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If you do think it's the overall system that matters, then the alignment that's needed is far less like training a virtuous child and more like managing a semi-virtuous corporation!
Every step we take that makes the models less governable and controllable also makes them less useful, so we will not be able to continue using them, which reduces the value, and which means delaying the release is the only option
LLMs constantly hallucinate and cannot be trusted. I still have to verify and iterate a lot but now I usually focus on architecture and design instead of code style.
As the context grows, a model starts paying less attention to instructions in the middle of the context in favor of what is at the beginning and the end.
And so when you look inside these big AI models, the mathematical objects that you see look a lot like the things that you see in neuroscience. So if you look at how do how do these AI models represent um just like represent concepts and you look at the parts of the brain that represent concepts, you see these you see very similar geometry.
I can't say enough how excellent a coding agent is at customizing Chrome with extensions. A huge productivity win. Every plugin interface you've ever blown off is now trivially usable.
I was thinking about how I recognize AI writing, and one big tell is excessively colorful verbs. A handful of journalists might write that a proposal "drew" 100 votes, but any normal person will just say "got".
AI may help an oil company emit less pollution per barrel of oil. But if the company is producing more barrels than it otherwise would have, total emissions can still rise.
The first is that new AI models have the know-how to escape containment in testing environments, which should challenge any assumption that advanced AI can be easily controlled by its makers.
An analysis by the AI Security Institute found that the capabilities of frontier models (i.e., from OpenAI and Anthropic) are roughly doubling every few months, and this doubling rate has actually gotten faster over time.
By 2027, almost every country-and every non-state group with sufficient computing power-will have the ability to download open-weight models even more powerful than the ones that attacked Hugging Face and use them for whatever they like.
the first is cognitive debt. So the more that you use AI, it's it's sort of the erosion of your ability to have good memory and have good understanding of the problems that you're working on. And the natural follow-up to that is cognitive surrender, which is where you, you know, you blindly give in to whatever the AI says as your answer.
I remember anytime I would work with a big site on their performance problems, uh you could easily spend half a day, um you know, just looking at traces before you've even written any fixes at all. And now that we have LLMs, it's very quick to like reason through massive stack traces and actually be able to get down to fixes you can make.
One of the things I found most exciting in the last couple of years was seeing as model qualities gotten better and and harnesses and tools have gotten better, how many people um that were directors or VPs or SVPs or any of these levels were actually rolling up their sleeves and trying things out.
Like seemingly everyone else at the end of last year, I began messing around with Claude Code, and have since made a small handful of apps that I really do use all the time.
We care very deeply about AI safety. We believe the entire field will have to coordinate on shared safety standards, but will act unilaterally in the meantime.
We expect confidence in safety to increasingly set the pace of AI progress.
have to abide by this rule that was written before LLMs were even a thing. And that shows how challenging sort of the the the situation is if you try to set the line too firm to begin with.
The thing about executive orders, which is a little secret, is when administration changes, you can just revoke the executive order and and start from scratch again. Um when something is a law, you you can't do that.
if the US wants to lead in artificial intelligence, we have to have a vibrant closed and open source ecosystem, and that's the only way they can all work together.
if you create a or allow for the creation of a patchwork of regulations, meaning there's one set of regulations for AI in California, another one in Maryland, another one in Texas. You know, the big tech guys, they can deal with that.
An excessively AI-polished proof may sand away both the "artificial" friction (typos, awkward phrasing, disorganization) and the "natural" friction, leaving a text that is easy to read and hard to learn from.
well but again these are functionally defined emotions and there's I think there's no problem assigning to AI to some extent and certainly to many animals uh those functional emotions
The basic answer is that I think of emotions as functional states of a particular type. They should be understood by what they do, their function rather than how they happen to be constituted in the human brain or differently in an octopus nervous system or in an AI which we should talk about.
The problem again was not reliance upon AI but that it was supporting a model of warfare with which the United States had been working for some decades, one that prioritized the rapid elimination of enemy capabilities.
The supercomputer inside our own skull runs on about 25 watts, which hints that the minimum energy needed for intelligence might be small - and that the giant, controversial AI compute centers we're building now may turn out to be a temporary blip rather than a permanent feature.
