Anthropic’s Felix Rieseberg: Claude Cowork, Mythos, and the SaaS Extinction

27:03When we first released Co-Work, it could already do fairly impressive things, right? It could write a 200-page VC report for you. You could ask it to start protein synthesis and modeling. You could ask it to design complex architectural drawings. But the thing that resonated most with people was clean up my desktop. A menial task for AI, right? You do not need Claw to help you clean up your desktop. Completely unnecessary. And I think the second piece that also really resonated with people was scheduled tasks, which, again, like from a technology point of view, it's not a big innovation. Like we've known how to like run a function in five minutes rather than right now for a long time.

27:43But what we're teaching people here is you start with a little task. You see Clark do that well. You then slowly grow the task. right like humans fairly independently will after seeing like a small task work increasingly offload more and more work and then with scheduled tasks you teach them it's actually okay if you don't watch this thing you're like you don't need to like supervise you don't need to sit in front of your computer and watch Claude do the thing you can just ask it to review your meetings every single day and write your report you can just have it do that it will send you an email once it's done you don't need to be involved And I think on this journey, we're slowly trying to teach people more and more what the capabilities are and how to integrate them into their life.

28:31And I think that's fundamentally where the trust comes from, right? Like trust is really built on top of cloud promising a particular output, that output actually being good, and you not having needed to either babysit it or intervene in some way. Would you say that UX is as important to the success of an AI agent as the technology itself? Like how you take users on a journey so that they are empowered? And if so, what are some other lessons learned building AI agents from a UX standpoint? It's a really good question because I actually think that's true. I do think the UX matters quite a bit, right?

29:13Even if you go back to one of our most popular products, Cloud Code, the very genesis of it was what if Cloud, but instead of in the cloud, it's running on your computer in your terminal. That is almost entirely UX. It's the same model. It's the same core capabilities. It's really all around what is the user experience and how do you interact with the model. But it's fundamentally the same model. And that's really where a lot of the benefits came from. And I think similarly today, the AI products I see resonate with people the most are rarely the ones that deliver the most raw potential, the most raw power.

29:51And I would actually go one step further and say this is probably true, not just with AI, but maybe with software overall, right? Like I'm going to blindly assume that plenty of startups out there offer email with more features than Gmail. There's plenty of companies that try to jump ahead by offering a larger amount of features or more buttons or more capabilities. I often think a lot about the silly times of mobile phones right before the smartphone was invented. All the things that people bolted onto phones. We had phones with projectors, phones with included game pads, a phone that didn't have a keyboard, phones with full keyboards.

30:36And in the end, I think technology that works really well is often more about the things that you take away rather than the things you add. It's more about what does it feel like. And to this day, I'm not convinced that most people buy a phone on the basis of a spec sheet. I could be wrong. I'm completely making this up. But I'm having the feeling that most phones are bought for reasons other than what are the specific chip capabilities. And I think AI probably works a very similar way. Like, obviously, a very powerful model gives you a bit of an edge. I'm not going to lie that it's probably much easier for me to build a good AI product because I work very closely with researchers and we have amazing models inside the company.

31:17But at the end of the day, if someone beats me, Felix, at building very good products, I suspect it's going to be not because they built a better model, but likely because they figured out a better user experience. So how do you improve the user experience very practically? Like you guys look at what people do. You mentioned you talk to customers fairly often, but you track very precisely what people do, what works, what doesn't work, and then spend more time on key use cases. How does that work? I think what we do is probably not super unique. There's one thing that is new to me. So I'm first going to say the things that like a lot of the listeners and they're going to say, ah, yes, of course.

31:58And those are pretty radical obsession with users, right? Like built for actual real humans that you talk to a lot. Try to prefer iteration over like these long running plans. We tend to plan not more than a month out. We try to be like fairly quick in how quickly we ship and also in how quickly we iterate. The entire roadmap for core work is one month? Yeah, at most. Amazing. Because we sort of like, we constantly think about, okay, what does this look like next week? What does it look like the week after? our confidence that we can all disappear into a room and envision the best product for most people out there, the way it looks like in a year, is pretty low.

32:34And in fact, I would argue that no one ought to have that confidence. If anyone tells me I know what AI looks like next year, I'm not going to be very impressed. Maybe some VCs will. Maybe, maybe. But I certainly don't have the confidence. And I think anything I've ever built that became very good became very good because I had many opportunities to course correct. I had many opportunities to be a little wrong and like many opportunities to figure out, okay, which one of these three works better. The thing that is new is that execution is essentially free, right? Like I can build, if you come to me with 10 different ideas, I can very quickly say, let's do all 10.

