How AI Starts Doing the Work in 2026 With Anthropic CPO Mike Krieger

0:00Today on the AI Daily Brief, the future of vibe coding and what's in store with AI 2026 with Mike Krieger, the chief product officer of Anthropoc The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:38intelligence service, check it out at aidbintel.com. Now, as we move forward into our end of year episodes, I'm excited to add a couple of conversations into the mix. You might know Mike Krieger as the co-founder of Instagram. Real Ones will also know him as the co-founder of Artifact, an AI-powered news app. However, for most of you right now, Mike's most important role is as the chief product officer of Anthropic. In this conversation, we talk about the origins of Anthropics focus on coding, how enterprise AI usage has changed over the course of the year, and some of the trends that Mike is most excited about heading into 2026.

1:13All right, Mike, welcome to the AI Daily Brief. Great to have you here. It's great to be here. Thanks for having me. Yeah. So this is super fun. Like I was just saying, some of my favorite episodes of the year are these end of year episodes where we get to kind of think big, look forward. And one of the big themes I think for me heading into the new year is sort of everything vibe coding everything agentic. And so I was super excited to have you join the show. What I wanted to do, though, is actually kind of go go way back a little bit. I think, you know, a lot of folks see Anthropik as as sort of the torchbearer in a lot of ways for AI coding.

1:45And I wondered, you know, I was thinking about when you joined the organization and just how how early was that sort of focus clear? You know, was that an emergent phenomenon as it became clear that there was something very differentiated in these models and that's how people were using it? Or is that sort of like intention from very early on that this is a broad sort of set of use cases that matter to you guys? Yeah. The thing I always like to say, whenever there's sort of product folks inside Anthropic, they're thinking about sort of which direction to take things in is the more you can align with the sort of company's general long-term perspective about where powerful AI will come from, like the smoother things will go because Anthropic is nothing but focused.

2:22And I think that that's shown through and sort of the, you know, the thing that that's that we choose to make versus not. And definitely there's this belief that, you know, for very powerful AI, you need the ability of the model to sort of reason about things, to plan agentically and work for a long time horizon, but then also to be able to write and run code, not only to produce software, but because it's a really useful tool for solving problems. And so that belief was in there and it predates me. I joined in May of last year, but it kind of coincided sort of with the outside world realizing it because Cloud3, which had come out, I think a month before that, was the first model, and I remember there was like that moment on Twitter, and everybody said, oh, wow, this model can actually write, like, not just like sort of function level, but like entire, you know, sort of files of code.

3:04And of course, compared to now, it was not very good at it, but it was already, you know, amazing what it could do then. And then we paired it with our first sort of more coding oriented product, which was artifacts. So you could have, you know, Claude kind of generate, you know, at the time was mostly React sites, you know, alongside the chat. And that was kind of, I think for a lot of people, the first moment they realized, oh, this is an interesting new experience of kind of coding alongside the model and not necessarily doing it in a development environment. Yeah, it's interesting. I think you can, in a lot of ways, almost chart people's sort of the viability of a lot of this to key releases alongside Anthropic.

3:38You know, I remember when I first started this show, it was actually April of 2023. And already sort of agent coding was like the thing that people were most excited about, like a GPT engineer, which would later actually become sort of morph into lovable at like 18 months later or something like that was, it was like my first viral YouTube episode was about GPT Engineer. And so it's interesting to see kind of like at each stage how more use cases get unlocked and sort of a broader set of people come into the fold. Coming into 2025, you know, I think that the odds on favorite for what the year was going to be about, at least if you had looked back at kind of all the AI content creators, It's going to be the year of agents, right?

4:22And I think looking back, it was, but it was the year of coding agents. Did you guys have a sense coming into this year that there's sort of that this was poised to be kind of, you know, the significant use case or the breakout based on what conversations you were having based on the capabilities that you were seeing? Yeah, it's a great sort of moment to reflect because going into the sort of last couple weeks of the year, last year, we had built something internally we called Cloud CLI, which we later released as Cloud Code. And the emergence of that came from our labs team, which is a team that really focuses on trying to do sort of disruptive zero-to-one ideas.

4:58And that was everything from early computer use explorations and some wacky things. And also this Cloud CLI thing. And between, I think, September, when the first version got sort of rolled out internally to December, it rapidly overtook every other sort of coding tool we had internally. And it was because it kind of had this bet that the models are going to be able to do more and more. Maybe not this model, but the next one and the next one and the next one. So let's let the model cook for longer. Let's let it sort of act for longer periods of time. And so that, you know, going to the holidays, it was that question of, do we release this?

