Why companies are becoming a series of loops | Anish Acharya (a16z)

It's a funny dark fantasy that we seem to have as Silicon Valley collectively. Like things have never been better by almost every measure.

This is a technology that really amplifies our agency. It kind of unbundles skill from desire. Not only can we dramatically drive productivity, we can dramatically drive ambition.

In the old days, 3 years ago, we would see a company and if what they were trying to do was too ambitious, we would, you know, not engage. Today, we're almost seeing the opposite problem. An idea that's too small is not something that we want to engage with.

We're going to see this sort of cascading set of everything from a loop per person to loops that can run large parts of the company. With that said, I think humans are a critical ingredient. The loop will help you climb to the local maxima, but then it plateaus. You need human intuition. You need somebody to actually help you land at the base of the next hill.

We believe that people want to be more productive, but they don't. I think more people want to spend time than save time. So, I think that the opportunity for this technology is the basics of consumer need. How do we feel more connected, more loved? How do we make progress? How do we have fun? I don't think it's a model or a capability challenge. It's just a product design challenge.

Thank you, Lenny. I'm so excited to be here, thrilled.

Not very seriously and it's a funny dark fantasy that we seem to have as, you know, Silicon Valley collectively. Like things have never been better really by almost every measure by how sort of distributed all the opportunities are, by the kind of technology we have access to, to the types of ambition we're allowed to have, and to the number of companies that are sort of independently working on things that are winning. And yet, there's this sort of discussion of permanent underclass, being outside of the light cone, I've heard it. And it's not just something for deep insiders or outsiders. It feels like there's a real fear kind of from, you know, researchers at Foundation Model Labs all the way through to the Silicon Valley layman. I mean, here's like a couple of points that I think are really important. So, first, I think the last era of tech was a lot more centralized. If you look at network effects, that's sort of the gold standard. You worked on a network effects product. Um that's the gold standard of businesses from the mobile era. And those things led to dramatic centralization, right? Of course, all of them are definitionally sort of end of one networks. If you look at what's happening now, it's like every part of the stack, there's not even two relevant players, there's like 20. You know, you've got labs, you've got open weight, you've got different variations within both. If you look at coding agents, we were talking about it, like your mental model for 2 years ago should have been would have been, I think it should be winner take all. And yet, Claude Code, Codex, Lovable, Replit, Wobby, like they're all sort of working. So, it's really really encouraging to see that. You know, the second thing, you've heard all the kind of economic data, everything from radiologists um who are supposed to be cooked uh every year for I think about 20 years now. And of course, job postings are higher than they've ever been, as well as programmers, you know. So, I don't know that the empirical data bears it out. I I think the final thing is that there's this sort of discussion about RSI, and I know RSI is like recursive self-improvement is a fun term to throw around. But, if you ask the most sophisticated individuals at the labs, it's not actually RSI that's occurring, which could lead to some sort of runaway winner cuz they were an epsilon ahead of the others. It's autocatalytic effects, which just means you're using the new technology to improve your process, but it's not truly recursive. So, I think everything from the most empirical to the most technical viewpoints in the other direction, and yet we can't seem to let go of this fantasy.

Yes.

That's right. Like the line of reasoning for that case is always everything up until now, then something happens that no one can quite articulate and then fast take off. So, I don't believe that that's going to happen. I do think that model progress is happening faster than ever before. But if you look at something like economic diffusion, you know, I grew up in a small town. I went back home last summer, like people's lives haven't changed that much. So, if nothing else, the sort of slow rate of economic diffusion will catch it. I think the other thing that's under discussed, Lenny, is, you know, how many problems are truly intelligence bound? Like if you had a, you know, a data center of PhDs working at FedEx or Domino's Pizza, are they going to be like exponentially dominating supply chain and pizzas? Like I don't think so. So, I think we might be overestimating how many problems are intelligence bound versus bound by other things.