33:16Let's try all 10, see which one we like more, which one feels better. We try to do most of our testing in-house. We try to not abuse our customers as like sort of free beta testers. But I think with most products, you very quickly know whether or not it's like roughly going the right direction or not. Like that feeling comes very, very quickly. As a company now, we've grown quite a bit. We have a decent amount of employees. It's like fairly easy for us to figure out does this resonate with more than five people or not. And this rapid speed of execution is like really what's new, right? Because previously, even like two years ago, if you wanted that rapid iteration, it required a very aggressive focus in which things you pick.

33:58Because you can only iterate so quickly with only a few things at a time. And now that execution is becoming so cheap, you can iterate with things. Like you can go deep and broad at the same time. And that's honestly wild to witness. So just to play it back, so you're saying that you'll actually create 10 products or 10 versions of the product actually running and then you'll have people at an anthropic test and sort of guide which one you should eventually pick? We probably have easily 100 different prototypes of various applications inside the company right now. None of them have necessarily yet hit the confidence of like, this is good enough to show to a user.

34:35but the amount of prototypes you can build internally very, very quickly completely dwarfs anything I've done in the past because of the cost of execution, right? Like in the past, the thing that would always hold you back is, you know, for me as the engineering leader, if you had a good idea, you would come to me and I would tell you, oh, we can work on this next month. It's going to take us three weeks. Until then, like go and talk to the customer, validate your ideas. And now you can come to me and say, oh, I have an idea. And I'm like, cool, give me 10 minutes. I'll send you something. and that is that is just it's like going from the painting to to the photograph you know fascinating so what becomes a bottleneck then then you have a hundred prototypes and then you need to to pick one and then somebody needs to do that is that is that where things slow down yeah i think the alignment piece is still pretty hard the alignment piece has always been hard for anyone anywhere right like as a company if you have people with competing ideas who do you pick How do you pick?

35:32How do you figure out how to take the best ideas from some things and combine them into another? That's probably a bottleneck because this is where the most humans are still active. This is where human taste comes in. Is taste the new fundamental capability that people need to have? I mean, that's certainly a word that's come back on this podcast many times. Is that what you're seeing? Yeah, I think it's probably becoming more important than it maybe has been in the past. That's in contrast to what we were saying a second ago, right? Like you'll test things, you'll see what users do, but ultimately that's a combination of like data-driven approaches and something that's much more intangible.

36:09Yeah, I think the data-driven approach really helps you in like trying to figure out whether or not your taste actually resonates with people, right? And like whether or not you're going in the right direction. And I think for most people, even the ones that we hold in very high regards when it comes to taste, sort of like the initial people behind the first versions of the iPhone, even they speak very highly of this notion of iteration and testing all the time. Like Ken Koscienda has written this beautiful book called Creative Selection that I think many people have maybe read before that talks about this combination of like, you need to have a lot of taste, but then you need to validate it.

36:40I do think it's both. And when it comes to software in particular, I'm kind of wondering how far away we are from a world where software maybe feels a lot like, say, the fashion industry. And I think phones are already kind of there. There's sort of like a baseline of quality and a baseline of features that you might like look towards, right? Like for performance clothing, there might be more secret sauce and like how you actually make the thing. But otherwise, for people who build products, it really matters what kind of story you tell about the thing, what kind of like onboarding you can give people, how you make people feel when they use the product.

37:17I think those things will probably be bigger differentiators than the actual raw capabilities inside. How does that work in the context of co-work? Putting myself in your shoes, you have the unique challenge of, maybe not unique, but you certainly have the challenge of addressing a broad group of professionals, smart people that are good at their jobs, trying to be good at their jobs. and some of them will be doing revenue ops, some of them will be doing marketing, some of them will be lawyers, some of them will be accountants. What does taste mean in a context where you have such a broad audience and how do you test for it?

37:58Yeah, I think a lot about, I've been mentioning it so much already in our conversation, I feel like almost silly about it, but I think a lot about the phone and how all of us start with the same phone, but like no two phones are the same. The exact apps you have installed probably makes your phone like unique among all the phones on the planet. It's almost like a fingerprint, same with my phone. We all start with a device that probably looks very similar to the other devices, but then the way it integrates itself into our lives is not always good, not always bad, but certainly very unique. It's certainly very personalized.

38:30And I think for co-work, our approach is similar, that we want something that generalizes extremely well, that we can apply to your life across a broad range of applications. And maybe just speaking from my personal life, currently in the process of moving and moving my family into a different house. And as many people, certainly the ones who are also listening in America, know that involves about 500 pages with a lot of words that I barely understand. Cowork here is extremely helpful, but it's also extremely helpful in like healthcare scenarios. I just had a daughter this year and working through all of that paperwork has been super helpful too.

