Mike Krieger: Product Building Lessons from Co-Founding Instagram to Leading Product at Anthropic

23:12But you're doing it off a base that doesn't assume that, right? So the product has to evolve. I was playing with Particle.News, which is another take on AI-powered news. They feel like if you had started Artifact in 2024 rather than in 2021, like we did, where would you start? Well, you'd have a lot more around AI aggregation and you'd have even the ability for AI to read you the top news of the day. You would approach the product in a different way. But I think the really fascinating question is, is that going to be true on a yearly basis? Like a year of pornography? Oh, that was great for 2024 and maybe it continued to evolve.

23:47But actually now we've got this different approach. It's just the capabilities that you have access to that previously might have taken you like years to build out as a startup, it might not even been achievable without a much larger research team or a much larger data set are now available. And like they get cheaper by the month, it feels like. So like, how do you like think about that in product and be willing to like actually question a lot of fundamental assumptions about how a product works? Like I think I would have been interesting to do with Artifact was like in the last few months, like actually like pare it down completely.

24:17Actually, that's great because at this point, we'd ingested like three years of news data. We had like a really rich corpus. We had one prototype that was fun, which was, right, like, what if it was more around, like, conversing with that knowledge base and asking questions? And maybe some of these are canned questions, but some of them aren't. And it was fun. It was very different. Super fascinating. So, Artifact, obviously, consumer product. Instagram, obviously, massive, massive consumer product. We talked a little bit about product strategy inside of Anthropic. But overall, like what's it like for you to be building inside a product that I think in many ways is now catering to enterprise customers, completely different type of customer than, say, Instagram in some respects?

24:54What has that been like? Yeah, it's interesting to kind of build both at once because, you know, there's plenty of people that go to Cloud AI, sign up for, you know, free and a pro subscription. You learn from them in some ways. But what's really interesting is our enterprise customers, because two things are happening simultaneously. Like one, you know, it's often not the individuals at a company that are making the decision about like which LLM like enterprise, you know, software are they adopting? And instead they might get CIO or CTO buys it for them. So the buyer is a bit different. But then what's really interesting is actually the opportunity for feedback and engagement is much greater because you're really, you know, hopefully mutually invested in the success of that sort of partnership.

25:31And so in some ways, we're getting richer, like actually much richer, like feedback on the ground from, you know, some of our enterprise deployments. And it's great because then you could send our applied AI team and go on their troubleshoot or go learn from those things. In the way that like Instagram, you have a data science team and they could try to like find the in aggregate what's happening across all of the United States. It's an interesting like societal question, but it's much harder to get at that texture of the individual product. And so that was a difference in what I expected. I was like, oh, like one of the things that's gonna be hard with enterprise is like, you're gonna lose that touch of the customer because it's gonna be like, uh, you know, the buyer is different.

26:06But actually it's, it's been quite different. We were able to lean in there. The other piece too, is you start from a different place of, all right, your company's paying for you, this for you, at least it will be, you know, while they're doing this, you know, either trial or like this engagement, how do you prove value to people there? And so that it's actually become successful. Cause the worst thing would be like a year in be like, oh, I guess we subscribe to, you know, cloud for enterprise and like, you know, not that many people are using that's a failure on our part. And so shifting the balance of like what user education looks like and what, that's a term I didn't even know before I really started here, which is like enablement.

26:38You know, there's a lot of like sales and enterprise terms that I've like ramped up quickly on or I'd heard, you know, tangentially. The principles are the same. You want to build things that are useful to people and then learn from what they're using it and then iterate on that. But the like approach is a bit different. Do you think about products and the product strategy? Do you, do you separate it by sort of consumer and enterprise or are you sort of building stuff and it's like oh let's now build the enterprise version of this or maybe vice versa how does that all work it's flowed the other way in a couple of ways i think it's interesting as well like as an example like we enterprise first we built out google drive and you know google suite integration get up integration and those are great uh you know starting points for enterprise because hopefully like the admin can configure that once and then everybody the company has access to and there's like some onboarding that you can do there.

