Podcast appearances and mentions of Drew Houston

In this episode, Dropbox founder and CEO Drew Houston joins Leadership Next to share how he's reimagining work in the age of AI. He traces Dropbox's evolution—from solving file-syncing in the early cloud era to launching Dropbox Dash, a new AI-powered search and knowledge-management tool. Houston reflects on the hard lessons from failed ventures into email and photo apps, the pressures of self-disruption, and what it takes to stay relevant in a crowded productivity space. He also opens up about distributed work, leadership growth, and the books and mentors that have shaped his journey toward becoming a “bionic CEO.” 

It's been almost 20 years since Drew Houston founded Dropbox in an effort to solve the problem of forgotten USB sticks. But how is the company innovating in a now much busier market? Plus Softbank makes a $40billion investment in OpenAI, while a new book allegedly tells the inside story of Sam Altman's 2023 (temporary) dismissal. Hosted on Acast. See acast.com/privacy for more information.

Guest: Ben Chestnut, Former CEO and Co-Founder of MailchimpIf you find yourself selling your startup, then Mailchimp co-founder Ben Chestnut has some important advice for you: Get a dog. When Intuit bought Mailchimp in 2021 for $12 billion, the company asked Ben if he wanted to stay on as CEO, but he chose to “walk off into the sunset” and let the new owners take over. After that, he estimates it took 6 to 12 months before he stopped checking his email, social media, and calendar with the same level of stress a CEO might have. Adopting a dog, he discovered, forces you to “get OK with the voices in your head."“After the acquisition, that's all I do, I walk the dog,” Ben says. “And the dog was good therapy ... No judgments from a dog.”Chapters:(01:09) - Growing slow (03:06) - The long journey (07:48) - Is money a burden? (09:35) - Building globally in Atlanta (11:22) - Ben's upbringing (12:59) - The first 10 years (17:58) - Scaling to one billion emails (19:22) - Freemium (23:32) - No equity (26:00) - Deciding to sell (33:55) - “I'm a sunset guy” (35:29) - Stress and support (37:25) - Time with the parents (39:07) - Get a dog (42:24) - The voices in your head (46:03) - Serial and “Mailkimp” (53:00) - Hiring interviews (57:14) - Fitness routines (59:27) - Lights off (01:01:46) - AI & reinvention (01:06:30) - The worst days (01:09:15) - What “grit” means to Ben Mentioned in this episode: Intuit, Wolt, DoorDash, LinkedIn, Dan Kurzius, Salesforce, ExactTarget, Pardot, Constant Contact, Rackspace, Free by Chris Anderson, Wired Magazine, Charles Hudson, the Freemium Summit, Drew Houston, Dropbox, Evernote, Phil Libin, TechCrunch, Brian Kane, Catalyst Partners, Georgia Pacific, Scott Cook, Bing Gordon, Vinay Hiremath, Loom, Joe Thomas, Caltrain, Flickr, Saturday Night Live, Droga5, Cannes Film Festival, Strava, Twitter, LinkedIn, Nvidia, Glean, Rubrik, Amazon AWS, and Mechnical Turk.Links:Connect with BenLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner Perkins

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

Drew Houston is the co-founder and CEO of Dropbox. Under his leadership, Dropbox has grown from a simple idea to a service used by over 700 million registered users globally, with a valuation exceeding $9 billion. Drew has led Dropbox through multiple phases, from explosive viral growth, to battling all the tech giants at once, to reinventing the company for the future of work. In our conversation, he opens up about:• The three eras of Dropbox's growth and evolution• The challenges he's faced over the past 18 years• What he learned about himself• How he's been able to manage his psychology as a founder• The importance of maintaining your learning curve• Finding purpose beyond metrics and growth• The micro, macro, and meta aspects of building companies• Much more—Brought to you by:• Paragon—Ship every SaaS integration your customers want• Explo—Embed customer-facing analytics in your product• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/behind-the-founder-drew-houston-dropbox—Where to find Drew Houston:• X: https://x.com/drewhouston• LinkedIn: https://www.linkedin.com/in/drewhouston/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Drew and Dropbox(04:44) The three eras of Dropbox(07:53) The first era: Viral growth and early success(14:19) The second era: Challenges and competition(20:49) Strategic shifts and refocusing(29:36) Personal reflections and leadership lessons(40:19) Unlocking mindfulness and building support systems(43:14) The Enneagram test(50:35) The challenges of being a founder CEO(58:11) The third era: Rebooting the team and core business(01:22:41) Lessons and advice for aspiring founders(01:27:46) Balancing personal and professional growth(01:42:38) Final reflections and future outlook—Referenced:• Dropbox: https://www.dropbox.com/• Y Combinator: https://www.ycombinator.com/• Paul Graham's website: https://www.paulgraham.com/• Hacker News: https://news.ycombinator.com/• Arash Ferdowsi on LinkedIn: https://www.linkedin.com/in/arashferdowsi/• Sequoia Capital: https://www.sequoiacap.com/• Pejman Nozad on LinkedIn: https://www.linkedin.com/in/pejman/• Mike Moritz on LinkedIn: https://www.linkedin.com/in/michaelmoritz/• TechCrunch Disrupt: https://techcrunch.com/events/tc-disrupt-2024/• Dropbox viral demo: https://youtu.be/7QmCUDHpNzE• Digg: https://digg.com/• Reddit: https://www.reddit.com/• Hadi and Ali Partovi: https://www.partovi.org/• Zynga: https://www.zynga.com/• Steve Jobs announces Apple's iCloud: https://www.youtube.com/watch?v=ilnfUa_-Rbc• Dropbox Carousel: https://en.wikipedia.org/wiki/Dropbox_Carousel• Dropbox Is Buying Mega-Hyped Email Startup Mailbox: https://www.businessinsider.com/dropbox-is-buying-mega-hyped-email-startup-mailbox-2013-3• 5 essential questions to craft a winning strategy | Roger Martin (author, advisor, speaker): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-strategy-roger-martin• Intel: https://www.intel.com/• Gordon Moore: https://en.wikipedia.org/wiki/Gordon_Moore• Netscape: https://en.wikipedia.org/wiki/Netscape• Myspace: https://en.wikipedia.org/wiki/Myspace• Bill Campbell: https://en.wikipedia.org/wiki/Bill_Campbell_(business_executive)• Enneagram type descriptions: https://www.enneagraminstitute.com/type-descriptions/• The Myers-Briggs Type Indicator: https://www.themyersbriggs.com/en-US/Products-and-Services/Myers-Briggs• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Ben Horowitz on X: https://x.com/bhorowitz• Why Read Peter Drucker?: https://hbr.org/2009/11/why-read-peter-drucker• GitLab: https://about.gitlab.com/• Automattic: https://automattic.com/• Dropbox Dash: https://www.dash.dropbox.com/• Welcome Command E to Dropbox: https://blog.dropbox.com/topics/company/welcome-command-e-to-dropbox-• StarCraft: https://en.wikipedia.org/wiki/StarCraft_(video_game)• Procter & Gamble and the Beauty of Small Wins: https://hbr.org/2009/10/the-beauty-of-small-wins• Teaching Smart People How to Learn: https://hbr.org/1991/05/teaching-smart-people-how-to-learn—Recommended books:• Guerrilla Marketing: Easy and Inexpensive Strategies for Making Big Profits from Your Small Business: https://www.amazon.com/Guerilla-Marketing-Inexpensive-Strategies-Business/dp/0618785914• Playing to Win: How Strategy Really Works: https://www.amazon.com/Playing-Win-Strategy-Really-Works/dp/142218739X• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884/• Only the Paranoid Survive: How to Exploit the Crisis Points That Challenge Every Company: https://www.amazon.com/Only-Paranoid-Survive-Exploit-Challenge/dp/0385483821• Zone to Win: Organizing to Compete in an Age of Disruption: https://www.amazon.com/Zone-Win-Organizing-Compete-Disruption/dp/1682302113• Warren Buffett's books: https://www.amazon.com/warren-buffett-Books/s?k=warren+buffett&rh=n%3A283155• Poor Charlie's Almanack: The Essential Wit and Wisdom of Charles T. Munger: https://www.amazon.com/Poor-Charlies-Almanack-Essential-Charles/dp/1953953239• Invent and Wander: The Collected Writings of Jeff Bezos: https://www.amazon.com/Invent-Wander-Collected-Writings-Introduction/dp/1647820715/• The 15 Commitments of Conscious Leadership: A New Paradigm for Sustainable: https://www.amazon.com/15-Commitments-Conscious-Leadership-Sustainable-ebook/dp/B00R3MHWUE—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

By now, the story is legend. When Drew Houston boarded a bus from Boston to New York and discovered that he had—yet again—forgotten to bring his thumb drive, he was frustrated. So frustrated that he sat down and began writing the first lines of code of what would eventually become Dropbox. After over a decade of changing the way files are stored, synced, and shared, Houston is changing the way people work, once again. This time, to solve a problem that likely plagues every single knowledge worker today: our fragmented, overcomplicated workspaces. In this episode, you'll learn more about Houston's journey—from ideation to launch—with Dropbox Spaces, as well as the most important lessons he's collected while building a multibillion-dollar company. Listen to Nathan and Drew discuss: The relatable experience that inspired Houston to come up with the idea for Dropbox Why Houston doesn't believe there's any “magic” involved in building a multibillion-dollar company The importance of decision making and learning continuously on the job How a conversation with a SpaceX engineer sparked the vision behind Dropbox Spaces Houston's advice on “harvesting” versus “planting” when it comes to your business Why Houston is such a huge believer in intentionally designing your environment—at work and with your personal relationships Click here to start your business for $1. You'll get all-access foundr+, where you'll find more in-depth, proven strategies from founders like our guest today and support and advice from our global community of 30,000 founders. If you loved this conversation and learned something new, rate and review this episode. Stay in touch with us, follow foundr on your favorite platform: Foundr.com Instagram YouTube Facebook X LinkedIn Magazine

Drew Houston has now been CEO of Dropbox for over 17 years. In my latest conversation, he opens up about the pivotal leadership lessons he's learned, the mistakes that shaped the company, and the true challenges of going head-to-head with big tech. We also dive into the highs and lows of fundraising, how valuations can make or break a company trajectory, and discuss the opportunities AI presents for the future of work. [0:00] Intro[0:44] AI Opportunities for Dropbox[5:57] Dropbox's AI Principles[7:52] Introducing Dropbox Dash[9:18] AI Enhancements in Search[12:33] Personal Productivity with AI[17:10] Memo-First Culture at Dropbox[22:15] Reflections on Leadership and Growth[31:21] Early Challenges and Viral Growth[48:22] Facing Fierce Competition[51:51] Strategic Shift to Collaboration[53:20] Navigating Internal Challenges[55:18] Reevaluating Productivity[1:03:07] The Birth of Dash[1:16:32] You Don't Want to Feel Like a Victim[1:30:29] Closing Thoughts and Lessons Learned  Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Sam Dewees and JR Bohannon Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

