Claude Code creator Boris Cherny on the end of the software engineer

Some people think the idea that we're all going to lose our jobs to AI is just hype. This week, I'm talking to one person who might make it a reality. This podcast is brought to you by Atlassian Rovo, the AI that takes your team from AI novice to AI native. Welcome to Platformer. I'm Casey Newton, and my guest this week is Boris Cherny of Anthropic. He's the creator and head of Claude Code, which is the fastest growing AI coding tool in the world and may also be a working preview of fully automated software development. So if our first two guests on this series, which is about AI and jobs, try to calm us down about the risks, I suspect that Boris is going to rile us up. He has said that software engineering jobs will start to go away as soon as the end of this year, and I'm really looking forward to pressing him on that one. But before that, as always, we're going to begin with the numbers. Each week, we kick off the show with fresh data trying to help us make sense of what is actually happening on the ground. And to do that, once again, we're bringing in Platformer Fellow and Gen Z AI Correspondent Ella Marcianus. Ella, how are you this week? I'm wonderful. I've been reading Lord of the Rings Return of the King, and it's kind of changing my life. That has nothing to do with AI. It's just truly excellent human-generated writing. I mean, there is like a parallel between the ring and AI that I always think about. It's like, you want the power, and then like maybe there's in fact something like insidious that comes with the power of AI. We see again and again. It's true. Well, you know, I will say, you know, I'm always glad to see you reading human-generated writing, as you call it, but I have one caution for you, which you may have already considered, which is that people in Silicon Valley who read The Lord of the Rings do then go on to start some of the most terrifying companies ever with names selected from Lord of the Rings. So, of course, Palantir, Anduril. So have you had any inkling yet of like a sort of a name that you've seen, and that's made you think I could probably do something really evil with a company named after this? You know, like minus Morgul, it's in stealth right now, so like maybe we should cut it out of the podcast, but I have some hope that we're going to do some, we're going to flip the narrative on Sauron-related tech company names. We're going to do really great things with our wonderful team of undead horsemen. Fantastic. We look forward to learning more about this company in the months ahead. In the meantime, though, I wonder if you have seen anything interesting this week related to AI and jobs. Yeah, so there's the study from Microsoft. They've surveyed 20,000 AI users. So worth keeping in mind, we're like narrowing down to the population of people who are like actually using AI at their jobs. And there's this thing they're calling the transformation paradox, which is basically 65% of people who are using AI at their jobs, they're worried that they'll fall behind if they don't adopt AI quickly. And also people are finding it useful, like 58% say they're producing work that they couldn't have if they didn't have the AI they have. Like a year ago, they wouldn't be producing this kind of work. 66% say it lets them spend more time on high-value tasks. So like, one, people are using it and they're liking it. Two, they're worried that that stuff will, like they won't be able to keep up if they don't use it more. However, the like really big contrasting statistic is only 13% say they're rewarded for experimenting with AI at work. So basically, there's this gap between like appetite for AI use and like the institutions themselves that people are working at. This is so interesting to me. And I think it gets at something very real based on my own conversations with people at their jobs lately, which is that AI is like all stick and no carrot, you know? The like the prototypical CEO is conducting an all hand saying, we're going to AI all of the things immediately. You must be AIing at all times. And yet, when workers go through with this, it doesn't seem like there's actually much reward waiting for them on the other side. Yeah. And I think another thing that a second Microsoft study found, like a little earlier this year, was when managers actively model AI use, AI use goes up like 17%. And people also trust agents 30% more, which like, should you be trusting the agents? That's another question. But I think that kind of, there are a few different ways you can like try to encourage your employees to use AI. One is like, AI is the future. Guys, please use AI. And another is like, hello, direct reports of mine. Here are like the specific ways like I use AI as they're surveying here at my job. And like, here's how you could try to do a similar thing. And it turns out like that kind of strategy does seem to get results. That makes sense. I mean, my question is like, what would this look like if managers were actually financially rewarding their workers for using AI? Or it's like, you know, right now, so many people understandably are