Podcast appearances and mentions of Ron Conway

Michael Kim is the Founder of Cendana Capital, a fund of funds that makes anchor investments in very early stage VC funds.We talk characteristics of the best investors, how Cendana does diligence on fund managers, portfolio construction best practices, Michael's “60x rule”, and why high ownership to fund size is the main driver of returns.We also get in to how VCs are using AI, the competition between Seed and multi-stage investors, why US endowments are under siege, and how secondaries are driving most early stage venture returns today.Michael also opens up about the early days of starting Cendana, the 18 month grind raising Cendana Fund 1, the day he almost died, and ranking in the top 2% globally in Call of Duty.Special thanks to Roger Ehrenberg, Kevin Hartz, Semil Shah, Jeff Claviar, Beezer Clarkson, Jack Altman, Jeff Morris Jr, Sheel Mohnot, Nichole Wischoff, Ted Alling, and Rick Zullo for their help putting this episode together.Thanks to Bolt for supporting this episode. Check out their world record largest (up to $1m in prizes) at: https://bit.ly/ThePeelBoltHackathonTimestamps:(4:24) The day Michael almost died(5:10) Call of Duty & video games(9:34) Hiring @ Cendana(10:31) How Cendana uses structured and unstructured data(16:51) How VCs are using AI(19:55) Why secondaries are driving most early stage venture returns(22:01) Deciding when to sell secondaries(24:28) Best performing venture funds ever(27:26) The best VCs have amazing access to the best founders(33:42) Why Cendana backs Solo GPs(35:57) How to invest over time and hype cycles(41:35) Why multi-stage firms are investing earlier(44:45) Cendana's current thesis: High ownership % to fund size(45:51) Why Cendana started backing non-lead VCs(48:41) How Cendana does diligence on fund managers(52:22) VC NPS Scores and Ron Conway's Silver Bullet(53:49) Good vs bad new VC firm strategies(56:36) Determining defensibility of a strategy(57:57) “Messy middle” software buyout fund(1:03:25) Portfolio construction best practice(1:08:11) Michael's 60x Rule(1:14:28) How Seed funds compete with multi-stage funds(1:20:05) Should you collect logos writing small checks?(1:21:07) Becoming an LP for the city of SF(1:24:42) Taking 18+ months to raise Cendana Fund 1 in the GFC(1:26:48) Warehousing the first Cendana Fund 1 investments(1:29:56) How to do a first close(1:34:29) Why it's hard to kill a VC firm(1:37:00) What happens to ZIRP tourist fund managers(1:40:22) How to raise a Fund 2 or 3 today(1:42:07) “US endowments are under siege”(1:44:55) What the best GP LP relationships look like(1:46:41) What Fund of Funds get wrong(1:50:43) The three most interesting trends in venture todayReferencedCheck out Cendana https://www.cendanacapital.com/Deep Checks https://www.deepchecks.vc/Prior episode with Eric at Bolt https://www.youtube.com/watch?v=7Q6n1vqUrF4Follow MichaelTwitter: https://x.com/MKRocksLinkedIn: https://www.linkedin.com/in/michael-kim-cendana-capital/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Guest: Blake Scholl, Founder & CEO of Boom Supersonic“Passion and drive trumps knowledge and experience,” says Boom Supersonic CEO Blake Scholl. Long before he was running Boom — which earlier this year successfully tested the world's first privately-developed supersonic jet — he was enabling “the world's most obnoxious spam cannon” at Groupon, or designing a barcode-scanning game for retail shoppers.But eventually, Blake found the courage to be more audacious and do something closer to his lifelong love of aviation. He began educating himself about things he had never thought to learn, and tapping his LinkedIn network to get intros to the smartest people in the industry. “If you imagine yourself on like the day of IPO, 99 percent of what you needed to know to get to that day, you didn't know on day one,” he says. “So, why not take 99 percent to 99.5 percent, and work on the thing you really want to exist, even if you don't know anything about it yet?”Chapters: (01:07) - Blake on Boom's beginnings (01:52) - Breaking the sound barrier (05:23) - Concorde's legacy (09:36) - Navigating regulations (12:08) - Boomless supersonic flight (16:48) - The test flight (20:11) - Day-of nervousness (24:26) - Carrying passengers (26:55) - Cost & wi-fi (30:19) - “No middle seats” (32:35) - Hard tech (36:48) - What if Apple made a plane? (39:08) - Blake's career journey (43:29) - The risk of failure (49:12) - Finding the courage (52:49) - Balancing life with Boom (56:42) - Learning how to build a jet (01:00:20) - The power of LinkedIn (01:02:38) - Y Combinator Demo Day (01:08:24) - Richard Branson (01:11:38) - Dividing yourself (01:14:19) - Being a focused dad (01:20:05) - Exuberance vs. fear (01:24:15) - Hiring slowly (01:27:17) - What “grit” means to Blake Mentioned in this episode: Chuck Yeager, ChatGPT, the Apollo program, Elon Musk, SpaceX and Falcon 1, Boom Overture, Starlink, Boeing, Airbus, iPhone, Jony Ive, Uber, Airbnb, Anduril, United Airlines, American Airlines, Eclipse Aviation, Tesla, Scott Kirby, Mike Leskinen, Inktomi, Yahoo!, Amazon, Pelago, Google Ads, Kima Labs, Barcode Hero, Groupon, iPad, Eric Schmidt, Steve Jobs, Khan Academy, Sam Altman, Loopt, Virgin Atlantic, Paul Graham, Michael Seibel, Ashlee Vance, Bloomberg, Hacker News, Jared Friedman, Sen. Mark Kelly, SV Angel, Ron Conway, Virgin Galactic, Lockheed Martin, Gulfstream, Jeff Bezos, Jeff Holden, and How It's Made.Links:Connect with BlakeTwitterLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm

If you're in SF, join us tomorrow for a fun meetup at CodeGen Night!If you're in NYC, join us for AI Engineer Summit! The Agent Engineering track is now sold out, but 25 tickets remain for AI Leadership and 5 tickets for the workshops. You can see the full schedule of speakers and workshops at https://ai.engineer!It's exceedingly hard to introduce someone like Bret Taylor. We could recite his Wikipedia page, or his extensive work history through Silicon Valley's greatest companies, but everyone else already does that.As a podcast by AI engineers for AI engineers, we had the opportunity to do something a little different. We wanted to dig into what Bret sees from his vantage point at the top of our industry for the last 2 decades, and how that explains the rise of the AI Architect at Sierra, the leading conversational AI/CX platform.“Across our customer base, we are seeing a new role emerge - the role of the AI architect. These leaders are responsible for helping define, manage and evolve their company's AI agent over time. They come from a variety of both technical and business backgrounds, and we think that every company will have one or many AI architects managing their AI agent and related experience.”In our conversation, Bret Taylor confirms the Paul Buchheit legend that he rewrote Google Maps in a weekend, armed with only the help of a then-nascent Google Closure Compiler and no other modern tooling. But what we find remarkable is that he was the PM of Maps, not an engineer, though of course he still identifies as one. We find this theme recurring throughout Bret's career and worldview. We think it is plain as day that AI leadership will have to be hands-on and technical, especially when the ground is shifting as quickly as it is today:“There's a lot of power in combining product and engineering into as few people as possible… few great things have been created by committee.”“If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a maniacal focus on outcomes.”“And I think the reason why is if you look at like software as a service five years ago, maybe you can have a separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of technological breakthroughs required for most business applications. And if you're making expense reporting software or whatever, it's useful… You kind of know how databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem. "When you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it and the capabilities of the technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself.”This is the first time the difference between technical leadership for “normal” software and for “AI” software was articulated this clearly for us, and we'll be thinking a lot about this going forward. We left a lot of nuggets in the conversation, so we hope you'll just dive in with us (and thank Bret for joining the pod!)Timestamps* 00:00:02 Introductions and Bret Taylor's background* 00:01:23 Bret's experience at Stanford and the dot-com era* 00:04:04 The story of rewriting Google Maps backend* 00:11:06 Early days of interactive web applications at Google* 00:15:26 Discussion on product management and engineering roles* 00:21:00 AI and the future of software development* 00:26:42 Bret's approach to identifying customer needs and building AI companies* 00:32:09 The evolution of business models in the AI era* 00:41:00 The future of programming languages and software development* 00:49:38 Challenges in precisely communicating human intent to machines* 00:56:44 Discussion on Artificial General Intelligence (AGI) and its impact* 01:08:51 The future of agent-to-agent communication* 01:14:03 Bret's involvement in the OpenAI leadership crisis* 01:22:11 OpenAI's relationship with Microsoft* 01:23:23 OpenAI's mission and priorities* 01:27:40 Bret's guiding principles for career choices* 01:29:12 Brief discussion on pasta-making* 01:30:47 How Bret keeps up with AI developments* 01:32:15 Exciting research directions in AI* 01:35:19 Closing remarks and hiring at Sierra Transcript[00:02:05] Introduction and Guest Welcome[00:02:05] Alessio: 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.[00:02:17] swyx: Hey, and today we're super excited to have Bret Taylor join us. Welcome. Thanks for having me. It's a little unreal to have you in the studio.[00:02:25] swyx: I've read about you so much over the years, like even before. Open AI effectively. I mean, I use Google Maps to get here. So like, thank you for everything that you've done. Like, like your story history, like, you know, I think people can find out what your greatest hits have been.[00:02:40] Bret Taylor's Early Career and Education[00:02:40] swyx: How do you usually like to introduce yourself when, you know, you talk about, you summarize your career, like, how do you look at yourself?[00:02:47] Bret: Yeah, it's a great question. You know, we, before we went on the mics here, we're talking about the audience for this podcast being more engineering. And I do think depending on the audience, I'll introduce myself differently because I've had a lot of [00:03:00] corporate and board roles. I probably self identify as an engineer more than anything else though.[00:03:04] Bret: So even when I was. Salesforce, I was coding on the weekends. So I think of myself as an engineer and then all the roles that I do in my career sort of start with that just because I do feel like engineering is sort of a mindset and how I approach most of my life. So I'm an engineer first and that's how I describe myself.[00:03:24] Bret: You majored in computer[00:03:25] swyx: science, like 1998. And, and I was high[00:03:28] Bret: school, actually my, my college degree was Oh, two undergrad. Oh, three masters. Right. That old.[00:03:33] swyx: Yeah. I mean, no, I was going, I was going like 1998 to 2003, but like engineering wasn't as, wasn't a thing back then. Like we didn't have the title of senior engineer, you know, kind of like, it was just.[00:03:44] swyx: You were a programmer, you were a developer, maybe. What was it like in Stanford? Like, what was that feeling like? You know, was it, were you feeling like on the cusp of a great computer revolution? Or was it just like a niche, you know, interest at the time?[00:03:57] Stanford and the Dot-Com Bubble[00:03:57] Bret: Well, I was at Stanford, as you said, from 1998 to [00:04:00] 2002.[00:04:02] Bret: 1998 was near the peak of the dot com bubble. So. This is back in the day where most people that they're coding in the computer lab, just because there was these sun microsystems, Unix boxes there that most of us had to do our assignments on. And every single day there was a. com like buying pizza for everybody.[00:04:20] Bret: I didn't have to like, I got. Free food, like my first two years of university and then the dot com bubble burst in the middle of my college career. And so by the end there was like tumbleweed going to the job fair, you know, it was like, cause it was hard to describe unless you were there at the time, the like level of hype and being a computer science major at Stanford was like, A thousand opportunities.[00:04:45] Bret: And then, and then when I left, it was like Microsoft, IBM.[00:04:49] Joining Google and Early Projects[00:04:49] Bret: And then the two startups that I applied to were VMware and Google. And I ended up going to Google in large part because a woman named Marissa Meyer, who had been a teaching [00:05:00] assistant when I was, what was called a section leader, which was like a junior teaching assistant kind of for one of the big interest.[00:05:05] Bret: Yes. Classes. She had gone there. And she was recruiting me and I knew her and it was sort of felt safe, you know, like, I don't know. I thought about it much, but it turned out to be a real blessing. I realized like, you know, you always want to think you'd pick Google if given the option, but no one knew at the time.[00:05:20] Bret: And I wonder if I'd graduated in like 1999 where I've been like, mom, I just got a job at pets. com. It's good. But you know, at the end I just didn't have any options. So I was like, do I want to go like make kernel software at VMware? Do I want to go build search at Google? And I chose Google. 50, 50 ball.[00:05:36] Bret: I'm not really a 50, 50 ball. So I feel very fortunate in retrospect that the economy collapsed because in some ways it forced me into like one of the greatest companies of all time, but I kind of lucked into it, I think.[00:05:47] The Google Maps Rewrite Story[00:05:47] Alessio: So the famous story about Google is that you rewrote the Google maps back in, in one week after the map quest quest maps acquisition, what was the story there?[00:05:57] Alessio: Is it. Actually true. Is it [00:06:00] being glorified? Like how, how did that come to be? And is there any detail that maybe Paul hasn't shared before?[00:06:06] Bret: It's largely true, but I'll give the color commentary. So it was actually the front end, not the back end, but it turns out for Google maps, the front end was sort of the hard part just because Google maps was.[00:06:17] Bret: Largely the first ish kind of really interactive web application, say first ish. I think Gmail certainly was though Gmail, probably a lot of people then who weren't engineers probably didn't appreciate its level of interactivity. It was just fast, but. Google maps, because you could drag the map and it was sort of graphical.[00:06:38] Bret: My, it really in the mainstream, I think, was it a map[00:06:41] swyx: quest back then that was, you had the arrows up and down, it[00:06:44] Bret: was up and down arrows. Each map was a single image and you just click left and then wait for a few seconds to the new map to let it was really small too, because generating a big image was kind of expensive on computers that day.[00:06:57] Bret: So Google maps was truly innovative in that [00:07:00] regard. The story on it. There was a small company called where two technologies started by two Danish brothers, Lars and Jens Rasmussen, who are two of my closest friends now. They had made a windows app called expedition, which had beautiful maps. Even in 2000.[00:07:18] Bret: For whenever we acquired or sort of acquired their company, Windows software was not particularly fashionable, but they were really passionate about mapping and we had made a local search product that was kind of middling in terms of popularity, sort of like a yellow page of search product. So we wanted to really go into mapping.[00:07:36] Bret: We'd started working on it. Their small team seemed passionate about it. So we're like, come join us. We can build this together.[00:07:42] Technical Challenges and Innovations[00:07:42] Bret: It turned out to be a great blessing that they had built a windows app because you're less technically constrained when you're doing native code than you are building a web browser, particularly back then when there weren't really interactive web apps and it ended up.[00:07:56] Bret: Changing the level of quality that we [00:08:00] wanted to hit with the app because we were shooting for something that felt like a native windows application. So it was a really good fortune that we sort of, you know, their unusual technical choices turned out to be the greatest blessing. So we spent a lot of time basically saying, how can you make a interactive draggable map in a web browser?[00:08:18] Bret: How do you progressively load, you know, new map tiles, you know, as you're dragging even things like down in the weeds of the browser at the time, most browsers like Internet Explorer, which was dominant at the time would only load two images at a time from the same domain. So we ended up making our map tile servers have like.[00:08:37] Bret: Forty different subdomains so we could load maps and parallels like lots of hacks. I'm happy to go into as much as like[00:08:44] swyx: HTTP connections and stuff.[00:08:46] Bret: They just like, there was just maximum parallelism of two. And so if you had a map, set of map tiles, like eight of them, so So we just, we were down in the weeds of the browser anyway.[00:08:56] Bret: So it was lots of plumbing. I can, I know a lot more about browsers than [00:09:00] most people, but then by the end of it, it was fairly, it was a lot of duct tape on that code. If you've ever done an engineering project where you're not really sure the path from point A to point B, it's almost like. Building a house by building one room at a time.[00:09:14] Bret: The, there's not a lot of architectural cohesion at the end. And then we acquired a company called Keyhole, which became Google earth, which was like that three, it was a native windows app as well, separate app, great app, but with that, we got licenses to all this satellite imagery. And so in August of 2005, we added.[00:09:33] Bret: Satellite imagery to Google Maps, which added even more complexity in the code base. And then we decided we wanted to support Safari. There was no mobile phones yet. So Safari was this like nascent browser on, on the Mac. And it turns out there's like a lot of decisions behind the scenes, sort of inspired by this windows app, like heavy use of XML and XSLT and all these like.[00:09:54] Bret: Technologies that were like briefly fashionable in the early two thousands and everyone hates now for good [00:10:00] reason. And it turns out that all of the XML functionality and Internet Explorer wasn't supporting Safari. So people are like re implementing like XML parsers. And it was just like this like pile of s**t.[00:10:11] Bret: And I had to say a s**t on your part. Yeah, of[00:10:12] Alessio: course.[00:10:13] Bret: So. It went from this like beautifully elegant application that everyone was proud of to something that probably had hundreds of K of JavaScript, which sounds like nothing. Now we're talking like people have modems, you know, not all modems, but it was a big deal.[00:10:29] Bret: So it was like slow. It took a while to load and just, it wasn't like a great code base. Like everything was fragile. So I just got. Super frustrated by it. And then one weekend I did rewrite all of it. And at the time the word JSON hadn't been coined yet too, just to give you a sense. So it's all XML.[00:10:47] swyx: Yeah.[00:10:47] Bret: So we used what is now you would call JSON, but I just said like, let's use eval so that we can parse the data fast. And, and again, that's, it would literally as JSON, but at the time there was no name for it. So we [00:11:00] just said, let's. Pass on JavaScript from the server and eval it. And then somebody just refactored the whole thing.[00:11:05] Bret: And, and it wasn't like I was some genius. It was just like, you know, if you knew everything you wished you had known at the beginning and I knew all the functionality, cause I was the primary, one of the primary authors of the JavaScript. And I just like, I just drank a lot of coffee and just stayed up all weekend.[00:11:22] Bret: And then I, I guess I developed a bit of reputation and no one knew about this for a long time. And then Paul who created Gmail and I ended up starting a company with him too, after all of this told this on a podcast and now it's large, but it's largely true. I did rewrite it and it, my proudest thing.