Anthropic's Labs Lead On Fable's Capabilities + Building AI-Native Products — With Mike Krieger

Mike Krieger is the head of Anthropic Labs and co-founder of Instagram. Krieger joins Big Technology Podcast live from the Big Technology AI Summit to discuss what it's like inside Anthropic the week the government forced the company to pull its frontier models, Fable and Mythos, off the market. Tune in to hear Krieger describe how working with Fable changed the way he builds — queuing up a full night of work before bed and waking to find it finished in an hour — why he insists Anthropic's safety warnings are material rather than marketing, and how Anthropic navigates being both a platform and a product as it competes with the companies building on top of it. Wired senior correspondent Lauren Goode joins as a co-interviewer. Hit play for a rare look inside the lab from the person building Anthropic's next breakout product.--- AI Agent documentary: https://www.gravitee.io/ai-agent-documentary Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  Anthropic is not just a massive model builder. It's a massive product builder as well. Products like Claude Code and CoWork that have taken off like crazy over the past few months. And Claude Code came out of a place that few of us know much about called Anthropic Labs. And Anthropic Labs is an organization within Anthropic that is working on building the next level of frontier products with AI at the center. And so today, we are lucky to hear from the person running that lab, Mike Krieger, who is the co-founder of Instagram and now the lead of Anthropic Labs at Anthropic. We're going to welcome him on stage along with Lauren Good of Wired, who will join me as a co-interview.

• 0:44Speaker 00

  Mike and Lauren, let's hear it for both of you guys.

• 0:49Speaker 00

  Let's see you. You're here. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis Chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, to go inside a large-scale agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute.

• 1:31Speaker 00

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• 1:56Speaker 01

  All right.

• 1:57Speaker 00

  All right, Mike. So chill time's in Anthropic land.

• 1:59Speaker 02

  Nothing going on.

• 2:01Speaker 01

  Slow week.

• 2:02Speaker 02

  Slow week.

• 2:04Speaker 00

  You want to start, Lauren?

• 2:05Speaker 01

  Yeah. First of all, I mean, we want to talk about what you're working on at Labs and explain your role to folks. But I want to ask you first, how close are you right now to the situation with the White House?

• 2:17Speaker 02

  Less than in my CPO role. So I transitioned about like five months ago into this Labs role. I think in the CPO role, I think would have been deep in it. Now I'm more, you know, obviously, you know, we want to restore it. And as a like product person, I want to make sure that that gets accessed. But less close to it than, you know, in that sort of C-level role that I had before.

• 2:38Speaker 01

  Okay. Alex, do you have a follow-up?

• 2:39Speaker 00

  Oh, yeah.

• 2:40Speaker 01

  I have like eight follow-ups.

• 2:40Speaker 00

  Definitely. Well, we, there was a guy named Ben on X who said, "I will mail Anthropic an original copy of my long-form birth certificate if they will enable Fable for me again. I sound like those lunatics who are obsessed with Foro now." Will you take Ben's long-form?

• 2:54Speaker 02

  I don't know that we'll take his long-form. But it has been interesting. I mean, Fable was only available for a few days, but I've definitely, every time I've tweeted since then, they have not read whatever I was tweeting. And they've mostly been like, "Bring back Fable." Which like, in Instagram, we got rid of Gotham. Do you remember Gotham, the filter? This is like, and then for the rest of like, the next eight years, all I heard was "bring back Gotham." So it struck a nerve. But Fable will, will come back before Gotham did. But yeah, it's, it's, it, clearly, the folks that have gotten to use it and started incorporating it. It's actually really interesting. I've learned to not really trust day of or even week of model reactions. You don't really know until you've put it through its paces. And so, like, I almost just completely block out the noise in the first couple of days

• 3:37Speaker 02

  of any new model release. Because, I don't know, everybody has maybe their like toy, like example thing that they like to do with a new model. But it's hard to actually put it through its paces until you've actually had real work done with it. And I think people were just starting to do that. And then, you know, we had to sort of pull back Fable. But I remember in December, when we put out Opus 4.6, it was like this interesting time where everybody went home for the holidays. And a lot of people had that week off between Christmas and New Year's. And then they came back and were like, "Oh, I spent a lot of time. And I really get why Opus is good, and I'm going to do it." So I don't think Fable has had that opportunity yet.

• 4:12Speaker 01

  But despite that, though, I mean, this was a pretty big reaction from the Trump administration. And I think everyone here, especially if you were listening to the Alex Stamos session, understands what's going on. But this happened within a few days of the model release. If I can give Wired some credit, Wired just reported last night that it was due to Anthropic's relationship with or having given access to the model to SK Telecom that could have raised flags within the administration. How surprised were you by how immediate that backlash was?

• 4:42Speaker 02

  Yeah, I think the sort of reaction decision was surprising and we were sort of immediately engaged with them to restore access as well. And so, at the same time, you know, the one thing that we think a lot about internally is, you know, there used to be a poster on the Facebook wall when we were so that was like, every day feels like a week. And I think that's becoming true in AI. And I think a good thing to remind ourselves of in general in the industry is we're dealing with unprecedented times, we're dealing with new situations, and they can develop really quickly as well. And so I think also developing the capabilities and the connections and to make sure those conversations can happen quickly is really, really important.