I mean, I have so many thoughts on this. I don't think we give the average employee enough credit. I think we have this abstraction of a white-collar employee. You know, the white-collar manager, the abstraction is like some Dilbert-esque manager who's just shuffling paper all day long. You know, it is abstraction of consumers that they're sort of these low-agency NBCs. Of course, that would never apply to us or our friends. You know, we have this abstraction that everybody else's job is super automatable by AI, but of course ours is not. So, I think when you actually get into the details, a lot of people are actually excited to you know, better themselves, get more leverage, and you see this with of course sophisticated companies like Google, but even a company like Kavak where they sell, you know, used cars in Mexico, they've got this concept of a Jedi Academy where they're teaching everybody at the company including the mechanics how to use the new tools and technologies and kind of at the end of the 6-week course, they ship a cutting-edge in-production agent. So, I actually think that more people are embracing the technology than we sort of like to discuss. I think a big change is going to be using AI um versus reorganizing your entire company around AI. And a great example of this is if you look at the diffusion of electricity as a technology, you know, it took 40 years for us to get from the inception of electricity to reorganizing factories. And that means like re- like burning the buildings down and starting from scratch versus taking what was previously coal and simply swapping it with electricity. So, I do think that like the most ambitious companies are rethinking everything around the models. And those that are a little less ambitious or perhaps a little earlier are thinking more about how do we give people and existing orgs existing job functions access to the technology.

I think so, man. I mean I I think if you even just think of the kind of incentives for the CEO and executives, you know, Sundar running Google, he doesn't want to run a more efficient $4 trillion company. He wants to build a $40 trillion company. So, anytime you have an economically productive unit, it's rational to kind of especially if it gets more productive to maintain that sort of presence in your organization. And then I don't know what you hear, but anecdotally I talked to a good friend who's an executive at Google and I said, "Hey, have you laid anyone off?" And he said, "No, we didn't. What we instead do is now rip through our road map. So, 2 years of road map happens in 3 months. And where actually our hardest problem is knowing what to add to the road map, which by the way is like every PM's fantasy. You know, how much emotion has been drained on prioritization conversations between you and I?" So, yeah, I actually think that people shouldn't be as stressed and I think they should feel really empowered. And and by the way, the best way to do it is just to ship stuff. I mean, Claire is my muse. Uh she is so awesome cuz she's always shipping. Yes, Claire Bow. She's shipping. She's trying things. She's not afraid to be a little embarrassed by it. And if you just see her whole kind of affect, she feels like the best version of herself that she's ever been. And I think we all have an opportunity to be that.

Totally.

Yeah, yeah. Well, I think the broad concept and you know, it's I the loops concept kind of gets teased a little on X cuz at sometimes it feels like maybe we're big braining it. I know there was a a big meme around graphs, like the next stage of loops is graphs. Um but let me make the kind of steel man for it, which is you know, we had prompts and then we invented agents. Agents, of course, are just models in a loop with tools and memory and skill files. And then we had sort of loops, which are, you know, sets of agents that are doing tasks. If you look at a lot of work in coding, coding such a great domain because you have the best models and you have the most sort of technically apt customer. Plus you have these established loops. Like bug fix or sort of bug report comes in, uh repro gets generated, bug fix gets created, bug fix gets reviewed. If high risk, then human should confirm that it's okay to ship to prod. And if low risk, it just gets shipped. And maybe you even email the customer and say, "Hey, we fixed the bug you reported." That happens in 5 minutes. There are many loops like that in engineering, everything from, you know, bug fixes to customer feedback to sales demos um to new feature development. Um So, coding sets this up well itself up well. So, you have a coding loop and the kind of change it makes is to the code base. My question is, what are the business loops? Right? So, if you're the GM of a business, you're looking across many job functions and you've got loops running in coding and marketing and sales and support and legal. The output of all of those loops is something that is itself a loop that you should be able to optimize for. And I think the strong form of this is that it sends a message to the CEO saying, "Hey, we need to actually make a change to one of the physical aspects of the business or to our business model or to our strategy." So, I think we're going to see this sort of cascading set of everything from a loop per person, loop per job function, loop across entire business units to, you know, loops that can run large parts of the company. With that said, I think humans are a critical ingredient. I just don't think that most work in the organization can be done fully autonomously. When you think of what a human will do in this like AI native company, sales, support, strategy, and exceptions. Right? And all those things are super critical. If we've seen one thing, Lenny, it's that the ability for models to do new thinking out of distribution thinking is still really limited. And I don't actually take the point that some of the new thinking in math is actually representative of new thinking in domains like business. So, you're still going to need a person to say, "Hey, here's the thing I think we should make." and have them be right about it.