[00:11:38] Bret: And I think JavaScript people appreciate this. Like the un G zipped bundle size for all of Google maps. When I rewrote, it was 20 K G zipped. It was like much smaller for the entire application. It went down by like 10 X. So. What happened on Google? Google is a pretty mainstream company. And so like our usage is shot up because it turns out like it's faster.[00:11:57] Bret: Just being faster is worth a lot of [00:12:00] percentage points of growth at a scale of Google. So how[00:12:03] swyx: much modern tooling did you have? Like test suites no compilers.[00:12:07] Bret: Actually, that's not true. We did it one thing. So I actually think Google, I, you can. Download it. There's a, Google has a closure compiler, a closure compiler.[00:12:15] Bret: I don't know if anyone still uses it. It's gone. Yeah. Yeah. It's sort of gone out of favor. Yeah. Well, even until recently it was better than most JavaScript minifiers because it was more like it did a lot more renaming of variables and things. Most people use ES build now just cause it's fast and closure compilers built on Java and super slow and stuff like that.[00:12:37] Bret: But, so we did have that, that was it. Okay.[00:12:39] The Evolution of Web Applications[00:12:39] Bret: So and that was treated internally, you know, it was a really interesting time at Google at the time because there's a lot of teams working on fairly advanced JavaScript when no one was. So Google suggest, which Kevin Gibbs was the tech lead for, was the first kind of type ahead, autocomplete, I believe in a web browser, and now it's just pervasive in search boxes that you sort of [00:13:00] see a type ahead there.[00:13:01] Bret: I mean, chat, dbt[00:13:01] swyx: just added it. It's kind of like a round trip.[00:13:03] Bret: Totally. No, it's now pervasive as a UI affordance, but that was like Kevin's 20 percent project. And then Gmail, Paul you know, he tells the story better than anyone, but he's like, you know, basically was scratching his own itch, but what was really neat about it is email, because it's such a productivity tool, just needed to be faster.[00:13:21] Bret: So, you know, he was scratching his own itch of just making more stuff work on the client side. And then we, because of Lars and Yen sort of like setting the bar of this windows app or like we need our maps to be draggable. So we ended up. Not only innovate in terms of having a big sync, what would be called a single page application today, but also all the graphical stuff you know, we were crashing Firefox, like it was going out of style because, you know, when you make a document object model with the idea that it's a document and then you layer on some JavaScript and then we're essentially abusing all of this, it just was running into code paths that were not.[00:13:56] Bret: Well, it's rotten, you know, at this time. And so it was [00:14:00] super fun. And, and, you know, in the building you had, so you had compilers, people helping minify JavaScript just practically, but there is a great engineering team. So they were like, that's why Closure Compiler is so good. It was like a. Person who actually knew about programming languages doing it, not just, you know, writing regular expressions.[00:14:17] Bret: And then the team that is now the Chrome team believe, and I, I don't know this for a fact, but I'm pretty sure Google is the main contributor to Firefox for a long time in terms of code. And a lot of browser people were there. So every time we would crash Firefox, we'd like walk up two floors and say like, what the hell is going on here?[00:14:35] Bret: And they would load their browser, like in a debugger. And we could like figure out exactly what was breaking. And you can't change the code, right? Cause it's the browser. It's like slow, right? I mean, slow to update. So, but we could figure out exactly where the bug was and then work around it in our JavaScript.[00:14:52] Bret: So it was just like new territory. Like so super, super fun time, just like a lot of, a lot of great engineers figuring out [00:15:00] new things. And And now, you know, the word, this term is no longer in fashion, but the word Ajax, which was asynchronous JavaScript and XML cause I'm telling you XML, but see the word XML there, to be fair, the way you made HTTP requests from a client to server was this.[00:15:18] Bret: Object called XML HTTP request because Microsoft and making Outlook web access back in the day made this and it turns out to have nothing to do with XML. It's just a way of making HTTP requests because XML was like the fashionable thing. It was like that was the way you, you know, you did it. But the JSON came out of that, you know, and then a lot of the best practices around building JavaScript applications is pre React.[00:15:44] Bret: I think React was probably the big conceptual step forward that we needed. Even my first social network after Google, we used a lot of like HTML injection and. Making real time updates was still very hand coded and it's really neat when you [00:16:00] see conceptual breakthroughs like react because it's, I just love those things where it's like obvious once you see it, but it's so not obvious until you do.[00:16:07] Bret: And actually, well, I'm sure we'll get into AI, but I, I sort of feel like we'll go through that evolution with AI agents as well that I feel like we're missing a lot of the core abstractions that I think in 10 years we'll be like, gosh, how'd you make agents? Before that, you know, but it was kind of that early days of web applications.[00:16:22] swyx: There's a lot of contenders for the reactive jobs of of AI, but no clear winner yet. I would say one thing I was there for, I mean, there's so much we can go into there. You just covered so much.[00:16:32] Product Management and Engineering Synergy[00:16:32] swyx: One thing I just, I just observe is that I think the early Google days had this interesting mix of PM and engineer, which I think you are, you didn't, you didn't wait for PM to tell you these are my, this is my PRD.[00:16:42] swyx: This is my requirements.[00:16:44] mix: Oh,[00:16:44] Bret: okay.[00:16:45] swyx: I wasn't technically a software engineer. I mean,[00:16:48] Bret: by title, obviously. Right, right, right.[00:16:51] swyx: It's like a blend. And I feel like these days, product is its own discipline and its own lore and own industry and engineering is its own thing. And there's this process [00:17:00] that happens and they're kind of separated, but you don't produce as good of a product as if they were the same person.[00:17:06] swyx: And I'm curious, you know, if, if that, if that sort of resonates in, in, in terms of like comparing early Google versus modern startups that you see out there,[00:17:16] Bret: I certainly like wear a lot of hats. So, you know, sort of biased in this, but I really agree that there's a lot of power and combining product design engineering into as few people as possible because, you know few great things have been created by committee, you know, and so.[00:17:33] Bret: If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a. Maniacal focus on outcomes.[00:17:53] Bret: And I think the reason why it's, I think for some areas, if you look at like software as a service five years ago, maybe you can have a [00:18:00] separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of like. Technological breakthroughs required for most, you know, business applications.[00:18:11] Bret: And if you're making expense reporting software or whatever, it's useful. I don't mean to be dismissive of expense reporting software, but you probably just want to understand like, what are the requirements of the finance department? What are the requirements of an individual file expense report? Okay.[00:18:25] Bret: Go implement that. And you kind of know how web applications are implemented. You kind of know how to. How databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem when you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it.[00:18:58] Bret: And the capabilities of the [00:19:00] technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself. And that's why I use the word conversation. It's not literal. That's sort of funny to use that word in the age of conversational AI.[00:19:15] Bret: You're constantly sort of saying, like, ideally, you could sprinkle some magic AI pixie dust and solve all the world's problems, but it's not the way it works. And it turns out that actually, I'll just give an interesting example.[00:19:26] AI Agents and Modern Tooling[00:19:26] Bret: I think most people listening probably use co pilots to code like Cursor or Devon or Microsoft Copilot or whatever.[00:19:34] Bret: Most of those tools are, they're remarkable. I'm, I couldn't, you know, imagine development without them now, but they're not autonomous yet. Like I wouldn't let it just write most code without my interactively inspecting it. We just are somewhere between it's an amazing co pilot and it's an autonomous software engineer.[00:19:53] Bret: As a product manager, like your aspirations for what the product is are like kind of meaningful. But [00:20:00] if you're a product person, yeah, of course you'd say it should be autonomous. You should click a button and program should come out the other side. The requirements meaningless. Like what matters is like, what is based on the like very nuanced limitations of the technology.[00:20:14] Bret: What is it capable of? And then how do you maximize the leverage? It gives a software engineering team, given those very nuanced trade offs. Coupled with the fact that those nuanced trade offs are changing more rapidly than any technology in my memory, meaning every few months you'll have new models with new capabilities.[00:20:34] Bret: So how do you construct a product that can absorb those new capabilities as rapidly as possible as well? That requires such a combination of technical depth and understanding the customer that you really need more integration. Of product design and engineering. And so I think it's why with these big technology waves, I think startups have a bit of a leg up relative to incumbents because they [00:21:00] tend to be sort of more self actualized in terms of just like bringing those disciplines closer together.[00:21:06] Bret: And in particular, I think entrepreneurs, the proverbial full stack engineers, you know, have a leg up as well because. I think most breakthroughs happen when you have someone who can understand those extremely nuanced technical trade offs, have a vision for a product. And then in the process of building it, have that, as I said, like metaphorical conversation with the technology, right?[00:21:30] Bret: Gosh, I ran into a technical limit that I didn't expect. It's not just like changing that feature. You might need to refactor the whole product based on that. And I think that's, that it's particularly important right now. So I don't, you know, if you, if you're building a big ERP system, probably there's a great reason to have product and engineering.[00:21:51] Bret: I think in general, the disciplines are there for a reason. I think when you're dealing with something as nuanced as the like technologies, like large language models today, there's a ton of [00:22:00] advantage of having. Individuals or organizations that integrate the disciplines more formally.[00:22:05] Alessio: That makes a lot of sense.[00:22:06] Alessio: I've run a lot of engineering teams in the past, and I think the product versus engineering tension has always been more about effort than like whether or not the feature is buildable. But I think, yeah, today you see a lot more of like. Models actually cannot do that. And I think the most interesting thing is on the startup side, people don't yet know where a lot of the AI value is going to accrue.[00:22:26] Alessio: So you have this rush of people building frameworks, building infrastructure, layered things, but we don't really know the shape of the compute. I'm curious that Sierra, like how you thought about building an house, a lot of the tooling for evals or like just, you know, building the agents and all of that.[00:22:41] Alessio: Versus how you see some of the startup opportunities that is maybe still out there.[00:22:46] Bret: We build most of our tooling in house at Sierra, not all. It's, we don't, it's not like not invented here syndrome necessarily, though, maybe slightly guilty of that in some ways, but because we're trying to build a platform [00:23:00] that's in Dorian, you know, we really want to have control over our own destiny.[00:23:03] Bret: And you had made a comment earlier that like. We're still trying to figure out who like the reactive agents are and the jury is still out. I would argue it hasn't been created yet. I don't think the jury is still out to go use that metaphor. We're sort of in the jQuery era of agents, not the react era.[00:23:19] Bret: And, and that's like a throwback for people listening,[00:23:22] swyx: we shouldn't rush it. You know?[00:23:23] Bret: No, yeah, that's my point is. And so. Because we're trying to create an enduring company at Sierra that outlives us, you know, I'm not sure we want to like attach our cart to some like to a horse where it's not clear that like we've figured out and I actually want as a company, we're trying to enable just at a high level and I'll, I'll quickly go back to tech at Sierra, we help consumer brands build customer facing AI agents.[00:23:48] Bret: So. Everyone from Sonos to ADT home security to Sirius XM, you know, if you call them on the phone and AI will pick up with you, you know, chat with them on the Sirius XM homepage. It's an AI agent called Harmony [00:24:00] that they've built on our platform. We're what are the contours of what it means for someone to build an end to end complete customer experience with AI with conversational AI.[00:24:09] Bret: You know, we really want to dive into the deep end of, of all the trade offs to do it. You know, where do you use fine tuning? Where do you string models together? You know, where do you use reasoning? Where do you use generation? How do you use reasoning? How do you express the guardrails of an agentic process?[00:24:25] Bret: How do you impose determinism on a fundamentally non deterministic technology? There's just a lot of really like as an important design space. And I could sit here and tell you, we have the best approach. Every entrepreneur will, you know. But I hope that in two years, we look back at our platform and laugh at how naive we were, because that's the pace of change broadly.[00:24:45] Bret: If you talk about like the startup opportunities, I'm not wholly skeptical of tools companies, but I'm fairly skeptical. There's always an exception for every role, but I believe that certainly there's a big market for [00:25:00] frontier models, but largely for companies with huge CapEx budgets. So. Open AI and Microsoft's Anthropic and Amazon Web Services, Google Cloud XAI, which is very well capitalized now, but I think the, the idea that a company can make money sort of pre training a foundation model is probably not true.[00:25:20] Bret: It's hard to, you're competing with just, you know, unreasonably large CapEx budgets. And I just like the cloud infrastructure market, I think will be largely there. I also really believe in the applications of AI. And I define that not as like building agents or things like that. I define it much more as like, you're actually solving a problem for a business.[00:25:40] Bret: So it's what Harvey is doing in legal profession or what cursor is doing for software engineering or what we're doing for customer experience and customer service. The reason I believe in that is I do think that in the age of AI, what's really interesting about software is it can actually complete a task.[00:25:56] Bret: It can actually do a job, which is very different than the value proposition of [00:26:00] software was to ancient history two years ago. And as a consequence, I think the way you build a solution and For a domain is very different than you would have before, which means that it's not obvious, like the incumbent incumbents have like a leg up, you know, necessarily, they certainly have some advantages, but there's just such a different form factor, you know, for providing a solution and it's just really valuable.[00:26:23] Bret: You know, it's. Like just think of how much money cursor is saving software engineering teams or the alternative, how much revenue it can produce tool making is really challenging. If you look at the cloud market, just as a analog, there are a lot of like interesting tools, companies, you know, Confluent, Monetized Kafka, Snowflake, Hortonworks, you know, there's a, there's a bunch of them.[00:26:48] Bret: A lot of them, you know, have that mix of sort of like like confluence or have the open source or open core or whatever you call it. I, I, I'm not an expert in this area. You know, I do think [00:27:00] that developers are fickle. I think that in the tool space, I probably like. Default towards open source being like the area that will win.[00:27:09] Bret: It's hard to build a company around this and then you end up with companies sort of built around open source to that can work. Don't get me wrong, but I just think that it's nowadays the tools are changing so rapidly that I'm like, not totally skeptical of tool makers, but I just think that open source will broadly win, but I think that the CapEx required for building frontier models is such that it will go to a handful of big companies.[00:27:33] Bret: And then I really believe in agents for specific domains which I think will, it's sort of the analog to software as a service in this new era. You know, it's like, if you just think of the cloud. You can lease a server. It's just a low level primitive, or you can buy an app like you know, Shopify or whatever.[00:27:51] Bret: And most people building a storefront would prefer Shopify over hand rolling their e commerce storefront. I think the same thing will be true of AI. So [00:28:00] I've. I tend to like, if I have a, like an entrepreneur asked me for advice, I'm like, you know, move up the stack as far as you can towards a customer need.[00:28:09] Bret: Broadly, but I, but it doesn't reduce my excitement about what is the reactive building agents kind of thing, just because it is, it is the right question to ask, but I think we'll probably play out probably an open source space more than anything else.[00:28:21] swyx: Yeah, and it's not a priority for you. There's a lot in there.[00:28:24] swyx: I'm kind of curious about your idea maze towards, there are many customer needs. You happen to identify customer experience as yours, but it could equally have been coding assistance or whatever. I think for some, I'm just kind of curious at the top down, how do you look at the world in terms of the potential problem space?[00:28:44] swyx: Because there are many people out there who are very smart and pick the wrong problem.[00:28:47] Bret: Yeah, that's a great question.[00:28:48] Future of Software Development[00:28:48] Bret: By the way, I would love to talk about the future of software, too, because despite the fact it didn't pick coding, I have a lot of that, but I can talk to I can answer your question, though, you know I think when a technology is as [00:29:00] cool as large language models.[00:29:02] Bret: You just see a lot of people starting from the technology and searching for a problem to solve. And I think it's why you see a lot of tools companies, because as a software engineer, you start building an app or a demo and you, you encounter some pain points. You're like,[00:29:17] swyx: a lot of[00:29:17] Bret: people are experiencing the same pain point.[00:29:19] Bret: What if I make it? That it's just very incremental. And you know, I always like to use the metaphor, like you can sell coffee beans, roasted coffee beans. You can add some value. You took coffee beans and you roasted them and roasted coffee beans largely, you know, are priced relative to the cost of the beans.[00:29:39] Bret: Or you can sell a latte and a latte. Is rarely priced directly like as a percentage of coffee bean prices. In fact, if you buy a latte at the airport, it's a captive audience. So it's a really expensive latte. And there's just a lot that goes into like. How much does a latte cost? And I bring it up because there's a supply chain from growing [00:30:00] coffee beans to roasting coffee beans to like, you know, you could make one at home or you could be in the airport and buy one and the margins of the company selling lattes in the airport is a lot higher than the, you know, people roasting the coffee beans and it's because you've actually solved a much more acute human problem in the airport.