CEOs of publicly traded companies are often in the news talking about their new AI initiatives, but few of them have built anything with it. Drew Houston from Dropbox is different; he has spent over 400 hours coding with LLMs in the last year and is now refocusing his 2,500+ employees around this new way of working, 17 years after founding the company.Timestamps00:00 Introductions00:43 Drew's AI journey04:14 Revalidating expectations of AI08:23 Simulation in self-driving vs. knowledge work12:14 Drew's AI Engineering setup15:24 RAG vs. long context in AI models18:06 From "FileGPT" to Dropbox AI23:20 Is storage solved?26:30 Products vs Features30:48 Building trust for data access33:42 Dropbox Dash and universal search38:05 The evolution of Dropbox42:39 Building a "silicon brain" for knowledge work48:45 Open source AI and its impact51:30 "Rent, Don't Buy" for AI54:50 Staying relevant58:57 Founder Mode01:03:10 Advice for founders navigating AI01:07:36 Building and managing teams in a growing companyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and there's no Swyx today, but I'm joined by Drew Houston of Dropbox. Welcome, Drew.Drew [00:00:14]: Thanks for having me.Alessio [00:00:15]: So we're not going to talk about the Dropbox story. We're not going to talk about the Chinatown bus and the flash drive and all that. I think you've talked enough about it. Where I want to start is you as an AI engineer. So as you know, most of our audience is engineering folks, kind of like technology leaders. You obviously run Dropbox, which is a huge company, but you also do a lot of coding. I think that's how you spend almost 400 hours, just like coding. So let's start there. What was the first interaction you had with an LLM API and when did the journey start for you?Drew [00:00:43]: Yeah. Well, I think probably all AI engineers or whatever you call an AI engineer, those people started out as engineers before that. So engineering is my first love. I mean, I grew up as a little kid. I was that kid. My first line of code was at five years old. I just really loved, I wanted to make computer games, like this whole path. That also led me into startups and eventually starting Dropbox. And then with AI specifically, I studied computer science, I got my, I did my undergrad, but I didn't do like grad level computer science. I didn't, I sort of got distracted by all the startup things, so I didn't do grad level work. But about several years ago, I made a couple of things. So one is I sort of, I knew I wanted to go from being an engineer to a founder. And then, but sort of the becoming a CEO part was sort of backed into the job. And so a couple of realizations. One is that, I mean, there's a lot of like repetitive and like manual work you have to do as an executive that is actually lends itself pretty well to automation, both for like my own convenience. And then out of interest in learning, I guess what we call like classical machine learning these days, I started really trying to wrap my head around understanding machine learning and informational retrieval more, more formally. So I'd say maybe 2016, 2017 started me writing these more successively, more elaborate scripts to like understand basic like classifiers and regression and, and again, like basic information retrieval and NLP back in those days. And there's sort of like two things that came out of that. One is techniques are super powerful. And even just like studying like old school machine learning was a pretty big inversion of the way I had learned engineering, right? You know, I started programming when everyone starts programming and you're, you're sort of the human, you're giving an algorithm to the, and spelling out to the computer how it should run it. And then machine learning, here's machine learning where it's like actually flip that, like give it sort of the answer you want and it'll figure out the algorithm, which was pretty mind bending. And it was both like pretty powerful when I would write tools, like figure out like time audits or like, where's my time going? Is this meeting a one-on-one or is it a recruiting thing or is it a product strategy thing? I started out doing that manually with my assistant, but then found that this was like a very like automatable task. And so, which also had the side effect of teaching me a lot about machine learning. But then there was this big problem, like anytime you, it was very good at like tabular structured data, but like anytime it hit, you know, the usual malformed English that humans speak, it would just like fall over. I had to kind of abandon a lot of the things that I wanted to build because like there's no way to like parse text. Like maybe it would sort of identify the part of speech in a sentence or something. But then fast forward to the LLM, I mean actually I started trying some of like this, what we would call like very small LLMs before kind of the GPT class models. And it was like super hard to get those things working. So like these 500 parameter models would just be like hallucinating and repeating and you know. So actually I'd kind of like written it off a little bit. But then the chat GPT launch and GPT-3 for sure. And then once people figured out like prompting and instruction tuning, this was sort of like November-ish 2022 like everybody else sort of that the chat GPT launch being the starting gun for the whole AI era of computing and then having API access to three and then early access to GPT-4. I was like, oh man, it's happening. And so I was literally on my honeymoon and we're like on a beach in Thailand and I'm like coding these like AI tools to automate like writing or to assist with writing and all these different use cases.Alessio [00:04:14]: You're like, I'm never going back to work. I'm going to automate all of it before I get back.Drew [00:04:17]: And I was just, you know, ever since then, I mean, I've always been like coding like prototypes and just stuff to make my life more convenient, but like escalated a lot after 22. And yeah, I spent, I checked, I think it was probably like over 400 hours this year so far coding because I had my paternity leave where I was able to work on some special projects. But yeah, it's a super important part of like my whole learning journey is like being really hands-on with these things. And I mean, it's probably not a typical recipe, but I really love to get down to the metal as far as how this stuff works.Alessio [00:04:47]: Yeah. So Swyx and I were with Sam Altman in October 22. We were like at a hack day at OpenAI and that's why we started this podcast eventually. But you did an interview with Sam like seven years ago and he asked you what's the biggest opportunity in startups and you were like machine learning and AI and you were almost like too early, right? It's like maybe seven years ago, the models weren't quite there. How should people think about revalidating like expectations of this technology? You know, I think even today people will tell you, oh, models are not really good at X because they were not good 12 months ago, but they're good today.Drew [00:05:19]: What's your project? Heuristics for thinking about that or how is, yeah, I think the way I look at it now is pretty, has evolved a lot since when I started. I mean, I think everybody intuitively starts with like, all right, let's try to predict the future or imagine like what's this great end state we're going to get to. And the tricky thing is like often those prognostications are right, but they're right in terms of direction, but not when. For example, you know, even in the early days of the internet, 90s when things were even like tech space and you know, even before like the browser or things like that, people were like, oh man, you're going to have, you know, you're going to be able to order food, get like a Snickers delivered to your house, you're going to be able to watch any movie ever created. And they were right. But they were like, you know, it took 20 years for that to actually happen. And before you got to DoorDash, you had to get, you started with like Webvan and Cosmo and before you get to Spotify, you had to do like Napster and Kazaa and LimeWire and like a bunch of like broken Britney Spears MP3s and malware. So I think the big lesson is being early is the same as being wrong. Being late is the same as being wrong. So really how do you calibrate timing? And then I think with AI, it's the same thing that people are like, oh, it's going to completely upend society and all these positive and negative ways. I think that's like most of those things are going to come true. The question is like, when is that going to happen? And then with AI specifically, I think there's also, in addition to sort of the general tech category or like jumping too fast to the future, I think that AI is particularly susceptible to that. And you look at self-driving, right? This idea of like, oh my God, you can have a self-driving car captured everybody's imaginations 10, 12 years ago. And you know, people are like, oh man, in two years, there's not going to be another year. There's not going to be a human driver on the road to be seen. It didn't work out that way, right? We're still 10, 12 years later where we're in a world where you can sort of sometimes get a Waymo in like one city on earth. Exciting, but just took a lot longer than people think. And the reason is there's a lot of engineering challenges, but then there's a lot of other like societal time constants that are hard to compress. So one thing I think you can learn from things like self-driving is they have these levels of autonomy that's a useful kind of framework in driving or these like maturity levels. People sort of skip to like level five, full autonomy, or we're going to have like an autonomous knowledge worker that's just going to take, that's going to, and then we won't need humans anymore kind of projection that that's going to take a long time. But then when you think about level one or level two, like these little assistive experiences, you know, we're seeing a lot of traction with those. So what you see really working is the level one autonomy in the AI world would be like the tab auto-complete and co-pilot, right? And then, you know, maybe a little higher is like the chatbot type interface. Obviously you want to get to the highest level you can to build a good product, but the reliability just isn't, and the capability just isn't there in the early innings. And so, and then you think of other level one, level two type things, like Google Maps probably did more for self-driving than in literal self-driving, like a billion people have like the ability to have like maps and navigation just like taken care of for you autonomously. So I think the timing and maturity are really important factors to include.Alessio [00:08:23]: The thing with self-driving, maybe one of the big breakthroughs was like simulation. So it's like, okay, instead of driving, we can simulate these environments. It's really hard to do when knowledge work, you know, how do you simulate like a product review? How do you simulate these things? I'm curious if you've done any experiments. I know some companies have started to build kind of like a virtual personas that you can like bounce ideas off of.Drew [00:08:42]: I mean, fortunately in a company you generate lots of, you know, actual human training data all the time. And then I also just like start with myself, like, all right, I can, you know, it's pretty tricky even within your company to be like, all right, let's open all this up as quote training data. But, you know, I can start with my own emails or my own calendar or own stuff without running into the same kind of like privacy or other concerns. So I often like start with my own stuff. And so that is like a one level of bootstrapping, but actually four or five years ago during COVID, we decided, you know, a lot of companies were thinking about how do we go back to work? And so we decided to really lean into remote and distributed work because I thought, you know, this is going to be the biggest change to the way we work in our lifetimes. And COVID kind of ripped up a bunch of things, but I think everybody was sort of pleasantly surprised how with a lot of knowledge work, you could just keep going. And actually you were sort of fine. Work was decoupled from your physical environment, from being in a physical place, which meant that things people had dreamed about since the fifties or sixties, like telework, like you actually could work from anywhere. And that was now possible. So we decided to really lean into that because we debated, should we sort of hit the fast forward button or should we hit the rewind button and go back to 2019? And obviously that's been playing out over the last few years. And we decided to basically turn, we went like 90% remote. We still, the in-person part's really important. We can kind of come back to our working model, but we're like, yeah, this is, everybody is going to be in some kind of like distributed or hybrid state. So like instead of like running away from this, like let's do a full send, let's really go into it. Let's live in the future. A few years before our customers, let's like turn Dropbox into a lab for distributed work. And we do that like quite literally, both of the working model and then increasingly with our products. And then absolutely, like we have products like Dropbox Dash, which is our universal search product. That was like very elevated in priority for me after COVID because like now you have, we're putting a lot more stress on the system and on our screens, it's a lot more chaotic and overwhelming. And so even just like getting the right information, the right person at the right time is a big fundamental challenge in knowledge work and these, in the distributed world, like big problem today is still getting, you know, has been getting bigger. And then for a lot of these other workflows, yeah, there's, we can both get a lot of natural like training data from just our own like strategy docs and processes. There's obviously a lot you can do with synthetic data and you know, actually like LMs are pretty good at being like imitating generic knowledge workers. So it's, it's kind of funny that way, but yeah, the way I look at it is like really turn Dropbox into a lab for distributed work. You think about things like what are the big problems we're going to have? It's just the complexity on our screens just keeps growing and the whole environment gets kind of more out of sync with what makes us like cognitively productive and engaged. And then even something like Dash was initially seeded, I made a little personal search engine because I was just like personally frustrated with not being able to find my stuff. And along that whole learning journey with AI, like the vector search or semantic search, things like that had just been the tooling for that. The open source stuff had finally gotten to a place where it was a pretty good developer experience. And so, you know, in a few days I had sort of a hello world type search engine and I'm like, oh my God, like this completely works. You don't even have to get the keywords right. The relevance and ranking is super good. We even like untuned. So I guess that's to say like I've been surprised by if you choose like the right algorithm and the right approach, you can actually get like super good results without having like a ton of data. And even with LLMs, you can apply all these other techniques to give them, kind of bootstrap kind of like task maturity pretty quickly.Alessio [00:12:14]: Before we jump into Dash, let's talk about the Drew Haas and AI engineering stuff. So IDE, let's break that down. What IDE do you use? Do you use Cursor, VS Code, do you use any coding assistant, like WeChat, is it just autocomplete?Drew [00:12:28]: Yeah, yeah. Both. So I use VS Code as like my daily driver, although I'm like super excited about things like Cursor or the AI agents. I have my own like stack underneath that. I mean, some off the shelf parts, some pretty custom. So I use the continue.dev just like AI chat UI basically as just the UI layer, but I also proxy the request. I proxy the request to my own backend, which is sort of like a router. You can use any backend. I mean, Sonnet 3.5 is probably the best all around. But then these things are like pretty limited if you don't give them the right context. And so part of what the proxy does is like there's a separate thing where I can say like include all these files by default with the request. And then it becomes a lot easier and like without like cutting and pasting. And I'm building mostly like prototype toy apps, so it's like a front end React thing and a Python backend thing. And so it can do these like end to end diffs basically. And then I also like love being able to host everything locally or do it offline. So I have my own, when I'm on a plane or something or where like you don't have access or the internet's not reliable, I actually bring a gaming laptop on the plane with me. It's like a little like blue briefcase looking thing. And then I like literally hook up a GPU like into one of the outlets. And then I have, I can do like transcription, I can do like autocomplete, like I have an 8 billion, like Llama will run fine.Alessio [00:13:44]: And you're using like a Llama to run the model?Drew [00:13:47]: No, I use, I have my own like LLM inference stack. I mean, it uses the backend somewhat interchangeable. So everything from like XLlama to VLLM or SGLang, there's a bunch of these different backends you can use. And then I started like working on stuff before all this tooling was like really available. So you know, over the last several years, I've built like my own like whole crazy environment and like in stack here. So I'm a little nuts about it.Alessio [00:14:12]: Yeah. What's the state of the art for, I guess not state of the art, but like when it comes to like frameworks and things like that, do you like using them? I think maybe a lot of people say, hey, things change so quickly, they're like trying to abstract things. Yeah.Drew [00:14:24]: It's maybe too early today. As much as I do a lot of coding, I have to be pretty surgical with my time. I don't have that much time, which means I have to sort of like scope my innovation to like very specific places or like my time. So for the front end, it'll be like a pretty vanilla stack, like a Next.js, React based thing. And then these are toy apps. So it's like Python, Flask, SQLite, and then all the different, there's a whole other thing on like the backend. Like how do you get, sort of run all these models locally or with a local GPU? The scaffolding on the front end is pretty straightforward, the scaffolding on the backend is pretty straightforward. Then a lot of it is just like the LLM inference and control over like fine grained aspects of how you do generation, caching, things like that. And then there's a lot, like a lot of the work is how do you take, sort of go to an IMAP, like take an email, get a new, or a document or a spreadsheet or any of these kinds of primitives that you work with and then translate them, render them in a format that an LLM can understand. So there's like a lot of work that goes into that too. Yeah.Alessio [00:15:24]: So I built a kind of like email triage system and like I would say 80% of the code is like Google and like pulling emails and then the actual AI part is pretty easy.Drew [00:15:34]: Yeah. And even, same experience. And then I tried to do all these like NLP things and then to my dismay, like a bunch of reg Xs were like, got you like 95% of the way there. So I still leave it running, I just haven't really built like the LLM powered version of it yet. Yeah.Alessio [00:15:51]: So do you have any thoughts on rag versus long context, especially, I mean with Dropbox, you know? Sure. Do you just want to shove things in? Like have you seen that be a lot better?Drew [00:15:59]: Well, they kind of have different strengths and weaknesses, so you need both for different use cases. I mean, it's been awesome in the last 12 months, like now you have these like long context models that can actually do a lot. You can put a book in, you know, Sonnet's context and then now with the later versions of LLAMA, you can have 128k context. So that's sort of the new normal, which is awesome and that, that wasn't even the case a year ago. That said, models don't always use, and certainly like local models don't use the full context well fully yet, and actually if you provide too much irrelevant context, the quality degrades a lot. And so I say in the open source world, like we're still just getting to the cusp of like the full context is usable. And then of course, like when you're something like Dropbox Dash, like it's basically building this whole like brain that's like read everything your company's ever written. And so that's not going to fit into your context window, so you need rag just as a practical reality. And even for a lot of similar reasons, you need like RAM and hard disk in conventional computer architecture. And I think these things will keep like horse trading, like maybe if, you know, a million or 10 million is the new, tokens is the new context length, maybe that shifts. Maybe the bigger picture is like, it's super exciting to talk about the LLM and like that piece of the puzzle, but there's this whole other scaffolding of more conventional like retrieval or conventional machine learning, especially because you have to scale up products to like millions of people you do in your toy app is not going to scale to that from a cost or latency or performance standpoint. So I think you really need these like hybrid architectures that where you have very like purpose fit tools, or you're probably not using Sonnet 3.5 for all of your normal product use cases. You're going to use like a fine tuned 8 billion model or sort of the minimum model that gets you the right output. And then a smaller model also is like a lot more cost and latency versus like much better characteristics on that front.Alessio [00:17:48]: Yeah. Let's jump into the Dropbox AI story. So sure. Your initial prototype was Files GPT. How did it start? And then how did you communicate that internally? You know, I know you have a pretty strong like mammal culture. One where you're like, okay, Hey, we got to really take this seriously.Drew [00:18:06]: Yeah. Well, on the latter, it was, so how do we say like how we took Dropbox, how AI seriously as a company started kind of around that time, that honeymoon time, unfortunately. In January, I wrote this like memo to the company, like around basically like how we need to play offense in 23. And that most of the time the kind of concrete is set and like the winners are the winners and things are kind of frozen. But then with these new eras of computing, like the PC or the internet or the phone or the concrete on freezes and you can sort of build, do things differently and have a new set of winners. It's sort of like a new season starts as a result of a lot of that sort of personal hacking and just like thinking about this. I'm like, yeah, this is an inflection point in the industry. Like we really need to change how we think about our strategy. And then becoming an AI first company was probably the headline thing that we did. And then, and then that got, and then calling on everybody in the company to really think about in your world, how is AI going to reshape your workflows or what sort of the AI native way of thinking about your job. File GPT, which is sort of this Dropbox AI kind of initial concept that actually came from our engineering team as, you know, as we like called on everybody, like really think about what we should be doing that's new or different. So it was kind of organic and bottoms up like a bunch of engineers just kind of hacked that together. And then that materialized as basically when you preview a file on Dropbox, you can have kind of the most straightforward possible integration of AI, which is a good thing. Like basically you have a long PDF, you want to be able to ask questions of it. So like a pretty basic implementation of RAG and being able to do that when you preview a file on Dropbox. So that was the origin of that, that was like back in 2023 when we released just like the starting engines had just, you know, gotten going.Alessio [00:19:53]: It's funny where you're basically like these files that people have, they really don't want them in a way, you know, like you're storing all these files and like you actually don't want to interact with them. You want a layer on top of it. And that's kind of what also takes you to Dash eventually, which is like, Hey, you actually don't really care where the file is. You just want to be the place that aggregates it. How do you think about what people will know about files? You know, are files the actual file? Are files like the metadata and they're just kind of like a pointer that goes somewhere and you don't really care where it is?Drew [00:20:21]: Yeah.Alessio [00:20:22]: Any thoughts about?Drew [00:20:23]: Totally. Yeah. I mean, there's a lot of potential complexity in that question, right? Is it a, you know, what's the difference between a file and a URL? And you can go into the technicals, it's like pass by value, pass by reference. Okay. What's the format like? All right. So it starts with a primitive. It's not really a flat file. It's like a structured data. You're sort of collaborative. Yeah. That's keeping in sync. Blah, blah, blah. I actually don't start there at all. I just start with like, what do people, like, what do humans, let's work back from like how humans think about this stuff or how they should think about this stuff. Meaning like, I don't think about, Oh, here are my files and here are my links or cloud docs. I'm just sort of like, Oh, here's my stuff. This, this, here's sort of my documents. Here's my media. Here's my projects. Here are the people I'm working with. So it starts from primitives more like those, like how do people, how do humans think about these things? And then, then start from like a more ideal experience. Because if you think about it, we kind of have this situation that will look like particularly medieval in hindsight where, all right, how do you manage your work stuff? Well, on all, you know, on one side of your screen, you have this file browser that literally hasn't changed since the early eighties, right? You could take someone from the original Mac and sit them in front of like a computer and they'd be like, this is it. And that's, it's been 40 years, right? Then on the other side of your screen, you have like Chrome or a browser that has so many tabs open, you can no longer see text or titles. This is the state of the art for how we manage stuff at work. Interestingly, neither of those experiences was purpose-built to be like the home for your work stuff or even anything related to it. And so it's important to remember, we get like stuck in these local maxima pretty often in tech where we're obviously aware that files are not going away, especially in certain domains. So that format really matters and where files are still going to be the tool you use for like if there's something big, right? If you're a big video file, that kind of format in a file makes sense. There's a bunch of industries where it's like construction or architecture or sort of these domain specific areas, you know, media generally, if you're making music or photos or video, that all kind of fits in the big file zone where Dropbox is really strong and that's like what customers love us for. It's also pretty obvious that a lot of stuff that used to be in, you know, Word docs or Excel files, like all that has tilted towards the browser and that tilt is going to continue. So with Dash, we wanted to make something that was really like cloud-native, AI-native and deliberately like not be tied down to the abstractions of the file system. Now on the other hand, it would be like ironic and bad if we then like fractured the experience that you're like, well, if it touches a file, it's a syncing metaphor to this app. And if it's a URL, it's like this completely different interface. So there's a convergence that I think makes sense over time. But you know, but I think you have to start from like, not so much the technology, start from like, what do the humans want? And then like, what's the idealized product experience? And then like, what are the technical underpinnings of that, that can make that good experience?Alessio [00:23:20]: I think it's kind of intuitive that in Dash, you can connect Google Drive, right? Because you think about Dropbox, it's like, well, it's file storage, you really don't want people to store files somewhere, but the reality is that they do. How do you think about the importance of storage and like, do you kind of feel storage is like almost solved, where it's like, hey, you can kind of store these files anywhere, what matters is like access.Drew [00:23:38]: It's a little bit nuanced in that if you're dealing with like large quantities of data, it actually does matter. The implementation matters a lot or like you're dealing with like, you know, 10 gig video files like that, then you sort of inherit all the problems of sync and have to go into a lot of the challenges that we've solved. Switching on a pretty important question, like what is the value we provide? What does Dropbox do? And probably like most people, I would have said like, well, Dropbox syncs your files. And we didn't even really have a mission of the company in the beginning. I'm just like, yeah, I just don't want to carry a thumb driving around and life would be a lot better if our stuff just like lived in the cloud and I just didn't have to think about like, what device is the thing on or what operating, why are these operating systems fighting with each other and incompatible? You know, I just want to abstract all of that away. But then so we thought, even we were like, all right, Dropbox provides storage. But when we talked to our customers, they're like, that's not how we see this at all. Like actually, Dropbox is not just like a hard drive in the cloud. It's like the place where I go to work or it's a place like I started a small business is a place where my dreams come true. Or it's like, yeah, it's not keeping files in sync. It's keeping people in sync. It's keeping my team in sync. And so they're using this kind of language where we're like, wait, okay, yeah, because I don't know, storage probably is a commodity or what we do is a commodity. But then we talked to our customers like, no, we're not buying the storage, we're buying like the ability to access all of our stuff in one place. We're buying the ability to share everything and sort of, in a lot of ways, people are buying the ability to work from anywhere. And Dropbox was kind of, the fact that it was like file syncing was an implementation detail of this higher order need that they had. So I think that's where we start too, which is like, what is the sort of higher order thing, the job the customer is hiring Dropbox to do? Storage in the new world is kind of incidental to that. I mean, it still matters for things like video or those kinds of workflows. The value of Dropbox had never been, we provide you like the cheapest bits in the cloud. But it is a big pivot from Dropbox is the company that syncs your files to now where we're going is Dropbox is the company that kind of helps you organize all your cloud content. I started the company because I kept forgetting my thumb drive. But the question I was really asking was like, why is it so hard to like find my stuff, organize my stuff, share my stuff, keep my stuff safe? You know, I'm always like one washing machine and I would leave like my little thumb drive with all my prior company stuff on in the pocket of my shorts and then almost wash it and destroy it. And so I was like, why do we have to, this is like medieval that we have to think about this. So that same mindset is how I approach where we're going. But I think, and then unfortunately the, we're sort of back to the same problems. Like it's really hard to find my stuff. It's really hard to organize myself. It's hard to share my stuff. It's hard to secure my content at work. Now the problem is the same, the shape of the problem and the shape of the solution is pretty different. You know, instead of a hundred files on your desktop, it's now a hundred tabs in your browser, et cetera. But I think that's the starting point.Alessio [00:26:30]: How has the idea of a product evolved for you? So, you know, famously Steve Jobs started by Dropbox and he's like, you know, this is just a feature. It's not a product. And then you build like a $10 billion feature. How in the age of AI, how do you think about, you know, maybe things that used to be a product are now features because the AI on top of it, it's like the product, like what's your mental model? Do you think about it?Drew [00:26:50]: Yeah. So I don't think there's really like a bright line. I don't know if like I use the word features and products and my mental model that much of how I break it down because it's kind of a, it's a good question. I mean, I don't not think about features, I don't think about products, but it does start from that place of like, all right, we have all these new colors we can paint with and all right, what are these higher order needs that are sort of evergreen, right? So people will always have stuff at work. They're always need to be able to find it or, you know, all the verbs I just mentioned. It's like, okay, how can we make like a better painting and how can we, and then how can we use some of these new colors? And then, yeah, it's like pretty clear that after the large models, the way you find stuff share stuff, it's going to be completely different after COVID, it's going to be completely different. So that's the starting point. But I think it is also important to, you know, you have to do more than just work back from the customer and like what they're trying to do. Like you have to think about, and you know, we've, we've learned a lot of this the hard way sometimes. Okay. You might start with a customer. You might start with a job to be on there. You're like, all right, what's the solution to their problem? Or like, can we build the best product that solves that problem? Right. Like what's the best way to find your stuff in the modern world? Like, well, yeah, right now the status quo for the vast majority of the billion, billion knowledge workers is they have like 10 search boxes at work that each search 10% of your stuff. Like that's clearly broken. Obviously you should just have like one search box. All right. So we can do that. And that also has to be like, I'll come back to defensibility in a second, but like, can we build the right solution that is like meaningfully better from the status quo? Like, yes, clearly. Okay. Then can we like get distribution and growth? Like that's sort of the next thing you learned is as a founder, you start with like, what's the product? What's the product? What's the product? Then you're like, wait, wait, we need distribution and we need a business model. So those are the next kind of two dominoes you have to knock down or sort of needles you have to thread at the same time. So all right, how do we grow? I mean, if Dropbox 1.0 is really this like self-serve viral model that there's a lot of, we sort of took a borrowed from a lot of the consumer internet playbook and like what Facebook and social media were doing and then translated that to sort of the business world. How do you get distribution, especially as a startup? And then a business model, like, all right, storage happened to be something in the beginning happened to be something people were willing to pay for. They recognize that, you know, okay, if I don't buy something like Dropbox, I'm going to have to buy an external hard drive. I'm going to have to buy a thumb drive and I have to pay for something one way or another. People are already paying for things like backup. So we felt good about that. But then the last domino is like defensibility. Okay. So you build this product or you get the business model, but then, you know, what do you do when the incumbents, the next chess move for them is I just like copy, bundle, kill. So they're going to copy your product. They'll bundle it with their platforms and they'll like give it away for free or no added cost. And, you know, we had a lot of, you know, scar tissue from being on the wrong side of that. Now you don't need to solve all four for all four or five variables or whatever at once or you can sort of have, you know, some flexibility. But the more of those gates that you get through, you sort of add a 10 X to your valuation. And so with AI, I think, you know, there's been a lot of focus on the large language model, but it's like large language models are a pretty bad business from a, you know, you sort of take off your tech lens and just sort of business lens. Like there's sort of this weirdly self-commoditizing thing where, you know, models only have value if they're kind of on this like Pareto frontier of size and quality and cost. Being number two, you know, if you're not on that frontier, the second the frontier moves out, which it moves out every week, like your model literally has zero economic value because it's dominated by the new thing. LLMs generate output that can be used to train or improve. So there's weird, peculiar things that are specific to the large language model. And then you have to like be like, all right, where's the value going to accrue in the stack or the value chain? And, you know, certainly at the bottom with Nvidia and the semiconductor companies, and then it's going to be at the top, like the people who have the customer relationship who have the application layer. Those are a few of the like lenses that I look at a question like that through.Alessio [00:30:48]: Do you think AI is making people more careful about sharing the data at all? People are like, oh, data is important, but it's like, whatever, I'm just throwing it out there. Now everybody's like, but are you going to train on my data? And like your data is actually not that good to train on anyway. But like how have you seen, especially customers, like think about what to put in, what to not?Drew [00:31:06]: I mean, everybody should be. Well, everybody is concerned about this and nobody should be concerned about this, right? Because nobody wants their personal companies information to be kind of ground up into little pellets to like sell you ads or train the next foundation model. I think it's like massively top of mind for every one of our customers, like, and me personally, and with my Dropbox hat on, it's like so fundamental. And, you know, we had experience with this too at Dropbox 1.0, the same kind of resistance, like, wait, I'm going to take my stuff on my hard drive and put it on your server somewhere. Are you serious? What could possibly go wrong? And you know, before that, I was like, wait, are you going to sell me, I'm going to put my credit card number into this website? And before that, I was like, hey, I'm going to take all my cash and put it in a bank instead of under my mattress. You know, so there's a long history of like tech and comfort. So in some sense, AI is kind of another round of the same thing, but the issues are real. And then when I think about like defensibility for Dropbox, like that's actually a big advantage that we have is one, our incentives are very aligned with our customers, right? We only get, we only make money if you pay us and you only pay us if we do a good job. So we don't have any like side hustle, you know, we're not training the next foundation model. You know, we're not trying to sell you ads. Actually we're not even trying to lock you into an ecosystem, like the whole point of Dropbox is it works, you know, everywhere. Because I think one of the big questions we've circling around is sort of like, in the world of AI, where should our lane be? Like every startup has to ask, or in every big company has to ask, like, where can we really win? But to me, it was like a lot of the like trust advantages, platform agnostic, having like a very clean business model, not having these other incentives. And then we also are like super transparent. We were transparent early on. We're like, all right, we're going to establish these AI principles, very table stakes stuff of like, here's transparency. We want to give people control. We want to cover privacy, safety, bias, like fairness, all these things. And we put that out up front to put some sort of explicit guardrails out where like, hey, we're, you know, because everybody wants like a trusted partner as they sort of go into the wild world of AI. And then, you know, you also see people cutting corners and, you know, or just there's a lot of uncertainty or, you know, moving the pieces around after the fact, which no one feels good about.Alessio [00:33:14]: I mean, I would say the last 10, 15 years, the race was kind of being the system of record, being the storage provider. I think today it's almost like, hey, if I can use Dash to like access my Google Drive file, why would I pay Google for like their AI feature? So like vice versa, you know, if I can connect my Dropbook storage to this other AI assistant, how do you kind of think about that, about, you know, not being able to capture all the value and how open people will stay? I think today things are still pretty open, but I'm curious if you think things will get more closed or like more open later.Drew [00:33:42]: Yeah. Well, I think you have to get the value exchange right. And I think you have to be like a trustworthy partner or like no one's going to partner with you if they think you're going to eat their lunch, right? Or if you're going to disintermediate them and like all the companies are quite sophisticated with how they think about that. So we try to, like, we know that's going to be the reality. So we're actually not trying to eat anyone's like Google Drive's lunch or anything. Actually we'll like integrate with Google Drive, we'll integrate with OneDrive, really any of the content platforms, even if they compete with file syncing. So that's actually a big strategic shift. We're not really reliant on being like the store of record and there are pros and cons to this decision. But if you think about it, we're basically like providing all these apps more engagement. We're like helping users do what they're really trying to do, which is to get, you know, that Google Doc or whatever. And we're not trying to be like, oh, by the way, use this other thing. This is all part of our like brand reputation. It's like, no, we give people freedom to use whatever tools or operating system they want. We're not taking anything away from our partners. We're actually like making it, making their thing more useful or routing people to those things. I mean, on the margin, then we have something like, well, okay, to the extent you do rag and summarize things, maybe that doesn't generate a click. Okay. You know, we also know there's like infinity investment going into like the work agents. So we're not really building like a co-pilot or Gemini competitor. Not because we don't like those. We don't find that thing like captivating. Yeah, of course. But just like, you know, you learn after some time in this business that like, yeah, there's some places that are just going to be such kind of red oceans or just like super big battlefields. Everybody's kind of trying to solve the same problem and they just start duplicating all each other effort. And then meanwhile, you know, I think the concern would be is like, well, there's all these other problems that aren't being properly addressed by AI. And I was concerned that like, yeah, and everybody's like fixated on the agent or the chatbot interface, but forgetting that like, hey guys, like we have the opportunity to like really fix search or build a self-organizing Dropbox or environment or there's all these other things that can be a compliment. Because we don't really want our customers to be thinking like, well, do I use Dash or do I use co-pilot? And frankly, none of them do. In a lot of ways, actually, some of the things that we do on the security front with Dash for Business are a good compliment to co-pilot. Because as part of Dash for Business, we actually give admins, IT, like universal visibility and control over all the different, what's being shared in your company across all these different platforms. And as a precondition to installing something like co-pilot or Dash or Glean or any of these other things, right? You know, IT wants to know like, hey, before we like turn all the lights in here, like let's do a little cleaning first before we let everybody in. And there just haven't been good tools to do that. And post AI, you would do it completely differently. And so that's like a big, that's a cornerstone of what we do and what sets us apart from these tools. And actually, in a lot of cases, we will help those tools be adopted because we actually help them do it safely. Yeah.Alessio [00:36:27]: How do you think about building for AI versus people? It's like when you mentioned cleaning up is because maybe before you were like, well, humans can have some common sense when they look at data on what to pick versus models are just kind of like ingesting. Do you think about building products differently, knowing that a lot of the data will actually be consumed by LLMs and like agents and whatnot versus like just people?Drew [00:36:46]: I think it'll always be, I aim a little bit more for like, you know, level three, level four kind of automation, because even if the LLM is like capable of completely autonomously organizing your environment, it probably would do a reasonable job. But like, I think you build bad UI when the sort of user has to fit itself to the computer versus something that you're, you know, it's like an instrument you're playing or something where you have some kind of good partnership. And you know, and on the other side, you don't have to do all this like manual effort. And so like the command line was sort of subsumed by like, you know, graphical UI. We'll keep toggling back and forth. Maybe chat will be, chat will be an increasing, especially when you bring in voice, like will be an increasing part of the puzzle. But I don't think we're going to go back to like a million command lines either. And then as far as like the sort of plumbing of like, well, is this going to be consumed by an LLM or a human? Like fortunately, like you don't really have to design it that differently. I mean, you have to make sure everything's legible to the LLM, but it's like quite tolerant of, you know, malformed everything. And actually the more, the easier it makes something to read for a human, the easier it is for an LLM to read to some extent as well. But we really think about what's that kind of right, how do we build that right, like human machine interface where you're still in control and driving, but then it's super easy to translate your intent into like the, you know, however you want your folder, setting your environment set up or like your preferences.Alessio [00:38:05]: What's the most underrated thing about Dropbox that maybe people don't appreciate?Drew [00:38:09]: Well, I think this is just such a natural evolution for us. It's pretty true. Like when people think about the world of AI, file syncing is not like the next thing you would auto complete mentally. And I think we also did like our first thing so well that there were a lot of benefits to that. But I think there also are like, we hit it so hard with our first product that it was like pretty tough to come up with a sequel. And we had a bit of a sophomore slump and you know, I think actually a lot of kids do use Dropbox through in high school or things like that, but you know, they're not, they're using, they're a lot more in the browser and then their file system, right. And we know all this, but still like we're super well positioned to like help a new generation of people with these fundamental problems and these like that affect, you know, a billion knowledge workers around just finding, organizing, sharing your stuff and keeping it safe. And there's, there's a ton of unsolved problems in those four verbs. We've talked about search a little bit, but just even think about like a whole new generation of people like growing up without the ability to like organize their things and yeah, search is great. And if you just have like a giant infinite pile of stuff, then search does make that more manageable. But you know, you do lose some things that were pretty helpful in prior decades, right? So even just the idea of persistence, stuff still being there when you come back, like when I go to sleep and wake up, my physical papers are still on my desk. When I reboot my computer, the files are still on my hard drive. But then when in my browser, like if my operating system updates the wrong way and closes the browser or if I just more commonly just declared tab bankruptcy, it's like your whole workspace just clears itself out and starts from zero. And you're like, on what planet is this a good idea? There's no like concept of like, oh, here's the stuff I was working on. Yeah, let me get back to it. And so that's like a big motivation for things like Dash. Huge problems with sharing, right? If I'm remodeling my house or if I'm getting ready for a board meeting, you know, what do I do if I have a Google doc and an air table and a 10 gig 4k video? There's no collection that holds mixed format things. And so it's another kind of hidden problem, hidden in plain sight, like he's missing primitives. Files have folders, songs have playlists, links have, you know, there's no, somehow we miss that. And so we're building that with stacks in Dash where it's like a mixed format, smart collection that you can then, you know, just share whatever you need internally, externally and have it be like a really well designed experience and platform agnostic and not tying you to any one ecosystem. We're super excited about that. You know, we talked a little bit about security in the modern world, like IT signs all these compliance documents, but in reality has no way of knowing where anything is or what's being shared. It's actually better for them to not know about it than to know about it and not be able to do anything about it. And when we talked to customers, we found that there were like literally people in IT whose jobs it is to like manually go through, log into each, like log into office, log into workspace, log into each tool and like go comb through one by one the links that people have shared and like unshares. There's like an unshare guy in all these companies and that that job is probably about as fun as it sounds like, my God. So there's, you know, fortunately, I guess what makes technology a good business is for every problem it solves, it like creates a new one, so there's always like a sequel that you need. And so, you know, I think the happy version of our Act 2 is kind of similar to Netflix. I look at a lot of these companies that really had multiple acts and Netflix had the vision to be streaming from the beginning, but broadband and everything wasn't ready for it. So they started by mailing you DVDs, but then went to streaming and then, but the value probably the whole time was just like, let me press play on something I want to see. And they did a really good job about bringing people along from the DVD mailing off. You would think like, oh, the DVD mailing piece is like this burning platform or it's like legacy, you know, ankle weight. And they did have some false starts in that transition. But when you really think about it, they were able to take that DVD mailing audience, move, like migrate them to streaming and actually bootstrap a, you know, take their season one people and bootstrap a victory in season two, because they already had, you know, they weren't starting from scratch. And like both of those worlds were like super easy to sort of forget and be like, oh, it's all kind of destiny. But like, no, that was like an incredibly competitive environment. And Netflix did a great job of like activating their Act 1 advantages and winning in Act 2 because of it. So I don't think people see Dropbox that way. I think people are sort of thinking about us just in terms of our Act 1 and they're like, yeah, Dropbox is fine. I used to use it 10 years ago. But like, what have they done for me lately? And I don't blame them. So fortunately, we have like better and better answers to that question every year.Alessio [00:42:39]: And you call it like the silicon brain. So you see like Dash and Stacks being like the silicon brain interface, basically forDrew [00:42:46]: people. I mean, that's part of it. Yeah. And writ large, I mean, I think what's so exciting about AI and everybody's got their own kind of take on it, but if you like really zoom out civilizationally and like what allows humans to make progress and, you know, what sort of is above the fold in terms of what's really mattered. I certainly want to, I mean, there are a lot of points, but some that come to mind like you think about things like the industrial revolution, like before that, like mechanical energy, like the only way you could get it was like by your own hands, maybe an animal, maybe some like clever sort of machines or machines made of like wood or something. But you were quite like energy limited. And then suddenly, you know, the industrial revolution, things like electricity, it suddenly is like, all right, mechanical energy is now available on demand as a very fungible kind of, and then suddenly we consume a lot more of it. And then the standard of living goes way, way, way, way up. That's been pretty limited to the physical realm. And then I believe that the large models, that's really the first time we can kind of bottle up cognitive energy and offloaded, you know, if we started by offloading a lot of our mechanical or physical busy work to machines that freed us up to make a lot of progress in other areas. But then with AI and computing, we're like, now we can offload a lot more of our cognitive busy work to machines. And then we can create a lot more of it. Price of it goes way down. Importantly, like, it's not like humans never did anything physical again. It's sort of like, no, but we're more leveraged. We can move a lot more earth with a bulldozer than a shovel. And so that's like what is at the most fundamental level, what's so exciting to me about AI. And so what's the silicon brain? It's like, well, we have our human brains and then we're going to have this other like half of our brain that's sort of coming online, like our silicon brain. And it's not like one or the other. They complement each other. They have very complimentary strengths and weaknesses. And that's, that's a good thing. There's also this weird tangent we've gone on as a species to like where knowledge work, knowledge workers have this like epidemic of, of burnout, great resignation, quiet quitting. And there's a lot going on there. But I think that's one of the biggest problems we have is that be like, people deserve like meaningful work and, you know, can't solve all of it. But like, and at least in knowledge work, there's a lot of own goals, you know, enforced errors that we're doing where it's like, you know, on one side with brain science, like we know what makes us like productive and fortunately it's also what makes us engaged. It's like when we can focus or when we're some kind of flow state, but then we go to work and then increasingly going to work is like going to a screen and you're like, if you wanted to design an environment that made it impossible to ever get into a flow state or ever be able to focus, like what we have is that. And that was the thing that just like seven, eight years ago just blew my mind. I'm just like, I cannot understand why like knowledge work is so jacked up on this adventure. It's like, we, we put ourselves in like the most cognitively polluted environment possible and we put so much more stress on the system when we're working remotely and things like that. And you know, all of these problems are just like going in the wrong direction. And I just, I just couldn't understand why this was like a problem that wasn't fixing itself. And I'm like, maybe there's something Dropbox can do with this and you know, things like Dash are the first step. But then, well, so like what, well, I mean, now like, well, why are humans in this like polluted state? It's like, well, we're just, all of the tools we have today, like this generation of tools just passes on all of the weight, the burden to the human, right? So it's like, here's a bajillion, you know, 80,000 unread emails, cool. Here's 25 unread Slack channels. Here's, we all get started like, it's like jittery like thinking about it. And then you look at that, you're like, wait, I'm looking at my phone, it says like 80,000 unread things. There's like no question, product question for which this is the right answer. Fortunately, that's why things like our silicon brain are pretty helpful because like they can serve as like an attention filter where it's like, actually, computers have no problem reading a million things. Humans can't do that, but computers can. And to some extent, this was already happening with computer, you know, Excel is an aversion of your silicon brain or, you know, you could draw the line arbitrarily. But with larger models, like now so many of these little subtasks and tasks we do at work can be like fully automated. And I think, you know, I think it's like an important metaphor to me because it mirrors a lot of what we saw with computing, computer architecture generally. It's like we started out with the CPU, very general purpose, then GPU came along much better at these like parallel computations. We talk a lot about like human versus machine being like substituting, it's like CPU, GPU, it's not like one is categorically better than the other, they're complements. Like if you have something really parallel, use a GPU, if not, use a CPU. The whole relationship, that symbiosis between CPU and GPU has obviously evolved a lot since, you know, playing Quake 2 or something. But right now we have like the human CPU doing a lot of, you know, silicon CPU tasks. And so you really have to like redesign the work thoughtfully such that, you know, probably not that different from how it's evolved in computer architecture, where the CPU is sort of an orchestrator of these really like heavy lifting GPU tasks. That dividing line does shift a little bit, you know, with every generation. And so I think we need to think about knowledge work in that context, like what are human brains good at? What's our silicon brain good at? Let's resegment the work. Let's offload all the stuff that can be automated. Let's go on a hunt for like anything that could save a human CPU cycle. Let's give it to the silicon one. And so I think we're at the early earnings of actually being able to do something about it.Alessio [00:48:00]: It's funny, I gave a talk to a few government people earlier this year with a similar point where we used to make machines to release human labor. And then the kilowatt hour was kind of like the unit for a lot of countries. And now you're doing the same thing with the brain and the data centers are kind of computational power plants, you know, they're kind of on demand tokens. You're on the board of Meta, which is the number one donor of Flops for the open source world. The thing about open source AI is like the model can be open source, but you need to carry a briefcase to actually maybe run a model that is not even that good compared to some of the big ones. How do you think about some of the differences in the open source ethos with like traditional software where it's like really easy to run and act on it versus like models where it's like it might be open source, but like I'm kind of limited, sort of can do with it?Drew [00:48:45]: Yeah, well, I think with every new era of computing, there's sort of a tug of war between is this going to be like an open one or a closed one? And, you know, there's pros and cons to both. It's not like open is always better or open always wins. But, you know, I think you look at how the mobile, like the PC era and the Internet era started out being more on the open side, like it's very modular. Everybody sort of party that everybody could, you know, come to some downsides of that security. But I think, you know, the advent of AI, I think there's a real question, like given the capital intensity of what it takes to train these foundation models, like are we going to live in a world where oligopoly or cartel or all, you know, there's a few companies that have the keys and we're all just like paying them rent. You know, that's one future. Or is it going to be more open and accessible? And I'm like super happy with how that's just I find it exciting on many levels with all the different hats I wear about it. You know, fortunately, you've seen in real life, yeah, even if people aren't bringing GPUs on a plane or something, you've seen like the price performance of these models improve 10 or 100x year over year, which is sort of like many Moore's laws compounded together for a bunch of reasons like that wouldn't have happened without open source. Right. You know, for a lot of same reasons, it's probably better that we can anyone can sort of spin up a website without having to buy an internet information server license like there was some alternative future. So like things are Linux and really good. And there was a good balance of trade to where like people contribute their code and then also benefit from the community returning the favor. I mean, you're seeing that with open source. So you wouldn't see all this like, you know, this flourishing of research and of just sort of the democratization of access to compute without open source. And so I think it's been like phenomenally successful in terms of just moving the ball forward and pretty much anything you care about, I believe, even like safety. You can have a lot more eyes on it and transparency instead of just something is happening. And there was three places with nuclear power plants attached to them. Right. So I think it's it's been awesome to see. And then and again, for like wearing my Dropbox hat, like anybody who's like scaling a service to millions of people, again, I'm probably not using like frontier models for every request. It's, you know, there are a lot of different configurations, mostly with smaller models. And even before you even talk about getting on the device, like, you know, you need this whole kind of constellation of different options. So open source has been great for that.Alessio [00:51:06]: And you were one of the first companies in the cloud repatriation. You kind of brought back all the storage into your own data centers. Where are we in the AI wave for that? I don't think people really care today to bring the models in-house. Like, do you think people will care in the future? Like, especially as you have more small models that you want to control more of the economics? Or are the tokens so subsidized that like it just doesn't matter? It's more like a principle. Yeah. Yeah.Drew [00:51:30]: I mean, I think there's another one where like thinking about the future is a lot easier if you start with the past. So, I mean, there's definitely this like big surge in demand as like there's sort of this FOMO driven bubble of like all of big tech taking their headings and shipping them to Jensen for a couple of years. And then you're like, all right, well, first of all, we've seen this kind of thing before. And in the late 90s with like Fiber, you know, this huge race to like own the internet, own the information superhighway, literally, and then way overbuilt. And then there was this like crash. I don't know to what extent, like maybe it is really different this time. Or, you know, maybe if we create AGI that will sort of solve the rest of the, or we'll just have a different set of things to worry about. But, you know, the simplest way I think about it is like this is sort of a rent not buy phase because, you know, I wouldn't want to be, we're still so early in the maturity, you know, I wouldn't want to be buying like pallets of over like of 286s at a 5x markup when like the 386 and 486 and Pentium and everything are like clearly coming there around the corner. And again, because of open source, there's just been a lot more com

