From Roadmaps to R&D: How AI Is Changing Product Development - with Richard White, Founder of Fathom AI

25:04You end up having some of these tools being thrown at you that just does a lot of life. And I've always been thinking about what is a founder's kind of role. And in many ways, I think about it in the context of Steve Jobs. My sense is that Apple was very consistent for a long time because people basically asked themselves, well, is Steve Jobs like this? And it became this kind of like really easy way to QA stuff because you can just go like, he probably wouldn't like it or he would like it. And if he would like it, he would put it in. And so I think in many ways, I'm increasingly obsessed with this narratives becoming source codes, almost like that.

25:37if you don't have a strong story about what it is that you want to be as a company, but you just define yourself of what you do, you have this very difficult time to basically share that articulation. And therefore, people don't really know if a feature that works in this way or that way should go in and on. It's funny you say that because my knee-jerk answer to your last question about if you're a large company, how do you get taste or whatever, is I think someone in the executive suite has that taste. They need to be stepping into the QA process. They need to start doing them. So they need to start basically being the backstop and start, you know, creating a culture around that.

26:11Like Steve did. Just like Steve did. Like Steve did. Yeah. Yeah. It's so fun. I think it's so important. I remember like at BarkBox, for example, we have this kind of rule called ban the bone. And the ban the bone was that I would have a little bit of a theatrical hissy fit if I saw somebody design anything with a bone. Because obviously when you design stuff for docs, the most obvious thing to do is a bone, right? All paw print. Ban the bone. And I just didn't want designers to be that lazy. It just meant that they hadn't really, they just done their homework, right? They just, somebody asked me to design something here, like a paw print and a bone.

26:41And so this ban the bone became a thing, but people didn't remember the rule. They remember that, that Henrik would get upset, right? And so sometimes when you had a new designer come on board and they design something with a bone or paw print, people go like, oh, have you seen, have you shown this to Henrik? I'll bet, better not show this to Henrik, right? And I think actually it's increasingly going to be important for us to your point to create these almost like folk laws of these systems, because obviously not only will people have to understand them, but models will have to understand them too.

27:09Yeah. Well said. So Henrik, I love your phrase, just to put a double click on it. Narrative is source code. I think it's beautiful. And that word code reminded me of something through, oh, there goes my notebook, something we talked about with Wade, actually, Richard, which I'd love to get your thoughts on. And so back in, what was it? Was it 23? Was it when GPT-4 came out, Henrik, that Wade declared code red at Zapier? And he basically said, everybody take two weeks off work because the period of time between 3.5 and 4 and the capability improvement was so significant. Wade and Mike Newp, his co-founder, basically said, we need everybody to stop everything they're doing and go deep.

27:53I contrast that slightly with your story. It sounds like you had an intimation through the grapevine, so to speak, that Gen AI is kind of coming down the pipe. And you actually said, what will we build if we assume somebody is going to do that thing? So I don't know if you didn't pre-code red in a way, but you kind of built with that mindset. And then the third kind of piece of this puzzle, I'd love for you just to riff on if you're willing. I've heard one strategy people talk about is in enterprises, what would we be if we could start today? Right. Knowing that AI is now here, how, and that's actually, I think a really difficult thing to imagine.

28:26But think about those kind of three things. You started knowing it's coming. What should I build? Wade said, oh, crud, it's here, code red. And then there's this kind of existential, what would we do if we started over? How should an organization think about AI and its kind of existential connection to the business? Okay. So to go back to my, like, we don't like plans, but we had a plan which was you know right of course we had kind of almost like a four-step plan here right for fathom going back five years ago one was okay we believe jane is coming um it's not here yet what should we build before it gets here what we're gonna do is work on all the things around the engine of the car we're gonna work on distribution channels we're gonna work on video streaming transcription like all the plumbing we think that part's really hard we're intentionally not try to build around models because we know that like they're coming we saw all of them should have competitors hire linguists and ML people.

29:25And we thought that was the wrong move. Then GPT-4 gets here, right? Great. Great. We can drop that engine out. It can take notes. It can use that in system structure to take notes. We always thought the step after that, sort of talking about earlier, is like it not as just like a scribe, but as it has like a second brain. And so we kind of built almost another car with another engine. Maybe it's a bigger car, bigger engine cavity. Thinking about like, okay, when it gets to this threshold, where we can actually horizontally scale it cheaply enough that we can have a do kind of massively parallel analysis of thousands of meetings at once to answering a question you've got.