5:29You know, like, do we now add a, you know, kind of third component to the product portfolio beyond just Cloud AI and then, and the API. And so that was the active conversation that was happening. But we really felt like, if not us, then at least somebody using our models would sort of co-discover this piece where you don't need to hold the models so closely anymore. You can let it operate over a sort of fuzzier task definition and over a longer time. It still needed a fair amount of handholding then, but you could see the shape of it. So it was definitely coming into this year, we felt like that was going to be a major shift in how people were going to build software.

6:03Well, you know, it's a super interesting question. One of the things, you're sort of, you know, you have a deep product experience. And one of the challenges I think now for product folks and just for entrepreneurs in general is there's this sense that to be successful, you have to, you know, not just give lip service to the idea of skating to where the puck is going, but actually sort of design and orient what you're building for capabilities that do not yet exist. And that's an extraordinarily hard thing to do. And it sounds like that was part of the genesis of Cloud Code was just some sort of attempt or, you know, some like, you know, scratching against that itch in some way.

6:42Yeah, we have product principles inside Anthropic. And one of them is Ride the Exponential, which is like we're trying to build products that both meet the moment so they're useful today, or at least they poke at something useful today. Maybe the ones that are a little early we won't release yet, but that they can naturally improve. And it's been interesting, even on the cloud code side, we've deleted parts of the harness over time rather than added to it because the model can do more. And it's really interesting. Also, we work with a lot of kind of downstream customers that are using the model.

7:08And sometimes, you know, we'll drop a new model, you know, a research model and they'll say, oh, it doesn't look like it improved very much. And then we'll send some applied AI folks to spend time with them. And they realize, all right, now we're actually harness bound and we need to actually let them evolve and let the model do a bit more to loosen that as well. But it's definitely an active conversation that we have with folks building on top of the platform. They have some visibility about where we're going. Maybe they'll be in a research program at early access, but they still have to do a fair amount of this.

7:34All right. So if the models are here now and I need to do this much additional scaffolding, what does this look like if I need to do less scaffolding? Is my product still useful in adding value? And can the model then do even more for me? Or is it now going to squeeze the piece that I thought I was adding value in? Have you been surprised at all with the way that people have used CloudCode since you released it? Because it is much broader uptake than just core audience of software engineers. Yeah, absolutely. Internally, we had this internal project that people were using, And then we've like buttoned it up and put on a more fancy suit to be able to release it publicly.

8:10But then, as you can imagine, like in the internal use cases kind of kept co-developing. And so we do like every two to three times a year, we do a hackathon. And it's been notable that every hackathon we've done has been around the time that some technology is like poised for a breakout. So the first one we did was around MCP and every single project was MCP based before really the rest of the world had kind of caught on to MCP. The second one we did was around the time that Cloud Code had been released. And what was really interesting was how many projects were not coding projects, but they were using Cloud Code as the underlying engine.

8:39So there was one that was using Cloud for doing bioinformatics, which we later kind of channeled into Cloud for Life Sciences. Another one that was using Cloud as a sort of SRE in a box and was able to use Cloud Code as a way of looking at data sources. There was Cloud as a data scientist. There's all these sort of pop-up projects that was nice so that they didn't have to reinvent the tool used kind of bit. They could just add value on top of that. And then when we launched it, we started seeing things externally too, like people using Cloud Code as their project manager, Cloud Code as their PM, Cloud Code as a data scientist externally.

9:09So we started seeing this much more. It's why we eventually renamed the underlying SDK to the Cloud Agent SDK, because we realized calling it code was doing it a disservice relative to what kind of use cases we were actually seeing. Yeah. So this is one of the questions that I'm most interested to see in coming year, but even the coming years is what it takes to kind of rewire people with these new tool set. It's like this whole language, this whole infrastructure that they have access to, especially if they haven't before, especially if they're not developers. Do you think that some of this, you know, if on the spectrum from this early kind of usage of cloud code for non-coding use cases is tinkerers who are kind of, you know, more technical than they let on on the one end of the spectrum versus actually kind of heralding a different set of interaction patterns that people are going to have.

10:02How do you see that evolving? Yeah, I think it's early still, like even when we look inside companies that have deployed like Cloud4 Enterprise and they have builders within their sales team or their ads team or whatever, different non-technical team, you will always find this sort of persona, which is the tinkerer, builder, like early adopter within that space that usually is not an engineer. It doesn't doesn't even have an engineering background, but has figured out enough and has learned the primitives and can then talk to Cloud enough about how to fix these issues that they can then kind of build something pretty powerful, whether it's automating part of what they were doing, sort of enriching what they were doing, making their teams live, these are like all these different pieces, but it does still take that person, which I think is probably a natural part of the kind of software life cycle where we are.