I mean, a great example is a growth team. You worked on the growth team at Airbnb, right?

Yeah. Awesome. Right. So, you remember those like war I don't know how you ran your team, but I'm guessing it was something like you got everyone together, you built a list of possible experiments, you prioritized them, you built, then you shipped them, you measured them.

So, the the the loop version of that should be that every variant gets generated, every variant gets measured. Once you get to stat sig with a high enough P value, you can merge and ship that variant. You then have a long-term holdout, and you start working on the next experiment. You know, and then you're going to hit some local maxima. And I think this is really important, you know, the loop will help you climb to the local maxima, but then it plateaus. And you need some sort of out of distribution thinking, you need human intuition, you need somebody to actually help you land at the base of the next hill.

Yes. Yeah, and you know what really illustrates that? If you've ever tried to have an agent come up with a business idea for you? Like, you know, "Hey Claude, make me a million dollars, make no mistakes." Like, why doesn't that work, you know? And it's because you sort of need to set it in the right direction and and nothing in the technology has shown us that that is not needed.

Yes. And and think of how happy that makes them. Like, what does the go-to-market team want to do? I mean, this is a caricature, but I'm going to stand behind it, which is they want to hit the gym, they want to go to steak dinners, and they want to like, you know, like raise the trophy up for being salesperson of the quarter of the year. Um so, I actually think that this is a distillation of their job into the thing that they're the best in the world at, that they're the most interested in, um and all the administration that goes around doing that core work is now handled for them. So, like that's where go-to-market is going and it's going to be awesome.

And you know what's so beautiful about that? Actually, there's two things. I'll tell you maybe the one that's less obvious to me, which is I feel like every PM at every company feels like they're a true zero-to-one thinker, but they're held back by the kind of, you know, the heavy hand of management and executives and founders and engineering capacity. And in a world where every story gets told, every product story gets told, every feature gets tried, I think a lot of PMs are going to realize they're actually not that good at zero-to-one and it's much more fulfilling to work on someone else's good idea than your own bad idea. So, I think even things like that are going to lead to a lot more organizational health than we've had in the past. You know, not to mention the fact that the idea that ends up winning doesn't have to be the one that's came up with by the person who can sell it best to an executive. It just gets tried and the best idea wins.

Yeah, I think that's right and I think that those are going to be the rate-limiting factors cuz you can only do one steak dinner a night. I guess you could do a steak lunch, but, you know, to some extent there's going to be these rate-limiting factors in every system. Um I think a useful way to think about it is anytime the model's making a mistake or doing something you wouldn't do, what do you know that it doesn't know? Um and there's a really interesting example I heard from Ale at Kavak. He's probably the most sophisticated thinker on this stuff that I get to hang out with, where he said anytime they were agent they have an agent per customer, they sell used cars online, and when the agent gets stuck it actually calls a human. And the human will coach the agent through. Now, the magic of that is not only does it unblock the agent, but of course the agent then captures all the traces and learns from it. Like that's a really interesting mental model. It's either a knowledge gap or a data gap that you have to give the agent, and the next time it shouldn't have to call you.

That's right. That's right. And then hopefully a lot of your day-to-day work, you know, when you come up with a new idea that works, the kind of implications of that idea, if it's a product, you know, there's marketing work, there's sales work, there's product marketing, there's communication, there's legal, all of that should be largely handled for you. And your idea, you know, your job is to go take a hike and dream the dreams and come up with the next hill to climb.

I mean, I One thing that's under discussed is that um it's unclear that the sort of competitive equilibria that exists in a lot of industries will really change. You know, so let's say Pizza Hut and Domino's and Papa John's and Round Table all get a data center of PhDs, and you know, they're all going to either adopt it or have a CEO change and adopt it. So, it'll be a rocky period and there'll be some, you know, relative shuffling, but I think that they're all going to embrace new technology cuz most companies do. I don't know that one of them is going to have 99% of the market. So, I think kind of what's under discussed is that yes, in the near term, I think there'll be winners and losers based on adoption of the technology and kind of how ambitiously you adopt it, but I do think there's a lot of industries that will sort of maintain their current competitive dynamics because they're not intelligence bound. Um I think the most useful question to ask yourself as a founder CEO is just, "Hey, if we assume these things are infinitely um intelligent and astonishingly cheap, how would we reorganize the company?" Cuz that's where we're going.