• 5:21Speaker 01

  I have a new motto suggestion for you, for the wall. Move fast and jailbreak things.

• 5:26Speaker 00

  I don't think they're going to use that, Lauren.

• 5:29Speaker 01

  Okay.

• 5:31Speaker 00

  You know, we just heard from Alex that some of these capabilities have been available on, you know, previous models, or, you know, you can find bugs with other models that are available today. So why do you think Anthropic got singled out on this front?

• 5:45Speaker 02

  I don't know why, like, Fable specifically was singled out as, you know, again, Fable being the non-cyber-intention model as well. I think that the thing that does change over time is there's, you know, the capabilities, if you knew what to prompt and knew what you were looking for, and then there's the capabilities, I think, you know, I'm not sure I resonate with the like juicing high school athletes metaphor from Alex, but like, you know, the uplift that you get, like uplift is a thing that we think about a lot when we think about model safety. So if you look at our model cards, for example, one of the ways that we look at risk in the bio domain is comparing the uplift. From sort of a layperson using the model versus, you know, an expert or a layperson

• 6:26Speaker 02

  just using the internet and seeing the comparison there is. And so that is one trend that is, you know, has been, you know, progressing as the models get more capable. And so maybe less why Fable is singled out and maybe more like what the overall trajectory is.

• 6:41Speaker 00

  Interesting. So we just had R. Karazian, the lead economist on Ramp on the podcast. And one of the interesting things that he said was, you know, we had during the Pentagon situation, there were these headlines, okay, this company will not use Anthropic anymore. But actually, the data from Ramp shows that spending actually increased to Anthropic models. It was apparently a good publicity moment for the company. So I think it sort of plays into a debate that we have like here on the show about like how much of this is like real concern for the issues and how much of it is, you know, from Anthropic is marketing. And like, we have somebody from Anthropic here who can actually shed some light on that. We just had Stamos talking a little bit about it from his perspective on the security side, but we're lucky to have you here today. So is it material or is it marketing or some combination?

• 7:26Speaker 02

  I mean, I think it is one of the hardest things to like really deeply believe that something is true and not marketing. And then due to not even just Anthropic, I think like, you know, I think people are generally right to be skeptical of any company saying anything and you should like put it through your own filters as well. But for me personally, I'm always like, no, but it's real. And like, we like both deeply care about safety and are like, to the extent that we are being vocal about anything, it is to either sort of help paint the picture of what very likely is coming or we believe is coming or what we've already seen and spotted. You know, for example, in the in the Mythos case, really just looking at like vulnerability scanning and bug finding and doing it in partnership with with companies that were in that kind of Project Glasswing initial announcement.

• 8:11Speaker 02

  And so

• 8:14Speaker 02

  The technology is awesome, not in the, like in the, just, you know, it is, it is doing really incredible things and therefore even calling out what we see as what is happening, I think can seem hypey. I wish I could press a button and like I could make everybody believe that we are not being hypey. I realize that's not the reality that we operate in, but at least from my perspective, we try to call it like it is.

• 8:36Speaker 01

  My understanding is that within labs in particular and research and development at Anthropic, that you're using the best AI models to actually build new products, to prototype new products and sort of test out your thesis. So is this, is this, is this ban essentially now limiting your ability to do that within labs?

• 8:56Speaker 02

  Yeah, I mean, definitely Fable is the best model I've ever used. And it's not to say that like work has stopped, but it's definitely like less good than the other models that, or sorry, we are using models that are less good than that as well. And it is also, I mean, maybe the reverse of the distrusting the first week, you know, response is what happens when you don't have Fable. And like, obviously, the Twitter reaction for the people that had already kind of like gotten into the model and reasoning it was strong. But I'd say even in my personal use, I'm like, oh, like, I'm on Opus 48, and it's good. Like, I'm still productive, I'm doing work. But, and we can go into sort of like how my work changed with sort of these like Fable or like, you know, the models of that sort of family. But it is noticeable for sure.

• 9:40Speaker 00

  Yeah, I think we'd like to know that. I mean, you know, we, the public had access to Fable for like a half a minute. Groups in this like Project Glass wing have had access to Mythos. We don't really know what the difference is between using a model that you can use today is and using one of these, you know, super Anthropic models. So what actually could you do differently with a Fable or a Mythos?

• 10:04Speaker 02

  I think for me, it's the sort of scope and scale of delegation. And all these things are really imperfect. Like, you know, people will say like, oh, is this now a level five software engineer or a level six? But anybody who's used these models extensively knows that they're still spiking in capabilities, right? In some ways, they're, you know, in many ways, they're better engineered.