I'd bet we learn more about our own brains from building a thousand AIs under a real theory of intelligence than we've learned from a century of neuroscience.
the score is the classifier's estimated probability for the AI-generated class based on its training distribution. However, we shouldn't interpreted it as a general probability that the text was written by AI.
AI checkers are essentially a cat-and-mouse game. AI checkers may learn to detect a certain pattern that is indicative of AI-generated content. Then, the next LLM may incidentally or deliberately not exhibit that pattern and avoid detection. The AI checker then has to be updated to detect said LLM, and so forth.
If you keep asking this question, what you realize is energy is the fundamental input. When we figure out AI and robotics that allows us to do semi-aututonomous manufacturing, energy will become the cost of all things, right? The cost of buying a thing will become the cost of energy used to make it.
Everything we see, whether it's this room or any room or anywhere you go, everything you see was either grown or mined, manufactured and moved. Everything. And so we look at what we do as industrial AI.
Chips come from the ground. Where's the energy come from? And a lot of people are like, "Oh, it comes from the sun." Yeah, it comes from the sun. But how are you capturing it from the sun? From stuff made from the ground, right?
buuuut my roadmap is’nt ten times shorter. and I’m definitely not ten times better at deciding what is worth building. teams haven’t started casually shipping a year of product work every month. you can look around and and it’s hard to say what software has gotten meaningfully better in the last year.
I still don't think it's a good idea though. The reason to have a cofounder is not just to get more done. It's to have someone to help bear the stress.
For the first time, AI has "hands" - that is, the ability to reach out and interact with the tools on your computer and in your browser, just as you would.
I'm so incredibly tired of seeing low-effort AI-designed web pages. I basically bounce off any new product immediately when I see it (never get a chance to judge the product itself). Lots of thin lines, glowy styles, inconsistent fonts, lots of monospace.
A lot of founders I talk to are worried about what will happen with AI. So am I frankly. But they're actually better off than anyone else. If the world is going to get turned upside down, the safest place to be is in a small, fast-moving company that can easily change direction.
One common question I get now is how much of the standard startup advice is still true in the AI era. So far almost all of it. There's more difference between the advice I give different startups in one batch than between what I've given pre-AI and post-AI startups.
But if you have to fine-tune a model, you actually aren't getting a general purpose model um for the things that you want it to do because you have to fine-tune it for each individual thing.
And in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision, this means that if the system makes a mistake, um, that's okay because usually the person can kind of recognize that or or decide what to do even despite that mistake.
Uh and this means that they're going to be far more useful when they're operating fully autonomously. And as a result, this requires us to develop physical AI systems that make far fewer mistakes than the machine learning systems that have been deployed thus far.
I mean at the very least I actually think that just starting with a generalist policy and then fine-tuning it even like right off the bat uh can be really effective.
It's like they have all the wisdom of their most senior engineers looking at every single diff and that is fantastic. Which means that you don't have to worry about remembering and looking and nitpicking and all the things that we're not good at anyway
You can use AI as a shortcut to help you not have to think too much. And you can use AI to help you think more deeply and more rigorously. And both of those use cases have their place. But when it comes to your core job function, we primarily want the second one, right?
There has also been an uptick in product shills, which, in some ways is worse than AI stuff-some human actually put effort into making straight garbage.
The translation & dubbing was a huge amount of work (which I deeply care about) with humans & AI collaborating. Thanks to the amazing team at @ElevenLabs for their help.
when AIs are extremely extremely capable my view is that those AIs will be harder to align than current systems. So for current systems, we have this feedback loop where we basically like we create an AI. We do some evaluations on it. We see that it has some kind of messed up behavior that we can kind of quickly understand. Then we like can like go look in training and be like, "Oh, the these training environments led to this problematic behavior. Let's like tweak that training data. Let's introduce some additional training data to like correct this other issue and then move forward from there." But in a regime where the AIs are extremely situationally aware, very very very very capable and um you know uh we don't necessarily understand what they're doing, this feedback loop breaks down.