39:10But these are two widely different things, right? Like one of them is like mortgage applications and like negotiating with movers and like figuring out various financial applications. And the other one is like much more health care. In theory, those are two completely different applications of the same underlying technology. But I'm noticing that the primitives that I think about are kind of the same. And like some of those primitives are a little better. Some of them feel a little better in my hand. And I think if you pay close attention as a person who's building things, if you use your own stuff a lot, you can sort of like feel when you're bumping into the software and it's not making you fly.

39:42And I want to create more and more instances where I can fly. And I can then validate with customers that even if they might be working in industries that I barely understand, I have no idea how they work. I can sort of tell from their stories how they're using it, what makes them fly and what really slows them down. And if you lean into those and you like just aggressively try to like enable that feeling of you becoming more productive, you're going into your inflow. You feel like this thing is taking over work that you find annoying. I think there's a lot of value to be found there. Just looking back on the journey, which at the end of the day is a five months, what, four months old journey.

40:18It's insane, the impact that you've had in such a short period of time. What was the hardest part? I'm thinking about your question to the lens of like, what is the hardest to replicate, right? Like if you told me, okay, now do it again, do it with another product, like what would be the most difficult to replicate? I think there's probably something about a point in time. and I mentioned that co-works sort of came on the heels of us like keeping our ear to the ground and saying oh there's something here there's latent demand latent demand is a gift I don't think it's I don't think you can you can go and try to look for it you can try to find it but it's very hard to create out of nothing recreating that would probably be the hardest thing now I do think software has always had ample latent demand like if you if you looked for it you could always find it quite a bit that's that's certainly one thing that is that i think is hard to like replicate in terms of actually building co-work i would not say that anything was particularly hard i think the things that are hard about building good products remain hard right like you there's sort of like the perils of success like what do you do if right like you open up a cafe and instead of 10 people 20 million people show up what do you do um that's that's that that was probably at times sometimes hard for us and like remains a challenge the overwhelming demand for anthropics products i'm probably going to be the last one to actually complain about people wanting to use my products any other lessons come to mind so if i'm listening to this and i'm building an ai agent of some sort um about the the process like building that harness and specializing it and it could be guardrails it could be like industry specialization any any thing that people could learn?

42:03I would probably recommend first not to actually build your, not to build too much of your infrastructure and use a product that we've launched today called Cloud Managed Agents that make this particular case very useful. You know what, I'm going to give you both. I'm both going to give you the advice and the reason for building custom agents and a lot of harnesses and trying to make a company on top of that. And then I'm also going to give you the case for. The case against is that as the models get more and more capable, what I'm noticing inside my products and inside my work is that we're sort of like pulling back the edge cases we account for.

42:41Right? And I mentioned earlier that memory is just a text file. If Claude needs a database, it will make a database. Those are all arguments against trying to come up with a hyper-specialized product. Because the idea would sort of be if we assume that the model doesn't need any of the special things that you as a builder can give it because it's just going to build it on the fly if it needs it that's probably not the best precondition to like building something however at the same time i think there is one one good argument for still investing in this area quite a bit and that is how far we'll have to go for the rest of the industry to truly harness this power i think the internet is a beautiful example here like i think so many people work in ai always like reach for these like very shiny analogies of like what is ai is it the internet is it the invention of the steam machine like you can pick whatever you want but i think there's one lesson in the internet that i find quite interesting which is just how long it took for the internet to really transform economy like we're talking multiple decades between the first working browser and like you considering amazon one of the behemoths of retail right like a lot of time has passed in between who's on top and who's at the bottom of like that that list of companies too has like changed quite a bit within that time and to me that is sort of an argument for like actually to lean in a little bit and like to find some opportunities in areas where you can like apply ai in a unique and novel way however i would probably say that that that sort of like akin to everything i've said so far is a lot of the value you can provide will probably be less on the agent side it will be less on the model intelligence there will be more about how do you help people organize their work right how do make that useful.

44:20So as I listen to this, I'm reminded that just a few weeks ago, when you made what sounded like a mundane announcement, the entire market collapsed, where the press eventually called the SaaS Park Ellipse, which I believe was just the addition of something like 10 or 11 files for legal and CRM and that kind of thing. Obviously, the market will do what the market will do. This is separate from you guys, but I think it gives people a sense for the just sheer importance and just global impact of what it is that you're building with co-work and, you know, Anthropic in general. What do you say when people ask you, and I'm sure you get the question all the time, you guys did Cloud Code, amazing solution for developers, then co-work, which is for everybody else.