27:24But ideally, like individuals can use GitHub just as much or connect their Gmail, right? Or connect their Google Drive. And so it's flowed sort of in both ways where some more like general purpose features mentioned projects and artifacts, like those started like fairly general purpose and then made up some customization for the enterprise. But then also like these like knowledge integrations, I think are ultimately like how we succeed is like, can we connect cloud to the knowledge at your company and actually help you do real work? That's a company problem. that's an enterprise problem, but individuals are trying to do that as well, just as much.

27:55And so you'll see that cross-pollination for sure in the product. And an active product strategy question I have on the team brings up is, right now there's manifestations of a similar product, but maybe with different feature sets. Do they drift further apart over time or do they stay fairly similar? And there's differences of it within internally. I think for the coming six months, they stay pretty similar. I think where they might start to diverge is in the kinds of work you might delegate to cloud over time, right? Where if you're actually automating parts of your workflow, we think a lot about like how cloud can be a virtual collaborator for you.

28:27That looks quite different, I think, for somebody working in marketing or somebody, you know, working as an engineer versus somebody using cloud for their personal life. Yeah, totally. I mean, even if you think about when I've tested computer use or, you know, some of the other products that are doing this, like the ideas of things I want to test it for always jump to more productivity or work oriented stuff. Like it feels like the things I want to do as a consumer or things I actually want to invest my time in. It feels like in work, I have all this stuff I want to delegate. So yeah, it feels like that would be a surface area that would get far more investment in the enterprise.

28:59Yeah, I think that that's right. And I think there's also sort of a modality to it as well, where you're like, you're doing work to probably like have an organized task list. I try to do it in my personal life. I don't always succeed. I do a better job of it at work. It kind of lends itself a lot. These are the kinds of things I want to carve off or like help me organize my thoughts on this. I actually think there's a lot that can be done in people's personal lives as well. Like we have two kids, um, they're going to two different schools because they're different ages. And I was talking to a founder of a successful, like sort of productivity company that uses a lot of bunch.

29:30And he's like, he's just like, let me share a project for you. I've got this whole workflow that ingests, we have their kids go to the same school, ingest all these emails because the emails are very, very dense, very long. And like actually produces an action list out of it. I'm like, that's really smart i should use ai for more things in my personal life just around like family life management as well that's fascinating by the way i'm in the same situation as you two two young kids two different schools two different like school newsletters and all this it's it's surprising we haven't really yet entered that moment where where products easily integrate with our personal life like ai products specifically like nothing yet is summarizing my email i imagine google will have some wedge there but yeah that level of productivity hasn't really crept into to personal and home life yet.

30:12I imagine that's coming soon. MARK MANDELYIERIENOVICIENCY In October or November, we open-sourced the model context protocol, which is our take on how do you bring data into LLMs and how do you get data out of them. And we made it open-source because we wanted to sort of foster an open ecosystem of people building on top of it. So you can use them with Cloud. You can also use it with several editors that have integrated MCP as well, like Block just open-sourced their agentic coding tool called Goose, and also has an MCP component. So it's gotten adoption even beyond Cloud. What's really cool is seeing what are the MCP servers that people build.

30:43So if you get cloud for desktop, you can write and integrate your own MCP servers. And if you look at the sort of things that people have built, it's things like Apple Notes. So being able to talk to your Apple Notes using cloud, like Google Calendar, like these things are actually personal. You know, even if they're like productivity oriented, they're still like, they're pretty personal in nature. And I think that's pretty neat. And like, especially as these models, like we think a lot about these models coding and Maybe in the first instance, people think, oh, great, it's going to help software engineers write code.

31:11But actually, it's going to help everybody solve problems that can be uniquely solved by writing code. And so that might be today writing an MCP server or like a pretty technical term, but like helping you connect to something that you already use, like Apple reminders or Apple notes. But in the future, it could also be, hey, I need to do some data analysis on those things I collected. that Cloud can actually write code to do that data analysis and give you that response. And that is Cloud coding, even if you're not producing software. Well, yeah, that's what I was going to say. Like, yeah, these things sound technical and challenging for your average person today.