30:00Great, now we're in this phase. And then there's a phase beyond that where it's not just answering questions you ask, it's gonna start pushing you the answers. And so the world we're really excited about is the world where you get off the meeting and it's done 80 % of your actions for you. And also, by the way, you're in a third of the meetings used to be because now you just have an agent that goes, runs around and finds all the topics you care about. and feeds them back to you at the end of the day. And so we haven't really had to remake these things because we already had like a, almost when it hits this capability, create, smash that red button.

30:31It's time for us to pause the assembly line and like rework it. So you've kind of pre-built some of this infrastructure, so to speak, and you're waiting for the new model to drop in. So is what happens, you know, Gemini 3 comes out, you go, let's drop in Gemini and see if it's got enough horsepower. Is that what you're doing? Basically, yeah. There's certain limitations that we run into and we're always looking for, does this new model unlock one of those limitations? Good example of this is GPT-5. GPT-5, I think, in the marketplace has been kind of panned, right? Not the most exciting launch.

31:01To us, it's the most exciting launch we've seen in almost over probably 18 months. Why so? Because it solved hallucination rates. And for us at scale, when we're trying to find needles in a haystack, trying to look at 2 ,000 meetings, we're trying to find, you're curious about something that only happened 0.1 % of the time, 25 % of the time, if the hallucination rates pre-GP5 are high enough that, well, we're going to, two thirds of what we're going to return you is hallucinated. And so like, it's interesting where it's like, that's a hallucination rates are not a thing that matters if we're just writing notes because it doesn't get it wrong.

31:33But when we're doing that use case, it matters a lot. So we do have, I think like a, we're on the lookout for certain things. The other thing is we kind of know there's a lot of times it's like, we can make this work today, but it's too expensive and too slow, which are basically the same thing in AI land, right? Costs speed of the same thing you don't want to ask question of father that takes 30 minutes to get the answer but if we can make it work in 30 minutes that means we can probably put it on the shelf and if we wait another lm cycle or two it'll get down to 30 seconds and that'll be both a latency and a cost we can stomach so sometimes we use expensive models to like prototype out like okay if the professor model can do it we see it we can't build it yet but like it's coming Clearly going back to your insight in how we as users should use a product like yours better.

32:20I read somewhere that if you have a partner for a long time, you basically and you do cat scannings of their brains. There are like whole prompts that you basically turned off because you assume your partner remembered, right? So if you're really good at maps and I'm not, then I'm basically I don't have to deal with maps anymore because my partner doesn't. Is the way to think about it is that that is kind of becoming these systems that you're creating. these basically libraries of everything that's been said in meetings are becoming this kind of very extended memory and if you have that what is the stuff that people are not doing today but they would get a lot of benefit from i think the thing we see a lot of is you come to fathom and you're trying to get fathom to approximate your current workflow which jeremy's illustrating well right he's taking notes the whole time he's going to afterwards he's going to clean it up etc etc he's gonna spend a lot of time with it so that it kind of like sinks into his brain and when people first come to fathom that's what they excited about oh wow it took really good notes for me but very quickly you realize like i don't even need those notes i don't need them now what i need them is when i go meet with jeremy again in two weeks and so instead of me even i don't review my notes after any meeting i just have the meeting i go on the next thing it's when i have the follow-up or when you need to do something about that meeting now i go back to the meeting and i ask the question hey what did we say we were going to do here or i watched the last two minutes or i read the summary and then i go into the next call just having reviewed that before so it's kind of like a just-in-time memory system so instead of putting all this work up front to try to like prime your brain to remember these things in the future yeah just when you need it come back to it i did a fun use case just recently so i write a sub stack like other people have a podcast and uh it has the same format it's like these eight to ten points and it's very structured but it's a little bit of a chore to make it because i want it to be high quality blah blah So what I do now is, as you suggested, I feed it my last 20 newsletters.

34:14And then I basically says, go through all the meetings I've had the last month or so and take anything that is interesting enough that I can package in the same way and then give me a source. And then, hey, presto, of course, it like finds all these interesting things that you and me have said. I'll then repackage them in my words. And then it works very well because then... I've wondered about that. I feel like a lot of my ideas are in your newsletter. What's up with that? No, okay, when you say go through your meetings, what is it going through? It's a good example. It just goes through all my meetings.