For our seventh episode of Working Smarter we're talking to Drew Houston, the co-founder and CEO of Dropbox. If you've been online long enough, it's likely Dropbox was your introduction to the cloud. The goal is still more or less the same—give you one organized place for all your stuff—but it's no longer just about storing and syncing files. A hundred files on your desktop is now a hundred tabs in your browser, and Houston believes AI is what will finally bring calm to the chaos that's been created by the tools of modern work.For Houston, AI's potential is so great that its arrival feels like a civilization shift. It's also not just a professional preoccupation; AI is a personal interest too. A few years ago he decided to teach himself machine learning in his spare time—and some of the AI tools Houston now uses to run Dropbox are ones he built himself. Hear Houston discuss why it's gotten so hard to find the information you need to do your job, the types of tasks we'll increasingly offload to our silicon brains, and what Dropbox is doing to help make modern work more meaningful and fulfilling.Show notes:To learn more about Dropbox Dash and try Dash for free, visit dropbox.com/dashThe two books Houston mentions are “High Output Management” by Andy Grove and “The Effective Executive” by Peter Drucker~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Mike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Jessica Livingston is a co-founder of Y Combinator, the first and most successful startup accelerator. Y Combinator has funded over 5,000 companies, 200 of which are now unicorns, including Airbnb, Dropbox, DoorDash, Stripe, Coinbase, and Reddit. Jessica played a crucial role in YC's early success, when she was nicknamed the “social radar” because of her uncanny ability to quickly evaluate people—an essential skill when investing in early-stage startups. She's also the host of the popular podcast The Social Radars, where she interviews billion-dollar-startup founders, and the author of the acclaimed book Founders at Work, which captures the origin stories of some of today's most interesting companies. In our conversation, we discuss:• How Jessica gained the affectionate title of the “social radar”• Why defensive founders are a red flag• How to develop your social radar• What she looks for in founders during YC interviews• How YC's early inexperience in angel investing led to the batch model• Her favorite stories from interviews with Airbnb, Rippling, and more• Lessons learned from hosting her own podcast• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston—Where to find Jessica Livingston:• X: https://x.com/jesslivingston• LinkedIn: https://www.linkedin.com/in/jessicalivingston1/• Podcast: https://www.thesocialradars.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Jessica's background(02:42) Thoughts on being under-recognized(07:52) Jessica's superpower: the social radar(15:11) Evaluating founders: key traits and red flags(21:00) The Airbnb story: a lesson in hustle and determination(25:57) A YC success story(28:26) The importance of earnestness(32:45) Confidence vs. defensiveness(34:43) Commitment and co-founder disputes(37:46) Relentless resourcefulness(40:00) Jessica's social radar: origins and insights(43:24) Honing her social radar skills(45:44) Conviction and scams: a Y Combinator story(46:50) The interview process: challenges and insights(48:20) Operationalizing founder evaluation(49:38) Advice for building social radar skills(52:08) The “Reading the Mind in the Eyes” quiz(55:19) Jessica's podcast: The Social Radars(01:00:34) Lessons from podcasting and interviewing(01:09:58) Lightning round—Referenced:• Paul Graham's post about Jessica: https://paulgraham.com/jessica.html• Paul Graham on X: https://x.com/paulg• Robert Tappan Morris: https://en.wikipedia.org/wiki/Robert_Tappan_Morris• Trevor Blackwell on X: https://x.com/tlbtlbtlb• Y Combinator: https://www.ycombinator.com/• “The Founders” examines the rise and legend of PayPal: https://www.economist.com/culture/2022/02/19/the-founders-examines-the-rise-and-legend-of-paypal• Patrick Collison on X: https://x.com/patrickc• John Collison on X: https://x.com/collision• Brian Chesky on LinkedIn: https://www.linkedin.com/in/brianchesky/• Nate Blecharczyk on LinkedIn: https://www.linkedin.com/in/blecharczyk/• Joe Gebbia on LinkedIn: https://www.linkedin.com/in/jgebbia/• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Parker Conrad on LinkedIn: https://www.linkedin.com/in/parkerconrad/• Zenefits: https://connect.trinet.com/hr-platform• Goat: https://www.goat.com/• Eddy Lu on LinkedIn: https://www.linkedin.com/in/eddylu/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• Arash Ferdowsi on LinkedIn: https://www.linkedin.com/in/arashferdowsi/• Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more | Dalton Caldwell (Y Combinator, Managing Director): https://www.lennysnewsletter.com/p/lessons-from-1000-yc-startups•Bitcoin launderer pleads guilty, admits to massive Bitfinex hack: https://www.cnbc.com/2023/08/03/new-york-man-admits-being-original-bitfinex-hacker-during-guilty-plea-in-dc-to-bitcoin-money-laundering.html• Paul Graham's tweet with the facial recognition test: https://x.com/paulg/status/1782875262855663691• SmartLess podcast: https://www.smartless.com• Jason Bateman on X: https://x.com/batemanjason• Will Arnett on X: https://x.com/arnettwill• Sean Hayes on X: https://x.com/seanhayes• The Social Radars with Tony Xu, Co-Founder & CEO of DoorDash: https://www.ycombinator.com/library/Ja-tony-xu-co-founder-ceo-of-doordash• The Social Radars with Brian Chesky: https://www.ycombinator.com/library/JW-brian-chesky-co-founder-ceo-of-airbnb• The Social Radars with Patrick and John Collison: https://www.ycombinator.com/library/Kx-patrick-john-collison-co-founders-of-stripe• The Social Radars with Brian Armstrong: https://www.ycombinator.com/library/K3-brian-armstrong-co-founder-and-ceo-of-coinbase• The Social Radars with Emmett Shear: https://www.ycombinator.com/library/KM-emmett-shear-co-founder-of-twitch• The Social Radars with Paul Graham: https://www.ycombinator.com/library/JV-paul-graham-co-founder-of-y-combinator-and-viaweb• The Social Radars with Adora Cheung: https://www.ycombinator.com/library/L0-adora-cheung-co-founder-of-homejoy-instalab• Founders at Work: Stories of Startups' Early Days: https://www.amazon.com/Founders-Work-Stories-Startups-Early/dp/1430210788• Startup School: https://www.startupschool.org/• The Social Radars with Parker Conrad: https://www.ycombinator.com/library/Ky-parker-conrad-founder-of-zenefits-rippling• Rippling: https://www.rippling.com/• Carry on, Jeeves: https://www.amazon.com/Carry-Jeeves-Dover-Thrift-Editions/dp/0486848957• Very Good, Jeeves: https://www.amazon.com/Very-Good-Jeeves-Wooster-Book-ebook/dp/B0051GST06• Right Ho, Jeeves: https://www.amazon.com/Right-Ho-Jeeves-P-Wodehouse-ebook/dp/B083FFDNHN/• Life: https://www.amazon.com/Life-Keith-Richards-ebook/dp/B003UBTX72/• My Name Is Barbra: https://www.amazon.com/My-Name-Barbra-Streisand/dp/0525429522• Clarkson's Farm on Prime: https://www.amazon.com/Clarksons-Farm-Season-1/dp/B095RHJ52R• Schitt's Creek on Hulu: https://www.hulu.com/series/schitts-creek-a2e7a946-9652-48a8-884b-3ea7ea4de273• Yellowstone on Peacock: https://www.peacocktv.com/stream-tv/yellowstone• Sam Altman on X: https://x.com/sama• Justin Kan on LinkedIn: https://www.linkedin.com/in/justinkan/• Alexis Ohanian on X: https://x.com/alexisohanian• Steve Huffman on LinkedIn: https://www.linkedin.com/in/shuffman56/• Breaking News: Condé Nast/Wired Acquires Reddit: https://techcrunch.com/2006/10/31/breaking-news-conde-nastwired-acquires-reddit/• Charles River Venture: https://www.crv.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