reluctant to use AI because they do not want to train their own replacement. They do not want to like hasten the end of their own job. But I think if they had reason to believe, hey, if you become more productive because of this tool and you raise the output of this company, like you are going to share in the spoils of this. I don't know if this idea just comes across as like pure communism to Silicon Valley, but like, I have not heard basically anyone who seems to be trying this lately. Yeah. I mean, I guess like, to some extent, there's this thing going on where at least workplaces are like subsidizing tokens, which sometimes I would like to bring up, doesn't end super well. So at the same time as we're seeing this like gap between, in fact, like worker demand and like how much managers are rewarding their workers for using AI, we're also seeing, quote unquote, token maxing at major tech companies where... Now, do you identify as a token maxer? I'm not token maxing. Like I don't have, I'm not like running agents at night. Like when I get an AI to do code for me, I'm kind of like sitting there. Like I know what it's doing. I just, there's just like, isn't any task for me as a journalist where I'm like, you know, I need to make, have Claude make like an enormous code repo where it's like constantly thinking to itself or something. Like, I don't, I don't even know what, like non-destructive activity I would do. Well, I told Claude to rebuild Palantir from first principles. And that started to burn so many tokens. We actually have to pull the plug on this. So thank you for your, your moderation. I'm in favor of token moderating. Okay. Token minimizing. I don't, we don't need to do that, but token maxing is a bridge too far. But you're saying that there are companies in Silicon Valley where they, where they are truly token maxing. Do you have a good example for us? Yeah. So Amazon. So previously we got some stuff Meta. This year, or this week, this year, a lot of stuff has happened. This week, some Amazon employees reported to the Financial Times that basically now that Amazon has adopted this new tool MeshClaw, which is an internal Amazon tool inspired by Openclaw and is encouraging workers to use it. And also has these leaderboards within teams, like various leaderboards of token usage. Some people, according to employees, are just like running agents, not even that do productive stuff, just like as we saw at Meta, like maybe just sometimes like randomly go in a loop so that they're like token usage goes up. And for example, at Meta previously, we saw a bit more numbers of like the biggest token usage in the biggest token leaderboard. It was like hundreds of billions of tokens and it was like an amount that clearly would have cost Meta literally millions of dollars. Where like some of that truly was going down the drain. Another thing that I found really interesting about the dynamic described in this Financial Times article is the official word from on high is that it's, in fact, your token usage is not supposed to be a metric your managers take into account, but employees still think that managers look at it. And so in fact, they're still just increasing their raw token usage. And then also Amazon has this high up corporate target for 80% of devs to use AI every week. And so it's like the signal from on high is we want you to be using AI to an extent that people are like, in fact, sometimes at least doing absurd stuff where like, to me, I'm not there. I'm not one of these managers at Amazon. I don't know as much about how you manage a team of software engineers, but like, I feel like as a dev, I would want less one double speak about like how my AI use was being tracked and like two more productive metrics that like, Yes. are clearly communicated and relate to how I'm adding value with AI. Yeah. And I think that this story speaks so well to that Microsoft work trend index that you brought to us at the top of the show, because if workers do not feel like they're going to be rewarded for using AI in a very specific way, they may use it in a very silly way, right? They're going to try to honor like the, the letter of the intent, which is use AI, but like miss the spirit of it, I tell you, it's still the same feeling today. There's still this like intense feeling the model can just do all these things. There's no product that lets it do that. And so, you know, we wanted to build a coding product, didn't know what it was going to be, and so I just wanted to learn how to use the Anthropic API because I was like, all right, we're going to build a product, I should learn how to use the API so I can build the product. And I just built the cheapest possible thing. It was a, you know, little thing that ran in the terminal. It was the thing that I could build so I didn't have to build a user interface or an app because it's just fast, you know? I built this thing in a couple of days and I just started giving it to people to see, like, if they would use it, how they would use it, just out of curiosity. And I remember, like, over the next few weeks, more and more people at Anthropic started using it. Like, first it was just the people