45:11As you just said, you just announced managed agents. I literally read the announcement as I was working to record this podcast, which is the ability to use anthropic infrastructure to build your own agent. What are the areas, as you guys keep going up the stack, that are left for the software industry to build around? Yeah, I think, and this is a very, very personal take, but I've now been around a few of these democratizing rounds where you needed less and less arcane knowledge in order to build things. I'll give you an example maybe just to make this like slightly more apt. Many years ago, I worked at Microsoft.

45:59At Microsoft, I was working on something called Electron, which is like a cross-platform. So it's a way to build applications that more or less work and look the same on both Windows and macOS. And one of the first things we used it for was Visual Studio Code, which is a code editor that has since become quite popular with people, and Cursor is built on top of it, and various other companies are. And when Visual Studio Code first was released, inside the company there was a feeling that this is a toy. This is not for real developers, because the real developers, they need Visual Studio, which is why Visual Studio Code is such a complicated long name, because Microsoft also had this big application for real developers with all kinds of like very, very advanced tooling.

46:43And what has happened since is that you just don't need to go that deep into your computer anymore. Like to the people who are listening who do work in software, like I reminisced this week that the amount of times I had to look at assembly this year was zero. Over the last five years, it has not been zero. I've looked at assembly at least once, but it's becoming very rare. Like it's not really a thing I look at anymore. and another thing that has happened is that margaret atwood the author has has published a beautiful piece on on talking to claude and using using using cloud i'm kind of wondering what like software made by margaret atwood would look like if she was to make it and i think it would be quite interesting to me and i'm pretty sure i would install it at least use it once and similarly i think my prediction is going to be that we are going to have a lot more software that software is probably going to be slightly more specialized i don't think everyone is going to build their own software i think people will still build things and like share them with others and others will still like to use good software that feels good but i think the the skills required to do that will shift slightly from you know just being someone who speaks the computer's language and will shift much more towards being someone who speaks human language like now sort of built for humans.

48:06And to double click on that, what does that mean? You mentioned like understanding, what was the term you used a minute ago? Understanding your industry and your users. And now you're mentioning the human aspect. So is that a question of UX to the earlier discussion? How does that manifest? Like I think successful software developers 20 years ago were very good at understanding computers, right? Like in order to build successful software, you need to be very good at computer. You were a computer expert. And I think the people who will build successful software going forward will increasingly understand humans and users very well.

48:42And I think this has been a gradient. This has already happened somewhat, right? Like building software 10 years ago was already much easier than 30 years ago. And I think AI is another step function change. When it comes to the market, I am not an economist. I'm a software engineer. I've never fully understood what the markets do. and I would recommend to other software engineers not to like base too much of what they do on what the markets do. That is my personal recommendation. But I really do think like to answer your specific question what is left to do, I think there's mountains upon mountains of like things we can automate for people, work we can make easier for people, problems we can solve.

49:25I think as long as humans have questions and problems, like the software will be a reasonable answer. Taking a step back, Where do you think things are going in terms of agent capabilities? At the very beginning of this conversation, we talked about, you know, an extraordinarily impressive new model and things seem to just like keep accelerating and realizing that your roadmap is one month. But what do you think agents will be capable of doing in a couple of years? You see, this is tricky for me because I, on principle, don't like to vaguely promise abilities or features before they actually exist.

50:04My marketing philosophy has always been build something cool and then show it to people. One thing that I find confusing and I don't have a good answer for is that people everywhere seem to very quickly forget how far we've come in AI and seem to sort of like be expecting that a plateau is going to come sometime soon. And I think it's probably because like technology has sort of like taught them that, right? Like we've gotten the iPhone and for a while, every single year of a new iPhone was like a big change. And like for the last couple of years, maybe it was like less big of a change. As someone observing AI, I have no reason to assume that that is happening to AI anytime soon.

50:40I'd like to remind people that it's been a single number of years, four, since AI has learned how to form sentences that make any sense. Now we have AI building entire applications. We're solving complex problems. And to me, this is just like, this is not the tip of the mountain, right? We're not there yet. We're just like, this is part of the journey. We have reasons to believe the journey is accelerating so that the steps are going to get bigger and bigger. And I think Mythos Preview is actually a pretty good argument for, this is not just a theory, like the models will get smarter and smarter And we currently have no reason to believe that on Anderson's side.