[00:30:19] Bret: And, and it's just worth a lot more to that person in that moment. It's kind of the way I think about technology too. It sounds funny to liken it to coffee beans, but you're selling tools on top of a large language model yet in some ways your market is big, but you're probably going to like be price compressed just because you're sort of a piece of infrastructure and then you have open source and all these other things competing with you naturally.[00:30:43] Bret: If you go and solve a really big business problem for somebody, that's actually like a meaningful business problem that AI facilitates, they will value it according to the value of that business problem. And so I actually feel like people should just stop. You're like, no, that's, that's [00:31:00] unfair. If you're searching for an idea of people, I, I love people trying things, even if, I mean, most of the, a lot of the greatest ideas have been things no one believed in.[00:31:07] Bret: So I like, if you're passionate about something, go do it. Like who am I to say, yeah, a hundred percent. Or Gmail, like Paul as far, I mean I, some of it's Laura at this point, but like Gmail is Paul's own email for a long time. , and then I amusingly and Paul can't correct me, I'm pretty sure he sent her in a link and like the first comment was like, this is really neat.[00:31:26] Bret: It would be great. It was not your email, but my own . I don't know if it's a true story. I'm pretty sure it's, yeah, I've read that before. So scratch your own niche. Fine. Like it depends on what your goal is. If you wanna do like a venture backed company, if its a. Passion project, f*****g passion, do it like don't listen to anybody.[00:31:41] Bret: In fact, but if you're trying to start, you know an enduring company, solve an important business problem. And I, and I do think that in the world of agents, the software industries has shifted where you're not just helping people more. People be more productive, but you're actually accomplishing tasks autonomously.[00:31:58] Bret: And as a consequence, I think the [00:32:00] addressable market has just greatly expanded just because software can actually do things now and actually accomplish tasks and how much is coding autocomplete worth. A fair amount. How much is the eventual, I'm certain we'll have it, the software agent that actually writes the code and delivers it to you, that's worth a lot.[00:32:20] Bret: And so, you know, I would just maybe look up from the large language models and start thinking about the economy and, you know, think from first principles. I don't wanna get too far afield, but just think about which parts of the economy. We'll benefit most from this intelligence and which parts can absorb it most easily.[00:32:38] Bret: And what would an agent in this space look like? Who's the customer of it is the technology feasible. And I would just start with these business problems more. And I think, you know, the best companies tend to have great engineers who happen to have great insight into a market. And it's that last part that I think some people.[00:32:56] Bret: Whether or not they have, it's like people start so much in the technology, they [00:33:00] lose the forest for the trees a little bit.[00:33:02] Alessio: How do you think about the model of still selling some sort of software versus selling more package labor? I feel like when people are selling the package labor, it's almost more stateless, you know, like it's easier to swap out if you're just putting an input and getting an output.[00:33:16] Alessio: If you think about coding, if there's no ID, you're just putting a prompt and getting back an app. It doesn't really matter. Who generates the app, you know, you have less of a buy in versus the platform you're building, I'm sure on the backend customers have to like put on their documentation and they have, you know, different workflows that they can tie in what's kind of like the line to draw there versus like going full where you're managed customer support team as a service outsource versus.[00:33:40] Alessio: This is the Sierra platform that you can build on. What was that decision? I'll sort of[00:33:44] Bret: like decouple the question in some ways, which is when you have something that's an agent, who is the person using it and what do they want to do with it? So let's just take your coding agent for a second. I will talk about Sierra as well.[00:33:59] Bret: Who's the [00:34:00] customer of a, an agent that actually produces software? Is it a software engineering manager? Is it a software engineer? And it's there, you know, intern so to speak. I don't know. I mean, we'll figure this out over the next few years. Like what is that? And is it generating code that you then review?[00:34:16] Bret: Is it generating code with a set of unit tests that pass, what is the actual. For lack of a better word contract, like, how do you know that it did what you wanted it to do? And then I would say like the product and the pricing, the packaging model sort of emerged from that. And I don't think the world's figured out.[00:34:33] Bret: I think it'll be different for every agent. You know, in our customer base, we do what's called outcome based pricing. So essentially every time the AI agent. Solves the problem or saves a customer or whatever it might be. There's a pre negotiated rate for that. We do that. Cause it's, we think that that's sort of the correct way agents, you know, should be packaged.[00:34:53] Bret: I look back at the history of like cloud software and notably the introduction of the browser, which led to [00:35:00] software being delivered in a browser, like Salesforce to. Famously invented sort of software as a service, which is both a technical delivery model through the browser, but also a business model, which is you subscribe to it rather than pay for a perpetual license.[00:35:13] Bret: Those two things are somewhat orthogonal, but not really. If you think about the idea of software running in a browser, that's hosted. Data center that you don't own, you sort of needed to change the business model because you don't, you can't really buy a perpetual license or something otherwise like, how do you afford making changes to it?[00:35:31] Bret: So it only worked when you were buying like a new version every year or whatever. So to some degree, but then the business model shift actually changed business as we know it, because now like. Things like Adobe Photoshop. Now you subscribe to rather than purchase. So it ended up where you had a technical shift and a business model shift that were very logically intertwined that actually the business model shift was turned out to be as significant as the technical as the shift.[00:35:59] Bret: And I think with [00:36:00] agents, because they actually accomplish a job, I do think that it doesn't make sense to me that you'd pay for the privilege of like. Using the software like that coding agent, like if it writes really bad code, like fire it, you know, I don't know what the right metaphor is like you should pay for a job.[00:36:17] Bret: Well done in my opinion. I mean, that's how you pay your software engineers, right? And[00:36:20] swyx: and well, not really. We paid to put them on salary and give them options and they vest over time. That's fair.[00:36:26] Bret: But my point is that you don't pay them for how many characters they write, which is sort of the token based, you know, whatever, like, There's a, that famous Apple story where we're like asking for a report of how many lines of code you wrote.[00:36:40] Bret: And one of the engineers showed up with like a negative number cause he had just like done a big refactoring. There was like a big F you to management who didn't understand how software is written. You know, my sense is like the traditional usage based or seat based thing. It's just going to look really antiquated.[00:36:55] Bret: Cause it's like asking your software engineer, how many lines of code did you write today? Like who cares? Like, cause [00:37:00] absolutely no correlation. So my old view is I don't think it's be different in every category, but I do think that that is the, if an agent is doing a job, you should, I think it properly incentivizes the maker of that agent and the customer of, of your pain for the job well done.[00:37:16] Bret: It's not always perfect to measure. It's hard to measure engineering productivity, but you can, you should do something other than how many keys you typed, you know Talk about perverse incentives for AI, right? Like I can write really long functions to do the same thing, right? So broadly speaking, you know, I do think that we're going to see a change in business models of software towards outcomes.[00:37:36] Bret: And I think you'll see a change in delivery models too. And, and, you know, in our customer base you know, we empower our customers to really have their hands on the steering wheel of what the agent does they, they want and need that. But the role is different. You know, at a lot of our customers, the customer experience operations folks have renamed themselves the AI architects, which I think is really cool.[00:37:55] Bret: And, you know, it's like in the early days of the Internet, there's the role of the webmaster. [00:38:00] And I don't know whether your webmaster is not a fashionable, you know, Term, nor is it a job anymore? I just, I don't know. Will they, our tech stand the test of time? Maybe, maybe not. But I do think that again, I like, you know, because everyone listening right now is a software engineer.[00:38:14] Bret: Like what is the form factor of a coding agent? And actually I'll, I'll take a breath. Cause actually I have a bunch of pins on them. Like I wrote a blog post right before Christmas, just on the future of software development. And one of the things that's interesting is like, if you look at the way I use cursor today, as an example, it's inside of.[00:38:31] Bret: A repackaged visual studio code environment. I sometimes use the sort of agentic parts of it, but it's largely, you know, I've sort of gotten a good routine of making it auto complete code in the way I want through tuning it properly when it actually can write. I do wonder what like the future of development environments will look like.[00:38:55] Bret: And to your point on what is a software product, I think it's going to change a lot in [00:39:00] ways that will surprise us. But I always use, I use the metaphor in my blog post of, have you all driven around in a way, Mo around here? Yeah, everyone has. And there are these Jaguars, the really nice cars, but it's funny because it still has a steering wheel, even though there's no one sitting there and the steering wheels like turning and stuff clearly in the future.[00:39:16] Bret: If once we get to that, be more ubiquitous, like why have the steering wheel and also why have all the seats facing forward? Maybe just for car sickness. I don't know, but you could totally rearrange the car. I mean, so much of the car is oriented around the driver, so. It stands to reason to me that like, well, autonomous agents for software engineering run through visual studio code.[00:39:37] Bret: That seems a little bit silly because having a single source code file open one at a time is kind of a goofy form factor for when like the code isn't being written primarily by you, but it begs the question of what's your relationship with that agent. And I think the same is true in our industry of customer experience, which is like.[00:39:55] Bret: Who are the people managing this agent? What are the tools do they need? And they definitely need [00:40:00] tools, but it's probably pretty different than the tools we had before. It's certainly different than training a contact center team. And as software engineers, I think that I would like to see particularly like on the passion project side or research side.[00:40:14] Bret: More innovation in programming languages. I think that we're bringing the cost of writing code down to zero. So the fact that we're still writing Python with AI cracks me up just cause it's like literally was designed to be ergonomic to write, not safe to run or fast to run. I would love to see more innovation and how we verify program correctness.[00:40:37] Bret: I studied for formal verification in college a little bit and. It's not very fashionable because it's really like tedious and slow and doesn't work very well. If a lot of code is being written by a machine, you know, one of the primary values we can provide is verifying that it actually does what we intend that it does.[00:40:56] Bret: I think there should be lots of interesting things in the software development life cycle, like how [00:41:00] we think of testing and everything else, because. If you think about if we have to manually read every line of code that's coming out as machines, it will just rate limit how much the machines can do. The alternative is totally unsafe.[00:41:13] Bret: So I wouldn't want to put code in production that didn't go through proper code review and inspection. So my whole view is like, I actually think there's like an AI native I don't think the coding agents don't work well enough to do this yet, but once they do, what is sort of an AI native software development life cycle and how do you actually.[00:41:31] Bret: Enable the creators of software to produce the highest quality, most robust, fastest software and know that it's correct. And I think that's an incredible opportunity. I mean, how much C code can we rewrite and rust and make it safe so that there's fewer security vulnerabilities. Can we like have more efficient, safer code than ever before?[00:41:53] Bret: And can you have someone who's like that guy in the matrix, you know, like staring at the little green things, like where could you have an operator [00:42:00] of a code generating machine be like superhuman? I think that's a cool vision. And I think too many people are focused on like. Autocomplete, you know, right now, I'm not, I'm not even, I'm guilty as charged.[00:42:10] Bret: I guess in some ways, but I just like, I'd like to see some bolder ideas. And that's why when you were joking, you know, talking about what's the react of whatever, I think we're clearly in a local maximum, you know, metaphor, like sort of conceptual local maximum, obviously it's moving really fast. I think we're moving out of it.[00:42:26] Alessio: Yeah. At the end of 23, I've read this blog post from syntax to semantics. Like if you think about Python. It's taking C and making it more semantic and LLMs are like the ultimate semantic program, right? You can just talk to them and they can generate any type of syntax from your language. But again, the languages that they have to use were made for us, not for them.[00:42:46] Alessio: But the problem is like, as long as you will ever need a human to intervene, you cannot change the language under it. You know what I mean? So I'm curious at what point of automation we'll need to get, we're going to be okay making changes. To the underlying languages, [00:43:00] like the programming languages versus just saying, Hey, you just got to write Python because I understand Python and I'm more important at the end of the day than the model.[00:43:08] Alessio: But I think that will change, but I don't know if it's like two years or five years. I think it's more nuanced actually.[00:43:13] Bret: So I think there's a, some of the more interesting programming languages bring semantics into syntax. So let me, that's a little reductive, but like Rust as an example, Rust is memory safe.[00:43:25] Bret: Statically, and that was a really interesting conceptual, but it's why it's hard to write rust. It's why most people write python instead of rust. I think rust programs are safer and faster than python, probably slower to compile. But like broadly speaking, like given the option, if you didn't have to care about the labor that went into it.[00:43:45] Bret: You should prefer a program written in Rust over a program written in Python, just because it will run more efficiently. It's almost certainly safer, et cetera, et cetera, depending on how you define safe, but most people don't write Rust because it's kind of a pain in the ass. And [00:44:00] the audience of people who can is smaller, but it's sort of better in most, most ways.[00:44:05] Bret: And again, let's say you're making a web service and you didn't have to care about how hard it was to write. If you just got the output of the web service, the rest one would be cheaper to operate. It's certainly cheaper and probably more correct just because there's so much in the static analysis implied by the rest programming language that it probably will have fewer runtime errors and things like that as well.[00:44:25] Bret: So I just give that as an example, because so rust, at least my understanding that came out of the Mozilla team, because. There's lots of security vulnerabilities in the browser and it needs to be really fast. They said, okay, we want to put more of a burden at the authorship time to have fewer issues at runtime.[00:44:43] Bret: And we need the constraint that it has to be done statically because browsers need to be really fast. My sense is if you just think about like the, the needs of a programming language today, where the role of a software engineer is [00:45:00] to use an AI to generate functionality and audit that it does in fact work as intended, maybe functionally, maybe from like a correctness standpoint, some combination thereof, how would you create a programming system that facilitated that?[00:45:15] Bret: And, you know, I bring up Rust is because I think it's a good example of like, I think given a choice of writing in C or Rust, you should choose Rust today. I think most people would say that, even C aficionados, just because. C is largely less safe for very similar, you know, trade offs, you know, for the, the system and now with AI, it's like, okay, well, that just changes the game on writing these things.[00:45:36] Bret: And so like, I just wonder if a combination of programming languages that are more structurally oriented towards the values that we need from an AI generated program, verifiable correctness and all of that. If it's tedious to produce for a person, that maybe doesn't matter. But one thing, like if I asked you, is this rest program memory safe?[00:45:58] Bret: You wouldn't have to read it, you just have [00:46:00] to compile it. So that's interesting. I mean, that's like an, that's one example of a very modest form of formal verification. So I bring that up because I do think you have AI inspect AI, you can have AI reviewed. Do AI code reviews. It would disappoint me if the best we could get was AI reviewing Python and having scaled a few very large.[00:46:21] Bret: Websites that were written on Python. It's just like, you know, expensive and it's like every, trust me, every team who's written a big web service in Python has experimented with like Pi Pi and all these things just to make it slightly more efficient than it naturally is. You don't really have true multi threading anyway.[00:46:36] Bret: It's just like clearly that you do it just because it's convenient to write. And I just feel like we're, I don't want to say it's insane. I just mean. I do think we're at a local maximum. And I would hope that we create a programming system, a combination of programming languages, formal verification, testing, automated code reviews, where you can use AI to generate software in a high scale way and trust it.[00:46:59] Bret: And you're [00:47:00] not limited by your ability to read it necessarily. I don't know exactly what form that would take, but I feel like that would be a pretty cool world to live in.[00:47:08] Alessio: Yeah. We had Chris Lanner on the podcast. He's doing great work with modular. I mean, I love. LVM. Yeah. Basically merging rust in and Python.[00:47:15] Alessio: That's kind of the idea. Should be, but I'm curious is like, for them a big use case was like making it compatible with Python, same APIs so that Python developers could use it. Yeah. And so I, I wonder at what point, well, yeah.[00:47:26] Bret: At least my understanding is they're targeting the data science Yeah. Machine learning crowd, which is all written in Python, so still feels like a local maximum.[00:47:34] Bret: Yeah.[00:47:34] swyx: Yeah, exactly. I'll force you to make a prediction. You know, Python's roughly 30 years old. In 30 years from now, is Rust going to be bigger than Python?