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Rather, the way that AI companies engage with communities-forcing NDAs, dangling billion-dollar promises, pushing environmental externalities far away from AI's wealthy user base-resembles a classic story about dark money in politics.
And that's why every hype cycle produces a wave of absolutely spectacular demos and very few real products. And the recurring mistake of every cycle is spending on the demo when you should be saving for the nines.
Given the high cost of errors, you need to have a very high level of safety and a very high level of confidence on day one before you deploy your first robot, before you drive your your first autonomous mile.
So, a deployment of your agent uh in the real world generates data. That data then grounds the simulator and makes it more realistic. The simulator generates harder edge cases for the critic to score and for the agent to learn from.
This is such a useful book. It makes the case that there's no such thing as "human error" - instead most catastrophes are caused by system issues, misaligned incentives, and unrealistic processes. You are not the custodian of an otherwise safe system that you need to protect from erratic human beings.
most of commerce in America isn't actually high intent. Like how many years are we into e-commerce now? Like 30 years into e-commerce and e-commerce has never exceeded 20% of retail spend in America.
absolutely agree that product skills are probably the most durable. My build would just be there's a lot of people who have the title PM who haven't spent a lot of time building those skills in the last 5 years, but have gotten really good at communicating frameworks to leadership.
as a product manager, I've spent less time in the last year talking to a data scientist than I ever have in my career, even though I've probably spent 10 times more time in data and understanding actually how the product's working than I have ever have in my career.
But based on what we can see at Stripe, the hunger and the intensity with which other companies are either getting started, taking advantage of these new capabilities, or existing companies are retooling, I don't worry about the centralization in the same way. Uh I think there I think there are going to be many thousands of winners.
And just human organizations are complicated and it's very hard to have um to manage to aggressively prosecute 100 different priorities and to deal with all the issues and interference that arises among them and so forth. And so, you know, Google has done incredibly well in a bunch of specific places, but it's not like Google has done all the things even if in some kind of basic material sense, uh Google maybe, you know, had that ability.
I mean, it's very interesting, right? Because these can prove the Jacobian conjecture, you know, whatever. Uh and so clearly they're capable of these monumental feats. Um but somehow I still haven't read the LLM essay that I found super compelling.
So, I um yeah, I think maybe maybe a better way of saying it is 20 20 years ago that whole lean startup thing was uh was almost the only thing to do because of capital available and you didn't have AI that made, I don't know, spinning up an organization with many different potentialities and capabilities so much easier, whereas now I think you can start these much more aggressive and ambitious things up front.
Yeah, I mean I feel like uh the models have been getting a lot better at sort of agent-based longer running coding tasks and it seems pretty clear that they are now actually pretty capable and depending on exactly your definition of of junior engineer it seems pretty spot-on I would say.
Um, and to give you an example of a a a use of a coding agent that works extremely well is you can ask today's models to translate software from one computer language to another very effectively because in that case you actually have a incredibly detailed specification.
Um, and sometimes that's because the model is trying to do something it doesn't have a lot of experience doing. So it's been trained on a whole set of things and as soon as you get a little bit off the distribution of things it knows how to do then like most machine learning models it will you know its performance will suddenly will start to degrade and the farther you get off the comfort zone of what it knows how to do the the more likely it is to to not work as well.
Yeah, I mean I think if you looked at what is important in AI systems these days, you would want to know things like the bandwidth between you know your main memory system on your accelerator to the onchip memory to the um you know the multiplier unit or whatever. You want to know how much energy does it take to do a single multiplier operation.
If you think about our large scale models today, they probably see a thousand times as much data as a human does by the age of 18. Yet, the human by the age of 18 is better in a lot of things and, you know, on par uh with those frontier models that have seen way more data. So could you come up with much more data efficient systems that can learn continuously learn from their own actions?
So, it's hard to tell how much of like the loss of the past few years was AI versus the end of like zero interest rate policy and like the post-COVID crash. And I think it's more the latter, but like again, LLMs are still getting better.
And often I found with my clients I have to tell them like it's doing a good job at generating the actual design, but in actually expressing what the design is supposed to do, it cannot do that yet. You have to do that part yourself.
As a general thing we've seen like to get good results you have to already know how to get good results without it. It just helps you get good results faster.
I predict that in the next 10 years software development will survive, but it will become like any other white-collar professional work. No more $200,000 salaries, unlimited vacation, or incredible employee bargaining power.
we don't believe in a world where these models are so expensive that, you know, they get rationed only for the most wealthy of developers and and companies.
we don't believe in this totalizing, you know, totalitarian view of, you know, AIs that control the world. We believe that these are going to enhance this very broad ecosystem.
so much of that debate is like I think um in some ways uh a little bit of a waste of time because, you know, I think it's inevitable that we're going to have very powerful models
we believe that everybody in the world, you know, all the billions of people in the world are going to have a super intelligence that is adapted and tailored to them, that is enables them to accomplish their goals, knows their context, and ultimately is an expander of their own agency.