At the absolute most basic, Dropbox is cloud storage for your stuff — but that puts it at the nexus of a huge number of today's biggest challenges in tech. As the company that helps you organize your stuff in the cloud itself goes all remote, how do we even deal with the concept of “your stuff?” Today I'm talking with Dropbox CEO Drew Houston about those big picture ideas — and why he thinks generative AI really will be transformative for everyone eventually, even if it isn't yet now. Links:  Dropbox AI and Dash make it easier to find your files from all over the web | The Verge Kids who grew up with search engines could change STEM forever | The Verge No, Dropbox's cafeteria didn't get a Michelin star | VentureBeat It's official: San Francisco's office vacancy rate just set a record | San Francisco Examiner Jeff Bezos: This is the 'smartest thing we ever did' at Amazon | CNBC Dropbox is laying off 500 people and pivoting to AI | The Verge Congress bans staff use of Microsoft's AI Copilot | Axios Transcript: https://www.theverge.com/e/23892647 Credits: Decoder is a production of The Verge, and part of the Vox Media Podcast Network. Today's episode was produced by Kate Cox and Nick Statt and was edited by Callie Wright. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This encore episode is a recording of a special masterclass roundtable session for our founders with John Donahoe. John is CEO of Nike and was previously CEO of ServiceNow and eBay. He is known as one of the most inspirational leaders in Silicon Valley and is a highly sought-after mentor to CEOs including Brian Chesky at Airbnb, Drew Houston at Dropbox, and Ben Silbermann at Pinterest. We're honored to have him among our small group of world-class executives and collaborators whose time and expertise help power our network of founders at Village Global. He shared advice on when to hire ahead, invest in and train, or replace personnel on your team and gave insight into his most common piece of advice on professional growth when advising CEOs. Quotes From This Episode "When you talk about priorities at an aspirational level, they overlap a lot. People start realizing we're more similar than we're dissimilar." "Adversity never feels fun. I don't seek adversity. But I'm no longer scared of adversity. When it emerges, instead of trying to run from it, I now accept that it is a reality and I say, 'well, at least I'm going to learn and grow.'" "My experience has been that around any issue that involves change, you have roughly 20-25% of people who want to be part of it, no matter what the topic is, you have 25-30% of people who want to fight it, and you have the 50% of people in the middle saying 'which side is going to win?'" "[When someone is let go] The fear is humiliation usually. That's almost a bigger fear than actually leaving the company." "We're never as good or as bad as labels make us out to be." "I would say in general, for every 10 hours of business development conversations, 8 of them are a waste." "I do gratitude practice driving into work every morning. It's proven in brain science that your brain becomes more negative over time. But it's also been proven in brain science that you can counteract that." "The older I get, the more I've made friends with uncertainty. I don't avoid uncertainty. Uncertainty is as present to me today as it was before but I'm a little more comfortable with it today." Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.

Holy SH*T, These two words have been said on this episode multiple times, way more than ever before I want to say, and it's because we got 2 incredible exciting breaking news announcements in a very very short amount of time (in the span of 3 hours) and the OpenAI announcement came as we were recording the space, so you'll get to hear a live reaction of ours to this insanity. We also had 3 deep-dives, which I am posting on this weeks episode, we chatted with Yi Tay and Max Bane from Reka, which trained and released a few new foundational multi modal models this week, and with Dome and Pablo from Stability who released a new diffusion model called Stable Cascade, and finally had a great time hanging with Swyx (from Latent space) and finally got a chance to turn the microphone back at him, and had a conversation about Swyx background, Latent Space, and AI Engineer. I was also very happy to be in SF today of all days, as my day is not over yet, there's still an event which we Cohost together with A16Z, folks from Nous Research, Ollama and a bunch of other great folks, just look at all these logos! Open Source FTW

The future of tech is 25-person companies powered by AI agents that help us accomplish our larger goals. Imbue is working on building AI agents that reason, code and generally make our lives easier. Sarah Guo and Elad Gil sit down with co-founders Kanjun Qiu (CEO) and Josh Albrecht (CTO) to discuss how they define reasoning, the spectrum of specialized and generalized agents, and the path to improved agent performance. Plus, what's behind their $200M Series B fundraise.  Kanjun Qiu is the CEO and co-founder of Imbue. Kanjun is also a partner at angel fund Outset Capital, where she invests in promising pre-seed companies. Previously, Kanjun was the co-founder and CEO of Sourceress, a machine learning recruiting startup backed by YC and DFJ. She was previously Chief of Staff to Drew Houston at Dropbox, where she helped scale the company from 300 employees to 1200. Josh Albrecht is the CTO and co-founder of Imbue. He also invests in other founders via his fund, Outset Capital. He has published machine learning papers as an academic researcher; founded an AI recruiting company that went through YC and a 3D injection molding software company that was acquired; helped build Addepar as an early engineer; and served as a Thiel Fellow mentor. He started programming as a kid and began working professionally as a software engineer in high school.  Show Links:  Kanjun's LinkedIn | Website | Google Scholar Josh's LinkedIn | Website | Google Scholar Imbue raises $200M to build AI systems that can reason and code Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Kanjun | @JoshAlbrecht Show Notes:  (00:00) - Introduction to Imbue (04:55) - The Spectrum of Agent Tasks (08:43) - Specialization and Generalization With Agents (13:03) - Code and Language in AI Agents

We're about to see a wave of big tech AI features "inspired" by third-party developers at a scale that makes the Sherlocking on Apple's platform seem like chump change. Plus, how Dropbox turned around their dev retention rates, and more.

In der Rubrik “Investments & Exits” begrüßen wir heute Otto Birnbaum, General Partner von Revent. Otto bespricht die Runde von ReOrbit und Speak:Das in Helsinki ansässige Unternehmen ReOrbit hat in einer Seed-Finanzierungsrunde 6,8 Millionen Euro eingesammelt. Die Runde wurde von Inventure VC geleitet und umfasste Beteiligungen von 10x Founders, Icebreaker.vc, Expansion und Yes VC. ReOrbit ist ein Anbieter von softwaregestützten Satelliten und ermöglicht den Echtzeit-Datenfluss im Weltraum. Das Unternehmen bietet Flugsoftware, Satellitenplattformen und komplette Systeme für Erdbeobachtungs- und SatCom-Betreiber. Durch die Software-First-Architektur kann ReOrbit Satelliten für verschiedene Missionen anpassen und dabei die Kosten und die Zeit bis zur Umlaufbahn minimieren. Die OpenAI-gestützte Sprachlern-App Speak hat in einer Series-B-2-Finanzierungsrunde unter der Leitung von Angel-Investor Lachy Groom insgesamt 16 Millionen US-Dollar gesammelt. Die Mitbegründer von Dropbox, Drew Houston und Arash Ferdowsi, haben ebenfalls in Speak investiert, was die Gesamtfinanzierung auf 63 Millionen US-Dollar erhöht. Das Geld wird verwendet, um die Markteinführung von Speak in weiteren Ländern, einschließlich der USA, zu unterstützen. CEO Connor Zwick plant, den KI-gesteuerten Tutor von Speak bis zum Ende des Jahres in den meisten wichtigen Märkten weltweit einzuführen, um Englischsprechern das Erlernen anderer Sprachen zu ermöglichen. 

Freshworks president Dennis Woodside copes with stress by running as often as he can, a habit that began when he was CEO of Motorola Mobility. So far, he has run “16 to 17” Ironman triathlons. He's also continually challenging himself in his professional life, leaving Motorola in 2014 to advise the founder-CEOs: Dropbox's Drew Houston, Impossible Foods' Pat Brown, and now Freshworks' Girish Mathrubootham. Dennis' advice for anyone working with founders is to “have empathy” for what they're going through, and to understand what motivates them. Without that understanding, he says, you won't be able to arrive at a shared vision for the company.In this episode, Dennis and Joubin discuss mega-acquisitions, the smartphone paradigm shift, triathlons and competitiveness, winning every category, “softening up,” global cities, Google interview questions, spreading Silicon Valley culture, the “chrome panda moment,” hiring the right people, “Where do you want to be in five years?”, evaluating new opportunities, and building trust with founders.In this episode, we cover: Google's acquisition of Motorola and how Dennis went from ad exec to first-time CEO (02:00) Did Dennis like being the CEO of Motorola? (08:04) The stress of the new job and dealing with it through exercise (13:02) Dennis' impressive résumé and what dinner conversation was like growing up (18:37) Going to Korea and choosing the harder path (23:00) Joining Google in 2003 as a general problem-solver (26:23) Hiring “scouts” all around the world to better understand the internet (30:41) Leaving Motorola to mentor Dropbox CEO Drew Houston (39:12) Checking your ego and the listening tour that wasn't (42:20) Dropbox's IPO and why the stock has been relatively flat (48:38) Changing jobs without breaks, and spotting new opportunities like Freshworks (52:19) Tips for working with founders and interrogating the status quo (58:02) Dennis' most unique OKR at Dropbox (01:02:39) Links: Connect with DennisLinkedIn Connect with Joubin Twitter LinkedIn Email: grit@kleinerperkins.com  Learn more about Kleiner Perkins This episode was edited by Eric Johnson from LightningPod.fm

Drew Houston is the Co-Founder and CEO @ Dropbox, for over 700 million users and +600,000 teams, Dropbox is the choice for storing and sharing their most important files. Prior to their IPO in 2018, Drew raised funding from some of the best including Sequoia, Index, Greylock, and IVP to name a few. Drew also currently sits on the board of Meta and is a seasoned angel with a portfolio including Gusto, Scale AI, Pilot and Superhuman to name a few. Prior to Dropbox, Drew founded Accolade, a bootstrapped online SAT prep company he started while in college. In Today's Episode with Drew Houston We Discuss: 1.) Entry into Startups and Y Combinator: How did Drew make his way into the world of startups with an SAT prep planning startup? How did Drew convince Paul Graham to accept him and Dropbox into Y Combinator? If we are all a function of our pasts, what is Drew running towards and what is he running away from? 2.) Drew Houston: The Leader and CEO: How does Drew define "high performance" today? How would Drew describe his style of management? How has it changed over time? How did taking an enneagram test change how Drew leads? What did he learn? What have been Drew's biggest hiring mistakes? What mistakes does he see others make? What have been Drew's biggest lessons in how to let people go the right way? 3.) Crucible Moments: Getting Sequoia, Acquisitions and Steve Jobs: How did Drew convince the Sequoia team to invest in Dropbox? How did it all start in a rug shop thanks to Pejman Nozad @ Pear? Has Drew had opportunities to sell the company? Why did he not take them? How does he advise founders on the decision to sell or not? How did Drew come to meet Steve Jobs? How did the meeting go? 4.) Drew Houston: AMA: Is Dropbox a B2B company or a B2C company? What is the hardest element of Drew's role with Dropbox? What has Drew recently changed his mind on? When press cycles were against him, how did Drew get through those tough times? What is Drew's biggest takeaway from joining the Meta board? Items Mentioned In Today's Episode: Drew's Fave Book: High Output Management by And

Washington Post tech at work writer Danielle Abril speaks with Ellyn Shook, chief leadership and human resources officer at Accenture, and Drew Houston, founder and CEO of Dropbox, about leadership and innovation amidst the growing shift to a hybrid workplace. Recorded on Friday, June 10, 2022.

Is work-life balance possible when you're always connected? The pandemic has forced advances in the digital underpinnings of remote and hybrid work. Dropbox has added apps and organizational advice to its ubiquitous cloud storage. Founder and CEO Drew Houston on the firm's virtual-first strategy and his vision for an AI-powered, uncluttered digital workplace.

词汇表consultancy 咨询公司to get rid of 除去，解雇flat （管理方式）扁平化的management structure 管理构架radical 根本性的，彻底的board member 董事会成员column （标明职责的）栏to drive (something) 推动（计划、项目等的）进展motivated 有积极性的company-wide 公司范围内的，整个公司的legal requirement 法律规定last resort 万不得已时的办法，最后的手段mixed results 好坏参半的结果leaderless 无领导的disorientating 失去方向感的，令人感到无所适从的to bump up against other people 与他人的职责重叠accountability 责任制，问责制hierarchy （机构）等级制度文稿:What would work be like if you had no boss? Imagine you could make all your own decisions and no one told you what to do.如果没有老板，工作会怎样？想象一下，您可以自己做出所有决定，而没有人告诉您该怎么做。One company decided to find out if it could work.一家公司决定看看它是否可行。The staff at Crisp, a Swedish software consultancy, chose to get rid of their CEO and create a completely flat organisation. 瑞典软件咨询公司 Crisp 的员工选择摆脱他们的 CEO 并创建一个完全扁平的组织。Having trialled several different management structures, the firm agreed to take the radical step of having no single leader.在尝试了几种不同的管理结构后，该公司同意采取没有单一领导者的激进措施。First, they needed to establish what a CEO actually did. Then they shared those duties out amongst staff and board members. 首先，他们需要确定 CEO 实际做了什么。然后他们在员工和董事会成员之间分担这些职责。Employee Yassal Sundman told the BBC: "When we looked at it we had nothing left in the CEO column, and we said, 'all right, why don't we try it out?'".员工 Yassal Sundman 告诉 BBC：“当我们查看它时，我们的 CEO 列中什么都没有，我们说，‘好吧，我们为什么不试试呢？'”。Which is exactly what they did. And Crisp employee, Heinrik Kniberg, believes it's been a good move. 这正是他们所做的。 Crisp 的员工 Heinrik Kniberg 认为这是一个很好的举措。He says the company can now act faster: "If you want to get something done, you stand up and start driving that". Kniberg also claims workers are more motivated.他说公司现在可以更快地采取行动：“如果你想完成某件事，你就站起来开始推动它”。 Kniberg 还声称工人更有动力。 Crisp regularly measures staff satisfaction, and say they average over four out of five.Crisp 定期衡量员工的满意度，并表示他们平均超过五分之四。Any big decisions are made during the company-wide four-day meetings several times a year. 任何重大决定都是在一年几次的全公司范围内的四天会议上做出的。They do still have a board: it's a legal requirement. But the board can be used as a last resort to resolve tricky issues.他们仍然有董事会：这是法律要求。但董事会可以作为解决棘手问题的最后手段。So, if it's working out for Crisp, could this model become more widespread? 那么，如果它适用于 Crisp，这种模式会变得更普遍吗？Online retailer Zappos tried out a similar plan but had mixed results. 在线零售商 Zappos 尝试了类似的计划，但结果喜忧参半。Almost a fifth of Zappos staff decided to leave, and chief executive Tony Hsieh admitted that "self-management is not for everyone".几乎五分之一的 Zappos 员工决定离开，首席执行官 Tony Hsieh 承认“自我管理并不适合所有人”。Founder of file-sharing service Dropbox, Drew Houston, believes that a leaderless structure is too chaotic.文件共享服务 Dropbox 的创始人 Drew Houston 认为，无领导的结构太混乱了。"Often infinite freedom like that can be pretty disorientating.“像这样的无限自由通常会让人迷失方向。 It doesn't always feel good, because you no longer know what you're supposed to do, what's important and you're bumping up against other people," Mr Houston says.感觉并不总是很好，因为你不再知道你应该做什么，什么是重要的，而且你正在与其他人发生冲突，”休斯顿先生说。So, what is the perfect balance? How do you keep accountability in a company without hierarchy?那么，什么是完美的平衡呢？您如何在没有等级制度的公司中保持责任感？ Would you like to work somewhere with no boss?你想在没有老板的地方工作吗？

Our anchors begin today's show breaking down the best and most cost-effective tech plays with CNBC's Dom Chu, and Credit Suisse Co-Head of Quantitative Research Patrick Palfrey shares his insight on undervalued stocks with low P/E ratios. Then, MoffettNathanson Co-Founder and Senior Managing Director Michael Nathanson takes a closer look at streaming service Roku's Q4 earnings miss, and Dropbox Co-Founder and CEO Drew Houston sits down for an exclusive interview on the cloud storage platform's latest results. “Margins” Editor Ranjan Roy also discusses his outlook for pandemic stocks including DoorDash, Airbnb and Peloton, and CNBC's Kate Rooney profiles new and more sophisticated trading tools for Ethereum. Later, CNBC's Morgan Brennan reports on Virgin Galactic Chairman Chamath Palihapitiya stepping down from the space tourism company's board.