10:44I think there's still this gap, and I think that's both a gap in interface in terms of how people think to interact with and how these products reveal their full capabilities. And then also the actual capabilities themselves, where if you had a human coworker and it was very creative at solving problems, like you gave it a high level task, I was able to do it most of the time, but sometimes it would sort of make a mistake that you would never have expected to make based on it having just done it great last week, you'd be like, I have a pretty complicated relationship with that coworker. I still, we're at that phase still of this like gap between understandability of these systems, but then also gap of, you know, how reliable and predictable are they when they do start working?

11:23And can they feel more like a thing that gets just predictably better over time? Yeah, I think that's true. And I also think that there's just, you know, I don't have the exact right words for this, but there's some lag in terms of just, you know, unwinding and undoing however many years or decades of the way that you've been doing a thing before. That is, it just, it just takes time. You know, I think about We're now, I guess, 10 months into VibeCoding as a named phenomenon, right? It was February of this year, same month that Cloud Code came out. And I'm still finding myself as someone who literally podcasts about this every day and is living inside these tools.

12:00I'm only just now starting to find myself actively ask on a regular basis, like, could I be building something to do this instead of using a Google Sheet or instead of however I used to do it? And again, that's me as someone who's as deep in this as you can get. I think there's something really to the, you know, building with one tool that gets you comfortable with it and familiar with it. It's easier to build the incremental N plus one, but it's that first one that requires that sort of uplift if you're not in the habit. So I was working on a project over the weekend. I was using Replit and using Opus under the hood.

12:32And then I also needed to create a secret Santa for my family. And that, you know, because I had been in the tools already, it was over breakfast while I was cooking eggs. I kind of kicked off this asynchronous request. And by the time I was done, it actually built the whole thing. And that was really cool. But I wouldn't have reached for it as my first tool had I just not been sort of interacting with that same software. So I do think that there's this sort of still like habit creation and adaptation of even knowing you can do that, that we still need to close. Yep, exactly. Sort of similar example.

13:03Someone had mentioned building a gift tracker with one of these tools. And I love that because we always end up, it's Christmas Eve and I'm like, we're shoving presents in the closet for later because we just bought too many things. And so I copied that, I imitated it. And it was probably for whatever reason, the first time in a month that I built something. And the just huge, basically since this last wave of sort of models had come out. And within, I don't know, three or four days, I had like six or seven different applications that had just sort of like spiraled from there. I think it's also interesting, like when we, you've seen this, not even just for our model launches, but other model launches, there's been the sense from people that are primarily maybe interacting with it, you know, in chat, and I'll say, oh, it seems a little smarter, maybe it's a little bit warmer, but there are very sort of vibes-based assessments.

13:51And if you're not sort of checking in and dipping back in and trying to build something with it, that's where I think you see the biggest leaps. And I definitely saw it over the weekend, actually getting to build with Opus, you know, for real over, you know, two days of getting really deep in there. I was like, oh, these are applications that would not have been doable even in Sonic 4.5. It would have hit some ceiling or it would have gotten stuck in some loop, but then just watching it hit a wall, debug itself, tail the logs, add debug logging, roll it out again, pull up a browser, check all of these capabilities that I think have just either been co-created along with the model or now the model is using better as it's improved.

14:27But you really got to sort of dip in and push it to really see that difference. But I think where there's some of that jadedness, I think from the outside sometimes of, oh, are we hitting a plateau? Whereas, you know, if you if you checkpoint it, you can definitely see that continued improvement.

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17:21Well, so this is actually an interesting point at which to sort of maybe try to fork the AI coding or vibe coding conversation into a few different buckets. You've got sophisticated, the sophisticated sort of software engineering conversation around what AI coding is going to mean and sort of, you know, the autonomy spectrum and sort of where these models are and what they can do. And you almost have, you've gone through kind of a full cycle this year where, you know, it was, you know, huge amounts of uptake, but now we're kind of sifting through the new challenges, right? You You know, new technology doesn't solve all problems.

17:55It trades one set of problems for a hopefully better set of problems, but then you still have to solve those. But then on the other end of the spectrum, you've got, it's incredibly nascent, the individual, the non-technical usage of these tools. I think we've barely started to scratch the surface on those things. And then somewhere in the middle, you've got kind of enterprise usage, which includes some of that software engineering reorganization, but also includes, I think, lots of folks who are thinking about how to use these types of tools for other aspects of the business rather than just sort of, you know, building software.

18:25How much do you think these are the same conversation versus, you know, again, three kind of, or two or three different conversations using all the same words? Yeah, no, I think you're right. I think even if they have an underlying model, that's the same. And even if some of the other building blocks that you might use in there, either from an SDK perspective, they all sort of end up needing different applications or different sort of manifestations. They feel quite different. I think you're right. So on the, you know, software development side, you have in general, like a pretty motivated population that has always been interested in tinkering with their tools.