that literally sat around me, then it was kind of like the next layer of the onion kind of outside of that, and then, like a few weeks in, just like a lot of Anthropic was using this every day. And it was weird because it was a little prototype in the terminal. It's like the most engineer-y possible product. A lot of engineers don't want to touch a terminal, but they did it, and they used it. I've read that within five days of the initial release, half of the engineering team was already using it. And I wonder, as that was happening, did you have a moment of thinking, okay, like, software engineering just changed forever? Or are you still sort of iterating on the product and pushing pull requests? Dude, I was so focused on just shipping this thing. Like, for me, as soon as I got this idea, I just spent, like, every night, every weekend. This is the only thing that I thought about, the only thing that I worked on. Like, I started having dreams about Quadcode back then. I'm like, that's still kind of all I dream about every night. It's just like, what should we do next? Like, what do we build for the product next? So, you know, I think now there's a chance to kind of zoom out a little bit because a lot of people are using it and we should kind of like... There's a lot to learn about the way people are using it. But for a long time, we were just so focused on building. I just didn't even have a chance to think about, like, what is this? Yeah. Was there a moment when you did sort of do that zooming back? Because I have to imagine part of the reason that you're dreaming about it is, like, you realize that, you know, it might be too, like, minimizing to say that you stumbled across it, but it does seem like there was a sense of, like, somewhat accidental discovery here. Wasn't there that moment of like, oh, gosh, like, yeah, this is different than some of the other things I've hacked on. Yeah, I mean, there's a lot of surprise. Like I said, broadly, we knew we wanted to build a coding product, but no one thought this coding product would be in a terminal. There was a lot of moments of surprise, just so many. The first one was when Claude told me what music I'm listening to. And there was a couple versions of this, and we actually have, like, there's a video demo I recorded of this and we actually just, like, donated it to a computer museum. Just, it's this, like, very weird historical artifact, like... And it was this video, I remember posting it on my Slack, and there was, like, two people that liked it. Like, two reactions. Because no one understood what, you know, that this would be it. But yeah, the first moment was like, I asked Claude, what music am I listening to? And it wrote, like, a little bit of code to, like, open my music player, and it wrote the code in AppleScript, which I don't know. And like, I wouldn't have thought to, like, write code to answer that. That's crazy. And it just sort of did it. And I was like, wow, this is surprising. It solved the problem in the way I wouldn't have as an engineer. And, you know, over the last year and a half, there's been so many moments like that. I just actually had one of these, like, with cowork. Every time that we release a new model, I kind of experiment with it and, you know, kind of like see what's the frontier of what this thing can do. Because that's like one of the hardest things about building on a model is it's advancing so fast. You just have to kind of, like, recalibrate every month, as I'm sure you know. Yeah. And I used cowork for the first time to book a bunch of flights. And usually it works okay. This time it was the first time it worked perfectly. And, you know, anytime I travel, I use cowork to book it. And yeah, it booked, um, it booked eight flights, five hotels. The only mistake was one of the hotels was just like way over budget. Yeah, I just sort of, okay, this might be like a little too pricey. I think it was like $5,000 a night or something. And I was like, please. Cowork wants you to have a great time when at your stay, you know? And I was like, please, please rebook this one. But then, you know, otherwise, like, it just like worked for like a couple hours and did all this. It was just, it was so cool. Like, I just feel the surprise, like, every week, every month. So I'm gonna get to cowork in a bit, but this feels like a moment to, to zoom out a bit, you know, from the story of initial discovery spreads rapidly through Anthropic and now has become a default tool for a very quickly growing number of engineers. And it is one of the products, I think, that is sort of making this question of jobs automation feel really salient, at least for the software engineers, but maybe more folks than that. So during our first episode, Aaron Levy was talking to me about this same subject. And he said he didn't see jobs going anywhere, that there's always going to be a kind of last mile of human work that the software can't do. You have publicly predicted that the title software engineer could start to go away as soon as this year. So is Aaron wrong about this? I think there's a bunch of stuff that's true and a bunch of stuff that we don't know. Okay. I