Dropbox has grown from a small indie file sync utility to a giant of cloud storage. During that time many users fell in love with its simplicity and reliability. But with rising prices and difficult software dependencies, it's hard to keep the flame alive. What happened to Dropbox? Blog Post: https://www.userlandia.com/home/2022/1/dropbox-drops-the-ball Published January 18, 2022 -=- Chapters -=- 00:00:11 - Intro 00:00:31 - Love at First Sync 00:03:45 - Steve Jobs Introduces the new iDisk 00:05:20 - Dropbox Giveth, and Dropbox Taketh Away 00:10:20 - The Decline of the Dropbox Client 00:16:22 - The Cautionary Tale of Quark 00:20:29 - Comparing the Competition 00:24:00 - My Future with Dropbox 00:26:29 - Outtro -=- Links -=- Dropbox - https://www.dropbox.com/ Apple Unveils Internet Strategy - Apple Press - https://www.apple.com/newsroom/2000/01/05Apple-Unveils-Internet-Strategy/ WWDC 2003 Keynote -https://tidbits.com/2003/06/23/mac-os-x-10-3-panther-springs-at-wwdc/ Dropbox Demo - Youtube - https://www.youtube.com/watch?v=7QmCUDHpNzE Dropbox Launches to the Public - https://blog.dropbox.com/topics/company/dropbox-launches-to-the-public Dropbox Limits Free Accounts to Three Devices - The Verge - https://www.theverge.com/2019/3/14/18265574/dropbox-3-device-limit-free-accounts-plus-professional Dropbox Increases Plus Plan Prices to $12 per month - Venture Beat - https://venturebeat.com/2019/05/29/dropbox-increases-plus-plan-to-2tb-for-12-per-month-adds-rewind-smart-sync/ Important Changes to the Dropbox Public Folder - https://help.dropbox.com/files-folders/share/public-folder Dropbox Drops Photo Galleries - Hacker News - https://news.ycombinator.com/item?id=14747526 Revealing Dropbox's Dirty Little Security Hack - applehelpwriter - https://applehelpwriter.com/2016/07/28/revealing-dropboxs-dirty-little-security-hack/ Dropbox Security Bug Made Passwords Optional for Four Hours - TechCrunch - https://techcrunch.com/2011/06/20/dropbox-security-bug-made-passwords-optional-for-four-hours/ Dropbox Touts New Collaborative Cloud Storage Management App - MacRumors - https://www.macrumors.com/2019/06/12/dropbox-touts-new-cloud-management-app/ Chromium Embedded Framework - https://en.wikipedia.org/wiki/Chromium_Embedded_Framework Dropbox Silently Installs New File Management App - Ars Technica - https://arstechnica.com/gadgets/2019/07/dropbox-silently-installs-new-file-manager-app-on-users-systems/ Apple Silicon Desktop Sync Compatibility - Dropbox Forums - https://www.dropboxforum.com/t5/Dropbox-installs-integrations/Apple-Silicon-M1-Desktop-Sync-Compatibility/td-p/527743 Drew Houston on Twitter - https://twitter.com/drewhouston/status/1453762478479843332 QuarkXPress - https://en.wikipedia.org/wiki/QuarkXPress Quark offers details on QuarkXPress 5.0 for Mac OS X - https://www.macworld.com/article/152401/quarkosx.html Pride Goeth Before the Fall - Forbes - https://www.forbes.com/global/1999/0531/0211032a.html?sh=5153e13d5ce1 Dropbox Mac App with Apple Silicon Support now available to all beta users - MacRumors - https://www.macrumors.com/2022/01/13/dropbox-apple-silicon-support-beta-users/ Maestral - https://maestral.app -=- Subscribe -=- Apple Podcasts: https://podcasts.apple.com/us/podcast/userlandia/id1588648631 Overcast: https://overcast.fm/itunes1588648631/userlandia Pocket Casts:   https://pca.st/m4tegn1u Spotify:  https://open.spotify.com/show/79LO3vO9avAt3yCLpNWark Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly91c2VybGFuZGlhLmxpYnN5bi5jb20vcnNz -=- Contact -=- Follow Userlandia: @userlandia - http://twitter.com/userlandiashow Follow Dan: @kefkafloyd -  http://twitter.com/kefkafloyd Visit The Website: https://www.userlandia.com Email us: feedback@userlandia.com   Join The Userlandia Discord: https://discord.com/invite/z2jmF93 Theme Song by Space Vixen: https://spacevixen.bandcamp.com Follow them on Twitter @SpaceVixenMusic: https://twitter.com/spacevixenmusic

Our anchors kick off this Friday morning with all the earnings movers: Peloton, Airbnb, Square, Pinterest and Uber. We also hear from Airbnb CEO Brian Chesky on the company's focus. Then, Dropbox Co-Founder and CEO Drew Houston joins to discuss the company's latest quarter with shares dropping today despite reporting a beat. Later, we return to Peloton with Truist Securities' Youssef Squali as the stock plummets by more than 30 percent today. Next, Microchip Technology CEO Ganesh Moorthy is here to talk his company's record results and the challenges facing the broader semiconductor industry. And, in an earnings triple threat, we have IAC CEO Joey Levin to discuss the company's quarter and competition with Big Tech companies like Facebook.

Drew Houston's first job was babysitting and he was making $3.00 per hour, but then he figured out that he could get paid more doing what he loved.. Game programming. He later started DropBox. He had many setbacks, and he had to face the demons in his head. He overcame all this and finally took Dropbox Public. Both he and his co-founder became billionaires. Let us see how he did it.

"Write an interesting story, not a perfect one." - Drew Houston

We're re-releasing some of the best episodes from the podcast this summer.We were thrilled to host a masterclass roundtable session for our founders with John Donahoe when he was CEO of ServiceNow. John is now CEO of Nike and was CEO of eBay for more than seven years. He is known as one of the most inspirational leaders in Silicon Valley and is a highly sought-after mentor to CEOs including Brian Chesky at Airbnb, Drew Houston at Dropbox, and Ben Silbermann at Pinterest. We're honored to have him among our small group of world-class executives and collaborators whose time and expertise help power our network of founders at Village Global.He shared advice on when to hire ahead, invest in and train, or replace personnel on your team and gave insight into his most common piece of advice on professional growth when advising CEOs. John also did an in-depth demonstration of how to let someone go with dignity and grace.Quotes From This Episode"When you talk about priorities at an aspirational level, they overlap a lot. People start realizing we're more similar than we're dissimilar." "Adversity never feels fun. I don't seek adversity. But I'm no longer scared of adversity. When it emerges, instead of trying to run from it, I now accept that it is a reality and I say, 'well, at least I'm going to learn and grow.'" "My experience has been that around any issue that involves change, you have roughly 20-25% of people who want to be part of it, no matter what the topic is, you have 25-30% of people who want to fight it, and you have the 50% of people in the middle saying 'which side is going to win?'" "[When someone is let go] The fear is humiliation usually. That's almost a bigger fear than actually leaving the company." "We're never as good or as bad as labels make us out to be." "I would say in general, for every 10 hours of business development conversations, 8 of them are a waste." "I do gratitude practice driving into work every morning. It's proven in brain science that your brain becomes more negative over time. But it's also been proven in brain science that you can counteract that." "The older I get, the more I've made friends with uncertainty. I don't avoid uncertainty. Uncertainty is as present to me today as it was before but I'm a little more comfortable with it today." Thanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.

The Buzz 1: “We can fly! we can fly! we can fly!…All it takes is faith and trust. Oh, and something I forgot. Dust! Dust? Yep. Just a little bit of pixie dust.” (“You can fly” song, Peter Pan, 1953 American animated adventure fantasy film) The Buzz 2: “What do you need to start a business? Three simple things: know your product better than anyone, know your customer, and have a burning desire to succeed.” (Dave Thomas, Wendy's founder) The Buzz 3: “Don't worry about failure; you only have to be right once.” (Drew Houston, Dropbox co-founder and CEO) The Buzz 4: “he best startups generally come from somebody needing to scratch an itch.” (Michael Arrington, TechCrunch founder and co-editor) The Buzz 5: “I knew that if I failed I wouldn't regret that, but I knew the one thing I might regret is not trying.” (Jeff Bezos, Amazon founder and CEO) We'll ask startup gurus Don DeLoach at Rocket Wagon Venture Studios, Chris A. Morgan at Lantern Partners, Jim Gagnard at Industrial IoT Studio, and Eric Simone at ClearBlade Inc. for their insights on The Future of Tech Startups: When Is THE Right Time?

The Buzz 1: “We can fly! we can fly! we can fly!…All it takes is faith and trust. Oh, and something I forgot. Dust! Dust? Yep. Just a little bit of pixie dust.” (“You can fly” song, Peter Pan, 1953 American animated adventure fantasy film) The Buzz 2: “What do you need to start a business? Three simple things: know your product better than anyone, know your customer, and have a burning desire to succeed.” (Dave Thomas, Wendy's founder) The Buzz 3: “Don't worry about failure; you only have to be right once.” (Drew Houston, Dropbox co-founder and CEO) The Buzz 4: “he best startups generally come from somebody needing to scratch an itch.” (Michael Arrington, TechCrunch founder and co-editor) The Buzz 5: “I knew that if I failed I wouldn't regret that, but I knew the one thing I might regret is not trying.” (Jeff Bezos, Amazon founder and CEO) We'll ask startup gurus Don DeLoach at Rocket Wagon Venture Studios, Chris A. Morgan at Lantern Partners, Jim Gagnard at Industrial IoT Studio, and Eric Simone at ClearBlade Inc. for their insights on The Future of Tech Startups: When Is THE Right Time?

The Buzz 1: “We can fly! we can fly! we can fly!…All it takes is faith and trust. Oh, and something I forgot. Dust! Dust? Yep. Just a little bit of pixie dust.” (“You can fly” song, Peter Pan, 1953 American animated adventure fantasy film) The Buzz 2: “What do you need to start a business? Three simple things: know your product better than anyone, know your customer, and have a burning desire to succeed.” (Dave Thomas, Wendy's founder) The Buzz 3: “Don't worry about failure; you only have to be right once.” (Drew Houston, Dropbox co-founder and CEO) The Buzz 4: “he best startups generally come from somebody needing to scratch an itch.” (Michael Arrington, TechCrunch founder and co-editor) The Buzz 5: “I knew that if I failed I wouldn't regret that, but I knew the one thing I might regret is not trying.” (Jeff Bezos, Amazon founder and CEO) We'll ask startup gurus Don DeLoach at Rocket Wagon Venture Studios, Chris A. Morgan at Lantern Partners, Jim Gagnard at Industrial IoT Studio, and Eric Simone at ClearBlade Inc. for their insights on The Future of Tech Startups: When Is THE Right Time?

History of the most famous file hosting service company based in California.

Our anchors start off this Monday morning with Plexo Capital's Lo Toney on the Big Tech bounce back. We also have the details on China's latest regulatory moves and Disney's Black Widow film bringing in $60 million over the weekend just on Disney Plus. Then, we have Dropbox Co-Founder and CEO Drew Houston to discuss return to work and how the company's redesigned offices allow for flexibility. Later, CNBC's Julia Boorstin joins to interview Former TikTok CEO Kevin Mayer on the recent tech crackdown in China. Mayer, also a Former Disney Senior Executive Vice President, shares his thoughts on Disney Plus and the broader streaming space. Plus, CNBC's Morgan Brennan has all the details on Sir Richard Branson's out of this world trip yesterday and the wild moves in Virgin Galactic's share price.

Dropbox was one of the first companies to go all-in on remote work. In October 2020, even as the Covid-19 pandemic continued to rage with no end in sight, CEO Drew Houston declared that “virtual first” was the future of Dropbox. Melanie Collins, the company's chief people officer, has been a leading force in figuring out what that actually means in practice. Melanie joined the show to discuss Dropbox's way of thinking about remote work, how it's redesigning offices, how to measure employees when you can't see their butts in seats, and much more. For more on the topics in this episode:Melanie Collins on LinkedInDrew Houston's original Virtual First blog postDropbox's Virtual First toolkitChief People Officer Melanie Collins shares her experiences with building the future of work

Drew Houston, head of Financial Planning and Investment Sales, chats with Jim about the impact to our business and the process-driven themes where he’s seeing advisors win. Fee based and non-fee based financial planning is offered by financial professionals who are investment advisor representatives of Equitable Advisors, LLC, a registered investment advisor. GE-3586928(5/21)(Exp.5/23)

Drew Houston is the new head of Citizens Project in Colorado Springs.

Today’s episode is with Kate Taylor, who recently joined Notion as their Head of Customer Experience. Previously, Kate spent 8 years at Dropbox, leading their SMB revenue and scaled sales operation before leaving in 2020. Prior to that, she started her career as a sales rep at Salesforce.   In today’s conversation, Kate shares a wealth of advice for building out product-led growth and self-serve motions. She shares tons of nuances around going up market, competing with sales and product planning, offering up tactical advice that any founder, product or go-to-market leader can learn from.   Kate also gives us a detailed look at how they approach product prioritization at Notion, including their system of 700 tags and examples of tradeoffs they’ve had to navigate. We also get into pricing and packaging, from specific experiments at Dropbox to why interestingly Notion’s trial isn’t time based.    We also chat about how to handle a wide range of use cases, as well as the “front door” customer experience her team is trying to build. From why customer service shouldn’t be focused on getting customers off the phone faster, to the questions she asks to find more signal in their product feedback, Kate shares some counterintuitive thoughts here.   Finally, we wrap up by talking about her approach to leading teams, including why she hires for curiosity, how she tries to teach her team to ride the ups and downs of startup life, and how working for three very different CEOs — Marc Benioff, Drew Houston and Ivan Zhao — has impacted her own leadership style.   Kate isn’t on Twitter, but you can email us questions directly at review@firstround.com or follow us on Twitter @ twitter.com/firstround and twitter.com/brettberson 

#FC_Leadership_Podcast #Leadership #Business #Entrepreneurs Pourquoi êtes-vous entrepreneur ? - FC Leadership Podcast # 109 « Ne vous souciez pas de l'échec, l'important est de réussir une fois » - Drew Houston, fondateur de Dropbox. Entreprendre un mot très simple qui signifie se mettre à faire quelque chose. D’une manière ou d’une autre nous sommes tous entrepreneurs. D’une manière ou d'une autre, nous prenons tous des risques. Mais quels sont les risques qui sont bons pour moi et ma mission et de risque qui me fait faire du surplace ? Si entreprendre c’est se mettre à faire quelque chose. « Faire » en est une autre question. Pourquoi je fais ce que je fais ? Dans quel objectif c’est peut-être la question à se poser ! Bonne écoute

In this epic roundup episode, we took our favorite moments from every interview this year and combined them to create our most jam-packed episode yet: Foundr Best of 2020! That’s right, in this very special episode, you’ll hear valuable insights from:  Drew Houston, CEO and founder of DropBox: On problem-solving, his formula for success in business, and how he— as a billion-dollar CEO — still learns every single day.  Dylan Mullen, Founder, and Director of Happy Skin Co. Mullen reveals how he built a $20 million dollar company in 24 months, and how they’re acquiring their customers.  Alexa Von Tobel, Founder of Learnvest, & Inspire Capital: Why you shouldn’t spend a dollar on marketing, and what it takes to be a ‘good entrepreneur’.  Gretta Van Riel, 4x Multi-Milion Dollar Founder. Van Riel discusses why she would spend $500k on a post from Kylie Jenner, and her $1.3m manufacturing horror story.  Henrik Werderlin, founder and CEO of Barkbox, and the strategy that Apple and Amazon have used to build global, beloved brands.  You’re about to learn the mistake that every new entrepreneur makes, as discussed by Alex Osterwalder, the Swiss business theorist who developed the “business model canvas”.  Author Kamal Ravikant reveals why you don’t need a mentor (from someone who’s been down the road a few times). Christina Stembel, founder of Farmgirl Flowers on how she managed to turn $49,000 into almost a million dollars in 3 years— all thanks to the success of her company. Here’s Skillshare founder Malcolm Ong… who’s about to reveal the one word that will make you a better entrepreneur.  Thor Ernstsson, Founder of Strata. The 2 tips that every single entrepreneur needs to hear. One of the internet’s greatest pioneers, cofounder of WordPress Matt Mullenweg on what motivates him.  GT’s living foods founder, GT Dave. He reveals to us the key to staying on your path, and not losing your identity.  Andy Frisella, founder of 1st Phorm with one of the most fired up conversations of the year. Enjoy this snippet where he’s going to tell you why building a brand is important, and the issue with comparing yourself to Steve Jobs.