• 10:23Speaker 02

  than me and in other ways like you know I was complaining today that it had missed a descender like a the G that bottom part is called the descender I'm like how did you put that in the UI and it's clipped in of course like there's vision capabilities that need to improve and there's debugging capabilities and there's sometimes even just sort of human common sense that is you know we're way better at than when the models are but overall I think the big shift for me working and it was really interesting because it sort of coincided with me going back into a builder role so I really got to see going from you know using these models as an executive you're trying to do the most of them but you know like not gonna have it write all your email and you know I think strategy still needs to come from you and then you can use the models to sort of pressure test it but going back into a builder role and going from okay I am delegating chunks like please fix this bug or I'm thinking of implementing this feature let's go back and forth to something that ends up being much more sort of all right like I got this bug report from one of our users or I have this notion of something that I want to build like can you sketch out two or three ways in which we could do it right like that seems plausible often I find actually sometimes it'll give me the the sort of explanation or proposal and be like okay that actually is over my head like you are clearly way smarter than me like explain it to me like I'm maybe not five but at least you know uh not you and it'll sometimes you know explain it that way but then go build it and gets it right getting it right like you know at a very very very high rate and I think that starts really changing how you operate like I moved much more to before going to bed making sure that I had like queued up for Fable like enough chunky work to last I would call it the whole night and I would check in later and it got it done in an hour and it was like I guess hanging out for the next seven hours but like really like delegating like a much more of a goal than just a

• 12:11Speaker 02

  so one one task for example yeah like give us an example of one task you would hand to it I mean, here's a kind of crazy one, which is for the programmers in the audience. Like, I had written one of our labs projects in Python. It's like the language I know all. Instagram was all Python. And for like some not super exciting reasons, we actually needed it to be in TypeScript to deploy it. And I was like, all right, that's going to be like, you know, in Instagram, we for years talked about moving from Python to PHP or Hack or the Facebook language after the acquisition. And at least when I was there, never did. But I basically, we have a feature called Dynamic Workflows, where you can have it like also break down the task into like a lot of subtasks. And I trusted it to sort of not just do the individual action, but here's like a whole

• 12:54Speaker 02

  language conversion of millions of, hundreds of thousands of lines of code at that point. Go off and do it. Go plan it. Go execute it. Go verify the work. Double verify the work. And then I came back to the work being complete. So that level of like, this is a big sort of chunky task.

• 13:10Speaker 01

  Hmm. So that was, so you're basically saying it was faster. Did it in an hour? You're guessing compared to, with Fable, compared to what it would have been before?

• 13:19Speaker 02

  I think the main difference is in the past, you would be like, great, I did it. And you'd be like, but did you? You kind of took a shortcut here.

• 13:25Speaker 01

  Yeah.

• 13:26Speaker 02

  Or this is not quite right.

• 13:27Speaker 01

  Right.

• 13:28Speaker 02

  Or I need to go verify it. Or like, oh, you cut this corner or.

• 13:30Speaker 01

  It's like the managing interns thing that everyone's been saying for the past year.

• 13:32Speaker 02

  Yeah.

• 13:33Speaker 01

  Which is very offensive to interns, by the way, but yes.

• 13:34Speaker 02

  Yeah, exactly.

• 13:35Speaker 01

  Exactly. I don't know, have you managed an intern? Yeah, that's true. And it was, and you're saying it was more correct.

• 13:40Speaker 02

  Yes.

• 13:41Speaker 01

  So it's faster, it's more accurate, more reliable, and then, according to the US administration, dangerous.

• 13:48Speaker 02

  I think the other piece is it has like a greater, theory of mind is the wrong word, but sort of like theory of project, so that it's less, you know, oh, I'm going to make this change. And it'll say, great, I'll make this change. But really, especially if you've done like software engineering at scale, the best engineers kind of keep in mind all the disparate parts of how this things. And they also see around the corners, like, I can make this change, but if I don't do it in this way, then the next change is going to be incrementally harder. And I think that's like been a significant difference I've seen in that kind of class of models.

• 14:18Speaker 01

  Okay. So I think when we talk about Anthropic Labs, right? People think of Claude code because it is really your breakout product. And it sounds like you've been tasked with basically figuring out what the next Claude code is. Would you say that's an accurate description of what you're doing at Labs? And also, why does Anthropic need Labs?

• 14:37Speaker 02

  Labs, yeah. It's also maybe worth thinking about why we needed Labs in 2024 when I arrived and why we need Labs today, because I think that the answer kind of shifts. I started the Labs, the original Labs team with Ben Mann, who's one of the co-founders of Anthropic, in my third week at Anthropic, and it had been something that I'd been bubbling under. And at the time, the reason was really different. It was all of our product engineering team was 25 people, and we didn't have the models, really. We had Claude, when I joined, it was Sonnet 3, like you, and Opus 3. Those were, for the time, good models, but you weren't going to, they were not even interns, right? They weren't even IC3 engineers. So if you have a team of only 25 or 30 engineers, they are working on like the next incremental thing.

• 15:20Speaker 02

  And we were feeling like the models are starting to get better, but we don't have any products that sort of show that off. Like, a good litmus test for me is when we get ready to release a model, do we have either a product, or a demo, or some other illustration of something that is very different? And it gets harder over time, like with Fable, even illustrating that weekend task, or this longer amount of work. So really, Labs at the time was, let's make sure we don't, like, our products don't fall behind the model exponential that's happening. And yeah, so Claude code came out of that initial one, because nobody in the rest of the product org, because people were thinking about coding, but nobody was, sort of, had the, like, space to go and think about, well, what if we totally change the form factor and we embrace the fact that the models were going to evolve in this way?