[00:47:42] Bret: I don't know this, but just, I don't even know this is a prediction. I just am sort of like saying stuff I hope is true. I would like to see an AI native programming language and programming system, and I use language because I'm not sure language is even the right thing, but I hope in 30 years, there's an AI native way we make [00:48:00] software that is wholly uncorrelated with the current set of programming languages.[00:48:04] Bret: or not uncorrelated, but I think most programming languages today were designed to be efficiently authored by people and some have different trade offs.[00:48:15] Evolution of Programming Languages[00:48:15] Bret: You know, you have Haskell and others that were designed for abstractions for parallelism and things like that. You have programming languages like Python, which are designed to be very easily written, sort of like Perl and Python lineage, which is why data scientists use it.[00:48:31] Bret: It's it can, it has a. Interactive mode, things like that. And I love, I'm a huge Python fan. So despite all my Python trash talk, a huge Python fan wrote at least two of my three companies were exclusively written in Python and then C came out of the birth of Unix and it wasn't the first, but certainly the most prominent first step after assembly language, right?[00:48:54] Bret: Where you had higher level abstractions rather than and going beyond go to, to like abstractions, [00:49:00] like the for loop and the while loop.[00:49:01] The Future of Software Engineering[00:49:01] Bret: So I just think that if the act of writing code is no longer a meaningful human exercise, maybe it will be, I don't know. I'm just saying it sort of feels like maybe it's one of those parts of history that just will sort of like go away, but there's still the role of this offer engineer, like the person actually building the system.[00:49:20] Bret: Right. And. What does a programming system for that form factor look like?[00:49:25] React and Front-End Development[00:49:25] Bret: And I, I just have a, I hope to be just like I mentioned, I remember I was at Facebook in the very early days when, when, what is now react was being created. And I remember when the, it was like released open source I had left by that time and I was just like, this is so f*****g cool.[00:49:42] Bret: Like, you know, to basically model your app independent of the data flowing through it, just made everything easier. And then now. You know, I can create, like there's a lot of the front end software gym play is like a little chaotic for me, to be honest with you. It is like, it's sort of like [00:50:00] abstraction soup right now for me, but like some of those core ideas felt really ergonomic.[00:50:04] Bret: I just wanna, I'm just looking forward to the day when someone comes up with a programming system that feels both really like an aha moment, but completely foreign to me at the same time. Because they created it with sort of like from first principles recognizing that like. Authoring code in an editor is maybe not like the primary like reason why a programming system exists anymore.[00:50:26] Bret: And I think that's like, that would be a very exciting day for me.[00:50:28] The Role of AI in Programming[00:50:28] swyx: Yeah, I would say like the various versions of this discussion have happened at the end of the day, you still need to precisely communicate what you want. As a manager of people, as someone who has done many, many legal contracts, you know how hard that is.[00:50:42] swyx: And then now we have to talk to machines doing that and AIs interpreting what we mean and reading our minds effectively. I don't know how to get across that barrier of translating human intent to instructions. And yes, it can be more declarative, but I don't know if it'll ever Crossover from being [00:51:00] a programming language to something more than that.[00:51:02] Bret: I agree with you. And I actually do think if you look at like a legal contract, you know, the imprecision of the English language, it's like a flaw in the system. How many[00:51:12] swyx: holes there are.[00:51:13] Bret: And I do think that when you're making a mission critical software system, I don't think it should be English language prompts.[00:51:19] Bret: I think that is silly because you want the precision of a a programming language. My point was less about that and more about if the actual act of authoring it, like if you.[00:51:32] Formal Verification in Software[00:51:32] Bret: I'll think of some embedded systems do use formal verification. I know it's very common in like security protocols now so that you can, because the importance of correctness is so great.[00:51:41] Bret: My intellectual exercise is like, why not do that for all software? I mean, probably that's silly just literally to do what we literally do for. These low level security protocols, but the only reason we don't is because it's hard and tedious and hard and tedious are no longer factors. So, like, if I could, I mean, [00:52:00] just think of, like, the silliest app on your phone right now, the idea that that app should be, like, formally verified for its correctness feels laughable right now because, like, God, why would you spend the time on it?[00:52:10] Bret: But if it's zero costs, like, yeah, I guess so. I mean, it never crashed. That's probably good. You know, why not? I just want to, like, set our bars really high. Like. We should make, software has been amazing. Like there's a Mark Andreessen blog post, software is eating the world. And you know, our whole life is, is mediated digitally.[00:52:26] Bret: And that's just increasing with AI. And now we'll have our personal agents talking to the agents on the CRO platform and it's agents all the way down, you know, our core infrastructure is running on these digital systems. We now have like, and we've had a shortage of software developers for my entire life.[00:52:45] Bret: And as a consequence, you know if you look, remember like health care, got healthcare. gov that fiasco security vulnerabilities leading to state actors getting access to critical infrastructure. I'm like. We now have like created this like amazing system that can [00:53:00] like, we can fix this, you know, and I, I just want to, I'm both excited about the productivity gains in the economy, but I just think as software engineers, we should be bolder.[00:53:08] Bret: Like we should have aspirations to fix these systems so that like in general, as you said, as precise as we want to be in the specification of the system. We can make it work correctly now, and I'm being a little bit hand wavy, and I think we need some systems. I think that's where we should set the bar, especially when so much of our life depends on this critical digital infrastructure.[00:53:28] Bret: So I'm I'm just like super optimistic about it. But actually, let's go to w

Get our Business Monetization Playbook: https://clickhubspot.com/monetization Episode 669: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) do a deep dive on the 7 strangers that made the greatest investment of all time.  — Show Notes:  (0:00) Andy Bechtolsheim (7:10) David Cheriton (10:10) Ron Conway (29:20) Alfred Lin (35:00) Shaquille O'Neill (37:20) Susan Wojcicki (38:50) Pejman Nozad (46:20) Jeff Bezos — Links: • SV Angel - https://svangel.com/  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Matt and Nic are back for another week of news and deals. In this episode: SALT Wyoming review Nic's next fight  How Bitcoin miners have a procyclical effect on the Bitcoin price Hunting Hill is launching a credit fund seeded by Kraken Wyoming is launching a stable token in Q1 of 2025 The SEC rejects CBOE's filings for SOL ETFs Is Gensler actually being slated to run Treasury under Harris? Tether is launching a UAE Dirham stablecoin Our upcoming Emerging Markets Stablecoin Report Our Bozo of the Week Babylon is launching Ron Conway defects from Fairshake PAC Chuck Schumer is evolving on crypto Would a CBDC actually be private for users? Sponsor notes:  Unwrapping Wrapped Assets & wBTC: In Coin Metrics State of the Network Issue 273, we understand wrapped bitcoin's (wBTC) custody transition, BitGo's business model and usage of WBTC in DeFi Withum's Digital Currency and Blockchain Technology Team specializes in crypto-assets, offering accounting, tax and advisory solutions to fortify trust in a dynamic industry. Contact them today to get started. - withum.com/crypto

On today's episode of Equity, Rebecca Bellan did a deep dive into the CrowdStrike outage that affected around 8.5 million Windows devices around the world, causing disruptions in air travel, banking, hospitals, media outlets, federal agencies and businesses of all kinds. The outage began when CrowdStrike, a cloud security giant, sent out a defective software update. While CrowdStrike quickly identified the issue and deployed a fix, the fallout continued over the weekend and will probably continue into this week, particularly for the travel sector. United, American and Delta airlines all collectively saw thousands of flights canceled and delayed, which will have ripple effects into the week. Rebecca went into how this outage – despite not being a cyberattack – has provided the world with a stark example of just how vulnerable our critical infrastructure systems are, a big problem if our adversaries decide to get any bright ideas. She also discussed the reputational damage CrowdStrike experienced, the startups that have smelled blood in the water and are poised to strike, and the potential need to regulate monopolies that offer essential services. Moving on, Rebecca took a look at what U.S. Vice President Kamala Harris's stance on technology has been, now that President Joe Biden has stepped out of the race for the presidency and officially endorsed his right hand. Harris appears to favor oversight for big tech companies to protect consumer privacy, as well as AI regulation to stop companies from prioritizing profits over people and society. While some big names in the VC and tech world have backed former President Donald Trump due to his laissez-faire approach to regulating AI and crypto (something we talked about on last week's Friday episode!), others in the industry have shown support for Harris. VCs like John Doerr and Ron Conway were among her early supporters, and as a presidential candidate, Harris was quickly endorsed by LinkedIn co-founder Reid Hoffman. Rebecca also looked at a Reuters report detailing Nvidia's plans to build a version of its new flagship AI chips for the Chinese market that are compatible with current U.S. export controls. The U.S. tightened controls of exports of semiconductors to China in 2023, a move designed to limit the Chinese military's breakthroughs in supercomputing, but it appears Nvidia isn't so keen to let that market go. Finally, Rebecca took a look at a deep dive from TechCrunch's Paul Sawers on Yandex, once referred to as the “Google of Russia” and its comeback from Nasdaq limbo. Yandex's publicly traded Dutch entity has severed all ties with Russia, selling off the entirety of its Russian assets in a fire sale earlier this year. The “new” company has adopted the name of one of its few remaining assets, a Finnish data center and AI cloud platform called Nebuis AI. The company is now operating as something of a corporation-startup hybrid. Its goal? To be a European AI compute leader. Equity will be back on Wednesday to interview Maven Ventures's Sara Deshpande about why the VC is bullish on consumer funding and how venture is looking at AI companies, so tune back in then! Equity is TechCrunch's flagship podcast, produced by Theresa Loconsolo, and posts every Monday, Wednesday and Friday. Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes over at Simplecast. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. Bryce Durbin is our Illustrator. We'd also like to thank the audience development team and Henry Pickavet, who manages TechCrunch audio products.

Ron Conway has been close to the center of things for longer than anyone else in Silicon Valley, from the point when he started his career at National Semiconductor in the early 70s to the AI conference he organized last month. He's the embodiment and the transmitter of Silicon Valley culture. He knows all the stories, usually because he was personally involved in them. In this episode we talk about what Silicon Valley was like when it was all about silicon, and how his career at Nat Semi led to working at and then running tech companies, and finally to angel investing.

A reminder for new readers. That Was The Week collects the best writing on critical issues in tech, startups, and venture capital. I selected the articles because they are of interest. The selections often include things I entirely disagree with. But they express common opinions, or they provoke me to think. The articles are only snippets. Click on the headline to go to the original. I express my point of view in the editorial and the weekly video below.This Week's Audio:Thanks To This Week's Contributors: @jeffbeckervc, @eshap, @stevesi, @gruber, @daringfireball, @SamuelStolton, @leah_nylen, @mattmday, @chrisheuer, @JoannaStern, @Om, @sarahpereztc, @GeorgeNHammond, @Tabby_Kinder, @NicholasMegaw, @PeterJ_Walker, @SteveAbbott415, @adamlashinskyContents* Editorial: * Essays of the Week* Changing the Customer of Venture Capital (Jeff Becker)* What A Drag It Is (Evan Shapiro)* Building Under Regulation (Steven Sinovsky)* Apple's Plans for the DMA in the European Union (John Gruber)* Amazon Drops iRobot Deal; Roomba Maker Cuts 31% of Staff (By Samuel Stolton, Leah Nylen, and Matt Day)* Envisioning the Future of Human Work in the Age of AI: The 2024 Forecast (Chris Heuer)* Video of the Week* Joanna Stern Wears a Vision Pro for 24 Hours* Product of the Week* The Vision Pro (Daring Fireball)* Apple's Vision Pro -The Meta-Review. (Om Malik)* My 4 magic moments with Vision Pro (Om Malik)* Apple Vision Pro Review: The Best Headset Yet Is Just a Glimpse of the Future (Joanna Stern)* News Of the Week* Spotify calls Apple's DMA compliance plan ‘extortion' and a ‘complete and total farce' (Sarah Perez)* Investors raise billions to buy discounted stakes in start-ups (George Hammond, Tabby Kinder, Nicholas Megaw)* Founders: getting to the next venture stage may take longer than you expect (Peter Walker)* The State of the SaaS Capital Markets: A Look Back at 2023 and Look Forward to 2024 (STEVE ABBOTT Partner, Capital Markets, KEVIN BURKE Partner, Strategy)* PayPal is laying off 2,500 employees (Pranav Dixit)* Startup of the Week* Zum Raises $140M At $1.3B Valuation To Help Kids Get to School Faster With AI (Chris Metinko)* X of the Week* For a moment, I almost felt sorry for Mark Zuckerberg. (Adam Lashinsky)EditorialYou didn't hear it here first but Apple's Vision Pro is a hit.Some wonderful essays in this week's newsletter. I lead with Jeff Becker's look at venture capital, focusing on who the customer is. The question “Who is the customer?” is crucial for any product. The answer is easy when the product is an asset class - the customer is the person investing money. Yet most of the venture world pretends that the customer is the entrepreneur. In reality, the entrepreneur is a supplier. She or He supplies opportunity, commitment, and execution; the goal is to grow value by investing customer cash into that supply.Now it is easy to understand why venture investors sometimes describe the recipient of funding as the customer. It is important that the company feels served by the VC. But serving an investee company is clearly a mission carried out for the VC fund investors, the real customer.Jeff is addressing a real problem - how to best invest in the supply. I will leave you to read his essay and ponder it, but he proposes a radical re-think of how to do early-stage investing, and for the most part, it argues for a more liberal spread of cash, in larger numbers, to far more founders. It's interesting, to say the least.Evan Shapiro focuses on the rapid aging of the US population. He makes a strong case:Since 2019, America's population has grown by 7.8 million. Yet, the US now has 2.7 million fewer kids under 15 than it did in 2019. Meanwhile, there are now 7.1 million more Americans 65-80 than five years ago. America now has half a million fewer people under 40 than it did in 2019 and almost 8.4 million more people over 40.At a time when politicians from both sides are falling over themselves to point a finger at immigration as a major problem, it is refreshing to see analysis demonstrating that the US needs more immigrants. And in a context where there is virtual full employment this needs to be across all skill levels and needs to trend young. The essay is great.Part of the anti-immigrant narrative has focused on DACA - Consideration of Deferred Action for Childhood Arrivals (DACA). Ron Conway is part of a group of over 50 businesses signing an amicus brief to support DACA. Bravo to him.Hostility to immigrants is never OK. It is even less OK when the economy is desperate for skilled and unskilled willing hands.Politically inspired propaganda dominated elsewhere this week. Amazon was prevented from closing the acquisition of iRobot due to EU objections based on competitive concerns. Well done, EU. Amazon dropped the deal, and iRobot may well be in trouble as a result. Thirty percent of staff were laid off. And more EU interference when Apple was ordered to allow alternative app stores on the iPhone. Steven Sinofsky's wonderful essay, “Building Under Regulation,” leverages his vast experience at Microsoft. It seems every day it becomes more obvious that the EU is against innovation, especially when it produces successful big companies.The Congress got in on the act too (see X of the Week), calling social media leaders to DC to be accused, show-trial-like, of being responsible for teen suicides. Sadly, the Meta CEO apologized as if admitting culpability.Teen suicide and causality is a non-trivial issue, but it is fair to say that Social Media does not cause it. Teens (I have one and another two recently in their post-teen phase). All have had growing up challenges. As I recall, I did also. The world can be harsh in the face of those challenges. But to see social media as the only factor, or even a major one, seems superficial and plain wrong. I wish one of the executives had the nerve to push back against the accusations. Adam Lashinsky's piece is interesting.Finally, Chris Heuer has a research piece on AI and the Future of Work. Well done, Chris, this is such an important issue. My PoV is that work, defined as paid labor, will inevitably decline and the average working day will decline. I believe this is a fundamental good for humanity. I also believe it poses enormous global questions about how the abundance made possible will be distributed to improve life for everybody. I do. not think this is the end of human effort. Just the beginning of the end of the need to do paid labor in order to live.Essays of the WeekChanging the Customer of Venture CapitalThe gift of technologyJEFF BECKERJAN 29, 2024TLDR: We need to change the customer of early-stage venture capital so that we can fund the future of technology and build global prosperity for decades to come.Recently, I hosted a group of students from Wharton at Antler's offices and we talked about the future of early-stage VC.I alluded to this a couple weeks ago when I said:…for $5B per year, you could seed the vast majority of meaningful tech companies for 8 years with the amount of money Elon Musk spent on Twitter. (Link here)The reality is, $5B per year just isn't that much money in the grand scheme of private equities—roughly .5-1% depending how you slice it.As a former salesperson, that fact often leaves me wondering, “what if you changed the customer of venture capital?”Could you attract more money, create more impact, and actually produce more returns?Classically, putting your name on building was a way to not only have a fairly durable legacy, but let's be honest, that gift is outdated.And it hardly does any good in the world.Instead, legacies and the world's most important problems alike would be better served by a consolidation of brilliant minds and capital, combined with the speed and leverage of startups.I think there are two interesting solutions, and both should be built.The first is something I'd call the 501-VC, and the second would be to fund all of venture capital for a decade or more through a new kind of Giving Pledge.I'm going to talk about the second one today.Famously,The Giving Pledge is a promise by the world's wealthiest individuals and families to dedicate the majority of their wealth to charitable causes.The problem is, charitable foundations and organizations aren't historically the most efficient way to solve the world's problems. They exist for good reason, but most operate like old corporates rather than savvy startups.However, what if we thought of economic opportunity and global prosperity as a more ubiquitous problem to solve, and instead of funding mission-driven work, we fund the entirety of the tech sector?What if instead of the average high net worth individual trying to get a 3-5X return over 10 years, you focused on the ultra high net worth population, the economic development groups, and the sovereign funds who are both trying to achieve these returns and trying to improve the world?What if you focused on their shared goals and values as customers, like creating economic opportunity and building a durable legacy?What if you could do it in every corner of the planet through access to entrepreneurship?What if instead of one PayPal Mafia, you had thousands?What if you had an investor who could actually deploy $5B per year at the formation stage?That has simply never existed before, and yet it is a defining opportunity for the human race and our evolution as a society.Currently, high potential employees are stuck in their corporate jobs.Our brightest minds handcuffed to benefits and addicted to a salary, never realizing their true potential or having a real impact on the world.Many go get their MBA where they spend money to learn new skills and acquire a network, rather than receive money for becoming a more productive citizen of the world.Many job hop looking for a low-risk way to get on a rocket ship.Some try to build their own, but quickly run out of runway and mental fortitude.It's a broken system, and we need to rebuild it.First it requires a product.The product needs to be for two groups—the founders and the investors.It starts with the infrastructure required to reduce the risk of being a founder which in turn attracts more of the brightest minds to the job itself. At the same time, the product also has to be an investment vehicle that attracts a new type of customer to early-stage VC.