mean, like, the trends are just, it's just exponential. Exponentials are very hard to think in. So honestly, everyone's saying that they know, no one actually knows. We're all guessing and some of these are, like, educated guesses based on what we're seeing and based on history. I think what's going to happen is a few things. One is, there's going to be a lot of companies that need less engineers because engineers are more productive, so you just don't need as many engineers to do the same work. I think at the same time, there's going to be a lot of companies that need a lot more engineers because every engineer is more productive, the company could do more things, it can start more products, it can create more businesses. And, like, you see this with our team. Like, we are constantly bottlenecked on good engineers. We are hiring as quickly as we can. And there's a lot of companies and a lot of our customers are exactly the same. So I think, I think both things are going to happen. And it sort of depends on the company, and it depends on the business. And I think there's this other thing happening where all the roles are kind of blending together in this kind of interesting way that I don't think anyone would have predicted. Like, you know, our manager, Fiona, she has not coded in like 15 years, and she joined Quadcode and now she's coding. And, you know, Kat, our product manager, codes. And, you know, like Megan, our designer, codes. Like, everyone on the team codes. Like, you don't have to be an engineer anymore. And so this is what makes me think that over time, if you kind of project this trend a little bit, what's going to happen is everyone that's not an engineer is going to code a little bit more. Engineers, you know, like me, I haven't coded in six months. I'm like building stuff all day, but I haven't written a line of code in six months, like, over six months now. And so I just see it all kind of blending into one thing. Like, we can call it a builder, we can keep calling it an engineer, we can call it a product manager. I don't know what the title is, but the role is changing. Got it. So the way that we conceive of these roles is definitely going to change, but what that means for, like, how many jobs are available at which companies is still very unclear. Yeah, and I think history, you know, like, has a lot of examples, like, in, you know, different ways. Like, you know, like the tractor was invented. I was actually just, like, reading about this the other day. Tractors were invented in the 1890s. It was this guy, John Froelich, invented it in, like, Iowa or something. That sounds right. And at the time, if you look at, like, the way that farm When they ask, like, you know, is it 100%? Let me ask about another fear that people have about a world where the engineers aren't writing as much code. And the fear is that people's understanding of their own profession will atrophy, and that might be dangerous in various ways. You haven't written any code in six months. Do you feel like that atrophy has started with you? And how do you feel about it? There's a, you know, there was one engineer on the team, Lena, that was still writing, like, C++ on the weekends by hand just for fun because she, like, still enjoys, like, writing the code. And I think there's like, there's always room for this. I think for me, this is part of, like, a much broader transition, and it's not about atrophy at all. It's just about, like, programming is always a thing that is in flux. Like my grandpa programmed in punch cards, like, back in the Soviet Union, you know, like, 70 years ago. And for him, that was programming. Like, there was no JavaScript. There was no, you know, Python. That didn't exist yet. For him, it was punch cards. It was like, piece of paper. There's a machine that, like, punches holes in it, then you feed it into this mainframe. It processes it. Like, a few lights light up. And when you talk about programming, like, that's what it was. And then, you know, like, before that, like, you know, like the Pablo program, it was like, there was a room full of people, you know, like, often, like, women, like, doing, doing math on paper, sometimes, like, by hand. That was called programming. Right. And nowadays, you know, like, this, this, this changed. Like, programming became writing machine code. Then it became writing assembly code. Then it became like JavaScript and Python, you know, Java, all these languages that people use nowadays. And now it's, like, changing again. It's now you talk to the agent and it's actually, I think, about to change one more time where you, you talk to an agent that talks to agents that does the coding. Um, but, you know, it's, it's just always changed like this. It doesn't feel like atrophy to me. It feels like a, it's like a sea change in the technology. My feeling about it has been that, like, I'm sure that using a graphing calculator, like, caused some of my math skills to atrophy, but my solution to that is that I will just continue to use a calculator, you know? Like, I'm sort of fine to see some of that stuff. You know, now, if over time, the calculator becomes super intelligent and tries to