• 16:03Speaker 02

  And a lot of the, like, the two most useful thought exercises we do in Labs, one is, visualize the gap between what the models can do today and how most people use it. Can we close that gap? That's one. And the other one is, Imagine what the models are bad at now that they're actually going to be really good at in six months, and let's make sure we have a product ready for that by then. And I think those are like the two guiding questions for labs. And then also out of that first incarnation came computer use. Computer use was different, though, because when we built it, it was really bad. Like, we tried a bunch of products with it, and this was around, you know, Sonnet 3.5. And you'd be like, Claude, can you help me, you know, clean up my desktop? And it would like click the thing, delete the file. Like, this is not safe for release. We're definitely not going to go and build this or to ship this.

• 16:46Speaker 02

  But we had that product, so that every new model that we'd release, we'd first check it internally and say, did computer use get better? And we'd tell the research team how it had gotten better or worse, until the moment where we said, it's good enough, we're actually going to put a product out around this. It also gives you this sort of beacon into the future that then you can kind of measure your future products against. But then compare it to now. So we have a thriving product team, there's cowork, there's, you know, Claude Code has grown a lot, we have our platform. And now I think it's actually much less about, none of these product teams are doing this sort of thinking. And I think it's much more that the models are advancing really quickly, and even our capability to interact with them needs to evolve.

• 17:28Speaker 02

  So one of the things we collaborated with labs in Claude Code that we ship today is Claude Code artifacts, having Claude Code not just be able to type back to you, but also sort of draw a picture or give you an illustration. And that partially came from spending a lot of time in labs saying, just a text box and a big text response is not going to cut it anymore. Like, when I mentioned that the models feel like they're way smarter than me when they talk to me, sometimes I'm like, can you draw me a picture? Because this is what I actually need to fully understand this. But it's really what we've been thinking about is, you know, yes, we have a lot more products, you know, we actually have a lot of consolidation to do in our products. Like, that's another initiative that we have. But within that, we still have an opportunity to make things much more accessible to a person that does not spend all of their time thinking about prompting and the exponential

• 18:11Speaker 02

  and the difference between high, low, and medium effort. Like, there's a lot we can say.

• 18:15Speaker 00

  Right. But Mike, so there's a... It puts people using anthropic models in an interesting place, right? Cursor, I think, just sold for 60 billion to SpaceX. And someone put this meme on Twitter that like, you know, Cursor would have sold for 300 billion if it wasn't for this guy. And it's a picture of Boris Cherny, the person who created Claude Code. And so for companies that are going to build on top of anthropic technology, you know, they're going to wonder, do I want to partner with anthropic or is anthropic going to go ahead and build the product that I'm going to want to build, potentially even after partnering with them?

• 18:51Speaker 02

  Yeah. I mean, we'll take the like agentic coding side, then I think the broader sort of aspect of, you know, being both a platform and a product, I think is really interesting. When we take on projects, it's, the goal is often to sort of push that area of the industry forward. So, you know, there were AI coding editors and some of them were really good and, you know, but nobody was quite thinking about it in as sort of free-form a way as we got to think about it with Claude Code. And now, a lot more products have that flavor than I think would have otherwise. And so, I think if we're ever, you can call me out on this, Alex, if we're ever entering an industry where like, all you're doing is the same thing everybody else is doing, but like, you've got the anthropic brand, I feel like that's a bad use of our time and a bad use of our either labs or product team time.

• 19:35Speaker 02

  Like, if we're going in somewhere, it should hopefully be to say, all right, we think that the direction of travel is this way, we can build a product of that. And then, by the way, there's no world, nor should there be a world where like, all the products are anthropic products. That would be a bad world, right? So like, that is hopefully either creating new space for companies, or sort of showing the way where other products can incorporate them.

• 19:54Speaker 01

  Yeah. It would almost be like working for a tech company that has like social messaging, video. Right? Mike? Yeah. Okay.

• 20:03Speaker 03

  You did leave.

• 20:05Speaker 01

  Well, there was some question, for example, when, you know, anthropic launched a product that was seen as competitive to Figma, and you had been on the Figma board prior to that, and I think you stepped down.

• 20:14Speaker 02

  I did, yeah.

• 20:15Speaker 01

  Is that correct? Yeah, and so, it's a good question that Alex has brought up, I think, where Silicon Valley is known for this really healthy, vibrant, risk-tolerant startup ecosystem. And when the bigs start coming in with tons of venture capital and, you know, a lot of resources, people say, well, wait, are they just, are they essentially just going to steal my idea?

• 20:32Speaker 02

  Yeah, no, I think I think our dual existence and it's something that other companies have to navigate. We talked, I'll talked about Amazon a lot in the in the previous panel, like they have to navigate this role where they are both the infrastructure provider. They obviously have a very large e-commerce thing. They do video, but they also serve video. And and then, you know, by and large, customers can live in that dual world of like, OK, I'm using their infrastructure. Also knowing that they they are also using their infrastructure to do that. And I think the you can talk to our customers and see how well we're doing at this. Like the thing I always try to do is like, at least approach it with a lot of transparency. So the cursor example is an interesting one where like Michael and I talked a lot over the, you know, time around. Here's where things are heading.