… Lots MoreWhat A Drag It IsAmerica Feels OldEVAN SHAPIROJAN 29, 2024Since 2019, America's population has grown by 7.8 million. Yet, the US now has 2.7 million fewer kids under 15 than it did in 2019. Meanwhile, there are now 7.1 million more Americans 65-80 than five years ago. America now has half a million fewer people under 40 than it did in 2019 and almost 8.4 million more people over 40.Because of the sheer size of the Baby Boomer Generation and the fact that younger Americans have pulled out on having kids, in the last five years, America has gotten old - not just compared to itself, but also compared to the rest of the world.In 2019, 63% of the world's population was under 40. Now, 64% of the people of the planet are 39 or younger. In short:Over the last half-decade the world has gotten one percent younger and America has gotten one percent older.One percent may seem small. However, the consequences of this demographic shift are consequential. For countries like the US, the UK, France, Italy, Germany, and Japan, with aging populations where the number of people over 60 is growing faster than the number of people under 15, the coming years will be filled with challenges brought on by their age: Workforce shortages, inverted dependency ratios where a diminishing tax-base struggles to fund a widening social safety net, health care infrastructures ill-equipped to deal with increased demand. As the world's wealthiest and most powerful nations continue to age faster than they reproduce, expect these issues to get increased and more urgent attention.After decades of aging down, the US population is now aging up quickly. In 2000, 58% of the US population was under 40 years old. Now just a slim majority of 51% is under 40. The impacts of this rapid maturation can be felt throughout our culture, but perhaps nowhere as dramatically as in America's Media and Tech industries.Over the last half century (but for some intermittent challenges from Japan and China), the US has led the world in entertainment and technology, setting the standard for the world's consumption of Media. While many TVs and phones are manufactured in other countries, most of the systems, software, and vision for these products has come from America - and the entertainment consumed on these devices has been, for many decades, the United States' most notable export.Now, America's Media Industrial Complex finds itself amidst a widely-reported bloodbath of its own making. Recently, this meltdown has been joined by America's leading Tech firms. Some of this is cyclical, driven by innovation cycles, advertising recessions, and even the aftermath of the worldwide pandemic. But muchof the current Media Apocalypse was as predictable as the upside-down aging ratio of our population.The first decade of the 21st Century was marked by an almost inconceivable level of innovation in American Media and Tech. The internet invaded all aspects of our lives. Broadband grew across the country like a high-speed weed, bringing the universe to our desktops, making all our worlds, at once, much bigger and infinitely smaller. By 2012, tiny supercomputers known as smartphones had reached a critical mass in the US and TV was streaming into our homes.Then, right around that time, America's Media C-Suite inhabitants seemingly started a shared mid-life crisis, through which we are all still living.Bob Iger took over Disney in 2005, when he was 53 years old. Through some of the most masterful deal-making in Media history, and (seemingly) a true vision of the future, Iger took a troubled company and turned it into the greatest proprietor of intellectual property the world has ever known. He bought Pixar in 2006, revitalizing Disney Animation. He bought Marvel in 2009, jump stating the most successful film and TV franchise in history. He bought Lucasfilm in 2012, completing what many see as bar-setting hat-trick of entertainment, bringing the most valuable collection of titles in entertainment all under one roof.… Lots MoreBuilding Under RegulationAn essay on the EU Digital Markets Act and Apple's "Update on apps distributed in the European Union" (and some personal history)STEVEN SINOFSKYJAN 27, 2024Readers note: This is a long post. There are enough hot takes on this super important issue. I welcome corrections as always.This week Apple detailed the software changes that will appear in an upcoming release of iOS to comply with the European Union Digital Markets Act (DMA).  As I read the over 60 pages of the DMA when it was passed (and in drafts before that, little of which changed in the process) my heart sank over the complexity of a regulation so poorly constructed yet so clearly aimed at specific (American) companies and products. As I read through many of the hundreds of pages of Apple documents detailing their compliance implementation my heart sank again. This time was because I so thoroughly could feel the pain and struggle product teams felt in clinging to at best or unwinding at worst the most substantial improvement in computing ever introduced—the promise behind the iPhone since its introduction. The reason the iPhone became so successful was not a fluke. Consumers and customers voted that the value proposition of the product was something they preferred, and they acted by purchasing iPhone and developers responded by building applications for iOS. The regulators have a different view of that promise, so here we are.To be clear, DMA covers a wide range of products and services all deemed to be critical infrastructure in the digital world. It is both an incredibly broad and sometimes oddly specific regulation. As written the regulation covers at least online intermediation services [commercial internet sites/markets], online search engines, web browsers, advertising services, social network services, video sharing platforms, number-independent interpersonal communications services [messaging], operating systems, virtual assistants, and cloud computing.If you're well-versed in online you can map each one of those to precisely who the target might be, or sometimes targets. It is all big tech, almost exclusively US-based companies. There are no EU companies that meet the criteria to be covered—hardcoded revenue of EUR 7.5 billion for three years, EUR 7.5 billion market cap, or 45 million MAU—with Alphabet, Amazon, Apple, ByteDance, Meta, Microsoft, and Samsung acknowledging the criteria apply to various units in addition to the following other “very large online platforms”: Alibaba AliExpress, Booking.com, Pinterest, Snapchat, Twitter, Wikipedia, Zalando [German fashion retailer]. Those thresholds seem strangely not round.I am going to focus on the Apple and primarily their App Store response because I think it is the most important and time critical and because iPhone is the most unique, innovative, and singular product in market. I can easily replace search, a browser, an ad network, a social network, a video site. Even cloud computing is not so sticky, and we all use multiple messaging services. What iPhone delivers is irreplaceable. At least for many of the subset of smartphone users that chose Apple.The thing is, as impressive as Apple has been it is not *that* successful by the measures that count for dominance. Worldwide Apple is clearly the number two smartphone to Google Android which has over 70% share. In the Europe (excluding Russia) Apple iPhone has about a 33% share (I won't debate exact numbers, units sold v in use, revenue v. profit v. units, etc. as all those do is attempt to tell a story that isn't obvious, which is Android is more popular). That's hardly a monopoly share by any standard. In some European countries Apple has a higher share, some data providers would say as high as 50% or nearly 60%, which by most legal standards is still not quite at a monopoly level especially in a dynamic market. Apple has not been fined, sued, or otherwise convicted of having a dominant share let alone abusing the market position it has. No consumer harm has been demonstrated. In Epic v. Applespecifically on the store, Apple prevailed in 9 of 10 claims of damages to Epic due to the store's costs. Of note, the same claims in Epic v. Google resulted in liability from Google and is being appealed. Many of most vocal competitors didn't even exist before the iPhone. They have become huge companies and don't appear to be struggling, and in fact benefit from being part of the iPhone ecosystem. Counter to the text of the DMA, innovation seems to be thriving as measured by the number of new companies and distinct new services.Yet, the EU DMA has declared that Apple is a “gatekeeper”—an ominous term applied to Apple among the others.… Lots MoreApple's Plans for the DMA in the European UnionFriday, 26 January 2024Apple yesterday announced a broad, wide-ranging, and complex set of new policies establishing their intended compliance with the European Union's Digital Markets Act, which comes into effect March 7. There is a lot to remark upon and numerous remaining questions, but my favorite take was from Sebastiaan de With on Twitter/X, the day before any of this was announced.After quipping “Oh god please no” to a screenshot of the phrase “Spotify also wants to roll out alternate app stores”, de With had this conversation:de With:The EU is once again solving absolutely no problems and making everything worse in tech. I gotta say, they are if anything highly consistent.“Anton”:Overly powerful, rent-seeking gatekeepers seem like a problem.de With:I love that I can't tell if you are talking about the EU or Apple in this case.My second-favorite take, from that same thread, was this from Max Rovensky:DMA is not pro-consumer.It's anti-big-business.Those tend to coincide sometimes, which makes it an easy sell for the general public, but do actually read the DMA, it's quite interesting.I'd go slightly further and describe the DMA as anti-U.S.-big-business, because as far as I can tell, nothing in the DMA adversely affects or even annoys any European tech companies. There are aspects of it that seem written specifically for Spotify, in fact.But Rovensky's framing captures the dichotomy. Anti-big-business regulation and pro-consumer results often do go hand-in-hand, but the DMA exposes the fissures. I do not think the DMA is going to change much, if anything at all, for the better for iOS users in the E.U. (Or for non-iOS users in the EU, for that matter.) And much like the GDPR's website cookie regulations, I think if it has any practical effect, it'll be to make things worse for users. Whether these options are better for developers seems less clear.I've often said that Apple's priorities are consistent: Apple's own needs first, users second, developers third. The European Commission's priorities put developers first, users second, and “gatekeepers” a distant third. The DMA prescribes not a win-win-win framework, but a win-win-lose one.Apple is proud, stubborn, arrogant, controlling, and convinced it has the best interests of its customers in mind.The European Commission is proud, stubborn, arrogant, controlling, and convinced it has the best interests of its citizens in mind.Ever since this collision over the DMA seemed inevitable, starting about two years ago, I've been trying to imagine how it would turn out. And each time, I start by asking: Which side is smarter? My money has been on Apple. Yesterday's announcements, I think, show why.APPLE'S PROPOSED CHANGESIt's really hard to summarize everything Apple announced yesterday, but I'll try. Start with the main Apple Newsroom press release, “Apple Announces Changes to iOS, Safari, and the App Store in the European Union”:“The changes we're announcing today comply with the Digital Markets Act's requirements in the European Union, while helping to protect EU users from the unavoidable increased privacy and security threats this regulation brings. Our priority remains creating the best, most secure possible experience for our users in the EU and around the world,” said Phil Schiller, Apple Fellow. “Developers can now learn about the new tools and terms available for alternative app distribution and alternative payment processing, new capabilities for alternative browser engines and contactless payments, and more. Importantly, developers can choose to remain on the same business terms in place today if they prefer.”Schiller is the only Apple executive quoted in the press release, and to my ear, his writing hand is all over the entire announcement. Apple was quite clear before the DMA was put into law that they considered mandatory sideloading on iOS a bad idea for users, and their announcement yesterday doesn't back down an inch from still declaring it a bad idea.Apple has also argued, consistently, that they seek to monetize third-party development for the iOS platform, and that being forced to change from their current system — (a) all apps must come from the App Store; (b) developers never pay anything for the distribution of free apps; (c) paid apps and in-app-purchases for digital content consumed in-app must go through Apple's In-App Payments system that automates Apple's 30/15 percent commissions — would greatly complicate how they monetize the platform. And now Apple has revealed a greatly complicated set of rules and policies for iPhone apps in the EU.MG Siegler has a great — and fun — post dissecting Apple's press release line-by-line. Siegler concludes:I'm honestly not sure I can recall a press release dripping with such disdain. Apple may even have a point in many of the points above, but the framing of it would just seem to ensure that Apple is going to continue to be at war with the EU over all of this and now undoubtedly more. Typically, if you're going to make some changes and consider the matter closed, you don't do so while emphatically shoving your middle fingers in the air.Some of these changes do seem good and useful, but most simply seem like convoluted changes to ensure the status quo actually doesn't change much, if at all. Just remember that, “importantly, developers can choose to remain on the same business terms in place today if they prefer.” What do you think Apple prefers?The puzzle Apple attempted to solve was creating a framework of new policies — and over 600 new developer APIs to enable those policies — to comply with the DMA, while keeping the path of least resistance and risk for developers the status quo: Apple's own App Store as it is.….Lots MoreAmazon Drops iRobot Deal; Roomba Maker Cuts 31% of Staff* IRobot CEO steps down and company cuts workforce by 31%* Tech giant to pay $94 million to iRobot over deal terminationBy Samuel Stolton, Leah Nylen, and Matt DayJanuary 29, 2024 at 5:33 AM PSTAmazon.com Inc. has abandoned its planned $1.4 billion acquisition of Roomba maker iRobot Corp. after clashing with European Union regulators who had threatened to block the deal.The fallout came quickly. IRobot, which has been struggling recently, said Chief Executive Officer Colin Angle has stepped downas the company embarks on a restructuring plan that will result in about 350 job cuts, or 31% of the workforce. The vacuum maker's shares tumbled 19% in New York to $13.80, their lowest level since 2009. Amazon's shares were up less than 1% at $160.07.The decision is a sign of the intense pressure Amazon is facing to prove its actions don't harm competition as its influence grows in retail, cloud-computing and entertainment. Antitrust regulators on both sides of the Atlantic have been keen to ensure that the biggest US tech companies don't snap up innovative startups before they have a chance to become formidable competitors on their own.Amazon met with the FTC's senior antitrust staff last week, who informed the company they were recommending a suit over the deal, according to a person familiar with the meeting. Executives and lawyers from the tech giant were scheduled to meet with the FTC's three commissioners this week to make a final push for the acquisition, said the person, who asked not to be named discussing the confidential probe.… Lots MoreEnvisioning the Future of Human Work in the Age of AI: The 2024 ForecastResearch Fellowship ProgramIntroductionAs technological change and the adoption of new technologies like artificial intelligence (AI) accelerate, the future of human work will be characterized by disruption, uncertainty, and opportunity. As 2024 approached, the Team Flow Institute Research Fellows gathered for a roundtable to discuss their visions for the future of human-focused work in the age of AI. As described by the institute's co-founder and Managing Director, Chris Heuer, “The Team Flow Institute is an organization dedicated to shaping a human-centric future of work as we face the choice of augmentation or automation in every industry and every function. This transformational decision will reshape what we call work and society itself, requiring us to abandon business as usual and finally design business as possible.” The Team Flow Institute Research Fellows' roundtable discussion delved into the potential opportunities and challenges of this technology revolution driven by the institute's “mission to gather like-minded individuals and organizations to steer our collective destiny toward a more sustainable future, where the essence of humanity and human work is valued and preserved as we increasingly adopt AI tools and technologies, explained Jennifer McClure, Senior Research Fellow, and Advisory Board member. This article analyzes key insights from the discussion, offering a glimpse into the work landscape of 2024 and beyond. As the Team Flow Institute embarks on its inaugural fellowship program, this analysis holds particular significance as it seeks to equip individuals with the knowledge and skills necessary to thrive in the evolving landscape of AI-enabled work. Through this program, the Team Flow Institute aims to foster a community of leaders who can guide organizations and individuals toward a future where humans and technology collaborate to create a more sustainable and fulfilling work environment.Part I: AI Progress and PromiseNo longer relegated to science fiction, AI has infiltrated our lives, transforming industries with its vast potential. From automating tedious tasks to streamlining complex decision-making processes, its applications are far-reaching. In the realm of design, AI-powered software is revolutionizing industries like architecture and fashion, enabling rapid prototyping and personalized creations. Team Flow Institute co-founder Jaime Schwarz says, “Imagine being able to prototype a new building or clothing line in minutes instead of weeks. This remarkable advancement accelerates design cycles and fosters increased customization, ultimately leading to more innovative and personalized consumer products.”The creative landscape is also poised for disruption with the emergence of generative AI. Team Flow Institute Research Fellow Shel Holtz describes its transformative potential: “Generative AI is blurring the lines between human and machine creativity. We're seeing machines create realistic text, images, and even music that is nearly indistinguishable from human-generated work.” This democratization of creativity opens doors for individuals with diverse backgrounds and abilities to express themselves in new and exciting ways. But it also opens up philosophical questions and debates about the nature of art and creativity, adds Jen McClure. Amidst these exciting advancements, Chris Heuer reminds us that “AI is not just a science fiction concept anymore; it's here, and it's changing the way we do everything.” This necessitates a thoughtful approach to the future of work, a need to ensure the value of human skills and their role in work, proactive workforce development initiatives to ensure that individuals are equipped with the necessary skills to thrive in the evolving job market, and an elevation of the need for constant communications within organizations, reminds Team Flow Institute Research Fellow Sharon McIntosh.As AI continues to permeate our lives, it is crucial to acknowledge its remarkable potential and challenges. By navigating this dynamic landscape with careful consideration and proactive planning, we can ensure that AI serves as a force for progress, innovation, and a brighter future for all. As Team Flow Institute Research Fellow Gina Debogovich reminds us, it will undoubtedly unlock economic growth. “The 20th century began with a global GDP of $3 trillion and, largely due to technological advancement, ended with a GDP of $33.8 trillion. AI is poised to boost the economy to unseen heights.”AI will be a catalyst for creating new jobs, just as the web did in the mid-1990s. Businesses must integrate these jobs and activities into existing workflows and business models and develop new ones. Indeed, innovative organizations are already experimenting with, if not embracing, the role of prompt engineers. The Team Flow Institute advocates for a Team Flow Facilitator to serve as a coach, a collaboration facilitator, and an AI pilot to support high-performing teams.Part II: The Risks and DownsidesWhile AI offers many benefits, possibilities, and opportunities, its advancements are not without potential pitfalls. AI and automation technologies bring both promise and peril to the workforce. While they offer the potential to augment human capabilities and business efficiencies significantly, understandable concerns persist surrounding job losses and the general impact on workers. Organizations must chart a thoughtful course that fully harnesses technical capabilities without losing sight of the humans at the heart of work.