Yeah, well, I think that there's so many interesting things that this touches on. You know, one is the question of open weight versus frontier. So, I I'm sure Are you familiar with kind of Pareto efficiency? I'm sure you are.

Yeah, so Pareto efficiency is just, you know, um for price performance, for example, what is the kind of the efficient frontier is what's considered the optimal trade-off of, you know, a unit of performance for a unit of price. And, you know, are you sort of if you're along that curve, you're always paying the rational amount for the performance. And what's interesting is that frontier models are actually irrationally priced in that, you know, first of all, Mythos is infinite dollars a token, you can't use it. Um but if you look at even something like Fable 5, you know, for one IQ, conceptually one IQ of extra intelligence, you're paying 100X more than Opus 48. So, it's not rational, but I think there's a lot of jobs in which you have unbounded upside like drug discovery, you know? And if that one IQ point lets you discover the next, you know, statin, it's a trillion-dollar outcome. So, it's sort of rational to pay for the highest intelligence in these types of jobs and industries. Now, on the other hand, um I'm going to pick on, you know, maybe legal. There's perhaps only so much upside to be had in legal or finance. And for those jobs, you actually do want to be very Pareto efficient and probably pay for, you know, good performance at a good price, not infinitely priced infinite potential upside performance. So, I think what we're going to see is a split between job functions that de- demand kind of mid-IQ um intelligence. And those will often be open-weight, sort of biased with reinforcement learning, you know, things that make the models even cheaper and more performant for a narrow job. Along with, you know, incredibly um quote-unquote expensive, but performant frontier tokens for sales, support, research, engineering. So, I think you're going to end up having both architectures. And, you know, we can touch on this in a little bit, but I do think there are these sort of comparative advantages amongst model families. Um and then also amongst, of course, individual models that we're seeing more and more of. It's not going to be one or the other.

Yeah.

Yeah, and I don't I don't want to be diminutive. Like, there's extraordinary people in those job functions. I just think that they're bounded upside problems that they work on. You know, you can only kind of uh close the books correctly. You know, you can't close the books 100x better. So, yeah, that that's exactly what I'm saying.

I'm not sure. I mean, I think for a verifiability is a good question is I think for a like a drug development company, you have infinite upside. It is verifiable, but closing the books is also a verifiable problem, which has limited upside. I think the question is really how how much upside is there and how hard is it to calculate it? Like here's a nuance. Customer support, a customer may call in and report a bug. That bug may actually be the first breadcrumb to a thing that changes our entire organization. And if the CEO is on that call or the smartest person, they could follow the trail. Um but if we actually have this quote-unquote mid IQ generalist then not, that actually makes the case for as a basket of problems always use frontier intelligence. Um and I can of course make the case for the mid IQ basket as well, which is simply that we've crossed an intelligence threshold for almost every economically useful problem. And anything beyond that threshold is is simply waste.

You're right cuz it's sort of like this thing is so crazy, you know, this is um this is AGI and then a day later it's like the old thing that you throw in the dustbin.

Yes, well, the you know, as you know, the secret of every sommelier is 10,000 hours or maybe 10,000 bottles. So I

I think the secret of being a model sommelier is just using them all.

I push myself really hard to ship something with every new model that comes out, and I think you learn so much. For people who believe the models are commodities or totally fungible, you just haven't actually used the models. For example, in the last few weeks, I've been obsessed with Qwen 38B. Qwen is an awesome model. It's very good at long-horizon tasks, and it's actually really creative. It's a great storyteller. So I've been using it to create these impossible documentaries, mostly of planets in the Star Wars universe. I did Tatooine, which turned out great. I did Bespin last night. And it can just work for four or five hours. It tells a great story. It generates all the video using the MiniMax model via file, generates audio via ElevenLabs. It actually directs the movie, the five-minute movie, in that it cuts all of the clips in. It overlays it. It watches it. It's just extraordinary.

And it's got a totally different shape than GLM 5.2. GLM 5.3 just came out, which I used for a bunch of product work. It doesn't have a vision component, and it's sort of like this neurotic PhD you put in the corner. And they both have their role, right? This is a little bit of this tension of models. It's not that one is ahead of another, one is more intelligent. It's rather that one has a mind shaped in one direction, perhaps creativity and openness for Qwen, and others that are shaped in other directions, like neuroticism and precision, like GLM 5.3. So I just make something with every model, and that's how I build my intuition.