Consumer sovereignty is a principle of Austrian economics. Here's how entrepreneurs apply the principle in business, as told by Martin Lünendonk, co-founder of FounderJar.com, as well as Finance Club and Cleverism.com. How to Make the Customer your Boss Download our "How To Make The Customer Your Boss" graphic at Mises.org/E4E_95_PDF. "There is only one boss. The customer. And he can fire everybody in the company, from the chairman on down, simply by spending his money somewhere else." —Sam Walton Though they are several decades old, these words by Walmart founder Sam Walton are still very relevant, especially in today's highly competitive world. This is particularly true for those trying to make money online. You are already in competition with hundreds, perhaps thousands of other businesses, and if you do not put your customers first, they can easily move to the competition. It's as easy as tapping a few buttons on their smartphone. Great business leaders understand that businesses exist for one sole purpose — to serve the needs of their customers. If you want your business to not only survive, but to thrive in this hyper-competitive world, it's time you started treating your customers like the boss. Below, let's take a look at the steps you need to take to place your customers in their rightful seat — the boss's seat. 1. Identify the Key Problems Customers Want To Get Solved To effectively serve your customers, you need to first identify what key problems the customer is trying to solve. Very often, entrepreneurs set out to solve problems they think the customer has, without trying to look at things from the customers' point of view and confirm whether the customer has this problem, and whether it is a problem they are trying to solve. For instance, Blackberry assumed that what its customers wanted was a laptop that could fit on the palm, so they focused on improving the physical keyboard. Apple, on the other hand, realized that what customers actually wanted was a device that was amazingly easy to use, and when they introduced a device with a touch screen and no physical buttons, they took Blackberry out of business. So, how do you identify the problems that customers are trying to solve? There are two ways to do this: Listen To Your Customers The easiest way to identify the problems your customers are trying to solve is to actually listen to them. They know what they are struggling with and why they need this problem solved. If you listen to your customers, you are unlikely to find yourself in a situation where you are solving a problem no one cares about. There are two main approaches you can take to listen to your customers and identify the problems they are trying to solve. Here are a few… Interview your customers: Your first option is to get proactive and ask the customers directly. You can do this using surveys on your website, by getting on the phone and talking to customers, through focus groups, and so on.Look at customer reviews: Your customer reviews present another great opportunity for you to learn about the problems your customers are trying to solve. Here, you should place more focus on the negative comments, since these are the ones that highlight customer needs that are not being met. However, even positive comments can give insights into customer problems that you're solving effectively. Listen To Your Salespeople The second approach to identifying the problems customers are trying to solve is to listen to your salespeople. Your salespeople are in direct contact with your customers, and they, therefore, have better insights into your customers' thought processes. They know the pain points that drive customers to purchase your products and services, they know the things that customers like or dislike about your products, they know the reasons that keep some customers from purchasing, and so on. By administering surveys to your sales teams, you can gain insights that will help you figure out your customers' key problems, which will in turn help you to serve them better. When trying to gain insights about customer problems, either from the customers themselves or from your salespeople, it's good to try to get to the root cause of the problem. Sometimes, what you think is the problem might not actually be the problem. For instance, at one point, Disney was experiencing lots of criticism because visitors felt the queues for the rides were too long. At first glance, the problem seems obvious – visitors spending too much time waiting for their rides. The solutions to this problem are obvious as well. To shorten the queues, Disney would either have to invest in more rides, or reduce the number of visitors getting into their parks. Both of these solutions would cost Disney millions. Disney hired a group of designers to help them solve this problem. After interviews with Disney visitors, the designers realized that the problem wasn't the long queues. The problem was that visitors were getting bored because they had nothing to do while waiting in the queue. To solve the problem, they had Disney add themed music and videos that visitors could listen to and watch while waiting for their rides. By getting to the root cause of the problem, they were able to come up with an effective solution that saved Disney millions. Similarly, do not take your customers' feedback at face value. Try to identify what the root problem is before you start developing a solution. 2. Make Sure Your Offering Solves Those Customer Problems Now that you have identified the problems that your customers are trying to solve, it's time to come up with solutions to solve those problems. The best way to ensure that the solution you are developing solves the actual problems your customers are struggling with is to involve your customers in the development process. One approach is to develop a minimum viable product (MVP) of your solution and show it to a group of customers with the problem you are trying to solve. You then collect their feedback, and use insights to improve your next iteration and ensure that your final solution solves the customer problem in the most effective way. [[{"fid":"94714","view_mode":"image_with_caption","fields":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Why Build a Minimum Viable Product?","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"SOURCE: Clevertap.com/blog/minimum-viable-product","field_image_file_link[und][0][value]":""},"type":"media","field_deltas":{"1":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Why Build a Minimum Viable Product?","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"SOURCE: Clevertap.com/blog/minimum-viable-product","field_image_file_link[und][0][value]":""}},"attributes":{"alt":"Why Build a Minimum Viable Product?","class":"media-element file-image-with-caption media-wysiwyg-align-center","data-delta":"1"}}]] For instance, when creating DropBox, founder Drew Houston didn't want to spend months, perhaps years, working on a product that no one was interested in, so he started with an MVP. Drew's MVP was a simple 3-minute video demonstrating how his product was meant to work. He shared the video on Digg, an online community of technology early adopters. After sharing his video, over 70,000 people joined the DropBox beta waiting list within a single night, which was enough validation that his product was solving the right problem. Another way to involve customers in the development of your solution is to form a small community of beta testers and give them access to your solution during the development process. This works even if you are developing a service-based product. For instance, if you are a digital marketing consultant, you could create a package — say a content marketing package — and test it among a small group of customers before you launch it in full scale. The aim here is to have a group of actual customers continually testing the solution you are developing to make sure that it addresses their key concerns in the best possible manner for them. This way, you don't have to worry about spending months or years coming up with a solution to your customers' problems, only to discover that it is not the kind of solution they were looking for. Another way to ensure that what you are offering solves your customers' actual problems is to conduct A/B tests. This basically involves creating two versions of your offering, giving two small groups of customers access to each version, and then tracking the results to identify the version that solves customers' most effectively. 3. Track Customer Satisfaction Ultimately, what matters is keeping your customers satisfied. If your boss is unsatisfied with your work, you can bet that you will be out of work soon. Similarly, if your customers are unsatisfied with your business, they will fire you – by spending their money on your competitors. Actually, while 96% of unhappy customers will not voice their dissatisfaction, 91% of them will never make another purchase from you. This is definitely something you don't want. To know whether your customers are happy, you need a way to track and measure customer satisfaction. Here are five of the most effective ways of measuring customer satisfaction: Customer Satisfaction Surveys This is one of the easiest ways of tracking customer satisfaction. With this approach, you simply need to put up a survey asking your customers how satisfied they are with your services. Depending on the medium you are using to administer the survey, you can add one to three open-ended questions to learn more about what they think of your services. Customer satisfaction surveys can be served through email, through your website, or through your app. Customer Satisfaction Score (CSAT) The CSAT is the standard metric for measuring customer satisfaction. Here, you ask customers to rate how satisfied they are with your products or services on a scale. The scale could be 1 – 3, 1 – 5, or 1 – 10. After receiving responses from various customers, you then find the average rating to determine your customer satisfaction score. The higher the score, the more satisfied customers are with your services. Net Promoter Score (NPS) This is another popular metric for measuring how happy customers are with your business and your services. Unlike the other metrics covered here, however, NPS does not measure how satisfied customers are with your business. Instead, it measures how likely they are to refer someone to your business. This is especially useful for those in the freelance business, which depends heavily on referrals. The NPS will ask a customer to rate on a scale of 1 – 10, how likely they are to recommend your business to their friends and acquaintances. [[{"fid":"94715","view_mode":"image_with_caption","fields":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Net Promoter Score","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"Source: Business2Community.com/strategy/using-customer-satisfaction-metrics-nps-best-practices-02261983","field_image_file_link[und][0][value]":""},"type":"media","field_deltas":{"2":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Net Promoter Score","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"Source: Business2Community.com/strategy/using-customer-satisfaction-metrics-nps-best-practices-02261983","field_image_file_link[und][0][value]":""}},"attributes":{"alt":"Net Promoter Score","class":"media-element file-image-with-caption media-wysiwyg-align-center","data-delta":"2"}}]] The NPS categorizes your customers into 3 groups: Promoters: These are customers who give you a rating of 9 – 10. They are willing to spread the word about your business and recommend your products and services. These customers are already satisfied with your business.Neutral/Passives: These are customers who give you a rating of 7 – 8. They are indifferent to your business. They aren't disappointed with your business, but they aren't satisfied either. They are unlikely to talk about your business to others.Detractors: These are customers who give your business a rating of 6 and below. They are unhappy with your business, and will spread negative word about your business in a bid to discourage others from doing business with you. The Net Promoter Score is a very useful metric. If someone is willing to recommend your business to others, then this means that your products or services are good enough that they would stake their reputation on them. Customer Effort Score (CES) This metric measures customer experience, particularly how hard it is for your customers to get what they want from your business. Customers are typically asked to rate their effort from 1 (very little effort) to 7 (very high effort). A high score means that customers have to work very hard to get what they need from your business, which translates to poor customer experience. Social Media Mentions Keeping track of what people are saying about your business on social media can also help you figure out how satisfied your customers are with your business. Satisfied customers will take to social media to praise your business, while unhappy customers will share their dissatisfaction with their social media followers. Monitoring the conversations about your business happening on social media will allow you to step in and respond to comments in time and control your brand perception, especially when people are sharing negative comments. Here are three tools that you can use to track social media mentions: Google AlertsMentionSocialMention 4. Put Customer Value First, Profits Will Follow A lot of entrepreneurs believe that the core purpose of a business is to make profits. Smart entrepreneurs, those with the right entrepreneurial mindset, on the other hand, know that the core purpose of a business is to serve its customers. Therefore, their core focus is on delivering customer value. Of course, this does not mean that businesses that put customer value first don't think about profits. They do. What differs is their approach. These businesses understand that when you keep your customers happy (by delivering great value), these customers will bring more business, and spread positive word about your business, leading to more business, and ultimately, greater profits. Actually, the findings of research by Deloitte and Touche show that companies that put customers first are 60% more profitable compared to those that don't. So, what exactly does it mean to put customer value first? Putting customer value first means that every single business decision made within your organization should have a positive impact on customer experience. For instance, when upgrading its systems, a customer-centric company will choose systems that allow it to deliver the best customer experience. Similarly, when hiring, customer-centric companies go for employees who show a knack for putting customers first. Basically, every decision is evaluated based on its impact on customer experience. Here are some tips on how to make your company customer-centric and put customer value first: Understand your customers deeply. It is impossible to put customers first when you don't even know who they are. To get a good understanding of who your customers are, you need to develop highly detailed buyer personas. Actually, gaining a good understanding of the customer segments you're targeting is a key component of the business model canvas.Make sure that all your team members are engaged and have a good idea of the impact of their work on customer experience.Make it a habit to collect customer feedback, and then use this feedback to gain insights on how to improve the customer experience.Don't just focus on getting customers to make the purchase. Focus on building relationships that will turn them into loyal customers and brand ambassadors.Be easily accessible. Make it easy for customers to get in touch with your business when they have an issue, or when they need any sort of help. Ready To Put Your Customers In The Boss's Seat? As an entrepreneur, you are in business to serve your customers, which means that your customers are your boss. If you want your business to thrive, you need to start treating them as such, by putting their needs first. In this article, we have gone over 4 key points on how to make the customer your boss. Here's a recap: Identify the key problems customers want to get solvedMake sure your offering solves those customer problemsTrack and measure customer satisfactionPut customer value first and profits will follow Additional Resource "How To Make The Customer Your Boss" (PDF): Mises.org/E4E_95_PDF

Consumer sovereignty is a principle of Austrian economics. Here's how entrepreneurs apply the principle in business, as told by Martin Lünendonk, co-founder of FounderJar.com, as well as Finance Club and Cleverism.com. How to Make the Customer your Boss Download our "How To Make The Customer Your Boss" graphic at Mises.org/E4E_95_PDF. "There is only one boss. The customer. And he can fire everybody in the company, from the chairman on down, simply by spending his money somewhere else." —Sam Walton Though they are several decades old, these words by Walmart founder Sam Walton are still very relevant, especially in today's highly competitive world. This is particularly true for those trying to make money online. You are already in competition with hundreds, perhaps thousands of other businesses, and if you do not put your customers first, they can easily move to the competition. It's as easy as tapping a few buttons on their smartphone. Great business leaders understand that businesses exist for one sole purpose — to serve the needs of their customers. If you want your business to not only survive, but to thrive in this hyper-competitive world, it's time you started treating your customers like the boss. Below, let's take a look at the steps you need to take to place your customers in their rightful seat — the boss's seat. 1. Identify the Key Problems Customers Want To Get Solved To effectively serve your customers, you need to first identify what key problems the customer is trying to solve. Very often, entrepreneurs set out to solve problems they think the customer has, without trying to look at things from the customers' point of view and confirm whether the customer has this problem, and whether it is a problem they are trying to solve. For instance, Blackberry assumed that what its customers wanted was a laptop that could fit on the palm, so they focused on improving the physical keyboard. Apple, on the other hand, realized that what customers actually wanted was a device that was amazingly easy to use, and when they introduced a device with a touch screen and no physical buttons, they took Blackberry out of business. So, how do you identify the problems that customers are trying to solve? There are two ways to do this: Listen To Your Customers The easiest way to identify the problems your customers are trying to solve is to actually listen to them. They know what they are struggling with and why they need this problem solved. If you listen to your customers, you are unlikely to find yourself in a situation where you are solving a problem no one cares about. There are two main approaches you can take to listen to your customers and identify the problems they are trying to solve. Here are a few… Interview your customers: Your first option is to get proactive and ask the customers directly. You can do this using surveys on your website, by getting on the phone and talking to customers, through focus groups, and so on.Look at customer reviews: Your customer reviews present another great opportunity for you to learn about the problems your customers are trying to solve. Here, you should place more focus on the negative comments, since these are the ones that highlight customer needs that are not being met. However, even positive comments can give insights into customer problems that you're solving effectively. Listen To Your Salespeople The second approach to identifying the problems customers are trying to solve is to listen to your salespeople. Your salespeople are in direct contact with your customers, and they, therefore, have better insights into your customers' thought processes. They know the pain points that drive customers to purchase your products and services, they know the things that customers like or dislike about your products, they know the reasons that keep some customers from purchasing, and so on. By administering surveys to your sales teams, you can gain insights that will help you figure out your customers' key problems, which will in turn help you to serve them better. When trying to gain insights about customer problems, either from the customers themselves or from your salespeople, it's good to try to get to the root cause of the problem. Sometimes, what you think is the problem might not actually be the problem. For instance, at one point, Disney was experiencing lots of criticism because visitors felt the queues for the rides were too long. At first glance, the problem seems obvious – visitors spending too much time waiting for their rides. The solutions to this problem are obvious as well. To shorten the queues, Disney would either have to invest in more rides, or reduce the number of visitors getting into their parks. Both of these solutions would cost Disney millions. Disney hired a group of designers to help them solve this problem. After interviews with Disney visitors, the designers realized that the problem wasn't the long queues. The problem was that visitors were getting bored because they had nothing to do while waiting in the queue. To solve the problem, they had Disney add themed music and videos that visitors could listen to and watch while waiting for their rides. By getting to the root cause of the problem, they were able to come up with an effective solution that saved Disney millions. Similarly, do not take your customers' feedback at face value. Try to identify what the root problem is before you start developing a solution. 2. Make Sure Your Offering Solves Those Customer Problems Now that you have identified the problems that your customers are trying to solve, it's time to come up with solutions to solve those problems. The best way to ensure that the solution you are developing solves the actual problems your customers are struggling with is to involve your customers in the development process. One approach is to develop a minimum viable product (MVP) of your solution and show it to a group of customers with the problem you are trying to solve. You then collect their feedback, and use insights to improve your next iteration and ensure that your final solution solves the customer problem in the most effective way. [[{"fid":"94714","view_mode":"image_with_caption","fields":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Why Build a Minimum Viable Product?","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"SOURCE: Clevertap.com/blog/minimum-viable-product","field_image_file_link[und][0][value]":""},"type":"media","field_deltas":{"1":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Why Build a Minimum Viable Product?","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"SOURCE: Clevertap.com/blog/minimum-viable-product","field_image_file_link[und][0][value]":""}},"attributes":{"alt":"Why Build a Minimum Viable Product?","class":"media-element file-image-with-caption media-wysiwyg-align-center","data-delta":"1"}}]] For instance, when creating DropBox, founder Drew Houston didn't want to spend months, perhaps years, working on a product that no one was interested in, so he started with an MVP. Drew's MVP was a simple 3-minute video demonstrating how his product was meant to work. He shared the video on Digg, an online community of technology early adopters. After sharing his video, over 70,000 people joined the DropBox beta waiting list within a single night, which was enough validation that his product was solving the right problem. Another way to involve customers in the development of your solution is to form a small community of beta testers and give them access to your solution during the development process. This works even if you are developing a service-based product. For instance, if you are a digital marketing consultant, you could create a package — say a content marketing package — and test it among a small group of customers before you launch it in full scale. The aim here is to have a group of actual customers continually testing the solution you are developing to make sure that it addresses their key concerns in the best possible manner for them. This way, you don't have to worry about spending months or years coming up with a solution to your customers' problems, only to discover that it is not the kind of solution they were looking for. Another way to ensure that what you are offering solves your customers' actual problems is to conduct A/B tests. This basically involves creating two versions of your offering, giving two small groups of customers access to each version, and then tracking the results to identify the version that solves customers' most effectively. 3. Track Customer Satisfaction Ultimately, what matters is keeping your customers satisfied. If your boss is unsatisfied with your work, you can bet that you will be out of work soon. Similarly, if your customers are unsatisfied with your business, they will fire you – by spending their money on your competitors. Actually, while 96% of unhappy customers will not voice their dissatisfaction, 91% of them will never make another purchase from you. This is definitely something you don't want. To know whether your customers are happy, you need a way to track and measure customer satisfaction. Here are five of the most effective ways of measuring customer satisfaction: Customer Satisfaction Surveys This is one of the easiest ways of tracking customer satisfaction. With this approach, you simply need to put up a survey asking your customers how satisfied they are with your services. Depending on the medium you are using to administer the survey, you can add one to three open-ended questions to learn more about what they think of your services. Customer satisfaction surveys can be served through email, through your website, or through your app. Customer Satisfaction Score (CSAT) The CSAT is the standard metric for measuring customer satisfaction. Here, you ask customers to rate how satisfied they are with your products or services on a scale. The scale could be 1 – 3, 1 – 5, or 1 – 10. After receiving responses from various customers, you then find the average rating to determine your customer satisfaction score. The higher the score, the more satisfied customers are with your services. Net Promoter Score (NPS) This is another popular metric for measuring how happy customers are with your business and your services. Unlike the other metrics covered here, however, NPS does not measure how satisfied customers are with your business. Instead, it measures how likely they are to refer someone to your business. This is especially useful for those in the freelance business, which depends heavily on referrals. The NPS will ask a customer to rate on a scale of 1 – 10, how likely they are to recommend your business to their friends and acquaintances. [[{"fid":"94715","view_mode":"image_with_caption","fields":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Net Promoter Score","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"Source: Business2Community.com/strategy/using-customer-satisfaction-metrics-nps-best-practices-02261983","field_image_file_link[und][0][value]":""},"type":"media","field_deltas":{"2":{"format":"image_with_caption","alignment":"center","field_file_image_alt_text[und][0][value]":"Net Promoter Score","field_file_image_title_text[und][0][value]":false,"field_caption_text[und][0][value]":"Source: Business2Community.com/strategy/using-customer-satisfaction-metrics-nps-best-practices-02261983","field_image_file_link[und][0][value]":""}},"attributes":{"alt":"Net Promoter Score","class":"media-element file-image-with-caption media-wysiwyg-align-center","data-delta":"2"}}]] The NPS categorizes your customers into 3 groups: Promoters: These are customers who give you a rating of 9 – 10. They are willing to spread the word about your business and recommend your products and services. These customers are already satisfied with your business.Neutral/Passives: These are customers who give you a rating of 7 – 8. They are indifferent to your business. They aren't disappointed with your business, but they aren't satisfied either. They are unlikely to talk about your business to others.Detractors: These are customers who give your business a rating of 6 and below. They are unhappy with your business, and will spread negative word about your business in a bid to discourage others from doing business with you. The Net Promoter Score is a very useful metric. If someone is willing to recommend your business to others, then this means that your products or services are good enough that they would stake their reputation on them. Customer Effort Score (CES) This metric measures customer experience, particularly how hard it is for your customers to get what they want from your business. Customers are typically asked to rate their effort from 1 (very little effort) to 7 (very high effort). A high score means that customers have to work very hard to get what they need from your business, which translates to poor customer experience. Social Media Mentions Keeping track of what people are saying about your business on social media can also help you figure out how satisfied your customers are with your business. Satisfied customers will take to social media to praise your business, while unhappy customers will share their dissatisfaction with their social media followers. Monitoring the conversations about your business happening on social media will allow you to step in and respond to comments in time and control your brand perception, especially when people are sharing negative comments. Here are three tools that you can use to track social media mentions: Google AlertsMentionSocialMention 4. Put Customer Value First, Profits Will Follow A lot of entrepreneurs believe that the core purpose of a business is to make profits. Smart entrepreneurs, those with the right entrepreneurial mindset, on the other hand, know that the core purpose of a business is to serve its customers. Therefore, their core focus is on delivering customer value. Of course, this does not mean that businesses that put customer value first don't think about profits. They do. What differs is their approach. These businesses understand that when you keep your customers happy (by delivering great value), these customers will bring more business, and spread positive word about your business, leading to more business, and ultimately, greater profits. Actually, the findings of research by Deloitte and Touche show that companies that put customers first are 60% more profitable compared to those that don't. So, what exactly does it mean to put customer value first? Putting customer value first means that every single business decision made within your organization should have a positive impact on customer experience. For instance, when upgrading its systems, a customer-centric company will choose systems that allow it to deliver the best customer experience. Similarly, when hiring, customer-centric companies go for employees who show a knack for putting customers first. Basically, every decision is evaluated based on its impact on customer experience. Here are some tips on how to make your company customer-centric and put customer value first: Understand your customers deeply. It is impossible to put customers first when you don't even know who they are. To get a good understanding of who your customers are, you need to develop highly detailed buyer personas. Actually, gaining a good understanding of the customer segments you're targeting is a key component of the business model canvas.Make sure that all your team members are engaged and have a good idea of the impact of their work on customer experience.Make it a habit to collect customer feedback, and then use this feedback to gain insights on how to improve the customer experience.Don't just focus on getting customers to make the purchase. Focus on building relationships that will turn them into loyal customers and brand ambassadors.Be easily accessible. Make it easy for customers to get in touch with your business when they have an issue, or when they need any sort of help. Ready To Put Your Customers In The Boss's Seat? As an entrepreneur, you are in business to serve your customers, which means that your customers are your boss. If you want your business to thrive, you need to start treating them as such, by putting their needs first. In this article, we have gone over 4 key points on how to make the customer your boss. Here's a recap: Identify the key problems customers want to get solvedMake sure your offering solves those customer problemsTrack and measure customer satisfactionPut customer value first and profits will follow Additional Resource "How To Make The Customer Your Boss" (PDF): Mises.org/E4E_95_PDF

In-office engagement or remote flexibility? We don't need to choose, says Dropbox co-founder and CEO Drew Houston. Facing the biggest shift in work habits in half a century, Houston has embarked on a radical experiment to reimagine how work gets done. The company's recently announced Virtual First plan dedicates all in-office activity to creative, team-based efforts, rebranding its offices as Dropbox Studios. Individual work will happen offsite, either at home or a self-chosen co-working space. Project teams set their own schedules. The unique opportunity of this moment, Houston says: How do we make work better? Yes, he admits, remote work feeds Dropbox's business, which now includes a collaboration with Zoom as part of a re-thought product roadmap. If Dropbox is going to design for the future of work, says Houston, then its own workforce needs to live in that future, right now.

