YouSaid · the spoken record
Yuanqing Yang
- lines on the record
- 53
- first
- 2017-08-30
- most recent
- 2017-08-30
- sittings or episodes
- 1
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- podcast
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“I think this is a wave of the future, in fact, because there's been this traditional adversarial portrayal and this idea that engineers don't collaborate well with designers and designers don't always collaborate well with engineers. And I think that's changed a lot in the last 10 years as you have a lot of these products. Examples we're talking about today that they're touching lives, but there's deep, deep tech behind them as well. Well, thank you guys for joining the A6&Z podcast.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I want to echo that. I actually really enjoy because they're fascinated about the challenges and I'm very supportive. When you have everyday conversation with a tech stack, like people, and you have formed a certain way because like implied this certain knowledge when you talk to a designer, ask questions like, wow, I never thought about this. And the process of thinking about how to answer this and discover there's some hole in my logic or discover like maybe we were to, you know, go down this path and maybe we should think in some way encourage you to think out of box.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I really enjoy it because Brian brings this incredible design sense, and it's not just to how the pixels look on the screen, it's to like, how are things going to work? How does this ultimately pan out down the road? And I actually think that that in combination with the technical discussion can be super powerful.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“This is like one of the most core fundamental premises behind engineering management is understanding the person, what motivates them, what are their skills, what are they trying to develop. That magic moment that I talked about, like being able to find those moments for people and that being a core part of the responsibility that a manager has because everything good happens then.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I love that you said that throughput because this connotation from the semiconductor industry is actually bigger than efficiency because it's about the social, cultural context that actually supports this idea. And I love it because it reminds me of creativity. You have moments where you can do like 10 edits a day and you have moments where you can maybe do one because you have a lot of meanings.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“My thinking on that is slightly different in that, I think that people have periods of time where they can be producing 10x what their peers might be and it's that magical moment when they're aligned with the right project, with the right skill set and like the right personal energy around it.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Interesting But it is a judgment call. I think the leader to decide whether there is a 10X or 2x difference, this is the beauty so fascinating. A good engineer, the amount of the throughput or the contribution they can bring on table, sometimes it's beyond what you can measure or imagine.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and like you can't hire your way out of that. Every person that you hire just ends up becoming less and less productive. And so you have to be able to look at like, what is your contribution per engineer or per engineering hour worked and seeing that that is like increasing over time through retirement of technical tech because then the organization has more throughput as a whole.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, like Yeah, I mean, because an engineering leader should be an owner of the business, right? Thinking about balancing these types of trade-offs. And the other angle that we think about technical debt on is really about what is our overall capacity as a technical organization to move? Like how much can we produce in any given amount of time? And you have to be watching that because if technical debt crops up, what happens is your overall throughput goes down. And the worst thing that you can do at that point is say like, well, we'll solve it by hiring more people, right?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“A great engineering leader finds the right balance between time spent fixing things that came from before and doing for development because neither end of that spectrum is correct. Like if you spend all of your time having like a technically perfect system, you probably didn't do anything to further your business and vice versa. So I think there definitely has to be a spectrum of time. The way we've been thinking about it and the way that we try to do it now is that the business leaders who are responsible for furthering the business also have goals that are associated with bugs performance, like all the things like system science.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“This is human nature, right? You want to have fascinating new things coming out. And look at that as like a whole system. But you need them. You don't want that the reliability will hurt you in the future.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Eyes from the belief that we, you know, in some way this is engineering excellence, right? You have to build a system or infrastructure that lasts a long time, right? Because it's ultimate efficiency. However, there will be a lot of features we iterate. And we don't know whether it will stick for three months, right? And maybe this features user don't want. Maybe something we will admit like shit, it's a failure, right? So it's okay. So we want to be, I use Hamilton quote a lot like recently Hamilton. You know, want to be young.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's definitely a good principle. It's just the constant debate, you probably agreed, is really to encourage them to understand the benefit of a consistency and the company principle versus their own creativity, right? I think the engineer always want there like, I have my think about it individualistically by definition. And then you just have to constantly balance.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“We did a couple things relatively early on to try to head some of that off. The first is that we standardized our supported technicals tax and said, we're just going to operate within these stacks and not deviate from them except for in exceptional cases. And we also got actually a pretty broad engineering effort a couple of years ago to come up with from an infrastructure standpoint, like what are the tenants by which we want to do development. And they're basically just guidelines. And the idea is if you're an engineer, if you're working within the architectural tenants for how we develop software, then you're pretty much good to go. And just stay within it. If you want to go outside, here's the group of people that you can talk to. And I think that that has sort of. Calm down a fair amount of that chatter. Don't get me wrong”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a progressed way, right? Because I think in my early days of career, like almost all the big internet companies do their own stack. And then we have tremendous progress in the open source community. Then you can say that principle without hurting your business efficiency. A lot of engineers would say believe they can build better. And then it's a judgment call to encourage that or say, let's look carefully. I totally agree when there is an open source tool that you can leverage. pay attention and consider that. Sometimes I smile when this question come out because I feel like engineered by nature, they are so practical, but sometimes they go to the extreme of philosophical discussion.