• 21:11Speaker 02

  And, you know, with similarly with with the other products that we think about like, can we? I think it's a couple of things. It's transparency and then it's shared building blocks. Like, yeah, I think in general, and I actually don't think there's any cases where this is even true. Like, we're trying to build on top of the same capabilities that are available elsewhere. The last time I was here in the Commonwealth Club on this stage was our healthcare day at the beginning of the year. And we didn't ship like Claude healthcare, only we have it like nobody else has it. We shipped a bunch of like plugins and skills and MCPs and like complimentary abilities. So that's how I'm not claiming it's easy or that it's a straightforward thing, but it is how we're trying to navigate. What is like admittedly a complicated sort of situation.

• 21:50Speaker 01

  Speaking of startups, Anthropic is still technically a startup. But you're worth a lot of money. I mean, what's the latest valuation? Is it?

• 21:59Speaker 00

  Nine sixty five.

• 21:59Speaker 01

  Nine hundred and sixty five billion dollars or something like that.

• 22:03Speaker 00

  They sold Instagram for a billion, right?

• 22:05Speaker 01

  Startup.

• 22:05Speaker 02

  In two thousand and ten.

• 22:06Speaker 01

  Right, right. Financials have changed quite a bit since then. And yet Anthropic has positioned itself, it is, you know, a PBC. And it's positioned itself as sort of a more ethical company around building AI. And I'm wondering if you could talk a little bit about how you see that. Positioning and Anthropic's role in particular, changing the culture of the valley. Like, I think back to how Google in the beginning of the 2000s really changed the culture of Silicon Valley in so many ways. And how do you see Anthropic's culture now dictating this next era?

• 22:40Speaker 02

  Yeah, that's a really interesting question. Maybe I'll start like insight, and I think there's an external component, too. I think the reason I joined in the first place, so I was winding down my second startup, and knew I wanted to go work at a frontier lab because I'd started to use these models for coding, and they were bad at coding, but I could see that they were as bad as they were ever going to be at coding. They were going to improve. And I had started building on top of these APIs. So the startup I was doing was called Artifact, and we did sort of AI-powered sort of news recommendations. And she read a lot of big technology via Artifact back in the day. Thank you. Things we added, so.

• 23:11Speaker 01

  But not wired.

• 23:12Speaker 02

  You know, you guys had a really hard paywall, to be honest.

• 23:16Speaker 01

  Fair enough.

• 23:18Speaker 01

  Do you need a discount on my subscription? Because I can get one for you. Okay.

• 23:22Speaker 02

  It's actually really funny, like the. Making deals. Yeah, making deals. It's the login cookies. It's like really hard to keep people.

• 23:28Speaker 01

  I know, I know. Please escalate this to Condé Nast. I know.

• 23:34Speaker 02

  And email login is very hard to do in an app. But so, but I was building on top of the APIs and be like, wow, okay, they're able to do really interesting things. But what ultimately made me go to Anthropic was like they walked the walk and they really like deeply believe in trying to make AI go well for humanity. And that is like in, that's like in the water internally, and I think has been why I think the company has remained as cohesive as it has, even as we've grown. And I think that it's like a testament also to the co-founders there on how often they are talking about this as well. It was a surprise for me coming from a world where at Instagram, we did a weekly all hands, and we talked about product 95% of the time. And maybe 5% of the time, we talked about something else that was going on in the world around the company.

• 24:17Speaker 02

  Probably maybe underselling our like go-to-market. Maybe it was like 80/20, but it was definitely a very, very heavy product. And I remember Anthropic, about six months in, myself and Kate Jensen, who's one of the leaders in the sales organization, did a joint all hands where we talked about our like, you know, how we're doing product and go-to-market together. And people were like, this is so great. I finally understand our product strategy and like what we've been doing. I was like, oh, right, this is not. Quote unquote, a product company, you know, it is a very mission driven AI company with like a very strong sense of like why it exists in the world. I think in terms of the overall impact on the valley remains to be seen. I think positive signs that I've seen or interesting signs that I've seen are like a renewed interest

• 24:59Speaker 02

  in philanthropy across the board. And I think that's something that has been written about. And I think it will be an interesting sort of outflow. Again, who knows how all of this goes, but like depending on how it goes, it could mean a lot of interesting new sort of philanthropic deployment. And then I think the other piece is, you know,

• 25:19Speaker 02

  the conversation around how AI could or should go is one that is happening in real time with the technology versus retrospectively, which I think has been the case for other technology waves. And I think that is a good thing.

• 25:30Speaker 00

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• 28:21Speaker 00

  Mike, you talked a little bit about Anthropic has this gap that it sees between the capabilities of the models and where everybody is building products. And with labs, what you try to do is get ahead of that so you can show people what AI might be able to do now and six months from now. So please tell us, please tell us like what you're building, where you see the potential. And what people should be on the lookout for.

• 28:45Speaker 02

  Yeah, it's a roadmap.