I think we're at this like in amazing moment in the world where the bottleneck is not the progress of the AI models, the bottleneck is diffusing that through the rest of the world and and helping the world adapt to this amazing technology that already exists. Like I think if the models didn't improve at all from today, there would still be like decades and decades of like total upheaval and change in the economy and how the world operates and and everything around us
it is both true that you know maybe creating super intelligence will be the most important thing yet to happen in the history of business or human society and also that it will pale in comparison to some new startup something that hopefully one of you will do.
In fact, if if we are right that AI is going to be such a big change, startups will be much more important to making sure that the power of this technology gets widely distributed throughout the economy and society and is not just concentrated in a few companies or models.
At boom we need far more software engineers in a postAI world than we need in a pre-AI world. Why? Because the cost of software development has dropped. anybody including hardware engineers can now become a coder and we need software engineers to make sure the architectures are right and make sense and are coherent.
So I think the worst advice is like work on what you know. Uh what you know can be changed. Particularly in a world where you everyone has personalized AI tutors. Anybody with passion and dedication can learn new knowledge and learn new skills. But what you can't change easily is what you love.
I think the skill nowadays is less about prompt engineering and more about figuring out how do you give Claude a hard task that seems a little bit too hard. And then how do you make it possible for Claude to verify its work along the way? And the verification I think is probably the single most important thing that people do not get right
So when I look at engineers that have been, you know, coding for a long for a long time, you know, like for for years or for decades, this is a really really common failure mode is trying to over specify and it's trying to be overly specific and then, you know, get the model to do the to do the task exactly the way that you would have done it. And that that's just not the way the model works.
for people that aren't building agentic products, but you're using Claude code, every 6 months delete your Claude MD. Delete your skills. Delete your hooks.
I'm seeing a trend here of declining revenue and traffic with indiehackers On my own projects too Maybe big VC products too but I wouldn't know cause they don't share revenue To me it seems clear BigAI is cannibalizing everything that used to be apps
The evidence would show that and it makes perfect sense that AI and automation is creating jobs everywhere. The narrative about AI destroying jobs is exactly backwards. AI eliminate tasks. AI automates tasks away. But it doesn't necessary doesn't necessarily eliminate jobs.
we want we want to encourage everybody and every company to build their own AIs. And and and who knows what innovation will come from the fact that it's open source.
likely this will be one of the largest industries in the world and um, uh, it'll take longer than a couple two, three years. It'll take less than 10. And so this will this will be our next $100 billion business.
we we kind of have coarse level uh recursive self-improvement already. And the fact that every time you use it, it improves the markdown files. Uh every time you use it, it updates its uh long-term memory.
in an intellectually dead field, AI will just produce worthless junk. If what you feed it is decades of complicated and meaningless computations, it will just produce even more complicated and meaningless ones.
So by far the funniest thing about using Claude and Codex together is how Codex is very earnest and businesslike while Claude treats Codex like an inferior but hard-working subordinate
So by far the funniest thing about using Claude and Codex together is how Codex is very earnest and businesslike while Claude treats Codex like an inferior but hard-working subordinate
Google, which led on benchmarks not that long ago, has fallen behind where it now counts: it has no leading frontier model and it has nothing close to Codex and Code.
But for LLMs, it shows that they have no skin in the game and will never take a stand for what they believe in regardless of what it costs. They are perspective generators, not perspective holders.
AI has the potential to assist with both the first and last element of that loop. What it cannot replace is the middle step—doing the work for yourself.
Asking an AI for help with the start of an essay, for instance, will invariably shape what direction you end up following, robbing you of the crucial experience of building your own judgement around a topic and selecting your own path to research and argue.
I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume OpenAI has sounder sandboxes.
For the first time, I ran all potential winners through Pangram, which is an AI-writing detector.1 So according to the machine (and to my ear), all of the winners are 100% human-written.
Martsinovich argues, via code snippets, that there is no such thing as a "conversation" with a chatbot. The AI is born anew every time it speaks, and it simply reads the dialogue so far and then tries to write the next line:
I told him what I believe: that in the age of AI, medicine will be bottlenecked more than ever by regulation and the grind of clinical trials, and that billions poured into faster pre-clinical research won’t touch that problem, unsexy as it is.
I think the challenge is that everyone can now build apps But 1) almost nobody has distribution (like an audience), or 2) the money to pay for distribution (ads or UGC), or 3) the creative genius to get distribution for free (classically called guerilla marketing)
Their words now
I thought it'd be distribution but who knows, obviously creativity and ideas, but if you can copy a successful app in an hour, then how does that differentiating work?
So AI has made everybody just as capable as everyone else, it equalized everyone in the world, as in everyone can make everything (software, music, images, art etc) now and everyone is equal, at least in the digital realm now
So, even in a world of AI where some things are easier, we were talking earlier about mindset, AI fluency. From my experience, younger folks are more open-minded. They tend to be more native in some of these new ways of working.