31:47But also creating software and creating products is going to get far easier as a result of Cloud or AI writing code for you. And I haven't yet been able to fully wrap my head around like what shape and what format that ends up coming in. like at what point does somebody who's not technical at all start creating these workflows out of software using AI? I'm really fascinated to see what that looks like and where that all goes. I mean, is that, is that something you're all thinking about? Like for sure, what does Claude do when consumer behavior intersects with software creation? Exactly. Or what, you know, one, like what problem we can solve for people?

32:23And then two, how do you help people sort of take an idea in their head and express it visually? I think that thing that was really fun over the holiday break is a couple of people in our go-to-market team, so non-engineers, like people without a coding background, started using things like V0 by Rissell to like prototype ideas they had for Cloud. And then they came back from a break and they were like, yeah, I've never written a line of code in my life, but I actually built a fully functioning web application. Let's talk about what Cloud could do in this situation. I think that's really interesting, right?

32:52I think the ability for AI, and not just like LLMs, but also more of these media creation ones as well to like help take ideas in people's head and then bring them to life so they can then either become sparks for further like human creativity or something else downstream i think is like a very cool flywheel speaking of that like how how has ai assisted you in your role as cpo i mean obviously you're dog food and clawed all day long but maybe in what ways and maybe what other ai tools are you using so um i'll talk a little bit about core just i I think it's a really interesting take on how LLMs can break up a traditional workflow.

33:28But even in the CPO role, I think two things that have really stood out. One is being a great critic slash sounding board slash just external voice. So I'll often be writing alongside Claude. So maybe I'll write an outline and have Claude produce something for a requirements doc or even just a product strategy document. But even if I'm writing my own things, I rely on Claude now to be like, all right, what am I not thinking about? Like, what did I forget? Or like, you know, what are the holes in this argument? And I think it does quite well. You know, it's not at the point right now where it's going to produce a like flawless strategy from whole clause, you know, but it is very good at like seeing what you've written and saying like, Hmm, you know, you haven't thought this through, you know, we actually did this as a leadership team.

34:10We did our whole like, okay, our process late last year. And the first thing that is like fed it to the cloud. And I was like, great. What are we not going on that we should be going on? Like, what are we missing? And I had three very good answers. Like, this is really interesting. So, so cool. Critic and then way of filling in. Also accelerant, I use this, we have our Google Docs integration now. And I was filling out a table, it was actually relevant to your question, which was how can Claude help accelerate product development itself? And I started building out this end by end table, and I did half the cells.

34:42When I was typing, I was like, I bet Claude could actually do the rest of these cells. It's got great examples now at this point. And I was basically just like, please just fill out the rest. And it did a great job. And I was like, okay, great. And then Adds about the cells I added, again, I had better ideas than I had in a lot of cases. So this companion assistant piece has been absolutely valuable, and I use that all the time. But some of our newer knowledge integrations, even stuff that we have internally, those have just been valuable in preparing for the day. I was like, Claude, help me get ready for the day.

35:11We've got these MCP integrations. And I was actually talking to this morning, it's like, you've got this podcast recording, and here's the briefing doc, better review that ahead of time, like be thinking about this. I was like, great. Like now I'm in that mindset as well. So as a increasingly aware of my work and the conversations that we're having, it is just a very valuable, like additional sounding board. Do you find a lot of people on the team are doing similar things? Like are people using it in this way or is it mostly just you? It's been interesting because, you know, three months in I wrote like observations about Anthropics so far.

35:46And one of the things was like, we need to do a much better job of like using Cloud internally to accelerate our own selves, because it's not that it's not happening. It's very unevenly distributed. I should go back to the conversation we're having earlier around, like what's the role of product design and product strategy in improving the ability of people to use these models to their maximum capacity? I think we could do that just even internally. And so we had a really fun hackathon in November. And one of the things that was really cool is that we took one of the go-to-market people and they had a whole workflow around like, all right, I'm meeting with these three prospective, prospects that I want to talk about Cloud for Enterprise to.