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Open source, it can be kind of a lightning rod on some of these things. But I think we've basically just taken a position on it and said, like, you know, this is the way we're going to manage it. Our strong believers in open source. We regularly open source our own internal technologies, particularly around data, like everything we do from managing data pipelines to how we do data analysis. We pushed out to open source. And then internally, we kind of have the philosophy that if there's a great open source tool that can solve this problem that we're trying to solve, let's look to use it before we look to build our own because we want a higher percentage of the hours, like engineering hours and thought and creativity work that happens here being focused on things that are very unique to our business.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“There's such high demand for people who have skills around computer science, data, AI machine learning. The technical challenge is so important, and that has to be there. It's like the foundation for, as an engineer, am I going to be fulfilled by my work? But when you can satisfy that at multiple places, because we all have interesting technology challenges to solve, then it starts coming down to like, what is the purpose behind my work? People more and more are choosing their work based on the mission behind the work and the purpose of that work as opposed to like the specific thing that they're going to work on.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Because I think that's so true, and I agree with you. Let's shift gears and talk about that. So, you guys essentially are at startups. You're still a startup, technically, which is really weird to think about given how big you are. And one of the biggest challenges is obviously getting to scale. And when you have that scale, you grow very fast. Who do you think about balancing building this kind of competency as heads of engineering when in a lot of traditional engineering jobs, I would imagine that you kind of know your roadmap already. And here in this kind of business, you shift direction. How do you think about building this organizationally and operationally? How do you hire people?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Just utilizing this technology, but we're figuring out ways to push it forward because we're going to have to, we're all going to have to be in leading positions in this technology in order to be competitive in the world that's coming in short order.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, at this particular point in time, and like on the spectrum of point of time, a year from now, two years from now, or five years from now, if you look at the advances that have been happening, just even really in the last couple of years in deep learning and AI technology, it is like an explosion over the 20 years before that. It's like these two huge forces sort of coming together and reaching a tipping point, the availability of massive, massive data sets, and then the processing power to be able to actually train models on them in a time-reasonable way, like the broad availability of GPUs. So I think what we're seeing is that we're at this moment of potential exponential growth in this field that has been promised multiple times before in history only to kind of not really have the ingredients there. The applications of this type of AI technology can go to every facet of how we live and work and how people sort of exist in the world and what technology companies are really going to be great in the futures are the ones that think about this as this needs to be core to what we do. And we're not.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say if you give a classic picture of dog versus classic picture of a bagel, the computer can solve at this point. However, if user generated random pictures with a dark background or with a dog in weird shape or whatever, it is hard. Remember, computer learn things from the data we train them, right? If you have a millions of pictures of a dog and with different shape, a different color, I'm sure it will get there. Right now, I will see a lot of domain is limited by the data. If you only have a limited data to teach, let's say fashion, how can we know this is a fashion that are high end and more for the runway instead of a daily, it's a lot of data because it is a subtleness. It's very subjective everyone view differently and then to have the computer really get there, it would take a while. I think the breakthrough recently in deep learning is that we can learn more sophisticated way. But it's a fascinating move so fast.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“You think about the future of where we're going with this connection between digital and physical, there is a world of sensorification happening all around us, whereas more and more sensors are embedded in our environment. You don't only have to rely on your smartphone today. But some of this is not”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“That you've related to. So then we will know those are the type of address or something you look for, right? It's not super fancy. For example, for me, it's more kind of business-like that I can wear. So I think all those signals we can pick up. And we also, I think when luxury we have is a user super engage with another platform. So sometimes they're willing to give more signals. Take a picture of your dish, take a picture of your living room, take a picture of your kids' artwork.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“You can actually model. So, what are the images is more inspirational than the other. Of course, it's not science, it's actually art. We learned, for example, if a same dress in a stark, very boring white background didn't sell. We also want to add a personalization signal. We have some experiment in-house if you take a picture of yourself and we learn your skin tone. But by the way, we can also learn from the pains you like. And we know there's certain style, certain shape of a model you like to see.