undermine me in subtle ways, like, that would sort of freak me out. But, you know, maybe we haven't, like, crossed that bridge uh quite yet. Let me ask about another criticism that I sort of feel like is, is in this realm, which is, it seems like, you know, every time a new model is released, we'll hear people say, this is really good. And then you check Reddit a few weeks later and they say, the product has massively regressed. My sense that sometimes this is, like, a real issue caused by bugs. Other times it's just sort of a vibe. But I feel like people are concerned that because it's all just sort of AI generated right now, there isn't maybe sort of the same craftsmanship that we once saw in code. So I'm just curious what you make of these, uh, periodic backlashes we seem to see. Yeah, um, it's actually sort of an open question what causes it. There's been a couple instances where it was real. And, um, there, there was two that I know of. And we published like engineering, you know, blog posts on the Anthropic blog about it. Because, you know, like if it's, if it's real, like, we found it. We've, we fixed it. And then we want to talk about it so people kind of understand exactly what happened. But I think in almost every other case, it's sort of like, Maybe it's, it's like a honeymoon period where you kind of get used to the model and, you know, at first it's magical and then you kind of get used to it. Maybe it's something like that. But I, I don't think it's really about craft because the models code at this point is just much better than the code I would have written. If you talked to me like a year ago, I would not have said that. I would have said the, the model is like kind of sloppy and like, the code's not really good. You have to like triple check everything. It can make silly mistakes all the time. But that's just, like, simply not the case anymore. And again, it's just, the model keeps changing. It's really weird because every other technology we use does not change this fast. But there's a, you know, like, if you tried the model for the last time, you know, like a year ago, the model now is completely different. And so if a year ago you had to handhold it and you have to triple check every line, now I just generally, like, let quad do its thing. I ask quad to double check the result. I ask quad to, like, open the app and test it by itself. And then, you know, while I do that, I have like, you know, 15 other quads running that are, that I've also asked to, like, do stuff like this. Um, but that's kind of what it is now. It's the code is actually just much better than what I would have written. Let me ask you about your Claude swarm then. You asked me earlier whether computers make me more productive. I think it seems clear that Claude is making you more productive, but it doesn't seem like it's actually reducing the amount of work that you're doing. And I think this is kind of, like, an important thing to dig into if we're curious, like, what AI means for jobs. Because, you know, it sounds like you believe companies are going to need fewer engineers. And yet, at least for you, like, you're never running out of things to do. So, like, how do you think about that, like, that question of, it's making me so much more productive. I'm not working any less. Will there ever be a case where making me more productive actually means I'm working less? Yeah, I mean, there's like, there's like a name of a, there's like some paradox. I forget what it was, but there was someone that named this thing. Okay. Yeah, I don't know. There, I, I think that it's actually really individual. There, there are some parts of it that where it's up to the company because, like, depending on the business, there might be more need for people or less need for people. But I think actually a lot of it is individual preference. There, there was like, you know, like, when the laundry machine was released, I'm going to give like historical in, because, you know, for me, I just, this is, this is a crazy technological change. Like, I need history to kind of anchor myself. No, I love it. I love the stories. Okay. Yeah. So this is just how I think about it. Yeah. So like, when, when the laundry machine was released, it, I think the average person to do a load of laundry, it took like five or six hours and you had to walk, there was some estimate of like 3,000 feet or something from laundry with laundry. Because like the way that it worked was like, you had to, you had to walk outside and you had to, you know, like collect the logs and the coals and you go back inside and you sort of fire your both water. Then you take it out. You put in the laundry, you like scrub it on the scrub board. Then you have to wring it out and then you might have to, like, repeat this for like your entire family, maybe like every day. And it was just like a lot of work. And at some point the laundry machine appeared and it just took it down like a lot. I think it, it took like three hours off the time it took to do a load of laundry. And this was one of the factors that let women enter the workforce in mass. Like without this, like usually it was the women of the house. Like