36:22I go through the same process every time around, researching the people who matter, getting more context on this company, what's going on with them, reading their S1, all of these things. A lot of that can be done really well by Cloud in terms of summarization. They partnered with an engineer and they basically built that out in an automated way. It was awesome. It was like, that's so cool. We need to do those kinds of things to take the seed of what is a good use of LMs in the workplace and then make it so that it's like, you don't have to think about it. It just actually happens for you. And that's like a push I've been having actually shifted a lot more of my own attention towards internal products and internal productivity more recently, because I think there's a lot we can learn from, hey, have we actually made our sales team more productive?

37:04Have we made our engineering team more productive using some more like cutting edge things that we can do with Cloud? And then can we externalize those ideas into like actual products? That's really, really cool. I meant to ask about the team. I mean, obviously, after you all sold Instagram to Facebook at the time, probably experienced, you know, massive hyper growth, not only in the product, but the team, scaling up the team. I imagine you're experiencing something very similar right now. Anthropic has gotten very, very big, very, very quickly, you know, still scaling massively, it seems, all the time.

37:35What's that been like, scaling up the team inside of Anthropic? but also while you're still, you know, I'm sure acclimating in some sense, this new type of company and new type of product. Yeah, tell us a little bit about that journey. Yeah, I mean, I think there's a few things that are different even from the compare contrast. Like one is you're scaling a team that is more distributed than Instagram was. Like with Instagram, we were doubling basically year on year, but for most of that growth, it was all in Melo Park, right? We moved to the middle part post-acquisition and we wanted like, stay close to other Facebook technical teams that we were collaborating with.

38:06But it just gave you, you visually saw how big the team was getting. It's much harder to see that when it's primarily growing in like Google Meet, Hollywood Square style or like Slack, you know, sort of participants. For better and for worse, like in some ways, like one sort of ritual that I did at Instagram with Kevin was we had product reviews. We realized that like too many people in the product review room started feeling a lot more, it was like a presentation rather than like a place where you could have like deeper, like nuance, like kind of like hard talk about like whether this thing was on the right direction or not.

38:39And here what I found is since a fair amount of people are like in New York or joining remotely, the room can actually feel smaller. You get a different feeling of scale with this like remote aspect of it. That's like one very notable difference that we've had here. And then the second piece is even the product has grown a lot. It is like a small chunk compared to like our research teams, the people working on trust and safety, the people thinking about all of like the threat modeling and like societal impacts. There's a lot more that gets into Anthropic. And so as much as we've grown, I think I still feel like, oh, we're like we're a small part of the company, even though, you know, proportionally we're growing similarly.

39:16And it's a different feeling to have that as well. Have you been able to take lessons from Instagram, either in terms of building out the team like we're talking about now, or even, you know, specific product lessons that you've been able to apply to Anthropic? Yeah, I think one of them has been, you know, thinking about, like, what is the ultimate problem that you're solving and, like, remaining obsessed with that. I actually think it's much harder to do. it's hard on Instagram to get on Instagram, you'd have people that internally that would come up with fantastic ideas that were interesting applications of technology, but weren't on the path of what we were like, what I really wanted to solve for like the most people as possible.

39:54In AI, I feel like it's even more tempting because there's no shortage of like very cool ideas that you will come up with, you know, in prototype using Cloud. And I think if we like staff teams are on all of them, we'd like to have like, you know, one, a pretty complex product, but two, I don't think we'd make enough progress because we'd be like sort of like peanut buttering ourselves over a lot of things. And so it's harder to say no, but even more important, you know, and having that focus within, within the team. And like, it's a discipline we've got to build up. And I don't think we've gotten it quite right yet around like harnessing bottoms up excitement.