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I figure that every paint you see like so beautiful looking, right? And this is something actually surprising enough when you train computer to learn why this image of the same living room. You take a picture of this way and that way that looks so different. One just looks so inspirational. The other like maybe just boring. And the computer will start to learn those cues and they already learn certain colors, a yellow is more inspirational and happy than other.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, exactly. And the reason that we went down that path again is also to make sure that we have good accuracy in the data, because there's a lot of information in that review that can come through and be a useful signal to us later on.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one of the things that we did was we changed it so that we have a simultaneous reveal of the reviews. So, both the host and the guest have two weeks to write a review, and then they're revealed at the same time. So that way you don't have to worry about the retribution.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“People who are guests can also be hosts. We got a high review rate from the host side as well. And I think that's also part of being a host and hosting, right? One thing we were worried about a while ago was that the reviews were really nice. You know, one of the reasons for that, of course, could be like fear of retribution, right? Like if I reviewed you, the host and said, like, oh yeah, this place wasn't that good. Then you say, well, this guest wasn't great either.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“By the way, you might have a unique benefit here too, which is that you have the both sides of your marketplace. Hosts can also be supply, and supply can be demand in the sense that people who are hosts are also guests, and people who are guests can also be hosts”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“There's such a personal connection when you're staying in somebody's home that I think there's sort of almost like a social contract a little bit, the percentage of trips that get reviewed are incredibly high.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Don't you guys have this problem, which is typical with all review behavior, I think, where you have this sampling from the extremes. You have this natural skew where only the people who are most extremely motivated because they loved it so much are extremely frustrated, like God, I hated that place. You regress to the mean when you sample from the extremes. How do you think about that?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“People have such emotional connection with these experiences that they have traveling. And I think as a result of that, the reviews that people write and the comments that they have and everything are very rich and filled with emotion about these experiences that they had.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's actually a lot of facets to this. It could be like, you know, I'm looking for something that's a little bit more secluded and out of the way, or I'm actually looking for something that's like in the middle of the nightlife. And we can start picking up on those signals again based on how you're searching and browsing through it. And then so that's sort of at the front end. But then, you know, all the way at the other end, we can look at, again, with that review information, there's some of it that is structured, right? Like give a star rating. But then there's also the content of the review itself. So we can do sentiment analysis. And some natural language processing on that to sort of suss out which aspects of this, like what feelings did it evoke? You might have given it a five on cleanliness, but maybe you felt like, oh, it wasn't really the right neighborhood.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Know similarly when you think about differentiating between aspiration and actual intent to do something, there's also another dimension of like feelings. When you think about what in the early days of Facebook, you could only like or not like something. And now you can express a variety of things. We have emojis and new communication form. And so we're essentially making emotions more machine readable. And that's a really useful thing. But you don't get those signals, actually, because you're talking about people both in the physical world and maybe, I don't know if you guys actually have the range of emoji, not just a star for how you liked or not like something or pinned or not pinned something. How do you guys think about that dimension?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. At that time, I think what we could do as a product expand your horizon and help you to discover new interesting ideas. We don't have to push you to deep, say purchase this or book this. But then later we can tell users behavior they're going narrow and narrow. Now I have this living room. I do plan. Looks like you are going down, down, where is this sofa? I want to get leathered. I want to get the recliner, things like that. Now we see those signals, we know, okay, you probably have the right intent. You're ready to do something more real. Then we will help you to drill down. All those need a computer to understand the user intent and which state you are. Not everyone is ready to purchase right away.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, yeah. You exactly have the same problem. I would say it depends on user state of mind. Sometimes users are ready to book or purchase. Sometimes they just explore, right? I plan my vacation months ahead and I start to explore. At that time, what I need is a creative inspirational ideas, what's possibilities.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, so you have, let's say you have users that are clicking on what they're interested in. They're browsing room listings, they're browsing pins. How do you distinguish between the aspirational and actual outcomes? But both, you can have people who are pinning things because they want to have their dream house one day. Or this is a type of Airbnb you're staying in because you can't afford to live in a beautiful industrial loft. But hey, when you go away, you can at least do it for a day or two. And that's a great way to get this experience. Do you actually weigh what they booked or actually pinned and bought more highly than the things that they might just be browsing and clicking through? Because one of the tricky things is differentiating intent when it's aspirational versus actual. And sometimes you don't always get an outcome that you can link to the choice that they've made because you might not know until weeks later that they've bought that dress after.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“We can do personalization for you. Now you go to say search, the same query, for example, reshu. We know if you are male, you are likely to want to have maybe a running shoe instead of a high heel pump for women. So those are the things we have to solve to personalize your favorite thing. And all those are power the massive data set. The understanding of the image, understanding connection between image and a user, and user machine learning technology to make those connections.