34:42But in what format? In Granola? In Fathom? Yeah, like, you know, like, I use this local model because some people get freaked out, you know, use cloud stuff, and you probably have a solution for that. So I use Mac Whisper, which is a known sort of thing. And it's just taking notes all the time. It just records everything and stores it, right? So this conversation will be in there, and then in... It's your whole transcript, I'm imagining. rich's point like in two three months when i've forgotten about all these small things that rich has said then it will find that point and then i go oh yeah i remember that that's actually is a really interesting point i should have put that in my newsletter but it's just it's a workflow that just couldn't have happened before because to your point i didn't have the transcript and i didn't even know that this was something that could be done and i think this is like the evolution of ai as first like an assistant like a scribe doing a specific job for you to ai is like a co-creator with you right and now i think we're very much into like we're co-creating things so i'm good at some parts of this process it's good at some parts process right it's much better at memory than than we are we're much better at taste than it is right so like you know i'm guessing you have some editing process gives you a bunch of stuff and you're like yeah these two are good those two are bad right and then i think the next phase is honestly it starts you know we start becoming it starts maybe they're passing us and then it starts suggesting things with less of a co-creation and we're more just more of like a steve jobs we're just kind of approving thumbs up and thumbs Can I give you one thing that I just thought was an interesting place that I've been talking more on this podcast than you have, so I apologize for that.

36:09So one of the companies I'm involved in is called Autos, and we help people build startups with AI. And we had a journalist from a major publication go through this process, and he ended up building something that basically helped him navigate his parents getting older. So like what kind of nursery homes could be, stuff like that. And so the system built it, built the website and all these different things. And then he basically started to get real customer through. And I think what he then realized was like, this might actually be a decent business, but not for me. I don't want to build that business.

36:37And so it's not just about taste. It's also just about preference, right? Like I don't want to be the one who posts that specific thing. Like it's just not for me. And so I think there's like an interesting used taste earlier, which I think is such a profound thing, but it kind of like very much doctails in with just preference. And of course, AI can't pick your preference because it doesn't know you well enough yet. Yeah. Yeah. I get all things in AI. We always have to throw, you got to throw yet on the end so that you look smart in the future, right? Yeah. One thing I'd be curious about, Richard, is can you talk for a second about organizational rhythms, rituals, mechanisms to drive exploration and adoption?

37:17One word actually that has not come up yet is adoption. You haven't spoken once, and that's not a criticism. Some people talk their entire thing is adoption. Yours, clearly not. And as As an example, I'll mention we had Humza Tehrani, who's the chief strategy and innovation officer at Maple Leaf Sports. So over the Toronto Raptors and the Maple Leafs and their soccer team and their arenas, he has a running list of ideas that get submitted by employees. And once every quarter, they have what they call a build day. And whatever the top three vote getting ideas are, and they have thumbs up and thumbs down on all these ideas, they bring the person who submitted the idea into their, they call it a V1 lab, and they build soup to nuts the idea in a day.

37:57and every quarter they do that. I thought it was a really elegant way of kind of demonstrating what's possible and stimulating engagement. What do you do to spark the imaginations of your team? One of the things that we got really excited about last year was this idea of AI ops, where it's kind of like, how do we, so we have an internal goal of getting to 100 million in revenue with less than 150 employees. And that's one from just, I've interviewed all my other startup friends who have gotten well beyond that point. And every time I tell them we're like a 90 person company, their eyes kind of glaze over and they just, they get rose colored glasses and like, oh, that's when it was fun.

38:35And they all, I asked them all, would it stop being fun? And they all say somewhere around 150, 200. But also because I just think from a, you know, conservation momentum, like how do you stay nimble and move fast? You want to stay small. And so for that reason, I get really excited about how do we have an internal team that kind of just partners with various other functional teams? Like, hey, what's almost like a work consultant-esque mindset of like let's let's look at all the stuff you're doing what's its lowest 20 of value and how do we automate that with ai or things like that turned out to be really challenging actually to do this and i think we found two things one is that like often the scope of these things was like somewhat ill-defined and having poor scope makes it really hard to build an ai project i think the other thing we found though is like one of the things we run into building ai features what i call the llm treadmill and the lm treadmill is like you build something on gemini 2.0 2.5 comes out it's not really forward compatible you almost have to generally build it again and oh by the way as soon as 2.5 comes out which came out six months after 2.0 uh google is now removing capacity from 2.0 very rapidly because there's finite compute in the world right especially gpu compute so you almost like have to constantly keep this thing up to date software update and so yeah so like it's the most aggressive eol cycle i've ever seen in my life right you imagine you would rebuild a feature every six to nine months and oh by the way you have like three weeks to rebuild it maybe that i mean we're in a situation right now gemini 3 came out they're already a deep revision 2.5 capacity we don't even have an api for 3.0 yet right so like it's insane and so a lot of our initial attempts to build stuff internally we're using these kind of models and we were building internally and we very quickly found that like we don't have the capacity to like for these things that are like nice little you know they're not huge movers of roi but they're decent little roi wasn't wasn't worth it to try to figure out how to keep them up to date and we just started shifting more into like great we're we're going to just be a buyer of ai products lord knows there's enough of them out there right so like why build internally when there's tons of stuff and we've just instead move to like, how do we just get better at evaluating AI vendors and getting more rigorous about it?