40:25Like that's really important. And like, I'm definitely not going to have all the best ideas. I might not even have like most of them, like there's going to, they're going to come from the team, but retaining that like user focus and like adherence to like what problem you're fundamentally solving, like it is just as important, but maybe twice as hard in this space. That's like, I think one, one lesson that we've, we've carried. The second one is something that, um, I learned, you know, in Instagram and somebody told me like, every time your company doubles, like your processes will break and your culture will break in some way.

40:52And Instagram, we basically doubled every year, um, in, in engineering. And so it kind of like, we had to evolve and unbreak ourselves like constantly. Um, we've grown even faster at Anthropic in the last like years since I've drawn on the products that offer a smaller base than you know Instagram is growing but still and I think we're probably like one organizational and process refactor wise away from like things really humming and I'll really get any given point I'm like oh that part's feeling good that part's not feeling good one thing I learned at the time at Instagram was you could put all the processes and like organizational stuff in place nothing beats like either getting on the ground and trying to do some work yourself like I would still IC engineer at Instagram until the year I left, or just having really high bandwidth communication with ICs on the ground.

41:40And so immediately after our conversation, I have a couple of one-on-ones with people that are all over the place on the org chart, but I want to hear from what is on-the-ground building product at Anthropic today and how can we make that better? And I think you need to maintain that contact. It's harder as a non-co-founder. Like Instagram, I knew who everybody was. I started there. I hired up a lot of that team. I was still engineering. I think one of the challenges coming in is like, I don't know everybody yet. Still, I probably know most of the people that, you know, I'm going to interact with, but two, like, I don't have as much that on the ground tactile feeling of like, what is the state of our code base?

42:13And like, what's it like building an anthropic today? Yeah, that's fascinating. Maybe zooming out a little bit, you know, thinking a little bit broadly about AI and sort of the future. Obviously, you know, one very topical think is DeepSeek, especially in the beginning of the week, seemed like the only thing anyone was talking about. What did you take away from that whole moment for, I guess, the state of AI and sort of model development moving forward and maybe Anthropik more specifically? My wife's great aunt texted us and I was like, OK, that's definitely. Oh, yeah. My parents were asking me about it.

42:45Crossed over to something. Three takeaways, like in no particular order, like one, open people's eyes up. you know, Cloud, I think we've gotten great business penetration. Our API has used, you know, really well, but I think we saw a lot of ways to go on, like just getting the word out about Cloud AI and like getting people to at least experience and like make a choice for themselves. I think it was interesting for people to like see that there was, you know, more to the world than just chat GPT, like transparently. I think that's like a good thing to have people like experiment with these different tools.

43:12So I think that I see some positives there. Two, like many sort of like personal opinion, like overreactions that I've like watched in the market over the last 10 years, like just fascinating watching it. Like one of my favorite writers is John Adley. He writes for Bloomberg and, you know, he's like a finance writer. And he had this great column once where he was like, if you had like fallen asleep two weeks ago and woken up today, and it had been like some like market swoon. And he'd be like, wow, the market moved like half a percent, like not that much. And like if you're awake for all of it, it was like the whole like sort of like bumpiness.

43:44I really try to take that perspective, which is like the short-term ability to like overcorrect and snowball is very real. Totally. And so now I'm just doing those things like, okay, let's write it out. What's actually changed? We need more compute than ever over here. So the role of chip companies and the role of scaling up on, if previously it was purely on pre-training, now more on the RL site is absolutely more important than ever. And then Darvay made this point this week and really resonated with me too. which is if you now have an even more like sort of high value scaling opportunity around RL, you should be incentivized to do that for even longer, you know?

44:24And like if you can train for five times as long now and have, you know, five times the intelligence, that's a very worthwhile investment to make. So that fundamentally has not changed at all. But in the third piece of like just perhaps the four sort of like the geopolitical implications of all these in a way that I've been bubbling over and is now like unignorable and like great. it's on that conversation now because, you know, as timely as time has ever to have. And I think like it's sparking the right conversation by WhatsApp groups or anything to be seen. And beyond just like the broader, you know, sort of reach that it's had for society, like it definitely sparked some real, I think, very timely conversations around that.