51:20And again, fully realizing that your roadmap is short, like any kind of like area that you guys are focused on that you could talk about. Speaking for ourselves, one question we're curious about is whether you're going to enable regulated industries to have better, easier access to co-work. Because as a venture capital firm, we don't have access to co-works. have access to co-work in my personal life, but not at work. Is there a roadmap for that? What I'm going to say is that you're not the only one who's asking for co-work for the particular regulated industry. It's something we hear quite a bit, and whenever users ask for something, we listen very carefully.

52:07Right? That's fundamentally our job. I can't particularly comment on anything that we're currently working on, but I can sort of mention the general concept of things that I'm still excited about. And the general concept of things that I'm very, very excited about still in 2026 is really the idea of helping people organize their work in a way that makes most use of the capabilities in AI. And if people are sort of listening to that, what does that mean? What is he talking about? Once upon a time, I spent five years working at a company called Slack. And Slack at the time was, we certainly felt like we were helping some companies revolutionize the way they work, but we were certainly not the first chat app.

52:46And we were also not the first company to tell you that your company will be more effective if you don't have all of these information silos. But very similarly, a huge part of the thing that we sold people was not just a chat application. It was this different way of working, a more transparent, more open way of working. And for AI, there's a similar change in this tool is most effective. if you examine how you do work and you think a little bit about what kind of pieces you can easily give away to the model and which kind of pieces you want to have full control over. That area is something I'm pretty excited about.

53:21The second area I'm excited about is we see that there's sort of two kinds of people who currently use AI. There's people who are, as we call them, very AGI-pilled, people who sort of go all in and are excited and spend a decent amount of time thinking about how do I set up my cloud, what kind of tools do I give it access to, what kind of MCP connectors do I install? They sort of end up flying, right? And they're very effective, very productive. And then there's people who like either don't care as much or like are not interested in us or just don't have the time to like set up all of those things.

53:54How do I reduce the amount of time you need to become one of those power users? It's like something I'm pretty excited about. And the potential there, I think is still very, very large. So in practice, if you are a co-work user you will probably continue to see fairly meaningful changes shipping every single week quite a bit um there's really no end inside i think i'd love to close with some hot takes if you're willing okay this was fun what is one idea that is underrated mcp connectors are underrated because we correctly me included a lot of us have moved from mcps to clis but there is a lot of things that are quite inherently good about separating the data from the, I want to say, the execution engine.

54:39This is a very technical take, but it's like one that I engage with people over quite a bit. Sort of MCP has kind of been like the really hot thing last fall, and we're not talking about it all that much right now, but I think for most people out there, MCPs are going to be like quite useful at the end of the year and next year. And I think that's sort of the same way that maybe web sockets are useful to people who go to Amazon or TikTok. MCP is a protocol and users shouldn't care. But I think engineers don't care enough about MCPs. What is one idea that is overhyped? Good question. You'd think this would be easier for me to answer because I work in AI.

55:15We certainly have our fair share of hype everywhere.

55:21Okay, I have a hot take for you. Not every product needs a chat. This might be a fairly spicy take in AI in 2026. Meaning what? Not everybody needs to be conversing or not every product needs to have AI built into it? I think AI can probably help with most software products. I think that is right. But I think many of my fellow software engineers have a knee-jerk reaction, which is, oh, you want me to put AI into my company and into my product? That means there's a sidebar on the right with a chat input at the bottom. And I would encourage my fellow AI builders to think one more turn. How do you make this thing useful?

56:07If you were starting from scratch today, what would you work on? Yeah, if you told me tomorrow, Felix, you don't get to work with any of your friends. You have to do it alone. What do you do? I would probably go after the long tail of the industry, which I mean like there's a bunch of Windows 7 devices out there in the world that are doing menial tasks and have a load-bearing role in our society. It's kind of terrifying if you think about it, but the amount of computers that are completely out of reach for any of the modern AI that are doing important work in our society is staggering. And I would probably think about that.

56:40The other area I would push into is if you are somewhat convinced by the idea that artificial intelligence as a concept, right? The idea of like computers is not just executing pre-determined functions, but non-deterministically making decisions and executing on those on your behalf, I would probably push into the physical world. And that might be my recommendation for young people. I think we're still so early. I really think we're so early. It is such early days for AI, for the products that exist in AI. And a thing I tell a lot of my colleagues is that we're really in the silly times of mobile phones.

57:18and then if we get really lucky maybe what we're currently working on is like the nokia 3320 like a good phone but it's not yet the smartphone it's not yet the iphone someone is going to build the iphone great well that's a wonderful place to live it felix thank you so much this was uh an amazing chat we appreciate it thank you matt for having me on that was so nice hi it's matt turk again thanks for listening to this episode of the mad podcast if you enjoyed it would be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from this really helps us build a podcast and get great guests thanks and see you at the next episode