a goal, uh there's no sense of right or wrong or better or worse. So large language models are trying to get by without having a goal or a sense of better or worse. That's just, you know, it's exactly starting in the wrong place.
A lot of it has to do with just how you feel about change. Um, and if you think the current situation is really really good, then you're uh more likely to be suspicious of change and averse to change than if you think um it's imperfect. And I think it's imperfect. In fact, I think it's pretty bad.
reinforcement learning is about understanding your world whereas large language models are about mimicking people doing what people say you should do. They're not about figuring out what to do.
used to be let's say in the Google world right I knew you were interested in shopping if you click the shopping tab I know you're interested in maps if you click the maps tab, we can measure clicks. Today, it's just all conversation. And so, it's actually harder for us to tease apart what is the user intent.
You can shortcut like hard feelings because now you can just watch TikTok instead of actually dealing with the very difficult emotion or tension that you had with a colleague or with your partner or with your children.
I think that managing change, it's always been manager's job to manage change and there's always the chaos of what's going on. I just think the rate of change is accelerating and we've seen that over the last couple of decades.
So yes, the tools are great. We can use cursor. It helps us. It autocompletes. It writes a bunch of things. But the acceleration of learning I think is another maybe underutilized tool in all of our arsenals
but if I was, you know, at the zero or 10th percentile, it can certainly get me even today very quickly up to like the 60th 70th in terms of um what the state of the art is.
I I sent a memo to my company like and we set the expectation that we require that people reflexively reach for AI now and and we require it because it's unfair not to because the people who do otherwise going to be the people who sequester all the best careers to themselves, right?
I I like the term context engineering because I think the fundamental skill of using AI well is to be able to state a problem with enough context in such a way that without any additional piece of information, the task is plausibly solvable.
uh common sense meaning the ability to make inferences about what might happen uh that are reasonable guesses but that do not require you to experience that mistake and learn and learn from it in advance that's tremendously important and that's something that we basically had no idea how to do uh about 5 years ago but now uh you we can actually use LLMs and VLMs ask them questions and they will make reasonable guesses
But the reason why it's not the most important thing for the kind of skills that you saw when you visited us, it at some level I think it comes back to Moravik's paradox. So Morovik's paradox is basically that it's like you know if you know one thing about if you want to know one thing about robotics it's like that's that's the thing. Morovik's paradox says that basically uh in AI the easy things are hard and the hard things are easy.
I think that it's optimistically that it's actually the other way around that the robotics uh element of the equation will make all the other stuff better. And there are two uh reasons for this that that I could tell you about. One has to do with representations and focus.
So traditional robots and factories uh they need to make motions that are highly repeatable and therefore it requires a degree of precision and robustness that you don't need if you can use cheap visual feedback. So AI also makes robots more affordable uh and lowers the requirements on the hardware.
With Web forms, the burden is on people to adapt to databases. Today's AI models, however, can flip this requirement. That is, they allow people to provide information in whatever form they like and use AI do the work necessary to put that information into the right structure for a database.
Agentic and sync are natural complements because local-first is multiplayer by default, and agents are another sort of "player" in addition to human collaborators.
I'm pretty sure people thought Microsoft had an advantage on the internet and Google and um Meta had an advantage on mobile and everyone thought IBM was going to win PCs. Like once IBM made a PC, that was it. It's all over now. And we kind of forget that like there were PCs before and then IBM made one and that kind of became the standard but then IBM lost it.
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