40:52Instead of having a team to be building, you've decided build versus buy, we're going to buy. The challenge is how do we make buying so easy and so quick that it's frictionless? Is that it basically? Yeah, basically. Yeah. I mean, I think probably most folks 18 months ago, we were just buying everything we get our hands on, right? Or just like, oh yeah, that looks like we're like a kid in the candy store. And then what's the process to validate? That's the thing. And I don't think we were as rigorous in process to validate because, you know, I think internally we do a lot of work to validate AI features.

41:24We take a lot of pride in the quality of the outputs of our AI. And then we think we learned in the marketplace that's not always the case. A lot of vendors don't maybe have that same pride. And so, you know, they'll sell you a thing and you find it like, you know, wild, we can't do that thing. So kind of similar in the same way we had to, you know, you have to develop taste and like a really good qualitative QA process for stuff we build internally. We do the same thing for stuff we buy. Right. And so like your AI purchases. Yeah. Aggressively. We almost won't buy anything unless you give us a 90 day pilot at a minimum.

41:57We just refuse. And we do the same thing for our customer treatment. We now assume you won't trust us either or knows there's enough people. So we do 90 day opt outs as well. Just by default. We make sure we have 90 days to pilot. we make sure we have a test plan, we make sure we know who's going to be testing it, et cetera, et cetera. Versus before, honestly, we were just going to like, the demo looked like it worked, right? Like ship it, put it in front of customers sort of thing. And so it varies from product to product, but we have a lot more rigor in that buy and evaluation process. You know what's interesting just as kind of a, this is a meta observation about innovation in general, but there's a question about what do we have to make really easy?

42:34And I think that's kind of first principle. I gleaned that. I was listening to, if you haven't listened to Jensen's interview on Joe Rogan, which I think just came out this week, it's one of the best interviews I've ever heard. I mean, it's truly fantastic. And Jensen's origin story and NVIDIA's origin story, I mean, it's truly incredible. But one of the things that I'm kind of a sucker for origin story. So I love hearing how companies get made. And Jensen tells a story, I've never heard it written about anywhere, that basically they got to the point that they had a new chip design. They had one purchase order and they knew that the typical way of kind of fabricating was you send your spec to a fab and then they send it back.

43:11It almost always is buggy. And then you got to redo it, redo it, et cetera, et cetera. And he said, we looked at our cash in the bank. We did not have enough money. We were going to run out of money if we had to go back and forth. So he said, we couldn't do it the traditional way. He said, I had heard of this product called an emulator, which basically would take onto the device your chip specifications and then act like it was the chip. And he said, I said, the only way that we're going to be able to to to preserve money is if we have an emulator and we do all the QA ourselves. And then we send the final design to the fab and we tell them, put it straight into production, which, by the way, no one had ever done.

43:50He said, so we spent this is a you heard the story. I haven't heard this. I mean, it's amazing. He said, 50 % of our remaining cash is what the emulator cost. 50%. He said, we reached out to the company. They'd gone bankrupt because they didn't have any customers. He said, oh no, I can't buy it. He said, we have one emulator in inventory if you want it. He said, I bought the one they had out of their inventory, despite the fact that they were bankrupt. We did all of our QA on the emulator. And then we sent to TSMC, who was small at the time. We sent them the final design and we said, go directly to production.

44:24with that design. I have chills because he said it ended up working. And to me, the amazing insight there is recognizing the thing that has to be tested as cheaply as possible is the back and forth, right? In that case here, you're saying, I don't want what I'm projecting. And I'd love for you to clarify if not, but it sounds like what you realized is it's too difficult to build. We want to buy. And what we got to do is make buying as easy as possible. And then we'll rigorously QA whatever we do buy, but we don't want anybody wasting any time or effort with the, you know, a new tool purchase process.