I do I do think that I wish that sort of my preferred constitution or like the way I would orient towards this like the thing I would prefer would be more like Claude is like look it would be structurally good for the way this technology work like the constitution should be like it would be structurally good for the way this technology works to be that AIS are like good fiduciaries, good representatives, the equivalent of a lawyer for a user
the reason why RL environments today are much better than they were in like you know 2024 is not that much because um we have hired way more human experts to make RL environments and is instead much more because we better know what how RL like what RL environments we even want to make and and like how we should structure them and also we're using huge amounts of AI labor to build RL environments.
Second, I think ML is a very shallow domain relative to math. So I think in math there's much more of a you find some true deep abstraction um and then like that like if you really understand that thing which is hard to understand then you get somewhere
The fastest-growing political movement in America is dead-set on stopping the construction of data centers, which are critical to the growth of the fastest-growing industry in America.
Facts and Fallacies of Software Engineering by Robert L. Glass. In essence, this is a book about an industry that refuses to learn. That was true 25 years ago when this book was published, and it's probably twice as true today. (Just think about all the AI adoption metrics being rolled out — back to productivity mistaken for lines of code produced, only more elaborate. And expensive). What I like about this book is that Glass doesn't present anything new. Quite the opposite, actually. Rather, it's about research lessons that we all should know, but tend to forget. Ever had to do an estimate, or plan according to a requirements spec? Or maybe you thought that enough eyeballs make all bugs shallow? Then this book is for you. A great work by a fantastic author.
Paradigms of Artificial Intelligence Programming by Peter Norvig. Learning the AI described in this book probably won't land you a job today. But reading the code examples will transform how you think about source code. The book shines when it comes to code comments, a topic that I've never seen demonstrated well in other sources. Here we get to see how comments become valuable as a narrative that explains both intent and reasoning. Brilliant, just brilliant.
It seems pretty clear that the only thing data center opposition is doing is pushing data center construction to the (many) places in America that won't regulate energy or pollution. Weird thing to pat oneself on the back for.
Claude Haiku is my current least favorite model - it hallucinates wildly, and is out-performed now by other similarly priced models like GPT-5.6-Luna
Even worse: it seems to still be used by the Claude Code WebFetch tool, which means hallucination risk any time you fetch a URL!
Coding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks off.
The absolute bad outcome is that our young generation, their agency and human level motivation of learning and living is taken away by tools. So doom scrolling, passive watching of shorts, all this are not helping agency, human agency.
They are the most important people in our society. We should be talking to them. We should be uplifting them. We should be supporting them. We should be providing resources to them.
In addition to reliability issues, it often engenders a mind-numbing workflow and an environment where junior developers will never acquire the expertise to become senior developers capable of designing complex systems.
But if you zoom out, it becomes clear that almost every “breakthrough” since last summer has concerned the narrow domains of computer code and math, which are defined by highly structured languages and come accompanied by massive amounts of specialized training data.
an ai is oddly well suited to this job. cassandra doesn’t want a promotion and doesn’t need the people in the channel to like her. disagreeing with the vp three times this month won’t show up in a performance review. she doesn’t have an ego invested in being right either.
His Lansing rally with Bernie and AOC, more than anything else, crystallized the connection between data center opposition and voters' anger about money in politics.
In the era of base LLM scaling (2022-2024), I believed the LLM line of research would reach a capability plateau (as later seen with base LLMs).
In late 2024, after the o3 test-time compute demo, I changed my views: the new models were showing genuine fluid intelligence, and with this new line of work, the LLM line of research could achieve unbounded capability scaling. "There will be no wall."
However, looking ahead, I still do not believe that future AI (say, in 15 years) will be based on the LLM stack. I believe it will necessarily have to move closer to its optimal, final form -- symbolic learning. Obviously this is a risky and contrarian belief -- the safe bet would be LRMs. But let's see.
I believe current techniques are 4-6 orders of magnitude away from optimality in terms of data efficiency and test-time compute efficiency. But far future AI will be near-optimal.
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
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
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.
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.
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.
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.
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 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.
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 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, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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
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