… Lots MoreVideo of the WeekProduct of the WeekThe Vision ProTuesday, 30 January 2024For the last six days, I've been simultaneously testing three entirely new products from Apple. The first is a VR/AR headset with eye-tracking controls. The second is a revolutionary spatial computing productivity platform. The third is a breakthrough personal entertainment device.A headset, a spatial productivity platform, and a personal entertainment device.I'm sure you're already getting it. These are not three separate devices. They're one: Apple Vision Pro. But if you'll pardon the shameless homage to Steve Jobs's famous iPhone introduction, I think these three perspectives are the best way to consider it.THE HARDWAREVision Pro comes in a surprisingly big box. I was expecting a package roughly the dimensions of a HomePod box; instead, a Vision Pro retail box is quite a bit larger than two HomePod boxes stacked atop each other. (I own more HomePods than most people.)There's a lot inside. The top half of the package contains the Vision Pro headset itself, with the light seal, a light seal cushion, and the default Solo Knit Band already attached. The lower half contains the battery, the charger (30W), the cables, the Dual Loop Band, the Getting Started book (which is beautifully printed in full color, on excellent paper — it feels like a keepsake), the polishing cloth1, and an extra light seal cushion.To turn Vision Pro on, you connect the external battery pack's power cable to the Vision Pro's power connector, and rotate it a quarter turn to lock it into place. There are small dots on the headset's dime-sized power socket showing how to align the cable connector's small LED. The LED pulses when Vision Pro turns on. (I miss Apple's glowing power indicator LEDs — this is a really delightful touch.) When Vision Pro has finished booting and is ready to use, it makes a pleasant welcoming sound.Then you put Vision Pro on. If you're using the Solo Knit Band, you tighten and loosen it using a dial on the band behind your right ear. VisionOS directs you to raise or lower the headset appropriately to position it at just the right height on your face relative to your eyes. If Vision Pro thinks your eyes are too close to the displays, it will suggest you switch to the “+” size light seal cushion. You get two light seal cushions, but they're not the same: mine are labeled “W” and “W+”. The “+” is the same width, to match your light seal, but adds a wee bit more space between your eyes and the displays inside Vision Pro. For me the default (non-“+”) one fits fine.The software then guides you through a series of screens to calibrate the eye tracking. It's all very obvious, and kind of fun. It's almost like a simple game: you stare at a series of dots in a circle, and pinch your index finger and thumb as you stare at each one. You go through this three times, in three different artificial lighting conditions: dark, medium, and bright. Near the end of the first-run experience, you're prompted to bring your iPhone or iPad nearby, just like when setting up a new iPhone or iPad. This allows your Vision Pro to get your Apple ID credentials and Wi-Fi password without entering any of that manually. It's a very smooth onboarding process. And then that's it, you're in and using Vision Pro.There's no getting around some fundamental problems with the Vision Pro hardware.First is the fact that it uses an external battery pack connected via a power cable. The battery itself is about the width and height of an iPhone 15/15 Pro, but thicker. And the battery is heavy: about 325g, compared to 187g for an iPhone 15 Pro, and 221g for a 15 Pro Max. It's closer in thickness and weight to two iPhone 15's than it is to one. And the tethered power cable can be an annoyance. Vision Pro has no built-in reserve battery — disconnect the power cable from the headset and it immediately shuts off. It clicks firmly into place, so there's no risk of accidentally disconnecting it. But if you buy an extra Vision Pro Battery for $200, you can't hot-swap them — you need to shut down first.… Lots MoreApple's Vision Pro -The Meta-Review.Apple Vision Pro reviews have started to roll in — and depending on who you read, the consensus vacillates between amazing and work in progress. In most cases, they reflect some version of reality. If one is looking for faults with Apple's face computer, then one will find them. And if you are looking at what it represents, you are going to be excited. I am in the ‘camp' of the amazed, though I am not blinded by the challenges that await Vision Pro in the real world.The Verge's Nilay Patel sums up the challenge of Vision Pro, writing:The technology to build a true optical AR display that works well enough to replace an everyday computer just isn't there yet. The Magic Leap 2 is an optical AR headset that's cheaper and smaller than the Vision Pro, but it's plagued by compromises in field of view and image quality that most people would never accept. So Apple's settled for building a headset with real-time video passthrough — it is the defining tradeoff of the Vision Pro. It is a VR headset masquerading as an AR headset. And let me tell you: the video passthrough on the Vision Pro is really good. It works! It's convincing. You put the headset on, the display comes on, and you're right back where you were, only with a bunch of visionOS windows floating around.Let's get on with the cons: The Verge points out problems like ‘motion blur,' ‘blurriness,' ‘color fringing,' ‘limited field of view,' and ‘vignetting.' I have not personally experienced any of these because, well, I don't have the device.The device is sometimes laggy. It's heavy, and the wired battery is limited to just over 2 hours. You can plug it into a ‘wall charger' with a USB-C cable, or daisy-chain it to another USB-C battery pack. And it does get a tad warm. You need to use the ‘dorky' headband to use the device without feeling the weight (or in some cases, a headache).None of this surprises me! Vision Pro is, after all, a full-blown computer. It's made from magnesium, carbon fiber, and aluminum. It has two high-resolution front-facing cameras (video pass-through), two cameras that face down to track your hands and gestures, a LiDAR, TrueDepth cameras, and some kind of infrared lights. The device has two tiny MicroOLED displays packed with a total of 23 million pixels. (As I noted in an earlier piece, these displays are the magic and the primary reason why Vision Pro is so expensive.)All these sensors, cameras, and displays are powered by an M2 chip and an R1 spatial coprocessor, and fans. Apple has packed this in an enclosure that is about three times the weight of the iPhone 15 Pro Max and is still lighter than the iPad 12.9. Paint me impressed purely from a technological standpoint.…. Lots MoreMy 4 magic moments with Vision ProNo, not again! Not another Vision Pro Review! I feel you — after all the reviews yesterday, I am pretty sure you don't want to read another review. Here's the good news — it's not a review. Instead, I will share my quick impressions from a deep dive at Apple Park, and my four magic moments with the Vision Pro.Unlike the reviewers who published their reviews, my access to the device has come in dribs and drabs. It has been a carefully managed experience — an early demo, exposure to the photos app, and the spatial video capabilities. A few days ago, I got to use the device for less than two hours.This was a highly curated experience — so this doesn't and won't qualify as a review. I am skipping all the stuff that has been covered by the deep dive that professional reviewers have already published. WSJ's Joanna Stern's review is amazing — especially the video version. It is best to consider these as my considered impressions.First, can I wax eloquent about the technological achievement of Vision Pro? As a chip and hardware nerd, I think Vision Pro is a witches' brew of the latest of all types of technologies. Let me quote my post from yesterday:Vision Pro is, after all, a full-blown computer. It's made from magnesium, carbon fiber, and aluminum. It has two high-resolution front-facing cameras (video pass-through), two cameras that face down to track your hands and gestures, a LiDAR, TrueDepth cameras, and some kind of infrared lights. The device has two tiny MicroOLED displays packed with a total of 23 million pixels. (As I noted in an earlier piece, these displays are the magic and the primary reason why Vision Pro is so expensive.)All these sensors, cameras, and displays are powered by an M2 chip and an R1 spatial coprocessor, and fans. Apple has packed this in an enclosure that is about three times the weight of the iPhone 15 Pro Max and is still lighter than the iPad 12.9. Paint me impressed purely from a technological standpoint.What's even more impressive is the sound — Apple is using beamforming to direct the sound into your ears. And unless you are really blasting it out loud — you could get away with wearing it in a public place — though people in Business Class will notice the slight din from the seat next to them. Apple is hoping you will splurge on AirPods Pro.No matter how you see the device — love it or hate it, you can't deny that it is yet another amazing computer built by a company that knows how to build great consumer computers.… Lots MoreApple Vision Pro Review: The Best Headset Yet Is Just a Glimpse of the FutureWorking, cooking, skiing, kicking back—our columnist wore Apple's new mixed-reality headset for a week to see what it's forBy Joanna Stern at the WSJJan. 30, 2024 at 9:00 am ETA few things surprised me after wearing the Vision Pro mixed-reality headset for nearly 24 hours straight:* I didn't puke. * I got a lot of work done.  * I cooked a delicious meal.Also, my Persona—the headset's animated video-call avatar—will haunt your dreams.For the last week, I have been testing Apple's boldest bet yet on the post-smartphone future. Strap on the 1.4-pound goggles and you see apps floating right in your living room. Living room a stress-inducing mess? Go full virtual reality and watch a 3-D movie on a giant screen perched on the mouth of a Hawaiian volcano.Let's get this out of the way: You're probably not going to buy the $3,500 Apple Vision Pro. Unless you're an app developer or an Apple die-hard, you're more likely to spend that kind of money on an actual trip to a Hawaiian volcano.And that's OK. Reviewing the Vision Pro, I wanted to understand the potential of the device, and the technical constraints that keep it from being a must-have, at least for now. Most importantly, I wanted to answer one question: In a world full of screens, what's the benefit of strapping one to your eyes?… Lots MoreNews Of the WeekSpotify calls Apple's DMA compliance plan ‘extortion' and a ‘complete and total farce'Sarah Perez @sarahpereztc / 2:41 PM PST•January 26, 2024Image Credits: Jakub Porzycki/NurPhoto (opens in a new window)/ Getty ImagesCount Spotify among those not thrilled with how Apple has chosen to comply with the EU's Digital Markets Act (DMA), which sets the stage for sideloading apps, alternative app stores, browser choice, and more. On Friday, the streaming music company issued its response to Apple's new DMA rules, calling the new fees imposed on developers “extortion” and Apple's compliance plan “a complete and total farce,” that demonstrated the tech giant believes that the rules don't apply to them.Apple earlier this week announced a host of changes that comply with the letter of the EU law, if not the spirit. The company said that app developers in the EU will receive reduced commissions, but it also introduced a new “core technology fee” that requires developers to pay €0.50 for each first annual install per year over a 1 million threshold, regardless of their distribution channel. It will also charge a 3% payment processing fee when developers use Apple's in-app payments instead of their own.Epic Games' CEO Tim Sweeney, whose company sued Apple over antitrust concerns, already condemned Apple's plan, saying it was a case of “malicious compliance” and full of “junk fees,” and now Spotify is essentially saying the same.…. Lots MoreInvestors raise billions to buy discounted stakes in start-upsBuyers return after secondary market for private shares was hit by higher interest ratesGeorge Hammond and Tabby Kinder in San Francisco and Nicholas Megaw in New YorkJANUARY 16 2024Investment firms are raising billions of dollars to buy stakes in venture capital-backed technology start-ups, as a long drought in acquisitions and initial public offerings forces early investors to offload their stock at discounts. The start-up secondary market, where investors and employees buy and sell tens of billions of dollars' worth of shares in privately held companies, is becoming an increasingly important trading venue, in the absence of traditional ways of cashing out and given a slowdown in start-up funding. Venture secondaries buyers are primed for a busy year as start-up employees look for a way to sell their stock and investors look to return capital to their own backers or reallocate it elsewhere. Secondary market specialist Lexington Partners last week announced a new $23bn fund to buy up stakes from “large-scale investors”. Lexington had originally aimed to raise $15bn, but upped its target on the back of high demand, and said it was “in the early stages of a generational secondary buying opportunity” that could last years.The fund will predominantly buy shares from private equity funds but also expects to invest as much as $5bn into venture capital secondaries, said a spokesperson.“We are seeing crazy amounts of [limited partner investors] that are distressed and need to lighten their venture load,” said the head of a $2bn venture capital firm. The latest Lexington fund “speaks to the sheer demand” from LPs that feel “over-allocated” to private capital including to start-ups, they said. Other specialist firms such as Pinegrove Capital Partners, a joint vehicle created by Brookfield Asset Management and Sequoia Heritage, and StepStone have also been raising multibillion-dollar funds to target venture secondaries.…. Lots MoreFounders: getting to the next venture stage may take longer than you expectPeter WalkerHead of Insights @ Carta | Data StorytellerThe median number of days between a priced seed and Series A round hit 679 in 2023, a new peak.Median for Series A to B was 744 days (over 2 years). Very similar for Series B to C (739 days, also over 2 years).Fascinating to watch the 25th percentile (green) and the 75th percentile (blue) trends as well. It looks as though the 25th pct has pulled closer to the median for the middle venture rounds - suggesting there are very few companies speed-running through venture fundraising right now. Some of that could be company choice, as founders have cut spend and become more capital-efficient over the prior 12 months. However, I'm certain a lot of the increase in time is due to VCs being far more choosy about where to invest.So what are founders doing if primary rounds are not on the menu? Getting creative.Founders are raising bridge rounds at record rates, usually from insiders already on the cap table. They are turning to SAFEs and Convertible Notes, even between named venture stages. Some are turning to non-dilutive financing and loans.And many are trying to make customer revenue their primary fundraising channel. But switching from growth at all costs to profitability in a short period of time is no easy track change. My bet is that the time between rounds plateaus in 2024 (or maybe even declines just a touch). Maybe that's wishful thinking

Jennifer is the founder of the Vegan Women Summit (VWS), a media and events platform of over 60,000 women founders, investors, and advocates. She is the author of the bestselling book The Future of Food is Female, the world's first book focused on women in alternative protein. Jennifer is an Independent Director of Natural Order Acquisition Co, a publicly-traded company focused on sustainable protein. She built her career under Ron Conway, Founder of SV Angel. Jennifer worked as the industry's leading lobbyist for the world's largest tech companies, including Google, Microsoft, and Meta.

Jennifer is the founder of the Vegan Women Summit (VWS), a media and events platform of over 60,000 women founders, investors, and advocates. She is the author of the bestselling book The Future of Food is Female, the world's first book focused on women in alternative protein. Jennifer is an Independent Director of Natural Order Acquisition Co, a publicly-traded company focused on sustainable protein. She built her career under Ron Conway, Founder of SV Angel. Jennifer worked as the industry's leading lobbyist for the world's largest tech companies, including Google, Microsoft, and Meta.

Jennifer is a multi-talented executive leader in the future of food and tech innovation space. Building her career in the tech industry under Silicon Valley's most prolific investor, Ron Conway, she has worked with the world's largest tech brands, such as Google, Microsoft, and Facebook. Pivoting to the burgeoning industry of alternative protein and food technology, Jennifer is an industry expert and thought leader, powering the world's largest global platform of mission-driven women founders working to create the future of food, fashion, beauty, and biotechnology. Jennifer has been featured as a speaker both domestically and internationally at many conferences, including the world's largest tech conference, Dreamforce, and the acclaimed World Knowledge Forum in Seoul, South Korea. She has travelled internationally as a government-appointed tech delegate in multiple missions, most recently in Germany, and is regularly featured in the national press. A tri-citizen of the US, Canada, and the UK, Jennifer is an avid traveller, athlete, rescue diver, and ethical vegan. Please email all inquiries to jennifer@veganwomensummit.com. LinkedIn messages are not read.