45:01Is that an accurate? A hundred percent. Yep. That's really cool. A hundred percent. Maybe that's a good time to wrap. I think we're coming to the end of the hour. Although it sounds like Jeremy and I have a bunch of more questions, so maybe we can invite you back to the pot another time. I hope we didn't scare you away. It's got to be honored to come on again. And it's super fun to get to talk to you. Thanks for coming on. Yeah, this is super fun. We love what you're doing. And hopefully it just drives more spotlighted attention to your great work. Take care. See you. It was a lot of fun. Thanks, guys.

45:30Thank you so much. Bye now. Mr. Oddly, interesting conversation. It's always so interesting to talk to entrepreneurs that are doing something cool because they obviously thought a lot about these things. Well, you know what I really like about a couple of the conversations we've had recently? If I think about Ilya, who anticipated Chagbiti, you know, five years too early as one example. Richard is similar in that because of the, you know, the pools he swam in, so to speak, he was anticipating Gen AI long before the rest of us were surprised by it. And to hear him talk about how the kind of structural decisions he made about the product he wanted to build.

46:13Basically assuming that model makers were going to drop in the engine, the product he wanted to build around that, I thought was a really fascinating approach to company building. And I couldn't help but wonder, we talk a lot about existing enterprises and how they grapple with adoption and things like that. But taking as a premise, models are going to improve. Almost what should we do if we take as a premise models will improve, I think is a very different question, even from what should we do given what's here? And folks right now are still trying to kind of catch up with what's here. And there's a whole kind of other strata of strategy, so to speak, which is what should we do anticipating what's going to come down the pipe?

47:01What I think is such an interesting dilemma that a lot of companies are facing right now, you know newer and older is this that the first two steps of getting introduced to ai seem to be pretty clear first you need to upgrade your capabilities of the organization and then second you need to kind of look at what workflows can be done with agents those two steps i can but they almost all like cost reduction thing we talked a lot about then what do you do all this unlocked energy that you might be able to get out of those exercises and i think there is this chasm between box one and box two and box three, which is basically living in this new world where if AI is electricity, you basically went from working before without electricity, and now you have a company in an age with electricity.

47:47And I think jumping that chasm is complicated. And some people do it, as Richard does, where he basically see where the puck will be going, and then he starts to build, you know, towards that already, even if it hasn't been released yet. And I think it's a way of saying the same thing as you guys. You can't just build from the reality that is there today. With the models releasing so fast and getting so much more capabilities every time they come out, you kind of already have to basically figure out what does GPT-6 or 7 potentially do and make some bets towards that. It's such a fun and interesting and scary time for strategy.

48:28I think in a way, because the last era has trained folks to think very incrementally about what the business could be. And in an era of uncapped intelligence, you know, as Ilya says, or Dario, maybe one of them says, you know, a country of PhDs in a data center. When you think about that, a country of PhDs or a country of geniuses in a data center are going to be available to you as a company. I mean, what Richard said was, we now have college seniors and doctoral students who have 800 hours a day. That's kind of another way of thinking about it. But very few companies can even allow themselves to imagine, what do we do with somebody who has 800 hours a day?

49:12With a totally, even if they only had one employee, a fully capable employee that had 800 hours a day. Let me ask you a question. As I was just saying this other things, I was like, that might actually be wrong. because there is these two competing thoughts that we were just introduced to. We talked about you had to be able to figure out where the puck is going. Richard was talking about that he was building software, you know, knowing that at one point he'll be able to run models on the client side, but at the time he couldn't do it. So clearly kind of was seeing where the future is going. Meanwhile, he and we were kind of freaking out about Jensen's argument about not having long-term plans.

49:52So he talked about like he only have a 90 day roadmap. He doesn't do long term planning, all these different things. How do you think people and I think I have an answer. Maybe I'll just say I think it's complicated for people to square off this thing about trying to be visionary of where things are going, but also be nimble enough to kind of work. So in an agile way. And so there seemed to be this tension. Right. And where I kind of come back is to the only thing you can stay true to is to be very good of having a narrative about what you want to be and who do you want to serve because that can stay consistent.

50:34And then you kind of have to have like an overall narrative of like, what is it, you know, what is the change you want to bring to the world? But not in a roadmap terms, but more kind of like bragg. It's like you want to make dogs and the people who love them happy. I say this as a statement, but I really mean it's a question because I think it's just so complicated on long-term strategy where it's just like agile, you shouldn't be an army, you should be like a SEAL team, but you can't think too far ahead because you should really like, how do you see that dilemma? that. No, I really agree. I resonate with what you're saying.