In-office engagement or remote flexibility? We don't need to choose, says Dropbox co-founder and CEO Drew Houston. Facing the biggest shift in work habits in half a century, Houston has embarked on a radical experiment to reimagine how work gets done. The company's recently announced Virtual First plan dedicates all in-office activity to creative, team-based efforts, rebranding its offices as Dropbox Studios. Individual work will happen offsite, either at home or a self-chosen co-working space. Project teams set their own schedules. The unique opportunity of this moment, Houston says: How do we make work better? Yes, he admits, remote work feeds Dropbox's business, which now includes a collaboration with Zoom as part of a re-thought product roadmap. If Dropbox is going to design for the future of work, says Houston, then its own workforce needs to live in that future, right now.

In 2006, Drew Houston got on a bus from Boston heading to New York. He planned to use the three-hour ride to get some work done, so he opened his laptop, and realized he had left his thumb drive with all of his work files at home. Drew decided he never wanted to have that problem again. On that bus ride, he started writing the code to build a cloud-based file storage and sharing service he called Dropbox. Fourteen years later, Drew and his co-founder, Arash Ferdowsi, have built Dropbox into a public company worth close to $8 billion. Order the How I Built This book at: https://smarturl.it/HowIBuiltThis

This week on the 50th episode of Just Another Podcast we meet with Drew Houston, to get his perspective on the duality of his existence as a Virginia Beach police officer and a black man. At (9:05) we jump right in with his perspective on the George Floyd case. After that Officer Houston shares about some of the experiences he's had on the job, how his unborn child is changing his mindset, and some information about the k-9 unit (18:20) . (21:30) On May 31, 2019 Virginia Beach had its first mass shooting Officer Houston reflects on how it changed him as well as the areas he sees room for improvement. Deem poses the question "Has the social contract between civilian and law enforcement been broken" (27:00) ? Rubber bullets have been used by officers on civilians maliciously and led to serious injury and death, they also end up in the hands of officers with questionable history. At (31:35) Houston weighs in on the use of rubber bullets and provides information about the internal disciplinary actions of the Virginia Beach Police Department. (37:25) The escalation of force, bootstraps, and Officer Houston's parting words. The second half of the pod the cast lightens the mood..sort of. Confederate statues and Confederate flags (52:25) . Would you leave your SO alone around your friends (1:02:05) ? YOU HAVE TO SAY IT (1:08:05) and how many times a week are you having sex with your partner. How do you declare that a person is your SO without saying it (1:14:40) and what is "relationship shi*"? Last but not least, (1:29:25) does time show real commitment or does marriage?  Podcast: https://podcasts.apple.com/us/podcast... Spotify: https://open.spotify.com/show/0LZP8NN... Make sure you like, subscribe, and share our content! Follow us on twitter: @justan0therp0d Subscribe to our Patreon! : Just Another Podcast --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

By now, the story is legend. When Drew Houston boarded a bus from Boston to New York and discovered that he had—yet again—forgotten to bring his thumb drive, he was frustrated. So frustrated that he sat down and began writing the first lines of code of what would eventually become Dropbox. After over a decade of changing the way files are stored, synced, and shared, Houston is changing the way people work, once again. This time, to solve a problem that likely plagues every single knowledge worker today: our fragmented, overcomplicated workspaces. In this episode, you’ll learn more about Houston’s journey—from ideation to launch—with Dropbox Spaces, as well as the most important lessons he’s collected while building a multibillion-dollar company with over 500 million users. Key Takeaways The relatable experience that inspired Houston to come up with the idea for Dropbox Why Houston doesn’t believe there’s any “magic” involved in building a multibillion-dollar company The importance of decision making and learning continuously on the job How a conversation with a SpaceX engineer sparked the vision behind Dropbox Spaces Houston’s advice on “harvesting” versus “planting” when it comes to your business Why Houston is such a huge believer in intentionally designing your environment—at work and with your personal relationships

Rachel Hepworth is VP of Marketing @ Pilot, the startup that offers the best bookkeeping, tax and CFO services for growing businesses. To date they have raised over $58M from some of the best in the business including Index Ventures, John Collison, Paul English, Drew Houston, Frederic Kerrest, Diane Greene and more incredible names. As for Rachel, prior to joining Pilot, she saw the hyper-growth of Slack firsthand enjoying a couple of different roles including Head of Growth Marketing and then also Head of Self Service and Platform Marketing. Before Slack, Rachel spent 4 years at LinkedIn where she led the product marketing team for content experiences. Finally, before LinkedIn, Rachel spent close to 3 years at Climate Corporation, prior to their $1Bn exit to Monsanto.   In Today’s Episode We Discuss: How Rachel made her way from marketing manager at Climate Corporation to VP of marketing at Pilot today? What were Rachel’s biggest takeaways from her time seeing the hyper-growth at Slack? How does Rachel think about organic growth and inciting word of mouth today? How does Rachel think they can be more accurately tracked and measured? How does Rachel think about the optimal ratio of paid to organic in growth? Would Rachel agree in paid, your payback period doubles every $5M you spend? With the rise of product-led growth, are we seeing a fundamental shift in the structure of sales and marketing? How does Rachel see marketing move ever close to the function of customer success today? What is the optimal way for customer success and marketing to work together?  How does Rachel think about the importance of getting in front of your customers? Why does Rachel believe that data tells you the what and customer conversations tell you the why? What is the right way to structure your customer conversations? Where do so many people go wrong here?       Rachel’s 60 Second SaaStr: Hardest element of your role with Pilot today? If Rachel could change one thing about SaaS today, what would it be? Who is killing it in SaaS marketing? Why? If you would like to find out more about the show and the guests presented, you can follow us on Twitter here: Jason Lemkin Harry Stebbings SaaStr Rachel Hepworth

Sam and Shaan are back talking news, trends, interesting products and businesses.The way to make a million is by surrounding yourself around other people who want it. Join our Facebook group where we share ideas and help each other out: www.facebook.com/groups/ourfirstmillion. Harry's (D2C razor brand) sale blocked & D2C selling to PE strategy (0:55), /r/WallStreetBets & Tesla shorts (6:05), Drew Houston sitting on Facebook board & Professional board sitters (11:39), Labor law posters, MailChimp for governments & other weird compliance rules (14:48), Turnkey compliance, contracting remote workers & Letsdeel.com (18:11), Huge unsexy business: exec headhunting (21:23), Influential people curating job boards (27:52), Crowd-sources salary comparison sites like levels.fyi (33:08) & The Ringer selling to Spotify & why it makes podcast acquisitions make sense (35:41). See acast.com/privacy for privacy and opt-out information.

Drew Houston, co-founder of Dropbox, talks about why while building one of the most popular productivity apps on the internet he got really frustrated with his lack of performance as CEO and how being available 24/7 contributes to an "epidemic of burnout". He also talks about his evolution as an entrepreneur, how reading helped him becoming a better CEO and his invention – the "No-Meeting-Wednesday". Houston started Dropbox at the age of 24 while still a student at MIT. He also gives us an insight into his private life, speaks about his “first love” and his hobbies like playing in a 90ies cover band. Hosts: Britta Weddeling (@bweddeling), Editor-in-Chief of Bits & Pretzels (@bitsandpretzels) Featuring: Drew Houston (@drewhouston), co-founder Dropbox https://www.instagram.com/drewhouston/ More to explore: https://www.bitsandpretzels.com/posts/how-dropbox-ceo-drew-houston-fought-his-burnout Visit our website: https://www.bitsandpretzels.com/podcast Follow us: Twitter: @bitsandpretzels Linkedin: https://www.linkedin.com/company/bits-&-pretzels If you like the show, please let us know by leaving a review. You can also send us feedback directly at podcast@bitsandpretzels.com.

In this episode, Dino and Simon quickly sum up the last episode and its characteristic focus which was solving problems instead of simply seeking answers; then move on to discuss the fourth characteristic of amazing and wildly successful entrepreneurs which is they share their passions and they engage with others. Others can be employees, contractors, customers, fans - essentially anyone who is around.  Using this characteristic startup companies that are being performed out of a back bedroom or shed can compete with global giants on the business scale. Simon gives an example using  Drew Houston, the founder of Dropbox. Startup companies can win because they are formed by a concentrated pool of talented, passionate, and hardworking people all pulling together toward one goal/one solution. How do we get the best people possible and make sure they will work well together? Ask this question instead: why would a really great person elect to leave a very safe corporate job with benefits and start working for a fledgling startup business? Engagement Cause Mission The above-bulleted items unlock potential. The mission is what will attract people who want to help propel you forward into success because what you’re doing matters! This should not be labeled as a person’s attitude. It is their character! If you deliver your vision for your business or product and convey it with passion other people will step up. Others will engage you (as you should engage them) because they already have something you’re speaking to inside them. Selling the mission and selling the vision Impossible - Simple It is either within a person or it’s not Passion can not be manufactured   If you are lacking in passion or drive it is unlikely you will successfully engage others. Dino and Simon don’t mean engage like the Human Resources checkbox definition which means essentially keeping people business. Our hots mean sharing our visions and ideas with others ie employees, partners, suppliers and so on.  A lot of startups were obviously built on passion because many entrepreneurs have forgone salary in order to build a team and generate their product. Simon takes a moment to explain his new project regarding character analysis for professional sports teams. No one on his team is currently making any salary. They are being asked to invest! Why do they do it? Passion! Diven for success. They believe in the cause. They believe it will change lives. Simon takes a moment to discuss Airbnb - which relies completely on word of mouth.  See a video where Brian Chesky, founder of Airbnb, describes it’s beginnings here.   How do these successful entrepreneurs make things happen? How do they get this high level of engagement and buy-in? Foundational characteristic from episode one: finding a problem and caring passionately about solving it Hard work - idea to tangibility Commitment Energy  Engagement is relationships People want to be a part of the story   In the next episode, our hosts will discuss the final characteristic of wildly successful entrepreneurs! Are you feeling lucky?   “Sam Altman (Y Combinator) says we need to create loves, not likes”  “Fans will forgive far more than customers. Fans will rave about you more than customers.” “Your biggest fans will also be your biggest critics. They will give you honest feedback.” “We can’t function on our own. A team is needed.”   Visit our Website! www.successengineers.co   Dino Tartaglia's LinkedIn Simon Hartley's LinkedIn Simon's website - Be World Class Simon's YouTube Channel

Drew Houston คือใคร

The best podcasts from week #36 talked about standing out and how to slay Goliath: The Minimalists: https://apple.co/2kGaUzd The Happier Podcast: https://apple.co/2kereao Masters of Scale with Drew Houston: https://apple.co/2ktSnGA Anonymous Feedback: bit.ly/MagnaVitaFeedback

When Drew Houston founded Dropbox, he knew he faced some fierce competition (hello, Google, Apple and Microsoft). But he didn’t back down. Why? Because he believed in his product, and he knew he had an advantage those big, cumbersome competitors could never exploit: Dropbox was lean, focused and fast. Hear how he outmaneuvered the big guys – and what's next for Dropbox. Cameo appearances: Mark Pincus of Zynga, Shellye Archambeau of MetricStream.Music: "Exciting Trailer" by Kevin MacLeod, licensed under a Creative Commons Attribution license.Books mentioned in this episode:Competing Against Luck, by Clay Christensen and Karen DillonThe Effective Executive, by Peter DruckerThe Hard Thing About Hard Things, by Ben HorowitzBecoming Steve Jobs, by Brent Schlender and Rick TetzeliHard Drive: Bill Gates and the Making of the Microsoft Empire, by James Wallace and Jim EricksonFounders at Work, by Jessica LivingstonHigh Output Management, by Andy Grove

Produced by Foundersuite.com, "How I Raised It" goes behind the scenes with startup founders who have raised capital. This episode is with Waseem Daher of PIlot.com, makers of an online bookkeeping platform for startups and SMEs. The Company recently raised $40 million of Series B venture funding in a deal led by Index Ventures along with Stripe. Previous investors include Drew Houston, Adam D'Angelo, John Collison and other angels. In this episode, Waseem talks about raising a seed round from 40 angels and 3 VCs, how to engage investors to help (and expectations for what percent will be active), why raising a Series B can embolden the company to take bigger bets, and more. SPECIAL OFFER: get 20% off by signing up here: https://pilot.com/foundersuite This series is produced by Foundersuite, makers of software to raise capital and manage investor relations. Learn more at www.foundersuite.com.

Our app publishes these earnings calls shortly after they are available. If you don't want to wait until we publish it on this podcast, use our app to listen right away. We've created a dedicated earnings calls listening app with a library of thousands of calls. Our app lets you set "new call" notifications, download earnings calls, and see the date of the earnings call. App Store: https://itunes.apple.com/us/app/borsa-earnings-calls/id1414117603?mt=8 Google Play Store: https://play.google.com/store/apps/details?id=com.borsahq.earningscalls Weekly Email List (week's earnings calls, product updates, special offers, etc.): http://eepurl.com/dCZ5AH Video Demo: https://www.youtube.com/watch?v=OALinunnRjc&t=20s Welcome to Earnings Season. Our goal is to make listening to earnings calls easier. We upload relevant and newsworthy earnings calls for easy listening. To request a company's earnings call, email borsaHQ@gmail.com. App Store Play Store Twitter Instagram StockTwits

Alexandr Wang is the Founder & CEO @ Scale, the startup providing high quality training and validation data for AI applications. To date, Alexandr has raised over $23m with Scale from some of the best in the business including Index, Accel, Y Combinator, Dropbox’s Drew Houston, Justin Kan, Thumbtack’s Jonathan Swanson and more. Prior to founding Scale, Alexandr was a Tech Lead at Quora, directly responsible for all speed projects and before that a software engineer at Addepar responsible for building and maintaining financial models. In Today’s Episode We Discuss: How did Alex make his way into the world of SaaS and come to found Scale? What were some of his biggest takeaways from seeing the first hand scaling of Quora and Addepar? Why does Alex take the contrarian view that “TAM in the traditional sense barely matter”? What two characteristics of the market should founders really look to examine? How does Alex approach the element of market sizing? Does he prefer top down or bottoms up and why? Why does Alex believe that you must invest in customer success before you think you need it? What were the benefits for Alex of investing early in customer success? Why does CS over sales ultimately drive the growth of your company? How does one know when is the right time to hire their first in customer success? What is the ideal profile of this candidate? How does Alex think about the integration of customer success and product teams? Why is it crucial from the product perspective that founders pick their first customers well? How can your customers drive your product decisions? How can one ensure to be customer informed and not customer driven?      Why does Alex believe that in the early days it is not important to focus on the size of the deals you are signing? What should founders be focusing on with these early customers instead? When is the right time to flip the switch and opt for value extraction as a more primary objective? How does Alex respond to the fact that VCs often look at these first customer deals as an indication of the size of the pain point you are solving?   60 Second SaaStr: What does Alex know now that he wishes he had known in the beginning? What does Alex believe is the hardest role to hire for today? Who does Alex think is crushing it in the world of SaaS today? Read the full transcript on our blog. If you would like to find out more about the show and the guests presented, you can follow us on Twitter here: Jason Lemkin Harry Stebbings SaaStr Alexandr Wang

We've created a dedicated earnings calls listening app with a library of thousands of calls. Our app lets you set "new call" notifications, download earnings calls, and see the date of the earnings call. App Store Link: bit.ly/FreeQuarterlyEarningsCalls Want to be notified when our Android app is out? Join the Android email list: http://eepurl.com/ggQMVX Free Newsletter: http://eepurl.com/dCZ5AH Video Demo: https://www.youtube.com/watch?v=OALinunnRjc&t=20s Welcome to Earnings Season. Our goal is to make listening to earnings calls easier. We upload relevant and newsworthy earnings calls for easy listening. To request a company's earnings call, email borsaHQ@gmail.com. Twitter Instagram Website App Store StockTwits

We were thrilled to host a Masterclass roundtable session with our founders and John Donahoe (@Donahoe_John), CEO of ServiceNow. Prior to ServiceNow, John was CEO of eBay for more than seven years. He is known as one of the most inspirational leaders in Silicon Valley and is a highly sought-after mentor to CEOs including Brian Chesky at Airbnb, Drew Houston at Dropbox, and Ben Silbermann at Pinterest. We’re honored to have him among our small group of world-class executives and collaborators whose time and expertise help power our network of founders at Village Global.When we asked John to deliver a Masterclass to 12 diverse and determined founders in our portfolio, John gladly invited us all to the ServiceNow HQ where he riffed on topics of leadership, culture building, talent development, and how to grow as a CEO in the tech industry.He shared advice on when to hire ahead, invest in and train, or replace personnel on your team and gave insight into his most common piece of advice on professional growth when advising CEOs. John also did an in-depth demonstration of how to let someone go with dignity and grace.Quotes From This Episode"When you talk about priorities at an aspirational level, they overlap a lot. People start realizing we're more similar than we're dissimilar." — John"Adversity never feels fun. I don't seek adversity. But I'm no longer scared of adversity. When it emerges, instead of trying to run from it, I now accept that it is a reality and I say, 'well, at least I'm going to learn and grow.'" — John"My experience has been that around any issue that involves change, you have roughly 20-25% of people who want to be part of it, no matter what the topic is, you have 25-30% of people who want to fight it, and you have the 50% of people in the middle saying 'which side is going to win?'" — John"[When someone is let go] The fear is humiliation usually. That's almost a bigger fear than actually leaving the company." — John"We're never as good or as bad as labels make us out to be." — John"I would say in general, for every 10 hours of business development conversations, 8 of them are a waste." — John"I do gratitude practice driving into work every morning. It's proven in brain science that your brain becomes more negative over time. But it's also been proven in brain science that you can counteract that." — John"The older I get, the more I've made friends with uncertainty. I don't avoid uncertainty. Uncertainty is as present to me today as it was before but I'm a little more comfortable with it today." — JohnThanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.Venture Stories is brought to you by Village Global and is hosted by co-founder and partner, Erik Torenberg. Colin Campbell is our audio engineer and the show is produced by Brett Bolkowy.