At the AI Pioneers Summit we announced Latent Space Launchpad, an AI-focused accelerator in partnership with Decibel. If you're an AI founder of enterprise early adopter, fill out this form and we'll be in touch with more details. We also have a lot of events coming up as we wrap up the year, so make sure to check out our community events page and come say hi!We previously interviewed the founders of many developer productivity startups embedded in the IDE, like Codium AI, Cursor, and Codeium. We also covered Replit's (former) SOTA model, replit-code-v1-3b and most recently had Amjad and Michele announce replit-code-v1_5-3b at the AI Engineer Summit.Much has been speculated about the StackOverflow traffic drop since ChatGPT release, but the experience is still not perfect. There's now a new player in the “search for developers” arena: Phind.Phind's goal is to help you find answers to your technical questions, and then help you implement them. For example “What should I use to create a frontend for a Python script?” returns a list of frameworks as well as links to the sources. You can then ask follow up questions on specific implementation details, having it write some code for you, etc. They have both a web version and a VS Code integrationThey recently were top of Hacker News with the announcement of their latest model, which is now the #1 rated model on the BigCode Leaderboard, beating their previous version:TLDR Cheat Sheet:* Based on CodeLlama-34B, which is trained on 500B tokens* Further fine-tuned on 70B+ high quality code and reasoning tokens* Expanded context window to 16k tokens* 5x faster than GPT-4 (100 tok/s vs 20 tok/s on single stream)* 74.7% HumanEval vs 45% for the base modelWe've talked before about HumanEval being limited in a lot of cases and how it needs to be complemented with “vibe based” evals. Phind thinks of evals alongside two axis: * Context quality: when asking the model to generate code, was the context high quality? Did we put outdated examples in it? Did we retrieve the wrong files?* Result quality: was the code generated correct? Did it follow the instructions I gave it or did it misunderstand some of it?If you have bad results with bad context, you might get to a good result by working on better RAG. If you have good context and bad result you might either need to work on your prompting or you have hit the limits of the model, which leads you to fine tuning (like they did). Michael was really early to this space and started working on CommonCrawl filtering and indexing back in 2020, which led to a lot of the insights that now power Phind. We talked about that evolution, his experience at YC, how he got Paul Graham to invest in Phind and invite him to dinner at his house, and how Ron Conway connected him with Jensen Huang to get access to more GPUs!Show Notes* Phind* BigScience T0* InstructGPT Paper* Inception-V3* LMQL* Marginalia Nu* Mistral AI* People:* Paul Graham (pg)* Ron Conway* Yacine Jernite from HuggingFace* Jeff DelaneyTimestamps* [00:00:00] Intros & Michael's early interest in computer vision* [00:03:14] Pivoting to NLP and natural language question answering models* [00:07:20] Building a search engine index of Common Crawl and web pages* [00:11:26] Releasing the first version of Hello based on the search index and BigScience T0 model* [00:14:02] Deciding to focus the search engine specifically for programmers* [00:17:39] Overview of Phind's current product and focus on code reasoning* [00:21:51] The future vision for Phind to go from idea to complete code* [00:24:03] Transitioning to using the GPT-4 model and the impact it had* [00:29:43] Developing the Phind model based on CodeLlama and additional training* [00:32:28] Plans to continue improving the Phind model with open source technologies* [00:43:59] The story of meeting Paul Graham and Ron Conway and how that impacted the company* [00:53:02] How Ron Conway helped them get GPUs from Nvidia* [00:57:12] Tips on how Michael learns complex AI topics* [01:01:12] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence and Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:19]Swyx: Hey, and today we have in the studio Michael Royzen from Phind. Welcome. [00:00:23]Michael: Thank you so much. [00:00:24]Alessio: It's great to be here. [00:00:25]Swyx: Yeah, we are recording this in a surprisingly hot October in San Francisco. And sometimes the studio works, but the blue angels are flying by right now, so sorry about the noise. So welcome. I've seen Phind blow up this year, mostly, I think since your launch in Feb and V2 and then your Hacker News posts. We tend to like to introduce our guests, but then obviously you can fill in the blanks with the origin story. You actually were a high school entrepreneur. You started SmartLens, which is a computer vision startup in 2017. [00:00:59]Michael: That's right. I remember when like TensorFlow came out and people started talking about, obviously at the time after AlexNet, the deep learning revolution was already in flow. Good computer vision models were a thing. And what really made me interested in deep learning was I got invited to go to Apple's WWDC conference as a student scholar because I was really into making iOS apps at the time. So I go there and I go to this talk where they added an API that let people run computer vision models on the device using far more efficient GPU primitives. After seeing that, I was like, oh, this is cool. This is going to have a big explosion of different computer vision models running locally on the iPhone. And so I had this crazy idea where it was like, what if I could just make this model that could recognize just about anything and have it run on the device? And that was the genesis for what eventually became SmartLens. I took this data set called ImageNet 22K. So most people, when they think of ImageNet, think of ImageNet 1K. But the full ImageNet actually has, I think, 22,000 different categories. So I took that, filtered it, pre-processed it, and then did a massive fine tune on Inception V3, which was, I think, the state of the art deep convolutional computer vision model at the time. And to my surprise, it actually worked insanely well. I had no idea what would happen if I give a single model. I think it ended up being 17,000 categories approximately that I collapsed them into. It worked so well that it actually worked better than Google Lens, which released its V1 around the same time. And on top of this, the model ran on the device. So it didn't need an internet connection. A big part of the issue with Google Lens at the time was that connections were slower. 4G was around, but it wasn't nearly as fast. So there was a noticeable lag having to upload an image to a server and get it back. But just processing it locally, even on the iPhones of the day in 2017, much faster. It was a cool little project. It got some traction. TechCrunch wrote about it. There was kind of like one big spike in usage, and then over time it tapered off. But people still pay for it, which is wild. [00:03:14]Swyx: That's awesome. Oh, it's like a monthly or annual subscription? [00:03:16]Michael: Yeah, it's like a monthly subscription. [00:03:18]Swyx: Even though you don't actually have any servers? [00:03:19]Michael: Even though we don't have any servers. That's right. I was in high school. I had a little bit of money. I was like, yeah. [00:03:25]Swyx: That's awesome. I always wonder what the modern equivalents kind of "Be my eyes". And it would be actually disclosed in the GPT-4 Vision system card recently that the usage was surprisingly not that frequent. The extent to which all three of us have our sense of sight. I would think that if I lost my sense of sight, I would use Be My Eyes all the time. The average usage of Be My Eyes per day is 1.5 times. [00:03:49]Michael: Exactly. I was thinking about this as well, where I was also looking into image captioning, where you give a model an image and then it tells you what's in the image. But it turns out that what people want is the exact opposite. People want to give a description of an image and then have the AI generate the image. [00:04:04]Alessio: Oh, the other way. [00:04:06]Michael: Exactly. And so at the time, I think there were some GANs, NVIDIA was working on this back in 2019, 2020. They had some impressive, I think, face GANs where they had this model that would produce these really high quality portraits, but it wasn't able to take a natural language description the way Midjourney or DALL-E 3 can and just generate you an image with exactly what you described in it. [00:04:32]Swyx: And how did that get into NLP? [00:04:35]Michael: Yeah, I released the SmartLens app and that was around the time I was a senior in high school. I was applying to college. College rolls around. I'm still sort of working on updating the app in college. But I start thinking like, hey, what if I make an enterprise version of this as well? At the time, there was Clarify that provided some computer vision APIs, but I thought this massive classification model works so well and it's so small and so fast, might as well build an enterprise product. And I didn't even talk to users or do any of those things that you're supposed to do. I was just mainly interested in building a type of backend I've never built before. So I was mainly just doing it for myself just to learn. I built this enterprise classification product and as part of it, I'm also building an invoice processing product where using some of the aspects that I built previously, although obviously it's very different from classification, I wanted to be able to just extract a bunch of structured data from an unstructured invoice through our API. And that's what led me to Hugnyface for the first time because that involves some natural language components. And so I go to Hugnyface and with various encoder models that were around at the time, I used the standard BERT and also Longformer, which came out around the same time. And Longformer was interesting because it had a much bigger context window than those models at the time, like BERT, all of the first gen encoder only models, they only had a context window of 512 tokens and it's fixed. There's none of this alibi or ROPE that we have now where we can basically massage it to be longer. They're fixed, 512 absolute encodings. Longformer at the time was the only way that you can fit, say, like a sequence length or ask a question about like 4,000 tokens worth of text. Implemented Longformer, it worked super well, but like nobody really kind of used the enterprise product and that's kind of what I expected because at the end of the day, it was COVID. I was building this kind of mostly for me, mostly just kind of to learn. And so nobody really used it and my heart wasn't in it and I kind of just shelved it. But a little later, I went back to HugMeFace and I saw this demo that they had, and this is in the summer of 2020. They had this demo made by this researcher, Yacine Jernite, and he called it long form question answering. And basically, it was this self-contained notebook demo where you can ask a question the way that we do now with ChatGPT. It would do a lookup into some database and it would give you an answer. And it absolutely blew my mind. The demo itself, it used, I think, BART as the model and in the notebook, it had support for both an Elasticsearch index of Wikipedia, as well as a dense index powered by Facebook's FAISS. I think that's how you pronounce it. It was very iffy, but when it worked, I think the question in the demo was, why are all boats white? When it worked, it blew my mind that instead of doing this few shot thing, like people were doing with GPT-3 at the time, which is all the rage, you could just ask a model a question, provide no extra context, and it would know what to do and just give you the answer. It blew my mind to such an extent that I couldn't stop thinking about that. When I started thinking about ways to make it better, I tried training, doing the fine tune with a larger BART model. And this BART model, yeah, it was fine tuned on this Reddit data set called Eli5. So basically... [00:08:02]Alessio: Subreddit. [00:08:03]Swyx: Yeah, subreddit. [00:08:04]Alessio: Yeah. [00:08:05]Michael: And put it into like a well-formatted, relatively clean data set of like human questions and human answers. And that was a really great bootstrap for that model to be able to answer these types of questions. And so Eli5 actually turned out to be a good data set for training these types of question answering models, because the question is written by a human, the answer is written by a human, and at least helps the model get the format right, even if the model is still very small and it can't really think super well, at least it gets the format right. And so it ends up acting as kind of a glorified summarization model, where if it's fed in high quality context from the retrieval system, it's able to have a reasonably high quality output. And so once I made the model as big as I can, just fine tuning on BART large, I started looking for ways to improve the index. So in the demo, in the notebook, there were instructions for how to make an Elasticsearch index just for Wikipedia. And I was like, why not do all of Common Crawl? So I downloaded Common Crawl, and thankfully, I had like 10 or $15,000 worth of AWS credits left over from the SmartLens project. And that's what really allowed me to do this, because there's no other funding. I was still in college, not a lot of money, and so I was able to spin up a bunch of instances and just process all of Common Crawl, which is massive. So it's roughly like, it's terabytes of text. I went to Alexa to get the top 1,000 websites or 10,000 websites in the world, then filtered only by those websites, and then indexed those websites, because the web pages were already included in Dump. [00:09:38]Swyx: You mean to supplement Common Crawl or to filter Common Crawl? [00:09:41]Michael: Filter Common Crawl. [00:09:42]Alessio: Oh, okay. [00:09:43]Michael: Yeah, sorry. So we filtered Common Crawl just by the top, I think, 10,000, just to limit this, because obviously there's this massive long tail of small sites that are really cool, actually. There's other projects like, shout out to Marginalia Nu, which is a search engine specialized on the long tail. I think they actually exclude the top 10,000. [00:10:03]Swyx: That's what they do. [00:10:04]Alessio: Yeah. [00:10:05]Swyx: I've seen them around, I just don't really know what their pitch is. Okay, that makes sense. [00:10:08]Michael: So they exclude all the top stuff. So the long tail is cool, but for this, that was kind of out of the question, and that was most of the data anyway. So we've removed that. And then I indexed the remaining approximately 350 million webpages through Elasticsearch. So I built this index running on AWS with these webpages, and it actually worked quite well. You can ask it general common knowledge, history, politics, current events, questions, and it would be able to do a fast lookup in the index, feed it into the model, and it would give a surprisingly good result. And so when I saw that, I thought that this is definitely doable. And it kind of shocked me that no one else was doing this. And so this was now the fall of 2020. And yeah, I was kind of shocked no one was doing this, but it costs a lot of money to keep it up. I was still in college. There are things going on. I got bogged down by classes. And so I ended up shelving this for almost a full year, actually. When I returned to it in fall of 2021, when BigScience released T0, when BigScience released the T0 models, that was a massive jump in the reasoning ability of the model. And it was better at reasoning, it was better at summarization, it was still a glorified summarizer basically. [00:11:26]Swyx: Was this a precursor to Bloom? Because Bloom's the one that I know. [00:11:29]Alessio: Yeah. [00:11:30]Michael: Actually coming out in 2022. But Bloom had other problems where for whatever reason, the Bloom models just were never really that good, which is so sad because I really wanted to use them. But I think they didn't turn on that much data. I think they used like the original, they were trying to replicate GPT-3. So they just use those numbers, which we now know are like far below Chinchilla Optimal and even Chinchilla Optimal, which we can like talk about later, like what we're currently doing with MIMO goes, yeah, it goes way beyond that. But they weren't trying enough data. I'm not sure how that data was clean, but it probably wasn't super clean. And then they didn't really do any fine tuning until much later. So T0 worked well because they took the T5 models, which were closer to Chinchilla Optimal because I think they were trained on also like 300 something billion tokens, similar to GPT-3, but the models were much smaller. I think T0 is the first model that did large scale instruction tuning from diverse data sources in the fall of 2021. This is before Instruct GPT. This is before Flan T5, which came out in 2022. This is the very, very first, at least well-known example of that. And so it came out and then I did, on top of T0, I also did the Reddit Eli5 fine tune. And that was the first model and system that actually worked well enough to where I didn't get discouraged like I did previously, because the failure cases of the BART based system was so egregious. Sometimes it would just miss a question so horribly that it was just extremely discouraging. But for the first time, it was working reasonably well. Also using a much bigger model. I think the BART model is like 800 million parameters, but T0, we were using 3B. So it was T0, 3B, bigger model. And that was the very first iteration of Hello. So I ended up doing a show HN on Hacker News in January 2022 of that system. Our fine tune T0 model connected to our Elasticsearch index of those 350 million top 10,000 common crawl websites. And to the best of my knowledge, I think that's the first example that I'm aware of a LLM search engine model that's effectively connected to like a large enough index that I consider like an internet scale. So I think we were the first to release like an internet scale LLM powered rag search system In January 2022, around the time me and my future co-founder, Justin, we were like, this seems like the future. [00:14:02]Alessio: This is really cool. [00:14:03]Michael: I couldn't really sleep even like I was going to bed and I was like, I was thinking about it. Like I would say up until like 2.30 AM, like reading papers on my phone in bed, go to sleep, wake up the next morning at like eight and just be super excited to keep working. And I was also doing my thesis at the same time, my senior honors thesis at UT Austin about something very similar. We were researching factuality in abstractive question answering systems. So a lot of overlap with this project and the conclusions of my research actually kind of helped guide the development path of Hello. In the research, we found that LLMs, they don't know what they don't know. So the conclusion was, is that you always have to do a search to ensure that the model actually knows what it's talking about. And my favorite example of this even today is kind of with chat GPT browsing, where you can ask chat GPT browsing, how do I run llama.cpp? And chat GPT browsing will think that llama.cpp is some file on your computer that you can just compile with GCC and you're all good. It won't even bother doing a lookup, even though I'm sure somewhere in their internal prompts they have something like, if you're not sure, do a lookup. [00:15:13]Alessio: That's not good enough. So models don't know what they don't know. [00:15:15]Michael: You always have to do a search. And so we approached LLM powered question answering from the search angle. We pivoted to make this for programmers in June of 2022, around the time that we were getting into YC. We realized that what we're really interested in is the case where the models actually have to think. Because up until then, the models were kind of more glorified summarization models. We really thought of them like the Google featured snippets, but on steroids. And so we saw a future where the simpler questions would get commoditized. And I still think that's going to happen with like Google SGE and like it's nowadays, it's really not that hard to answer the more basic kind of like summarization, like current events questions with lightweight models that'll only continue to get cheaper over time. And so we kind of started thinking about this trade off where LLM models are going to get both better and cheaper over time. And that's going to force people who run them to make a choice. Either you can run a model of the same intelligence that you could previously for cheaper, or you can run a better model for the same price. So someone like Google, once the price kind of falls low enough, they're going to deploy and they're already doing this with SGE, they're going to deploy a relatively basic glorified summarizer model that can answer very basic questions about like current events, who won the Super Bowl, like, you know, what's going on on Capitol Hill, like those types of things. The flip side of that is like more complex questions where like you have to reason and you have to solve problems and like debug code. And we realized like we're much more interested in kind of going along the bleeding edge of that frontier case. And so we've optimized everything that we do for that. And that's a big reason of why we've built Phind specifically for programmers, as opposed to saying like, you know, we're kind of a search engine for everyone because as these models get more capable, we're very interested in seeing kind of what the emergent properties are in terms of reasoning, in terms of being able to solve complex multi-step problems. And I think that some of those emerging capabilities like we're starting to see, but we don't even fully understand. So I think there's always an opportunity for us to become more general if we wanted, but we've been along this path of like, what is the best, most advanced reasoning engine that's connected to your code base, that's connected to the internet that we can just provide. [00:17:39]Alessio: What is Phind today, pragmatically, from a product perspective, how do people interact with it? Yeah. Or does it plug into your workflow? [00:17:46]Michael: Yeah. [00:17:47]Alessio: So Phind is really a system. [00:17:48]Michael: Phind is a system for programmers when they have a question or when they're frustrated or when something's not working. [00:17:54]Swyx: When they're frustrated. [00:17:55]Alessio: Yeah. [00:17:56]Michael: For them to get on block. I think like the single, the most abstract page for Phind is like, if you're experiencing really any kind of issue as a programmer, we'll solve that issue for you in 15 seconds as opposed to 15 minutes or longer. Phind has an interface on the web. It has an interface in VS code and more IDEs to come, but ultimately it's just a system where a developer can paste in a question or paste in code that's not working and Phind will do a search on the internet or they will find other code in your code base perhaps that's relevant. And then we'll find the context that it needs to answer your question and then feed it to a reasoning engine powerful enough to actually answer it. So that's really the philosophy behind Phind. It's a system for getting developers the answers that they're looking for. And so right now from a product perspective, this means that we're really all about getting the right context. So the VS code extension that we launched recently is a big part of this because you can just ask a question and it knows where to find the right code context in your code. It can do an internet search as well. So it's up to date and it's not just reliant on what the model knows and it's able to figure out what it needs by itself and