Well, I think almost all of us have got — you know, the most insufferable thing for a long time was your app-idea friend. You know, every time you went to have a beer, they're like, 'Let me tell you my app idea.' You're like, 'Ugh, here we go again.' Like, we have got to be the app-idea guys now. And all the silly ideas, actually, especially the silly ideas, because those are the ones that often have the most alpha, are the ones that we should all be building. So I've probably got, you know, two dozen apps that I build. I've got one or two large apps that I iterate on. And I think that if you don't have a chassis on which to use the models, it's just really hard to come up with an idea from scratch every time. So I'd say work on something. It's actually better if it's not important with a capital I. And then keep finding new ways to invest in it and add to it, with a model as a kind of tool rather than as a goal. Does that make sense?

Totally.

Yeah, no, I think that's right. I'm also a long-time meditator — we should talk about that more later. But yeah, I think that's right. I think in our day-to-day knowledge work, sometimes it's less obvious to me. I guess my mind is — I've always been a consumer product person. I love products. So for me it's easier to think of a new feature to add to my DJ streaming app, or you know, my sort of Google Reader for X that I use, than it is to find a part of my life to automate. But you're right, there's some fun examples. You know, I posted about one a few weeks ago where I had my laptop transcribe everything that was happening in the kitchen. And then it would award or deduct screen time from my son's iPad depending on whether he was being good or bad. So it was like a fun little social experiment. It also had a fun outcome, which is that he recorded a video of himself saying, 'I love you, Dad' over and over and put it next to the mic.

To hack the metrics. So yes. So yes, the kind of, you know, anything becomes a measure, it's no longer useful. But it was just a cool little social experiment. I think those things are super fun.

Whether he was saying things that are positive and prosocial, or negative and antisocial? And saying 'I love you' is very prosocial.

Oh, that's nice.

Isn't that so... And this is what I mean, you know, man. Like, I think we spent a lot of the time on the show so far really intriguing, talking about productivity, job loss, kind of all these heady, important topics with a capital I. But what you just described — how often you laugh — like that's not a startup. That's probably not an economically consequential idea, but it is for the quality of our life. And I think we tend to think that happiness is fixed, but what if it's not? You know, if you and I were doing jobs 100 years ago, what would our jobs be? I promise they'd be less cool than they are right now. And maybe 100 years from now, they'll be that much cooler. So I think the thing that often gets missed, and this is why I'm a fan of Claire and others, is just how can this thing add texture to our human lives even if they don't have economic consequences?

Yeah. I mean, I think that we believe that people want to be more productive, but they don't. I think more people want to spend time than save time. There's a reason the biggest products in the world are kind of entertainment and social. So we get at the heart of how do we sort of deliver value to the consumer. I think for most consumers, you know, I sometimes tease and I say it's like the Instagram AI user versus the X AI user. The X AI user is, you know, fearful of being outside of the permanent underclass, is really opinionated on GLM 5.3 versus Kimi K3, like they're just so pilled. And then the Instagram person is like, 'Oh, this is like a better Google search, kind of. It's cool. You know, I don't get what all the hype is about.'

And it's really a product design failure. We have the capabilities to radically transform people's lives. I mean, Lenny, in many ways we spent 40 years building a technology that enables better spreadsheets, right? We built this technology that extends our intellect, but nothing to extend our soul. And I think we have a little bit of a spiritual hunger, especially as a lot of these cultural institutions have gone away that fulfilled that, especially in the rest of America, you know, where you don't have as many hot yoga classes and Pilates and fasting and Friendsgiving. So I think the opportunity for this technology is like: 'Hey, the basics of consumer need — how do we feel more connected, more loved? How do we make progress? How do we have fun?' Like the things that we all aspire to, the basics. How do we apply this technology to those areas? That's what I want to see more of. And again, I don't think it's a model or a capability challenge — it's just a product design challenge.

Yeah, and also, you know, look, we have to dial this in, but I think there's a way that AI can sort of challenge you, can push you, can be disagreeable. This is also why I think startups are advantaged over incumbents. You know, the idea that there's a thousand Google committees who would roll in their graves at the idea that they're going to release a model that's disagreeable, or — God forbid — it should be like sexually suggestive. But guess what? Those are all parts of human existence. So I think exploring the uncomfortable parts of our social existence are things that startups are uniquely set up to do.