not always, but usually doing this work. And then this means like you were just like stuck at home and you couldn't do anything else. But now it's like three hours freed up every day. And so like different people could choose how they want to spend this time. And maybe for some people, the choice was, well, I just want to like hang out with my kids or like, I want to go like, you know, walk the dog or read a book or hang out with my friends. But actually for a lot of people, the answer was, okay, I'm ready to enter the workforce. Like I want to go, like, work at a factory or like, I want to go work in an office job. And because the time was freed up, now you have this choice of what you want to do with this time. And I think it's kind of similar right now. It's like, it's similar for any technology. It gives you more choice. Yeah. A couple last questions about the, the software engineering. I've been asking all of our guests, if a 22 year old just finished their CES degree this month and came up to you and said, okay, now what? What do More jobs are disappearing due to automation or maybe just transforming quite a lot. I think you've said in the past that you do think that this transition is going to be painful for a lot of people. And Anthropic is in a unique position here, right? Like potentially it will be a source of unemployment among software engineers or people in other jobs. Does the company have an obligation to those people? Is that something that the government needs to be paying attention to? Or what do we do about this world that seems to be coming into being? Yeah, like I said, I think it's going to be mixed, like it is for any technology. There's going to be good effects and there's going to be bad effects. And we don't know the exact timing. We don't know the exact mix. Like you can just never predict it in the moment. You know, I feel like I do feel this pretty immense obligation, just like as an engineer, that, you know, there's always more that we should be doing to tell people about kind of what's coming. Make sure they're able to use the tools, kind of educate them and bring them along into the future. So I actually feel this very strongly. And it's something that, you know, like the team and I actually talk a lot about. I think that, you know, broadly, this is not a problem that we can solve. This is bigger, you know, than one company. And you actually like really don't want one company to solve it because it could be the wrong solution. So I think like this is a society-wide question. It's something that we should be talking about. It's something that we should be debating. And I think the thing that Anthropic is trying to add to the mix is we put out economic reports, we talk about policy, and we generally just, you know, try to make it really obvious what we're seeing so that everyone else can decide what we do about it. Yeah. I mean, I do think the number one thing that has gotten people to take these issues more seriously is just improvement in the quality of models, which I guess makes sense, you know, like when it's just purely at the realm of the theoretical, people have a hard time thinking through, okay, what do we do next? But then, you know, once you like see a computer use itself, for example, I think a lot of people have that moment of like, okay, it's time to develop a strategy here. Yeah. Yeah. And this is, this is, I think, like one of the reasons why we build product at Anthropic. You know, like we're, we're an AI safety lab. It's actually kind of weird to even build product. It's like really not obvious like why we should be like doing that at all. But actually one of the biggest arguments for building product, and this is like actually a debate, like early on in Anthropic days, and one of the biggest arguments was, we want people to experience it so that they can understand it so that they can kind of play a part in figuring out like as a society what we should do. But if you like keep this technology locked away, so, you know, no one knows what it can do and no one can experience it. It's much harder for people to form a point of view about it. Yeah. I also wonder if you've thought at all about, like, the... Let me, let me start that one again. Our, our most recent guest on the show is James Manyika from Google, who's kind of studied, like, technology at the level of economies and whole societies. And he's really worried about what started out as a kind of digital divide. So not everyone having equal access to technologies like, you know, the internet or maybe a good laptop. And he's worried that that's about to transform into an AI divide. And it seems like the data that we've seen so far shows that the people who are getting the most out of AI are the ones already sort of, like, near the top of the income ladder. From your perspective, like, does ClaudeCode make that better or worse? Like, who do you see actually using it and not using it? And are there efforts to try to, you know, maybe get it into the hands of people who haven't always had access to these kinds of cutting edge technologies? Yeah. So I think there's a couple programs to do things like this to kind of like