51:07It reminds me of the Viktor Frankl quote, not to get sober, but you know, someone who has a why can survive anyhow or any what, I can't remember exactly. I hate to invoke him so kind of superficially, but the why matters a lot. And I think one of the things I took away, a ritual that Richard didn't reference when I asked him about rituals, but he referenced it earlier. He's meeting with the AI team once a month. And he's told them 60 % of the stuff that you do, I hope it comes from my wishlist and from the team's wishlist, but fully 40 % of the stuff you do, I expect you to do because I want you to follow your interestingness, right?

51:43To use your word, right? And so the why, obviously it's going to be in service of the mission of the organization, but folks who are tightly coupled to the mission of the organization probably can be given a lot of leash or a lot of leeway to explore lots of different kinds of things. And especially if your expectation is, hey, 50 % of the stuff you're going to try isn't going to work. That seems like you've created the conditions to succeed. But I think that statement, and this is why I think increasingly AI is both the capabilities, but it's also a way of thinking, right? That statement is true also because that he talks about basically what seemed to be a Dunbar theory of the firm, right?

52:22Which is basically, he believes that a company should be 150 people max. He'd like to get to$100 million of revenue with 150 people. That's why our other founder seems that basically the intimacy breaks down, everything gets complicated, right? So he can, I think, deploy all this trust because he kind of understand the 150 people. And he said like one out of four people was these R &D AI engineers, right and so let's say like he only have 100 people now blah blah it's probably a handful or two people that he has to do so the trust is probably immense and so there's this interesting kind of like issue that when we talk about how to think ai think about how we build our companies with ai there's these underlying kind of assumptions where we a lot of companies i think have organizational debt to pay.

53:13They don't have trust. They don't have originality. They don't have these other things, you know, entrepreneurship and resourcefulness. They have all these, there's all these things that they probably be complaining about that they didn't have in the past, but it didn't really matter as much. But now with AI, it suddenly matters a lot, right? And so it's fascinating. Wait, what matters a lot? Say it again. In the age of AI, what matters a lot? A lot of these capabilities that I think companies have always had, and it was envious that startups had, they didn't matter as much as they do now where more people can do much more with less.

53:49So suddenly the issue just becomes much more magnified and all these kind of hidden issues, bad culture, bad originality or innovation or creativity, not having people who are resourceful and can do a lot with a little, very clear department structures that means that people don't feel they can go into other swim lanes. All these things that we've all talked about in innovation entrepreneurship in a bigger company context for a long time is now becoming these like super obvious that it will also have to fix. But what's weird about it is, of course, these are not AI issues. These are secondary AI issues because they become a much bigger problem in an HOAI.

54:31Right. Right. Well said. Well said. I think that, you know, just to summarize some of those things that at least I got very fascinated about. He basically has a frontline deployed R &D team, which is an interesting way of thinking about it that basically don't speak in ideas and workshops. They work in code and prototypes. And so I think that was just a fascinating thing to hear about. I think, you know, he talks about the Jenga model, which is basically use AI and use cases. Program. And they're Jenga blocks. And you press a little bit and see which loose and you can kind of pick up easy. I loved his point there, by the way, about the Hail Mary, that every once in a while you throw a Hail Mary and it works.

55:11And if you aren't throwing those long passes and if you aren't taking big bets, you don't see the stuff that could surprise you. And the last thing that really resonated with me, which is kind of also a softy kind of thing, is he said something that people who are not making good products, because of the QA process is broken in the age of AI, but they have this issue because they have no taste. And so it is a super interesting question of who in an organization have the permission to be the one whose taste we're following. And for startups, it's easier because the founder often have like a very big voice.

55:50But if you walk into a P &G or whatever, is it the CEO? Is this a CMO? Is the head of strategy? Is the tastemaker? Like, is it all these people that creates consensus? Like, who is this person whose taste we're going to go for? And how do we define that? And I think that's kind of back to this whole point about your narrative is your source code. If you can now make everything, what do you want to make? And what is the thing where your taste picks out and saying, this is the thing that I want to put to the world? And I just find this to be super fascinating kind of issue and problem to solve for the future.

56:29It was a great conversation. Thanks to Anna Leyva for introducing us to Richard and other listeners. If you have interesting guests that we should be speaking with, put us in touch. We love learning from the folks who are at the front lines. And for everybody who has been through the whole episode and now listen to the Still Deep Rift, thank you for staying all the way through. And with that, Jeremy and I only have one thing to say, and that is bye-bye.