• 28:46Speaker 01

  Oh, and if I can throw in and what you're building now, but also if you had, if you have a pie in the sky, like Elon Musk data centers in space type ambition, I want to hear about that, too. Tell us, tell us, tell us everything.

• 28:59Speaker 00

  We have 13 minutes left.

• 29:01Speaker 02

  Exactly. The rest of 13 minutes is the monologue in my product. I think maybe two themes I'm really excited about that we've been exploring a lot. The first one is giving Claude an environment where it has more agency and it also has more self knowledge. And I'm going to unpack that because that's like a lot of AI words. But I'll give you an example of where we are currently doing a bad job of this. Like, if you are in a Claude project and you make a file with Claude, you're like, that's great, can you add it to our project? Claude will be, no, you have to go download the file and go drag and drop into this thing. And you're like, what? Until yesterday, I would have said the same thing about Claude design and Claude code, where if you're in Claude code, you're like, cool, like, I need a design for this thing that we're building.

• 29:44Speaker 02

  Let's, or you're in Claude design and you make a mock-up and you want to go build it, be like, cool, here's a zip file. And you're like, what? You know, and I think, so that's a little bit of interoperability. But in general, this theme of giving, if you give Claude a lot of notion of its environment, I was talking to actually a customer, like an API customer, and one of the things that they were experimenting with is actually even giving Claude like a secure version of their source code while it's running in the agent loop in their product, so that if it hits an issue, it doesn't go like, I don't know, I hit an issue. It could be like, well, it's probably this thing, you know, at least when it's talking to one of the sort of maintainers of the software. And so that overall theme, and of course, you have to do it with safeguards and like, be really careful about what you unlock with it. It sounds kind of obvious, but it's actually night and day in terms of how expressive these

• 30:27Speaker 02

  products end up being able to be, right? And you can even see it going from like, maybe like Core Chat or Classic Chat in Claude AI, and something like Co-Work, where it's got a little bit more agency and it has a runtime and it's able to sort of like, understand a little bit about its environment. But I think we are at like 10% of the journey about where we could go. Actually, one of the reasons I think people got excited about things like OpenClaw is seeing how a, you know, harness that is modifiable and you can talk to it about things. And you don't ever get the sense of like, oh, sorry, I can't do that. You're gonna have to go to this like setting screen and turn it on. It's just a thing it has access to and hopefully with like the right guarding and permission. So that's like theme one that I'm like extremely excited about. And I think

• 31:09Speaker 02

  If we do it right, should actually like transform all of our products like from head to toe. The other piece is, and I'll maybe like share like the, if not the internal product we're working on, but like the phrase I got as feedback was like, I think closing the gap, I mean, I talked about closing the gap between capabilities and reality. I think it's also closing the gap between how people understand their own work and then how the actual day to day is to do that work. I was talking to somebody internally who's on our privacy team and to move a ticket from like one queue through another one via the task tracker into another was like eight different steps of copying and pasting of like manually moving a pretty, you know, like

• 31:52Speaker 02

  kind of annoying to have to do, probably error prone. I have to like keep spot checking it and we helped her with one of our labs projects to basically like make that not a pain. And she's like, ah, this is the first time in my career. And she's like been working for 30 years, we're like, what's in my head and what I am using is like now this, like it is now closed. And I want to like bring that feeling to everybody who like, you know, of course Claude unlocked a lot of, you know, non-technical people to be able to code, but like it's, we're still asking people to understand way too many concepts of like, what is, what is, you know, the difference between like my sandbox environment and production or like connected MCP as myself or others, or how should I store data? And of course you can't abstract everything, but like if you combine both of those themes, if you give Claude a lot of self-knowledge and you're creating an environment where can

• 32:36Speaker 02

  actually solve complex problems for people in like repeatable ways, I think I get like very, very excited about.

• 32:42Speaker 01

  And your moonshot?

• 32:43Speaker 02

  Yeah.

• 32:44Speaker 01

  Not letting you off the hook, what's your moonshot?

• 32:45Speaker 02

  Moonshot?

• 32:46Speaker 01

  Yeah.

• 32:47Speaker 02

  Nothing in space, although I guess we're, you know, we're talking to SpaceX about spacey things. Wait, you're talking to SpaceX? I mean, that was an announcement.

• 32:54Speaker 01

  Well, it was the announcement for compute, yeah.

• 32:58Speaker 02

  Is it, sorry?

• 32:59Speaker 01

  It was exploring extra, extra orbital, what was the phrase? Something about exploring like post-orbital world things.

• 33:05Speaker 02

  Definitely not my department, but yeah, there's stuff, stuff in.

• 33:08Speaker 01

  Are you, so you're, the labs isn't working specifically with the team on compute?

• 33:11Speaker 02

  Right, exactly.

• 33:12Speaker 01

  Or chips?

• 33:13Speaker 02

  Separate, separate.

• 33:14Speaker 01

  Totally separate, okay. Okay, so your moonshot?

• 33:16Speaker 02

  Do you personally believe in data centers in space? I had a conversation, I, I, by far from a data center expert, but I talked to somebody who is a, like a person who sends things to space, who's not Elon Musk. That's what, that's what you would say.