45:01And I think that's a plus. On the last point, like it definitely seems like it's forcing conversations and forcing people to kind of like confront how or why this happened. And I agree, like, that's probably a conversation we should have. And yeah, it's forcing people to ask questions like, you know, did a major distillation happen? And what's your thinking on that? It's really hard to say. I mean, I think like the whole separate topic that he gets to come like model tells, like we, not the refresh sonnet, but the previous one used to start a lot of answers. But certainly it became like an internal meme where we'd be like, so you tell if a model been distilled from it.

45:33If you ever saw like, you know, instantly start like a, certainly like that sounds like sonnet-ish. So I don't know the OpenAI models well enough to know if like some of those kind of tells were present or not. But yeah, I think it's on all the labs, I think now to be like, OK, great. Like if there's things that we need to lock down and protect or at least detect like that work to be done. And then like the imperative to continue innovating and scaling is like more important than ever, I would say. And then, yeah, I totally agree on the second point. And as far as like the overreaction goes, Anthropic, OpenAI, like these are companies, they're products, they're more than just models.

46:08And yet it feels somewhat reductionist to be like, oh, there's another model here. Let's wipe a trillion dollars worth of value off NVIDIA just like that. Like there are lots of other factors here. I spoke to the CEO Summit on Monday and people are like, well, what do you think the sort of impact is on the enterprise? And like my honest answer was like little to none because every enterprise conversation I have is not we want to buy your model or like we want to exchange input tokens for output tokens. Like that's not what people are looking for. They're like, we want an AI partner that will help us co-design our big bet on our internal transformation and also assist with getting our products to be more AI focused and also be part of your customer advisory board.

46:50So we're also part of the community of excellence around using AI. That is the relationship that you want to have. You're picking an AI partner, which just transparently, the list that you're going to choose from is going to be quite small. And like, that's a lot of the capability that we've been building up in the last year is going beyond that tokens in, tokens out and being more of like a partner that can connect you with the right solution as things evolve. And that's going to continue to be more and more important. And like, if you look at like the, you know, valuations of these AI companies and you think it's just purely on model quality, like model quality absolutely matters.

47:23But it's also like the company and the sort of support and the infrastructure that you build around that is like equally important. Yeah, I feel like this has forced a big conversation in recent days. People have not only recognized and acknowledged exactly what you just said, but it feels like it's put a whole new spotlight on the product, which obviously you're in charge of, and the application layer in general. Do you think now is the moment in AI where the application layer is going to start getting a lot more attention and people will be more and more excited about the products, which is something I think people have been waiting for for some time?

47:56I think there's a lot that will happen there in two ways. One is as the models get more and more intelligent, especially as they think for longer, the things that they excel at, like competition math and what a lot of people are using them for, are going to be more and more disconnected. I think we have a really interesting product challenge just to find what are the real problems we can solve for people that are solved by models that use more test on compute. Nobody has an answer to that that's really solved yet. If you talk to most people, how are you using these reasoning models in your day of life?

48:27They're like, I don't know, ask them to check them out something for a long time. That's an unsolved problem. So I think it's an exciting problem still. But then the second part is how do these models actually start playing longer and longer roles in people's lives? Because it's not just the ability to reason about something for a while, it's also the ability to sort of agentically act and then reflect on that action and go off and do things like our computer use thing or operator that that uh open ad put out like that's really interesting that's a whole new front on on the consumer and business product that we'll definitely see over the next few months looking back your former life building in the age of social media you know you experienced that whole whole cycle and now like you're right in the center of this sort of next cycle around ai again sort of with a zoomed out view like what what parallels are are you seeing between these two eras?