We were thrilled to host a Masterclass roundtable session with our founders and John Donahoe (@Donahoe_John), CEO of ServiceNow. Prior to ServiceNow, John was CEO of eBay for more than seven years. He is known as one of the most inspirational leaders in Silicon Valley and is a highly sought-after mentor to CEOs including Brian Chesky at Airbnb, Drew Houston at Dropbox, and Ben Silbermann at Pinterest. We’re honored to have him among our small group of world-class executives and collaborators whose time and expertise help power our network of founders at Village Global.When we asked John to deliver a Masterclass to 12 diverse and determined founders in our portfolio, John gladly invited us all to the ServiceNow HQ where he riffed on topics of leadership, culture building, talent development, and how to grow as a CEO in the tech industry.He shared advice on when to hire ahead, invest in and train, or replace personnel on your team and gave insight into his most common piece of advice on professional growth when advising CEOs. John also did an in-depth demonstration of how to let someone go with dignity and grace.Quotes From This Episode"When you talk about priorities at an aspirational level, they overlap a lot. People start realizing we're more similar than we're dissimilar." — John"Adversity never feels fun. I don't seek adversity. But I'm no longer scared of adversity. When it emerges, instead of trying to run from it, I now accept that it is a reality and I say, 'well, at least I'm going to learn and grow.'" — John"My experience has been that around any issue that involves change, you have roughly 20-25% of people who want to be part of it, no matter what the topic is, you have 25-30% of people who want to fight it, and you have the 50% of people in the middle saying 'which side is going to win?'" — John"[When someone is let go] The fear is humiliation usually. That's almost a bigger fear than actually leaving the company." — John"We're never as good or as bad as labels make us out to be." — John"I would say in general, for every 10 hours of business development conversations, 8 of them are a waste." — John"I do gratitude practice driving into work every morning. It's proven in brain science that your brain becomes more negative over time. But it's also been proven in brain science that you can counteract that." — John"The older I get, the more I've made friends with uncertainty. I don't avoid uncertainty. Uncertainty is as present to me today as it was before but I'm a little more comfortable with it today." — JohnThanks for listening — if you like what you hear, please review us on your favorite podcast platform. Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.Venture Stories is brought to you by Village Global and is hosted by co-founder and partner, Erik Torenberg. Colin Campbell is our audio engineer and the show is produced by Brett Bolkowy.

In this episode, filmed at Goldman Sachs' Builders + Innovators Summit, Dropbox co-founder and CEO Drew Houston discusses what he has learned as he continues to grow the cloud computing company into one of the biggest disruptors in Silicon Valley. The interview is moderated by Goldman Sachs' George Lee. Date: October 18, 2018 This podcast should not be copied, distributed, published or reproduced, in whole or in part, or disclosed by any recipient to any other person. The information contained in this podcast does not constitute a recommendation from any Goldman Sachs entity to the recipient. Neither Goldman Sachs nor any of its affiliates makes any representation or warranty, express or implied, as to the accuracy or completeness of the statements or any information contained in this podcast and any liability therefore (including in respect of direct, indirect or consequential loss or damage) is expressly disclaimed. The views expressed in this podcast are not necessarily those of Goldman Sachs, and Goldman Sachs is not providing any financial, economic, legal, accounting or tax advice or recommendations in this podcast. In addition, the receipt of this podcast by any recipient is not to be taken as constituting the giving of investment advice by Goldman Sachs to that recipient, nor to constitute such person a client of any Goldman Sachs entity. Copyright 2018 Goldman Sachs & Co. LLC. All rights reserved.

I know I take for granted many of the freedoms that are offered to us in this country and the world we live in. We are privileged, and I know I need to be better at appreciating these great fortunes. As 2018 ended, the word “freedom” rang in my mind a lot and today’s episode talk more about those thoughts. “Instead of trying to make your life perfect, give yourself freedom to make it an adventure, and go ever upward.” – Drew Houston

Ted Wang is a Partner @ Cowboy Ventures, one of Silicon Valley's leading early-stage funds with the likes of Philz Coffee, Dollar Shave Club, Brandless, DocSend, Accompany and Brit + Co all in their portfolio. As for Ted, prior to VC, Ted spent X years as a leading Silicon Valley lawyer with Fenwick & West where he worked with some of the most notable companies of our times including Facebook, Dropbox, Twitter, Square and Spotify just to name a few. Ted also created the Series Seed Documents - a set of open-sourced financing documents posted on Github used by thousands around the world today. In Today’s Episode You Will Learn: 1.) How Ted made his way from one of the most renowned lawyers in the valley with Fenwick & West to partner @ Cowboy alongside Aileen Lee? 2.) How does Ted fundamentally approach risk today? Given this mindset, how does this impact Ted's thinking on optimizing portfolio construction? On the flip side, how has Ted seen many founders wrongly approach the theme of risk? What is the question they need to be asking? What is Ted's story about risk related to his time working with Jet? 3.) What is it that makes Ted believe that "advice is often oversimplified"? If so, how can VCs provide tangible advice to their portfolio companies today? How can founders determine what is the right advice to accept and integrate vs listen and disregard? How does this lead Ted's thinking on the 2 core value adds a VC can provide? What advice did Dropbox Founder, Drew Houston give Ted on when to accept advice? 4.) What does Ted mean when he says "there are 4 parts to venture"? How does Ted think about the theme of learning and self-improvement when assessing founders? How does he look to do this pre-investment? What questions reveal the most? Applying it to himself, where will Ted place his biggest efforts on learning within the realm of venture over the next 12 months? Items Mentioned In Today’s Show: Ted’s Fave Book: 7 Habits of Highly Effective People Ted’s Most Recent Investment: Fullcast As always you can follow Harry, The Twenty Minute VC and Ted on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC. Much like how Carta changed how private companies manage their cap tables and 409A valuations, Carta are now doing the same for fund administration. With Carta’s new, modern fund administration software and services, you get a real-time dashboard of your general ledger, can securely share info with your LPs, and issue capital calls–from the same platform, you accept securities and request cap table access. So essentially, Carta simplifies how startups and investors manage equity, fund administration, and valuations. Go to carta.com/20VC to get 10% off.

Listen to any earnings call on demand with the Borsa Earnings Call mobile app now on the App Store. Download here: bit.ly/FreeQuarterlyEarningsCalls Welcome to Earnings Season. Our goal is to make listening to earnings calls easier. We upload relevant and newsworthy earnings calls for easy listening. To request a company's earnings call, email borsaHQ@gmail.com. This podcast episode is Dropbox's Q3 2018 earnings call. Listen to Drew Houston discuss his company's performance. About Earnings Season: Earnings Season posts relevant earnings calls for an easy listening experience. Email borsahq@gmail.com to request a company.

Thoughts? Comments? You can contact me by calling or texting 1-201-429-0274. If you leave a voicemail please be aware, you only have 3 minutes.   Email me at improveandhavefun@gmail.com    Join the conversation on the blog by going here https://bit.ly/2wJckfl   ..Thanks for watching/listening!   There are affiliate links in the show notes.   BIGGEST TAKEAWAYS-Robert Cialdini's 'Influence'   This book was tough to get through.   It felt like I was back in school and I was doing homework.    I did glean some useful information from here.    This material I feel is especially useful if you're in sales, a public figure working on attracting others to you(what I'm doing with this podcast), or being an everyday consumer and knowing when someone is trying to 'sell' you on something.      These are some of my biggest takeaways from Robert Cialdini's 'Influence':   -'One favorite and profitable tactic of certain compliance professionals is to give something before asking for a return favor.' Page 49.   A great sales tactic!   -'Even though they had committed themselves under anonymous circumstances, the act of writing down their first judgments caused them to resist the influence of contradictory new data and to remain consistent with their preliminary choices.' Page 72.    This goes hand in hand with first impressions.   -'The general idea is to pave the way for a full-line distribution by starting with a small order...Look at it this way-when a person has signed an order for your merchandise even though the profit is so small it hardly compensates for the time and effort of making the call, he is no longer a prospect-he is a customer.'   Page 64.     -'Oh, those 'harmless' concessions.   We've already seen how apparently trifling commitments can lead to further consistent behavior.   As a commitment device, a written declaration has some great advantages.'   Page 67.    I feel this also applies if you sign up online for an email list, a subscription as examples.    Signing up makes it real as opposed to committing superfluously.   -'No matter which variety of low-balling is used, the sequence is the same: An advantage is offered that includes favorable purchase decision.    Then sometime after the decision has been made, but before the bargain is sealed, the original purchase advantage is deftly removed.'   Page 85.    Sign up now at this discounted price for Verizon FIOS, it includes a year of Netflix.    But after a year you pay the full monthly, regular price.   -'What if physical appearance is not much at issue?    After all, most people possess average looks.    Are there other factors that can be used to produce liking?   As both researchers and compliance professionals know, there are several, and one of the most influential is similarity.   We like people who are similar to us.'   Page 148.    This is fascinating, it's from a chapter which also discusses how we are quicker to gravitate to someone selling to us, particularly if they are physically attractive.    But this gives credence to the fact that you don't have to have looks to make something happen.   -'A potentially effective strategy for reducing the unwanted influence of liking on compliance decisions requires a special sensitivity to the experience of undue liking for a requester.   Upon recognizing that we like a requester inordinately well under the circumstances, we should step back from the social interaction, mentally separate the requester from his or her offer, and make any compliance decision based solely on the merits of the offer.'   Page 172.    A great tip for everyday consumers.   -'According to the scarcity principle, people assign more value to opportunities when they are less available.     The use of this principle for profit can be seen in such compliance techniques as the 'limited number' and 'deadline' tactics, wherein practitioners try to convince us that access to what they are offering is restricted by amount of time.'   Page 225.   -'Although we all wish to make the most thoughtful, fully considered decision possible in any situation, the changing form and accelerating pace of modern life frequently deprive us of the proper conditions for such a careful analysis of all the relevant pros and cons.    More and more, we are forced to resort to another decision-making approach-a shortcut approach in which the decision to comply(or agree or believe or buy)is made on the basis of a single, usually reliable piece of information.'   Page 234.   I would say this being the case, especially in our present-day, short-attention-span society.   If you enjoyed this material, please support the author and myself by buying this book through the provided Amazon link.   Thank you! LINKS Robert Cialdini's Influence https://amzn.to/2Q9pbiX    Going Where the Wind Blows   After a day's work, I enjoy coming home, eating dinner and watching YouTube, Netflix or Amazon Prime on my iPad.   I'm always looking for something to watch.   I finally committed to watching Orange is the New Black.   From the beginning.   I barreled through five episodes(of the first season)in the two days.   The show was funny, smart and very entertaining with some drama.   Once I started to notice that a few of the storylines run together and that several episodes were self-contained(like the chicken episode), I decided to go straight to the three final chapters of the season, and it didn't seem like I missed much.    But where I really started resonating with the show was in one of Piper's characteristics(she is the main character).    She seems to go where the wind blows. People she cares about end up suffering because of this.   This spoke to me because I've done this often.   On this very podcast, I've stated numerous times that 'I've discovered this new technique, approach, skill and I want my world to revolve around it because it will give me some edge or I had a dream about my mother and I should go and move back in with her.'    I tell anyone who will listen to me about this new thing.   I'll try it for a bit, then drop it and go back to my regular patterns.   Some of these new ideas have stuck.    Most haven't   Examples being; gaining inspiration from the Scavenger Life podcast and listing an item every day on eBay.   I'd begin that for a week then go back to listing 3-4 things a week.    Another instance was watching YouTuber Casey Nesitat.   I thought I want to start vlogging because it seemed like fun.   Maybe I can get millions of YouTube views, make some money I thought!   I purchased a ton of video equipment.   Quickly I realized my bookbag is heavy enough and  I don't want to travel with all this gear.      There are pros and cons to trying new things.   The ONE Thing book says you should put your time and energy into what you enjoy and already have some proficiency in.    On the flipside on a recent day off I listened to a combination of MWF podcast (episode 472), Marie Forleo's podcast interview of Jaclyn Johnson, and Tim Ferriss interview of Drew Houston.   The alphabet soup message I got from listening to these podcasts is to try many things and fail because you don't know what you are good at and can potentially love until you discover it.       Also, straight quitting on something is a waste of time.   Because if you failing, you're learning.   Apply what you've learned on the second go round and continue growing/failing/learning from there.   But if you quit, you have truly wasted your time if the lesson learned from the failure isn't used.   So is trying different things a waste of time?    Does this take away from working on your main goal?       When I pay attention to my patterns, I don't regularly do much of the stuff I discovered in a book, podcast, a video.   But now I'm starting to notice my time spent other than on my creative endeavors and it makes me feel guilty.    I spend 3-4 hours at night watching TV.    At work when I'm not 'working' I'm on the internet looking at entertainment news.       I believe there is a time to procrastinate.    If I spent all my time documenting and creating, I'd get sick of it.    Even if I do so for little bits at a time, I dance, draw, write a little bit every day or every other day.    Weekly I work on the podcast.   Small actions done consistently make big things happen.   Piper Chapman is free-spirited and determined, and I like that.   Thanks to this fictional character for showing me my own indecision and going where the wind blows.    9 times out of 10 I believe many answers to my own questions exist within me.   I just need a reflection of myself in others, in a movie, in a book, in a podcast, to find it.   LINKS What is 'Orange is the New Black? https://bit.ly/1QNWvqg  The ONE Thing book https://amzn.to/2wOZigd    You are Born with Love   I was listening to the audiobook version of Marianne Williamson's 'Return to Love' for the 3rd time.   Presently I'm highlighting sections of interest in the ebook on the Kindle app.    Using this new method will help create more content for the podcast.   Reading and now listening(more often)I'm completing more books.   My brain is getting bigger!!     In the early part of the book, Mrs. Williamson talks about how we are born with love, how it is our nature.   Fear is something we learn.  This is the quote here from page 4: 'Love is what we were born with.   Fear is what we have learned here.   The spiritual journey is the relinquishment-or unlearning-of fear and the acceptance of love back into our hearts.   Love is the essential existential fact.   It is our ultimate reality and our purpose on earth.   To be consciously aware of it, to experience love in ourselves and others is the meaning of life.'   These next quotes come from 'Way of the Peaceful Warrior' by Dan Millman.   I will link the connection I've made to the previous quote right after.   This is on page 159.   'As a child, all this would appear before your eyes and ears and touch as if for the first time.   But now you've learned names and categories for everything:  'That's good that's bad, that's a table, that's a chair, that's a car, a house, a flower dog, cat, chicken, man, woman, sunset, ocean, star.'   You've become bored with things because they only exist as names to you.   the dry concepts of the mind obscure your direct perception."   "You now see everything through a veil of associations about things, projected over a direct, simple awareness.   You've 'seen it all before': it's like watching a movie for the twentieth time.   You see only memories of things, so you become bored, trapped in mind.   This is why you have to 'lose your mind' before you can come to your senses."   The mind is a powerful, multipurpose device.   According to these recent books I've read(including Eckhart Tolle's 'Power of Now'), it can be problematic when it starts going in all directions.    Unrelated thoughts to the task at hand, hang-ups from the past.   Worrying about the future.    Jealousy, lack of focus, ego, envy.   This is my mind's wheelhouse when it starts running around the neighborhood like a dog off its leash.   Let's say a co-worker, I feel does not appreciate a small gesture of kindness which I've imparted.    My ego is hurt.   I start to think, 'next time I will let them earn my generosity, my love.'   This egoic sentiment is learned.   I wasn't born with this.   I, like so many of us, was born with passion, curiosity, no fear.      There is a method to combat this(which I learned from the books mentioned above).   Basically to turn off your mind.   The times I notice when my mind is turned off; when I'm watching TV, listening to podcasts or when I'm drawing.   Now when my mind and emotions are all over the place, I gently tell myself 'Attention!   Be here now.   How's your breathing?    How's your body feeling, what's your posture like?'   It's not easy, it's tough. In the past, I would knock myself for turning my brain off and going on auto-pilot.   I would talk down to myself, badly.    Listening particularly to Mrs. Williamson's book, it's reminded me how toxic this is.    When is it a good time to flip the switch and turn on my mind?    Solving issues, problems. Focusing.    At work, I've been making fundamental mistakes.    Like being late to work, or not crossing all the 'T's' or dotting the 'I's.'    I used my mind and came up with solutions. I started waking up 10-15 minutes earlier to prevent being late; I also started keeping a log of all my work, as a reference point to follow up on what I'd already done.   I'm happiest when I'm coming from a place of fearless love and generosity to my family, friends, co-workers.    I want to re-discover my childlike curiosity.       Honestly, when my ego shows up, or out of fear I put up my defenses, I'm a lesser version of myself.   When I put someone down mentally or judge them, I'm putting myself down because how I see another human being, in essence, is how I look at myself.   This is a benefit of the negative emotions.    It shows me my capacity, where I need to show more love, forgiveness, patience, compassion or allow things to be.   I realize I've written these articles on all these beautiful lessons I learn through books, podcasts, and videos.   I want to try to incorporate many of these lessons.   Most don't stick unfortunately as I fall back into my patterns and behaviors.    But that doesn't control me.   I do.   I'm not perfect; I want to be better.   This lesson regarding the mind is one of the most impactful I've learned.   I want it to stay with me. LINKS Marianne Williamson's 'Return To Love' https://amzn.to/2oLRTd2  Dan Millman's 'Way of the Peaceful Warrior' https://amzn.to/2wONaLz  Eckhart Tolle's 'Power of Now' https://amzn.to/2NRMQCV    Crazy Rich Asians REVIEW-SPOILERS   I saw this movie over Labor Day weekend.    To date, it has been the number one movie in the box office 3 weeks in a row.    On a budget of $30 million dollars, it has grossed over $110 domestically.    A sequel was already announced a few days after the film's release.    Several movie critics I trustingly follow praised this film.   The Rottentomatoes score is 93%.   I was curious and wanted to check this out.   This movie was only ok for me until it got to Colin and Araminta's wedding.    Up until this point, the movie was ho-hum, I felt the comedy was forced.    I wasn't really invested in the main characters.     But I now realize during that first half, seeds were being planted which would pay off by the film's end.   The movie takes place for the most part in Singapore.   What is shown of this country in this film is gorgeous.    The colors pop and are vibrant.   I started thinking, 'man I gotta go visit this country!'   As I mentioned above the movie really picks up for me after the wedding.   Nick's family acceptance angle with Rachel was great.   The romance kicked into the next gear with Nick and Rachel during the wedding scene.   This is first time(and not the last)where I got emotional.   When everyone was tearing up and Nick was mouthing 'I love you' to Rachel.   Colin is crying while Araminta is walking down the aisle.    Eleanor played by Michelle Yeoh watching all of this happen.   You know when you see her, she is not crazy about what's going down between her son and his girlfriend.    Good stuff.   The strongest performances in the movie were by Michelle Yeoh who plays Nick's mother.    She may come across wooden and emotionless but subtle things in the face or how she turns her head, or what she says delivers the point across nicely.   Showing how doing little can say so much.    Another great performance and familiar face was the beautiful Gemma Chan.   I remember her from the TV show Humans, where she plays a humanoid who has emotions.   I enjoyed her in that and binged two seasons of it on Amazon Prime.       Ms. Chan's(who played Astrid)story, with her husband, the infidelity and accepting oneself as they are and not making excuses because of it I also thought was great.    I resonated with this especially when it comes to Nice Guy Syndrome.    You can found my articles on that and the related book here.   I started caring more about Rachel's character near the end section of the movie.   I thought the scenes with her mother and Eleanor at the mahjong game where fantastic.   This movie was labeled as a rom-com but I found it more drama/romance than comedy.   The comedy was fun but as I mentioned forced at times with Awkwafina and Ken Jeong(from the Hangover series).    I smiled and chuckled a bit but that's about it.    A beautiful looking movie, nice romance.   I hope the sequel is even better.    LINKS What is Crazy Rich Asians? https://bit.ly/2JYWlhw  Crazy Rich Asians book https://amzn.to/2wPyoVr  My Biggest Takeaways 'No More Mr. Nice Guy' https://bit.ly/2oJynxT      Enjoyed this? Find all of my content on the website at http://improveandhavefun.com   Social Media Instagram: https://www.instagram.com/paul_pvp_perez  Facebook: https://www.facebook.com/pvpluvzlieff    Twitter: https://twitter.com/Paul_PVP_Perez    Rate, like, leave a review! I will shout you out for sure!   If you've enjoyed this, please support this podcast by doing any, all your shopping through my affiliate links:   my eBay link: EBAY http://ebay.to/2e5mvmj    or my AMAZON link: http://amzn.to/2dRu3IM    or DONATE here https://bit.ly/2LD1mwy     Thank you!   Subscribe/watch/listen here:   iTunes http://apple.co/2pnmMqa    Android http://bit.ly/2p5fgQx    YouTube http://bit.ly/2ixiRo4    iHeartRadio http://bit.ly/2oBLZdX    Stitcher http://bit.ly/2p8oTi2    TuneIn http://bit.ly/2oE6xUQ   Google Play http://bit.ly/2oEizNZ   SPOTIFY http://spoti.fi/2ALfgHr