18:56Like hence the Emacs versus, you know, Vim, like, you know, tabs versus spaces, like programmers maybe notoriously have the desire to sort of optimize their own building environment, which other disciplines might have, but not always to the same degree. And it's not always as easy to swap one thing out for another. So the adoption there, the evolution has become, I think, this flywheel where it's really clear for, you know, engineers and Anthropic where the model needs to improve. And that's very helpful for us to coordinate with research and close that flywheel as well as feedback from external folks.

19:24In the middle piece, there's still this sort of ceiling of complexity that you can hit. Now, there's been really impressive sort of zero to one, five coded applications that have even been released. But you're still, I think the gap I was perceiving, even just like watching my wife, who's a sort of product manager, UX designer, right by training, not a software engineer, use some of these is that you still sometimes need to know the right sort of magic incantation words to use. we were building a product together, like a side project. And the way it was using LLMs was just filling the context window.

19:54And I was like, okay, well, you actually probably need to move to some like semantic retrieval piece. But Opus wasn't suggesting that out of the box. And she didn't know the magic words. And it took me saying, all right, we actually probably need to move to this embedding solution. It was just like a layer of complexity above. And so I think one thing that our models, all these coding models can do a better job of in that middle category of helping non-technical people build things that are, you know, effectively software is helping them move up that complexity ladder in a sort of more structured or thoughtful way where yes, the five coded front end only thing is great to show off an idea.

20:26And then you want to persist data. Okay. That's the next step. Or how you want to persist data. You're thinking about launching this. That's going to take a whole other step of security reviews and thinking about things and like, Oh, now you've launched it. And if the thing is melting under, you know, load, okay, great. Now I got to put on my performance engineering hat and then build from there. the same way with Instagram, we went through that process of first, we were just building the UI, then we built the backend, then we launched and it totally fell over because it got attention. And then we kind of rebuilt it over the next, you know, weeks and months to sort of manage, you got to speed run that, but with model assistance now.

20:56So that feels like the big piece on the middle one. And then the last one, I think on the, on the enterprise software side, you know, I saw you cover the, you know, like famous MIT report of that like gap of expectations. And I think that was such a, it was one of those things that was truthy. And even if there was like sort of methodological problems underneath the study, it did point to something that a lot of people had, which is like, I got AI rolled out to me at work, but I'm not sure I'm more productive. And I think the place to close that gap, I think there's a bunch of things. One is just making sure the output quality is actually good enough where you're saving yourself time, where something is half done.

21:27I think for most people, they say, well, I probably would have been faster doing this myself rather than hanging up with something that's like not quite there. And then I'm struggling to get it to iterate with me to where I will need it to be. So a lot of the emphasis that we've been doing is actually less on the agentic side. It's less like, take my two-sentence description and generate an entire PowerPoint deck out of it. And it's much more, you know, require a little bit more upfront work, but really focus on making sure that that initial, you know, sort of thing that got created was high quality enough where you felt relief and happy that it saved you time rather than, oh man, I've just created more work for myself by using AI.

22:01As you look into 2026, where do you see enterprises starting this year? Maybe especially as compared to where they were starting in 25. What do you think the big goals are that you have sort of thinking about, you know, both model design, but also product design? I think maybe two things that feel markedly different now versus a year ago. One is enterprises getting more interested in rolling out, what we've been calling horizontal agents. But basically, you know, if you think about the sort of companion agent or co-pilot agent, where there's a strong human in the loop and you're kind of co-creating either a document or an email or, you know, whatever that may be, seeing also a lot more interest now in, great, we have this repetitive back office task.

22:40We're trying to scale up to handle international, know your customer requests, whatever those sort of complicated but repeatable processes that have something that is bespoke to that enterprise, but also something that is sort of regulatory, for example. We're seeing a shift there where there's a lot more interest. And we've been deploying applied AI engineers into these enterprises to help them get those agents running. And it's often about sort of translating what those requirements are into that process, again, where the model can be creative and flexible, but still repeatable enough to follow their operating procedures.

23:12That feels markedly over a year ago, we weren't really having really any of those conversations as well. And the second piece, which also feels nascent, is I think all of these enterprises, especially any that have this public-facing product that they might be shipping, is kind of going beyond V1, which was like, let's kind of sprinkle AI on these different surfaces and hope that improves the product too. Do we need to rethink some fundamental pieces of the product to be more, you know, agent native to use a buzzword? But what I really see it as is, you know, have you unlocked the full power of your product to any AI that is sort of running on top or alongside it?