just spread access and Anthropic does this. I think the thing that I've been just continuously surprised by is the people that get the most value out of ClaudeCode and use the most are just not at all the people I expect most of the time. Like we just did this, this hackathon for, you know, Opus 4.7 release. And the people that won the hackathon are not actually professional engineers, largely. There was like one person that was a, there was an electrician. There was a, there was a doctor. There was a carpenter that used it to build an app. And we actually saw the same thing with our last hackathon with the 4.6 hackathon. Before that, I think it would have been like mostly engineers, but I think now the models are sophisticated enough that it's actually like not even engineers a lot of the time that are learning how to like really harness them. And this is the thing that we're seeing at kind of big customers also. So like as companies think about how do you adopt AI tools, Obviously, you have to think about how do you do this kind of like business process change? How do you put Claude at the center? This is like the biggest question. And every company is approaching it kind of differently. One of the ways that I've seen work the best is you just give everyone tokens. You make everyone feel safe experimenting and the ideas will come from the people that you don't expect a lot of the time. It's not actually the, like the super senior engineer that was like the most productive in the past. Actually, the best idea might be like an accountant somewhere in the corner of the org. Or it might be like a GTM person somewhere in a different corner of the org that like build some amazing internal dashboard that just like sped everything up a lot. Or, you know, like solve some important problem that like no one even realized the business had. So I think actually like this is the future. And like this is the thing that we're going to start seeing a lot more of. And it's going to keep being surprising. And so, like, yeah, it's it's important that people learn how to use the tools, because it's not necessarily the people that are, you know, the most productive and the best with the tools of today that are going to be the best with the tools of tomorrow. All right, well, we've talked a few times so far about how difficult it is to, like, think through what life is going to look like in an exponential. So I'm going to try not to ask you to predict out too far. But if we had you back in a year, how much of the world of let's just maybe keep it to software engineering, will look very recognizable. And how much would look a little crazy to us from today's perspective a year from now? Is there a world where you think we do start to see some of that automation kicking into gear, some, you know, more big layoffs at companies that where they're citing AI as an example? What do you think we're going to be looking at? I think there's going to be a lot of disruption in the next year. I think there's going to be a lot of big players that try to figure it out. And I think a lot of them will be successful. I think we're going to see some like traditional business modes go away. You know, like, there's all these different modes that companies have. And some of them are here to stay, even despite AI. And then I think some of them are going to matter a bunch, you know, less because of AI. And, you know, so like, like network effects. That's actually like not going to go away. Like if you have like a, you know, an app that gets more valuable with more people being on the app, it doesn't matter, like who's building the app or, you know, whether, you know, AI exists or kind of is an important force or not. Like that's, that's still like just as important. If you think about like scale economies, so like, you know, as you manufacture the marginal cost of goods kind of goes down over time. There's like kind of a natural advantage your business gets from that. And I don't think that's really going to go away. But actually other modes are going to go away. And so this is, for example, like switching costs. So like, you know, you're on vendor A and you want to move to vendor B. Actually Claude can probably just move you from A to B and like the, you know, the switching cost is not a giant moat anymore. So I think, I think businesses that depended on some of these moats that are going away are, you know, they're not going to do well. I think a lot of them are going to figure out new moats. And actually if you look at like all the big businesses today, like the biggest companies, they actually have a number of moats because this is something they think about all the time. Like how do we have a defensible business? So it's actually like not that new to them. And then I think, you know, some companies are going to continue to do well. And I, I think there's just going to be, um, if I had to predict something that would be surprising today, it's that there's going to be a lot more innovation than we expect. I think there's going to be a lot of new ideas coming from not big companies, but tiny startups of like one or two or 10 people. And I think the number of these