I think that's right. And you know, I think the thing that's held back consumer so far a little bit, and 'held back' is maybe not strong enough, is just that if we kind of put this into iPhone terms, we're in iPhone 2010, right? What is iPhone 2010? I think it's pre-Airbnb, pre-WhatsApp, pre-Uber, pre-all the important consumer companies. So it is early days, but what's held us back is, I think, three things. One is that the models have been expensive. So if you want to do a kind of free-to-use product, that hasn't been easy. The second is that we've kind of had an interface problem. Like, chat makes sense if you're the highest-agency person in the world, which is Elon and Sam. But for the average consumer, like, their ideal interface is TikTok. So we need to find something between chat and TikTok. And then the fact that the technology has been so much more focused on productivity than things like human connection and entertainment. I think all those things are kind of up for grabs. Like, the open-weight models mean things are way cheaper. I think we're starting to have conversations about things like 'loop, make me happier.' And I think founders like Eugenia, who should have been on the show, she's tremendous, are thinking really ambitiously about user interfaces. Brian Chesky from Airbnb, he started a foundation lab focused on next-gen user interfaces. So I think all those problems will get solved, or they're at least in a better position to be solved than they were two years ago.

One, I think it has the potential to be a sort of emotional, spiritual interface on which we can kind of get leverage and explore aspects of ourselves that have been really buried. If you look at the effect of the Industrial Revolution, it's that there are these scale advantages which are insurmountable. And as much as obviously I'm very pro-capitalism and I love the economy that we live in, I think centralization sort of discourages the individual in some ways, and it maybe detracts from their identity. So I think one of the really magical things is this technology that really amplifies our identity, our agency. It kind of unbundles skill from desire. For example, if you want to make music, you can make music now — you don't have to know how to play the piano. If you want to be a programmer and make software, you can make software now. So it really amplifies our individuality. It allows us to explore aspects of our lives that we were never able to explore before. And you know, just in terms of the nuts and bolts, we have been in this sort of morass of 2% GDP growth. Like, who said that we have to be there? Why can't we be 10 or 15 or 20%? And this is a technology with which not only can we dramatically drive productivity, we can dramatically drive ambition. Like, think of the — maybe this is a caricature — the 1950s and 1960s. We believed that we could do anything, right? We were coming out of a world war where the entire economy, the entire world mobilized in a way that we didn't think was possible. And one of my theories, Lenny, is that when the stakes are high, we are awesome. When the stakes are low, we are at our absolute worst. And in a lot of ways, I think the world we lived in five years ago felt like a low-stakes world, which is why we kind of collectively had a lot of these side projects as a society which weren't necessarily productive or making any of us happier. And now you've got — it's not just Elon doing everything he's doing, but I think everybody feels like they're climbing the ambition ladder. You ship your bad ideas so you can discover your good ideas. And if you want to build software, great. If you want to build a bridge, you don't have to be a civil engineer to know how to do that anymore. So I really think we have the makings of a dramatically happier, more fulfilled, more productive society. And yet we're here talking about the permanent underclass.

Yeah, you're right. Moderna just did. I mean, the fact that Dario can write a blog post and say, 'What happens when we cure every disease?' and then we debate it as a serious topic — like, what world are we living in here, you know? I also think that we're collectively worried, always. Like, these abstractions — the world, the average worker, the middle manager, the person who lives in the vicinity of a data center — these are abstractions. But our actual lives feel like they're getting more fulfilled, we're more capable, we're more empowered. So I think that's also something — the revealed preferences tend to show how people are experiencing it individually, and the stated preferences tend to show how people are sort of observing it or believe it's playing out societally. You know, if you ask people if they want a data center in their neighborhood, most will say no. But if you ask them if they used ChatGPT today, most people will say yes. So that's kind of the dissonance.