• 33:34Speaker 02

  And they're really bullish and I was like trying to talk about why and it was basically like I guess effectively infinite power if you convert it well and you know infinite land and I was like okay you can buy that I mean and I think they feel good about the shielding you have to do again clearly not my area of expertise but after that talk I was like okay I see it you know even if it's gonna take a few years at first I admittedly thought it was a crazy idea in general but now I'm like oh actually really can understand why this might make sense.

• 34:03Speaker 01

  When you were talking earlier about the ways that the work in Claude is gonna get compressed in all those steps I couldn't help but think of tokens and how you know maybe it's good for your business model in the short term if people have to take so many steps and use so many tokens but tokens have become this unit of economics that we're using to describe the industry now and people are token maxing and now they're tokenizing and and and one I wanna see I wanna hear how we're used on that spectrum if you're a token maxer and two is there a near future in which the industry is not actually measured by tokens you know it goes the way of MIPS or dial up or some other you know there's some other unit of measurement that actually defines the economics of this era.

• 34:45Speaker 02

  Yeah I think both of those are really interesting questions it was interesting earlier this year when you started hearing about like companies that have like dashboards showing like who used it the most and we of course have like internal metrics as well and we found that there's not a lot of correlation between like the person who's using the most tokens and like the person that I like it was an interesting thought I should maybe do at your companies like write down your 10 most productive people that you think are most productive and then like get your top 10 token users and see how closely they correlate at least for us it wasn't that correlated it seemed dangerous to sort of like sort of purely glorify the like maximum usage obviously it's like very gamable but even beyond that I think it's it's you know yes you can ask Claude to do 10 different variants on something but if you thought about it deeply maybe you would do choose two that you thought were most promising and the third one if you then had like some

• 35:29Speaker 02

  iteration on that as well so I would not say like a token maxer the actually the token iest thing was that conversion thing I did was just like a couple million tokens just like a lot of tokens that it took to convert the the thing from from Python to TypeScript. But I think people are being more thoughtful about these different pieces. And one of the things we look at whenever we look at a model launch is not just model intelligence, but we're also really thinking about model intelligence and effort and token efficiency as that combination. And I think that's a big lever we have to improve is how do we continue to be more and more token efficient for a given task so that you can also, hopefully you don't have to think very hard about this, we can do this automatically. But like, we're able to tune the solution to the problem a little bit more. And then to your second question, yeah, I, you know, when I was still in the CPO seat,

• 36:14Speaker 02

  I was thinking a lot about sort of outcome-based pricing as something that would be really interesting to do if you could do. And of course, if you talk to like the Tiaras and Finns of the world that have like a really clear, like, we kept this, you know, we were able to solve this customer request and not have it go escalated, like, that's really clear. It gets so much fuzzier on these like tasks that we actually ask Claude these days, like, I had a strategy document, I use Claude to critique my strategy document, like, what was the outcome? It's like, well, I don't know, it's like, tell me how the strategy goes six months from now. It feels like it's going to be very hard to capture that as well. But I would like to see some more experimentation around, can you better capture like what it's worth, you know, to the individual and then, or to the company, and then can we find the best way to do that as well?

• 36:57Speaker 02

  And I guess the most concrete thing we've moved towards that in, we have a product called Claude Vanished Agents, where we'll run all of the infrastructure for you in terms of doing all of the, you know, agentic harness and calling the tools, et cetera. And you can either do it in sort of the normal mode, which is you give it tasks, it, you know, will go through tokens, it'll tell you when it's done, or we have an outcome-based mode where you can say, here's what good looks like, here's a rubric, go and do it and, you know, you know, it'll go off and make it more outcome. So like, if everybody had moved on to that API, then I think maybe we could have a different sort of outcome-based pricing, but we'll see how that gets adopted.

• 37:29Speaker 00

  John or the guys in the back, can, do we have the random image? Can we show the random image? If we can, great.

• 37:35Speaker 02

  I'm excited, the random image.

• 37:36Speaker 00

  Oh, here it is. Okay, it's just because we didn't have a good label for it. So we just called it the random image because it might come up at any point. But this is a chart from the Financial Times. Speaking of utility, where it shows the amount of app releases that have come out, which are skyrocketing, and then apps with significant usage that seem to be going down, and app reviews which seem to be going down. So Mike, I'd love to hear you respond to what we're seeing in the image here. Is it possible that like, everybody's coding and releasing, but we're not really seeing a big boom in productivity?

• 38:07Speaker 02

  That's really interesting. I mean, I think there's definitely a power law in app usage in general. It'd be interesting to see if any of those app releases became one of the apps with significant usage.

• 38:17Speaker 00

  All right, we can take it down. Yep, go ahead.

• 38:19Speaker 02

  I think this ties into something that I've been thinking a lot about, which obviously my background is in consumer, and I've been wondering what the consumer AI breakouts will end up being. And I don't know, we've seen a lot of them yet. And I think part of it is, you know, I don't know how far back that that chart goes, but when we were releasing Instagram, it still felt a little bit Wild West in terms of the apps. Like, people were excited about apps. And like, two kind of random people released an app and were able to get to like, number one in photos and video within three months, right? I think that is much harder now when you think about how consolidated the top 10 is, and how much time spent is spent on like, the TikToks and Reels of the world. It's a lot, right? And so I think getting that breakthrough consumer experience, I think, is really, really hard.