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“First is you start with data, and I think both companies have massive interest in data that user expressly shared their interest, right? You click or you book. The first step is really understand that data. We have computer vision technology to identify, there's a sofa, there's a table, and we can train, label all those like millions, actually billions of hints. We will have a subset of labeling data, say this is a white sofa, and the computer will pick up all those labeling and start to learn. This is a white sofa. This is a leather sofa. This is a table with a brown color, things like that. Once they learn, you will then.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Everything with filters. That kind of technology can be used to detect all kinds of different objects that could potentially be like correlated with what would be interesting for you. And so it's that same kind of computer vision techniques that can be brought in to bring that unstructured data forward and turn it into something you can actually use.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, I do like that. I want that. I like that too. Tell me how you technically solved that challenge of moving from unstructure to structured data in that case. How do you now extract that data, feed it in”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Every single one is unique. And so a similar problem for us is like, how can we look through the click behavior of somebody who's going around and looking to stay in a certain area if you're thinking about staying in Paris next weekend, you might have to sort through like 50,000 different places. Like obviously we can narrow some of that down with filtering, but then we can also look at what are the similarity and characteristics between the listings that you're clicking on or the ones that you're expressing interested in. And then the deeper you go into it, the more we can be re-ranking and surfacing other things that share some of those attributes, some of the areas that we're exploring now is like how can we find other embeddings in those images that can take the unstructured data of the image, turn it into something that can actually be tagged and labeled and then used in that ranking algorithm. Like maybe you particularly like places where you have views of trees from the window and all the listings happen to have those attributes.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I mean, you've got millions of travelers who are traveling, you've got millions of homes that they could potentially stay in. Every home is completely unique, right? Like totally different. Like we're not selling like a block of hotel rooms, right?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Love that idea and like the sort of act of helping inspire somebody for what they might like that they don't necessarily know that they're looking for I think is really fascinating and it's it's you know it's actually kind of analogous to like the the challenge that we have to solve on airbnb because you think”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“This is not the classic Google rabbit hole you click on a bunch of page because you're interested in one topic. For me, it's fascinating because it's actually understanding yourself in a weird way. Computers augmenting humans. I'll give you a concrete kind of dumb example, but this literally happened to me on Pinterest in the early days. I had no idea. I love the combination of dark green, black, and gray. So whenever I did searches on shopping sites, I would always put my favorite colors, but I never knew I loved that combo. And then I noticed one day that one of my Pinterest boards for dresses was all dark green, gray, and black. And I was like, oh my God, I love this combo. And in a weird way, the system kind of taught me what I like.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“This is exactly it's not necessary you have to describe in the English text. You give me the image, I will tell you like a thousand other users match your cabinet with this one or match this sofa with the other table or something like that. You may not think your white sofa metric is a red table like outrageous but when you see the images say wow that looks good. We want to give you that inspiration and in some way this is different from Google. There's no right or wrong.”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“First difficulty or challenge is don't know what they don't know, right? For me, like, for example, very hard to describe this is contemporary classic traditional. And also, you don't know whether this is ultimately saying you want to match upon. Maybe you should put in the two different rooms, right? So I think one powerful thing we help user to do is chances are some users have the same struggle with you. And the hard part is maybe that user is in Scotland and they use different language to express. That's why lens can be powerful because you don't know how to describe instead of asking user to input a text as a query, take your camera, point it to whatever you want to understand, and we will give you ideas what are the related ideas. So you're actually...”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, you guys are both talking about something so fascinating, which is the complexities, not just of search and matching and relevance, but that a lot of people don't explicitly express what their intent. I'll have a George and Nelson desk and a vintage French country bench. And those two categories do not go together. If you were to do an explicit search, one would do mid-century modern and a different category for country or cottage chic. But now you have to infer this collection, this cluster of traits of what people are interested in. Can you guys talk to me about some of the challenges of doing this?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“In some way, we have a unique advantage that users, when they use Pinterest, a lot of them start with explicit action. They ping something they like. Those explicit signal user give us is a unique assets and this is fascinating data set our engineer can work with so that we know one out of ten times they are likely to go down deeper and click by click and sometimes went out of say 20 they will buy something”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source
“Meaning the prediction for like if you book this listing, how likely are you to give it a good review? So we can actually sort of collect some amount of offline data from the actual experience that you want to have. And then use that as a feedback mechanism into our ranking model in terms of what listings we're going to show you next time. Because again, if your objective function for ranking is really like how good of an experience are you going to have, then all this data you can get about what kind of experience did you actually have out there in the world can be then used to your next booking, but also to like be able to look at using that data towards how other people are going to book?”
2017-08-30 · a16z Podcast · a16z Podcast: Engineering Intent · IDENTIFIED FROM THE TRANSCRIPT · source