The most important thing that we can do with AI to change the kind of conversation around it is make important things cheap. There's two things that are extraordinarily important in America that have only gotten more expensive, right? Health care and education. If you look at health care, 45% is administrative. So if you take a lot of that administrative burden out, you can actually see deflationary health care costs. Also, things like curing every disease — that sounds awesome. You know, GLP-1s, also obviously not an AI thing, but I think is a reason to be optimistic about deflationary health costs. And then education as well. I think education now has the strongest form of competition — sort of traditional education — that it's had in 200 years. And I think it's going to be very, very good to kind of unbundle learning from institutions. And also, by the way, status from credentials. You know, like you don't need a Harvard degree, you just need to get things done. And that's pretty cool.

Yeah. I mean, without commenting on OpenAI specifically, I think that maybe I'm a little skeptical on some of these things, where I think the aura that Anthropic got from having a model that was too dangerous to release was extraordinary. And maybe they had a GPU shortage. Now, maybe it was, you know, the capabilities were more advanced than they actually wanted. You know, maybe they actually wanted to keep that proprietary model internal to extend their own lead. So I think there's a lot of sort of confounding factors that would cause you to pull back a little bit. Look, I do take the points about offensive cyber seriously, which is we should harden all of our systems before we make them trivial to penetrate. But I think the sort of concept of the model that's too dangerous to release — it kind of conflates marketing, inference capacity, and then also economic considerations like: do you want to externalize your competitive advantage, or use it to make yourself better?

It's crazy, though, you know, to take the other side for a moment. Like, it felt like Anthropic was unassailable, and now OpenAI has had an amazing six months, and open weights are also ripping. So despite the sort of scary concept of this supremely intelligent model that's totally proprietary to one company, so far the industry trends haven't played that way at all.

Oh man, I mean, we should talk about the personal-agent thing. Like, actually the three big products that I love here — Grok bot totally nailed it, and also the model underneath it is awesome, right? They came out right out of left field. And that's — I know a lot of the good work that Chris's team did. I think ChatGPT Work is kind of buried in the UI, but it's really, really good. It's one of the best products they've released. And then there's a startup that's really getting some buzz called Instinct, which has made some more aggressive and interesting trade-offs, but all in the same domain.

Oh, really?

Oh man, they did such a good job on it. It's really — I don't know how much you use it, but it's really well done.

It runs in the cloud. It does a good job of kind of caching browser credentials, though it's not as aggressive as Grok or Instinct. And also the remote feature — it's got the full deep-duplex voice mode. So you can actually just call it. It can see all your threads, and you can just talk to it and say, 'Hey, what's happening across all my coding agents, across this? Can you change that?' So it's just because the full-duplex voice is so good, you really feel like you're calling your assistant who knows everything that's happening in your world, whereas with Grok it's a sort of one-way transcription, right? Or with some of the other assistants, it's a text. So the voice, plus the kind of model of it seeing all threads, is really well done.

I mean, I think the entire trend of human existence has been that our desires grow faster than our ability to fulfill them. If you look at the things that are expectations today, they were unimaginable luxuries 500 years ago, even 50 years ago, for things like therapy, right? Or things like antibiotics 100 years ago, right? I mean, it didn't matter how rich you were; you simply didn't have access to it. So I think we're underestimating human ambition, human desire. You know, people are going to be mad that they don't have a vacation home on Mars in 20 years. Like really mad — like, really mad. They'll be seeing it on Insta and be like, 'Come on, babe. We've got to work harder and make this happen.' Every CEO is going to want to build a much bigger company. So, don't they already? It feels like we're living a much larger form of sort of human existence than we could have imagined 100 years ago. And there's no reason that trend won't continue or accelerate.

A lot of things get conflated, because when you and I say ambition on this pod, I think people have a very specific idea of what that ambition is, you know? Ambition to be a founder, ambition to build a beautiful software product. Like, those are types of ambition, but there are other types of ambition. Let's talk about creative ambition. You know, when you're five, nobody says, 'Lenny, you're good at painting, but you're bad at drawing.' You know, you just have an ambition or a desire to make something, and you make it. Like that's something that's very unique and can now actually be encouraged.

You know, think of a very local ambition. So, you know, they have the NHS in the United Kingdom. I think it's a sort of treasured institution that's not working well. Maybe the way AI shows up in their society is making the NHS as good as the iPhone, right? That's very specific and local to them. Or simply the ambition to be more connected to our family, to be more present parents. So the ambition doesn't have to be sort of ambition in the narrow economic sense. It can be really anything that we want to do more of — and who doesn't have that in their bones?