• 39:02Speaker 02

  So I think that is as much a story about how sort of consolidated consumer products are these days, number one. Number two, how entrenched or how powerful it is to have that sort of data, people call it data gravity, like the data gravity of something like, your Google Docs are in your Google Docs. So even if somebody has a like, 2X better AI-powered, you know, doc editor, you gonna move all your stuff up? Maybe, probably not. So I think that it speaks to, you know, the things that are sticky. I think about it a lot, it's like, the hard stuff is still hard. Like, making something people want is still really hard. You know, we have amazing models internally. On the topic, not all of our products work, right? And so I think that's a bullish sign for like, product people like me, because it means that I think we hopefully still add value.

• 39:47Speaker 02

  But I think that chart is maybe in other places, just like, it's harder in many ways than ever to break. Through, even if you can code more quickly. And could we have done Instagram in, you know, a month instead of three or four? Probably, but we got there after like a long winding turns and twists and turns process.

• 40:08Speaker 00

  You had, I think, 18 people at Instagram when you sold it?

• 40:11Speaker 02

  13.

• 40:11Speaker 00

  With these tools, do you think you would have, how many people you think you would have had?

• 40:16Speaker 02

  It's really interesting because of those 13.

• 40:18Speaker 00

  Just give us a number because like everyone's like, oh, one billion, one billion dollar, one person startup. How close could you guys have gotten?

• 40:24Speaker 02

  I think we could have gotten there with like four to six, you know?

• 40:29Speaker 00

  Okay.

• 40:29Speaker 02

  Yeah. Or the thing that we would have done, if we'd grown it, we'd be able to do things in more than a single track. Like Instagram was, if you ever watched my five year old play soccer now, by which I mean like the ball is there and every single person runs the ball. Like that was our product team. It was like video, go. And everybody like goes and works on the one thing and like we'd be able to like play positions. Like Android, we built in about a month for Instagram. We could have done it probably in a week with the models. And but and to build Android, we took everybody off iOS and we all like relearned to code in, you know, code Android OS. And then we went off and do that. And like for that whole month, we were barely shipping updates on iOS. So I think you can be a lot more, actually a really good example, there's a lab project I have internally that helps accelerate how a topic engineers like code and do code review.

• 41:14Speaker 02

  And that, that project, I am maintaining an iOS and an Android version of. And I basically have the Claude that works on the iOS one basically like ping the Android one and be like, hey, I implemented this. Sorry, Android users, it's still the second one, even in the L world, sorry.

• 41:31Speaker 02

  And then the Android version is like, okay, I'm going to do this. Oh, that doesn't count because that feature doesn't make sense here. I'm going to drop it. And of course, we wouldn't have been able to delegate all of that on Instagram, but we sure could have done a lot by having sort of platform parity. Like this dream of platform like close to parity is now actually quite doable.

• 41:49Speaker 01

  You're probably going to get calls now from the remaining six or seven people on your Instagram team going, was I, did I make the cut in the new era? Also, it sounds like you probably could bring Gotham back now if you really wanted to.

• 41:59Speaker 02

  I forget if we eventually, I think for April Fools, maybe we brought it back one day.

• 42:03Speaker 01

  Yeah, do we have time for one more question?

• 42:04Speaker 02

  Yeah, yeah, last question.

• 42:05Speaker 01

  Okay, I mean, my last question for you is, you worked on a product that now, as it has evolved, is in many ways ethically fraught because of some of the harms that people are concerned about with children. And when you talk about the fact that there hasn't really been a big breakout consumer app for AI, I think there has, and it's chatbots, right? And chatbots have also led to some real dangers and harms for young people. And so when you are building in labs, how are you thinking about the, you know, the potential harms and the risks that come with just making this technology that much better?

• 42:38Speaker 02

  Yeah, I mean, I think there are certainly products that we have either prototyped or conceptualized and been like, this product, because it's not so hypey, I hate this, but like, this product, if shipped, would be bad for the world, or like, would nudge people in the wrong direction. Or even if we did it right, the like, wrong or like, more morally fraught version of this would be actively, we think, bad. And so I think asking that question a lot internally makes a difference. And having, it's a luxury to have core products and models that are doing really, really well, so we don't, like, that's, in some ways, an easy decision if we think it could get a lot of, a lot of use. But yeah, I think going back to an earlier conversation, I think front-loading it is

• 43:19Speaker 02

  really valuable and really thinking through, like, it is now more normalized to have people at a company, and definitely on the topic, as for like, economists thinking about the impact of the thing that you're building on the world, and that just was not the case in, along the years on, on most of social media, I think.

• 43:33Speaker 00

  Mike, it's always great to speak with you.

• 43:35Speaker 01

  Thanks, Mike.

• 43:36Speaker 00

  Thank you again for bringing your insight today. And let's see you again soon. Let's hear from Mike and Lauren. Thank you. Great job. Thank you.

• 43:46Speaker 03

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• 44:17Speaker 04

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