49:18A Cambrian maybe explosion of companies that happen in social media kind of mirrors a lot of what's happening somewhat in AI as well. Like either companies like doing their own models or building software, take your incubator, startup accelerator, like AI, AI, AI, AI, AI, right? And like we saw that actually in social media as well. And definitely some breakthrough and become sort of a important long-term company that is like self-sustaining. And then lots like get absorbed and good ideas end up somewhere else or they consolidate or they sell like i think that parallel is definitely real as well you know remember like what's who's going to be the instagram of video and it turns out instagram and there being the instagram video but also like not social merge yeah exactly but like other apps like did emerge in carpool their own space and so the moment i think is definitely one of like the sort of outward sort of ideation and creation i think is great and there's inevitable consolidation that we saw on social media i don't know if we've seen in every way but like we'll see in ai as well and then another one of these and then we'll expose.

50:17So that's one piece. The second one is like, and in some ways the deep seek part contributed to this too. Like you would have these moments of like greater awareness, right? Like for us at Instagram, it was like the first time Instagram was in like a major hip hop song. You're like, this has now emerged beyond like early adopter, you know, Instagram photographer. It's now culture beyond that. Right. And so that's cool. Like, you know, the first time it started in a movie, you know, it's in a trailer. So we're like, okay, great. You know, and I think it only ratchets up because like it gets ever sort of wider reaching implications and like the name DeepSeek did that, but like not the usage, right?

50:56Like regardless of where they were in store, I don't think like, you know, then your answer might be super technical, but like, yeah, it didn't break through like usage. Like they're not like everyday people being like, I'm going to compare and contrast the chain of thought between, you know, three, five, sign and I don't know what it's like not happening right but like that will happen they'll continue to happen and i think the really exciting part being in product in person social media and now here is that you get to play a part in in shaping what that sort of looks like and how you introduce it to people and then also like one of the reasons i joined the topic was like hopefully shaping in a way that is like really positive for people and solve their problems in a way that they feel is like really serving them um and that sort of you know step by step is is it mirrors it's probably gonna happen faster than into social media.

51:40And that whole journey for me was about eight years. And it obviously continued afterwards. But, you know, I'm a year in here. And how do you feel like we've got like four of those moments in the last year? Last question. If we were to look out into the future, maybe 10 years as we sort of race towards AGI or ASI or wherever we're going to end up with that, what role do you envision Anthropic playing in that future? There's two things I think a lot about. One is, have we helped people be their maximal versions of themselves? So if you're a person that is creative and you want to explore that creativity, 10 years from now, is there basically no impediment to realized ideas or realized creations other than your creativity, your time, your effort, et cetera, and multiplied by Red Dropic?

52:29I think we'll be serving people well if we are doing that piece. and then like i often joke you read machines of living grace which is an essay that dario wrote like he's like in some ways it's like manifesto or sort of you know like visionary piece and like i joke with him i'm like i also think of it as a roadmap like it's not going to be one of your roadmap but it is like what i want to get to like we should be helping in life sciences we should be helping in civic society um and we should be helping and like helping the economy thrive and all of those things. And that's not today, you know, but I think the greatest challenge, I think the reason I am here is can you translate the model capabilities and potential?

53:09They're not going to magically have societal impact. They need products. They need people using them and they need to be put into the context that they're actually going to be successful. They need the right guardrails. That's how we succeed. You know, like there's The word I learned last year is viscosity, at least as applied to society. The world has a fair amount of viscosity and the way that you get ideas to penetrate. And so it's not just the future tiered, just unevenly distributed, but actually evenly distributed or more evenly distributed. I think it's through good design and good product and good building.

53:42That's how those two things coalesce. Love ending on optimism. Mike, thanks so much. This has been a blast. Really, really appreciate you joining. Same. Really enjoyed it as well. Great to see you. You too. Thanks so much for listening to Generative Now. If you liked what you heard, please rate and review the show on Spotify, Apple Podcasts, and YouTube. And of course, subscribe. All that stuff really, really does help. And if you want to learn more, follow Lightspeed at LightspeedVP on X, YouTube, or LinkedIn. Generative Now is produced by Lightspeed in partnership with Pod People. I am Michael Magnano, and we will be back next week.

54:17See you then.