It does feel like we have to be more more explicit about the types of people and talent that tend to thrive at Netflix versus other companies like some of the frontier labs.
So, the most useful thing is not to make it level specific or role specific, but to encourage everyone towards the expectation on AI fluency, which doesn't mean use it as a tech for the sake of tech. It's tech where it's useful, to have good judgment about that, and to have the mindset to be open-minded to explore and try new things.
So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI.
In a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work, common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important.
This saturation can be seen more clearly for the GPT 5.6 Sol model, which also shows that increasing reasoning budgets can become uneconomical at some point.
AI is developing at a very rapid pace, far more rapidly than most outside of the industry fully understand, and far more rapidly than almost anyone in the industry predicted.
the external tools that we use to help us reason, remember, associate, calculate-from counting on our fingers to chatting with an advanced LLM-should be properly understood as extensions of our own cognitive process.
the problem with training models on maintainability is like the cost function of bad architecture and bad program design can't be evaluated by running the unit test because it hits you 3 to 6 months later
you can slow way down and read every PR and read every line of code. Uh, and then you're only going to really get modest benefits from AI because that becomes I I think you should expect maybe 30 to 50% lift in productivity is kind of what I see when we go into teams
yes it will catch things and it will raise your floor but I don't believe like the model writing the code is the same model reading the code and if you ask a model hey is this code good it's going to be like oh yeah it's great comprehensive it's got unit tests
But if you don't have good LLM intuition, like 100K for smaller models, 200K for these like really beefy like Codeex and Opus 4.8 models is usually a good like training wheel guideline of like if you pass there, your quality of results may be degrading.
And at the end of the day, they're all like different ways to pass tokens into a model and ask it to produce usually some structured output. And understanding that is a lot more powerful than trying to learn memory and trying to pick some agent framework off the shelf and some memory framework off the shelf.
Geo-politically, countries will be weighing open weight models as a way to get frontier-level tokens inside controlled environments that may not be otherwise possible.
Organizations are increasingly looking for control over how their data is used and are willing to trade off some access to frontier level tokens for this control.
The result is something completely worthless, but not obviously distinguishable from many other things on hep-th, which is in serious danger of going from something sad and unhealthy to something completely overwhelmed with crud.
While in mathematics AI agents are starting to have a major positive impact on research (e.g. by resolving the Jacobian conjecture), the situation is very different in hep-th.
AI flattens everything it touches. The way you stand out is by getting clearer about what you really want, your taste, your perspective, your opinions.
I think if you ask an AI just for a strategy lazily, you're not going to get something great. You're going to get something pretty predictable that probably the competition would expect you to do.
I believe that if you are too prescriptive as a leader with a team you end up stifling good ideas but if you're too open-ended sometimes teams just waste time um going in the wrong direction.
But I don't think we should judge content based on the tool that made it. Um I think we should judge it based on the content, the point of view, the person behind the content.
But they just by virtue of having less people to coordinate they can often move faster and make um better decisions a little bit less design by committee.
These developments inform our position that AI systems are now capable of meaningful contributions to formal mathematics, not merely informal problem-solving.
However, these LLM reasoners that generate informal reasoning in natural language are fundamentally limited by the lack of precise, machine-checkable semantics, making their outputs prone to hallucinations [Huang et al., 2025b] and precluding autonomous verification, a prerequisite for tackling open-ended mathematical research.
We argue that the next leap in AI4Math systems requires a decisive shift from predefined problem-solvers to research agents that can address frontier mathematical challenges with rigorous formal mathematical reasoning.
So one of the things is that the pace of development is definitely accelerated. One thing I wonder the pace of business hasn't accelerated though and that mismatch is going to become more and more apparent.
which is why when I see manifestos today I'm just like too soon. Not a bad idea. Would love to have one just too soon. It took 15 years for the technical change of object-oriented programming to come before we could say here are the consequences of it. Here's how in a simple way we can express how to effectively use this technology that we've been using day in and day out for 15 years. The genie comes along. People are like, "Well, what's the new manifesto? It's just not manifesto time yet.
the genie runs out of g runs itself out of options it can't make further forward progress and so I'll wipe it away start over I won't try and tweak I'll start over and say all right well if I implement things in a different order. If I implement with this markdown file or if I implement it with this commit hook, we collectively need to try absolutely everything.
Nobody knows now. That playbook has been wiped clean and people whose identity is I know the playbook are now terrified. Who who am I? Now, it turns out that the skill of writing a playbook is completely different than the skill of applying a playbook.
What is happening is that GenAI's climate harm comes from the system design, such as Google's AI overview triggering constantly whether users want it or not, in addition to significantly worse digital bloat tools dominating overconsumption.
That’s why we’re launching the Preliminary Report of the Independent International Scientific Panel on AI — an initial evidence-based assessment of the current state of AI science — to help the public and policymakers better understand the unprecedented moment we’re in.
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.
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.
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.
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.
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.
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.
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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.
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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.
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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.
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.
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.
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.
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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
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.
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