answer your question based on that. If it needs some help, you can also get yourself kind of just, there's opportunities for you yourself to put in all that context in. But the issue is also like not everyone wants these VS code. Some people like are real Neovim sticklers or they're using like PyCharm or other IDEs, JetBrains. And so for those people, they're actually like okay with switching tabs, at least for now, if it means them getting their answer. Because really like there's been an explosion of all these like startups doing code, doing search, etc. But really who everyone's competing with is ChatGPT, which only has like that one web interface. Like ChatGPT is really the bar. And so that's what we're up against. [00:19:50]Alessio: And so your idea, you know, we have Amman from Cursor on the podcast and they've gone through the we need to own the IDE thing. Yours is more like in order to get the right answer, people are happy to like go somewhere else basically. They're happy to get out of their IDE. [00:20:05]Michael: That was a great podcast, by the way. But yeah, so part of it is that people sometimes perhaps aren't even in an IDE. So like the whole task of software engineering goes way beyond just running code, right? There's also like a design stage. There's a planning stage. A lot of this happens like on whiteboards. It happens in notebooks. And so the web part also exists for that where you're not even coding it and you're just trying to get like a more conceptual understanding of what you're trying to build first. The podcast with Amman was great, but somewhere where I disagree with him is that you need to own the IDE. I think like he made some good points about not having platform risk in the long term. But some of the features that were mentioned like suggesting diffs, for example, those are all doable with an extension. We haven't yet seen with VS Code in particular any functionality that we'd like to do yet in the IDE that we can't either do through directly supported VS Code functionality or something that we kind of hack into there, which we've also done a fair bit of. And so I think it remains to be seen where that goes. But I think what we're looking to be is like we're not trying to just be in an IDE or be an IDE. Like Phind is a system that goes beyond the IDE and like is really meant to cover the entire lifecycle of a developer's thought process in going about like, hey, like I have this idea and I want to get from that idea to a working product. And so then that's what the long term vision of Phind is really about is starting with that. In the future, I think programming is just going to be really just the problem solving. Like you come up with an idea, you come up with like the basic design for the algorithm in your head, and you just tell the AI, hey, just like just do it, just make it work. And that's what we're building towards. [00:21:51]Swyx: I think we might want to give people an impression about like type of traffic that you have, because when you present it with a text box, you could type in anything. And I don't know if you have some mental categorization of like what are like the top three use cases that people tend to coalesce around. [00:22:08]Alessio: Yeah, that's a great question. [00:22:09]Michael: The two main types of searches that we see are how-to questions, like how to do X using Y tool. And this historically has been our bread and butter, because with our embeddings, like we're really, really good at just going over a bunch of developer documentation and figuring out exactly the part that's relevant and just telling you, OK, like you can use this method. But as LLMs have gotten better, and as we've really transitioned to using GPT-4 a lot in our product, people organically just started pasting in code that's not working and just said, fix it for me. [00:22:42]Swyx: Fix this. [00:22:43]Alessio: Yeah. [00:22:44]Michael: And what really shocks us is that a lot of the people who do that, they're coming from chat GPT. So they tried it in chat GPT with chat GPT-4. It didn't work. Maybe it required like some multi-step reasoning. Maybe it required some internet context or something found in either a Stack Overflow post or some documentation to solve it. And so then they paste it into find and then find works. So those are really those two different cases. Like, how can I build this conceptually or like remind me of this one detail that I need to build this thing? Or just like, here's this code. Fix it. And so that's what a big part of our VS Code extension is, is like enabling a much smoother here just like fix it for me type of workflow. That's really its main benefits. Like it's in your code base. It's in the IDE. It knows how to find the relevant context to answer that question. But at the end of the day, like I said previously, that's still a relatively, not to say it's a small part, but it's a limited part of the entire mental life cycle of a programmer. [00:23:47]Swyx: Yep. So you launched in Feb and then you launched V2 in August. You had a couple other pretty impactful posts slash feature launches. The web search one was massive. So you were mostly a GPT-4 wrapper. We were for a long time. [00:24:03]Michael: For a long time until recently. Yeah. [00:24:05]Alessio: Until recently. [00:24:06]Swyx: So like people coming over from ChatGPT were saying, we're going to say model with your version of web search. Would that be the primary value proposition? [00:24:13]Michael: Basically yeah. And so what we've seen is that any model plus web search is just significantly better than [00:24:18]Alessio: that model itself. Do you think that's what you got right in April? [00:24:21]Swyx: Like so you got 1500 points on Hacking News in April, which is like, if you live on Hacking News a lot, that is unheard of for someone so early on in your journey. [00:24:31]Alessio: Yeah. [00:24:32]Michael: We're super, super grateful for that. Definitely was not expecting it. So what we've done with Hacker News is we've just kept launching. [00:24:38]Alessio: Yeah. [00:24:39]Michael: Like what they don't tell you is that you can just keep launching. That's what we've been doing. So we launched the very first version of Find in its current incarnation after like the previous demo connected to our own index. Like once we got into YC, we scrapped our own index because it was too cumbersome at the time. So we moved over to using Bing as kind of just the raw source data. We launched as Hello Cognition. Over time, every time we like added some intelligence to the product, a better model, we just keep launching. And every additional time we launched, we got way more traffic. So we actually silently rebranded to Find in late December of last year. But like we didn't have that much traffic. Nobody really knew who we were. [00:25:18]Swyx: How'd you pick the name out of it? [00:25:19]Michael: Paul Graham actually picked it for us. [00:25:21]Swyx: All right. [00:25:22]Alessio: Tell the story. Yeah. So, oh boy. [00:25:25]Michael: So this is the biggest side. Should we go for like the full Paul Graham story or just the name? [00:25:29]Swyx: Do you want to do it now? Or do you want to do it later? I'll give you a choice. [00:25:32]Alessio: Hmm. [00:25:33]Michael: I think, okay, let's just start with the name for now and then we can do the full Paul Graham story later. But basically, Paul Graham, when we were lucky enough to meet him, he saw our name and our domain was at the time, sayhello.so and he's just like, guys, like, come on, like, what is this? You know? And we were like, yeah, but like when we bought it, you know, we just kind of broke college students. Like we didn't have that much money. And like, we really liked hello as a name because it was the first like conversational search engine. And that's kind of, that's the angle that we were approaching it from. And so we had sayhello.so and he's like, there's so many problems with that. Like, like, like the say hello, like, what does that even mean? And like .so, like, it's gotta be like a .com. And so we did some time just like with Paul Graham in the room. We just like looked at different domain names, like different things that like popped into our head. And one of the things that popped into like Paul Graham said was fine with the Phind spelling in particular. [00:26:33]Swyx: Yeah. Which is not typical naming advice, right? Yes. Because it's not when people hear it, they don't spell it that way. [00:26:38]Michael: Exactly. It's hard to spell. And also it's like very 90s. And so at first, like, we didn't like, I was like, like, ah, like, I don't know. But over time it kept growing on us. And eventually we're like, okay, we like the name. It's owned by this elderly Canadian gentleman who we got to know, and he was willing to sell it to us. [00:26:57]Michael: And so we bought it and we changed the name. Yeah. [00:27:01]Swyx: Anyways, where were you? [00:27:02]Alessio: I had to ask. [00:27:03]Swyx: I mean, you know, everyone who looks at you is wondering. [00:27:06]Michael: And a lot of people actually pronounce it Phind, which, you know, by now it's part of the game. But eventually we want to buy Phind.com and then just have that redirect to Phind. So Phind is like definitely the right spelling. But like, we'll just, yeah, we'll have all the cases addressed. [00:27:23]Swyx: Cool. So Bing web search, and then August you launched V2. Is V2 the Phind as a system pitch? Or have you moved, evolved since then? [00:27:31]Michael: Yeah, so I don't, like the V2 moniker, like, I don't really think of it that way in my mind. There's like, there's the version we launched during, last summer during YC, which was the Bing version directed towards programmers. And that's kind of like, that's why I call it like the first incarnation of what we currently are. Because it was already directed towards programmers. We had like a code snippet search built in as well, because at the time, you know, the models we were using weren't good enough to generate code snippets. Even GPT, like the text DaVinci 2 was available at the time, wasn't that good at generating code and it would generate like very, very short, very incomplete code snippets. And so we launched that last summer, got some traction, but really like we were only doing like, I don't know, maybe like 10,000 searches a day. [00:28:15]Alessio: Some people knew about it. [00:28:16]Michael: Some people used it, which is impressive because looking back, the product like was not that good. And every time we've like made an improvement to the way that we retrieve context through better embeddings, more intelligent, like HTML parsers, and importantly, like better underlying models. Every major version after that was when we introduced a better underlying answering model. Like in February, we had to swallow a bit of our pride when we were like, okay, our own models aren't good enough. We have to go to open AI. And actually that did lead to kind of like our first decent bump of traffic in February. And people kept using it, like our attention was way better too. But we were still kind of running into problems of like more advanced reasoning. Some people tried it, but people were leaving because even like GPT 3.5, both turbo and non-turbo, like still not that great at doing like code related reasoning beyond the how do you do X, like documentation search type of use case. And so it was really only when GPT 4 came around in April that we were like, okay, like this is like our first real opportunity to really make this thing like the way that it should have been all along. And having GPT 4 as the brain is what led to that Hacker News post. And so what we did was we just let anyone use GPT 4 on Fyne for free without a login, [00:29:43]Alessio: which I actually don't regret. [00:29:45]Michael: So it was very expensive, obviously. But like at that stage, all we needed to do was show like, we just needed to like show people here's what Fyne can do. That was the main thing. And so that worked. That worked. [00:29:58]Alessio: Like we got a lot of users. [00:29:59]Michael: Do you know Fireship? [00:30:01]Swyx: Yeah. YouTube, Jeff Delaney. [00:30:03]Michael: Yeah. He made a short about Fyne. [00:30:06]Alessio: Oh. [00:30:07]Michael: And that's on top of the Hacker News post. And that's what like really, really made it blow up. It got millions of views in days. And he's just funny. Like what I love about Fireship is like he like you guys, yeah, like humor goes a long a long way towards like really grabbing people's attention. And so that blew up. [00:30:25]Swyx: Something I would be anxious about as a founder during that period, so obviously we all remember that pretty closely. So there were a couple of people who had access to the GPT-4 API doing this, which is unrestricted access to GPT-4. And I have to imagine OpenAI wasn't that happy about that because it was like kind of de facto access to GPT-4 before they released it. [00:30:46]Alessio: No, no. [00:30:47]Michael: GPT-4 was in chat GPT from day one. I think. OpenAI actually came to our support because what happened was we had people building unofficial APIs around to try to get free access to it. And I think OpenAI actually has the right perspective on this where they're like, OK, people can do whatever they want with the API if they're paying for it, like they can do whatever they want, but it's like not OK if, you know, paying customers are being exploite by these other actors. They actually got in touch with us and they helped us like set up better Cloudflare bot monitoring controls to effectively like crack down on those unofficial APIs, which we're very happy about. But yeah, so we launched GPT-4. A lot of people come to the product and yeah, for a long time, we're just we're figuring out like what do we make of this, right? How do we a make it better, but also deal with like our costs, which have just like massively, massively ballooned. Over time, it's become more clear with the release of Llama 2 and Llama 3 on the horizon that we will once again see a return to vertical applications running their own models. As was true last year and before, I think that GPT-4, my hypothesis is that the jump from 4 to 4.5 or 4 to 5 will be smaller than the jump from 3 to 4. And the reason why is because there were a lot of different things. Like there was two plus, effectively two, two and a half years of research that went into going from 3 to 4. Like more data, bigger model, all of the instruction tuning techniques, RLHF, all of that is known. And like Meta, for example, and now there's all these other startups like Mistral too, like there's a bunch of very well-funded open source players that are now working on just like taking the recipe that's now known and scaling it up. So I think that even if a delta exists, the delta between in 2024, the delta between proprietary and open source won't be large enough that a startup like us with a lot of data that we've collected can take the data that we have, fine tune an open source model, and like be able to have it be better than whatever the proprietary model is at the time. That's my hypothesis.Michael: But we'll once again see a return to these verticalized models. And that's something that we're super excited about because, yeah, that brings us to kind of the fine model because the plan from kind of the start was to be able to return to that if that makes sense. And I think now we're definitely at a point where it does make sense because we have requests from users who like, they want longer context in the model, basically, like they want to be able to ask questions about their entire code base without, you know, context and retrieval and taking a chance of that. Like, I think it's generally been shown that if you have the space to just put the raw files inside of a big context window, that is still better than chunking and retrieval. So there's various things that we could do with longer context, faster speed, lower cost. Super excited about that. And that's the direction that we're going with the fine model. And our big hypothesis there is precisely that we can take a really good open source model and then just train it on absolutely all of the high quality data that we can find. And there's a lot of various, you know, interesting ideas for this. We have our own techniques that we're kind of playing with internally. One of the very interesting ideas that I've seen, I think it's called Octopack from BigCode. I don't think that it made that big waves when it came out, I think in August. But the idea is that they have this data set that maps GitHub commits to a change. So basically there's all this really high quality, like human made, human written diff data out there on every time someone makes a commit in some repo. And you can use that to train models. Take the file state before and like given a commit message, what should that code look like in the future? [00:34:52]Swyx: Got it. [00:34:53]Alessio: Do you think your HumanEval is any good?Michael: So we ran this experiment. We trained the Phind model. And if you go to the BigCode leaderboard, as of today, October 5th, all of our models are at the top of the BigCode leaderboard by far. It's not close, particularly in languages other than Python. We have a 10 point gap between us and the next best model on JavaScript. I think C sharp, multilingual. And what we kind of learned from that whole experience releasing those models is that human eval doesn't really matter. Not just that, but GPT-4 itself has been trained on human eval. And we know this because GPT-4 is able to predict the exact docstring in many of the problems. I've seen it predict like the specific example values in the docstring, which is extremely improbable. So I think there's a lot of dataset contamination and it only captures a very limited subset of what programmers are actually doing. What we do internally for evaluations are we have GPT-4 score answers. GPT-4 is a really good evaluator. I mean, obviously it's by really good, I mean, it's the best that we have. I'm sure that, you know, a couple of months from now, next year, we'll be like, oh, you know, like GPT-4.5, GPT-5, it's so much better. Like GPT-4 is terrible, but like right now it's the best that we have short of humans. And what we found is that when doing like temperature zero evals, it's actually mostly deterministic GPT-4 across runs in assigning scores to two different answers. So we found it to be a very useful tool in comparing our model to say, GPT-4, but yeah, on our like internal real world, here's what people will be asking this model dataset. And the other thing that we're running is just like releasing the model to our users and just seeing what they think. Because that's like the only thing that really matters is like releasing it for the application that it's intended for, and then seeing how people react. And for the most part, the incredible thing is, is that people don't notice a difference between our model and GPT-4 for the vast majority of searches. There's some reasoning problems that GPT-4 can still do better. We're working on addressing that. But in terms of like the types of questions that people are asking on find, there's not that much difference. And in fact, I've been running my own kind of side by side comparisons, shout out to GodMode, by the way. [00:37:16]Michael: And I've like myself, I've kind of confirmed this to be the case. And even sometimes it gives a better answer, perhaps like more concise or just like better implementation than GPT-4, which that's what surprises me. And by now we kind of have like this reasoning is all you need kind of hypothesis where we've seen emerging capabilities in the find model, whereby training it on high quality code, it can actually like reason better. It went from not being able to solve world problems, where riddles were like with like temporal placement of objects and moving and stuff like that, that GPT-4 can do pretty well. We went from not being able to do those at all to being able to do them just by training on more code, which is wild. So we're already like starting to see like these emerging capabilities. [00:37:59]Swyx: So I just wanted to make sure that we have the, I guess, like the model card in our heads. So you started from Code Llama? [00:38:07]Alessio: Yes. [00:38:08]Swyx: 65, 34? 34. [00:38:10]Michael: So unfortunately, there's no Code Llama 70b. If there was, that would be super cool. But there's not. [00:38:15]Swyx: 34. And then, which in itself was Llama 2, which is on 2 trillion tokens and the added 500 billion code tokens. Yes. [00:38:22]Michael: And you just added a bunch more. [00:38:23]Alessio: Yeah. [00:38:24]Michael: And they also did a couple of things. So they did, I think they did 500 billion, like general pre-training and then they did an extra 20 billion long context pre-training. So they actually increased the like max position tokens to 16k up from 8k. And then they changed the theta parameter for the ROPE embeddings as well to give it theoretically better long context support up to 100k tokens. But yeah, but otherwise it's like basically Llama 2. [00:38:50]Swyx: And so you just took that and just added data. [00:38:52]Michael: Exactly. [00:38:53]Swyx: You didn't do any other fundamental. [00:38:54]Michael: Yeah. So we didn't actually, we haven't yet done anything with the model architecture and we just trained it on like many, many more billions of tokens on our own infrastructure. And something else that we're taking a look at now is using reinforcement learning for correctness. One of the interesting pitfalls that we've noticed with the Phind model is that in cases where it gets stuff wrong, it sometimes is capable of getting the right answer. It's just, there's a big variance problem. It's wildly inconsistent. There are cases when it is able to get the right chain of thought and able to arrive [00:39:25]Alessio: at the right answer, but not always. [00:39:27]Michael: And so like one of our hypotheses is something that we're going to try is that like we can actually do reinforcement learning on, for a given problem, generate a bunch of completions and then like use the correct answer as like a loss basically to try to get it to be more correct. And I think there's a high chance I think of this working because it's very similar to the like RLHF method where you basically show pairs of completions for a given question except the criteria is like which one is like less harmful. But here we have a different criteria. But if the model is already capable of getting the right answer, which it is, we're just, we just need to cajole it into being more consistent. [00:40:06]Alessio: There were a couple of things that I noticed in the product that were not strange but unique. So first of all, the model can talk multiple times in a row, like most other applications is like human model, human model. And then you had outside of the thumbs up, thumbs down, you have things like have DLLM prioritize this message and its answers or then continue from this message to like go back. How does that change the flow of the user and like in terms of like prompting it, yeah, what are like some tricks or learnings you've had? [00:40:37]Michael: So yeah, that's specifically in our pair programmer mode, which is a more conversational mode that also like asks you clarifying questions back if it doesn't fully understand what you're doing and it kind of it holds your hand a bit more. And so from user feedback, we had requests to make more of an auto GPT where you can kind of give it this problem that might take multiple searches or multiple different steps like multiple reasoning steps to solve. And so that's the impetus behind building that product. Being able to do multiple steps and also be able to handle really long conversations. Like people are really trying to use the pair programmer to go from like sometimes really from like basic idea to like complete working code. And so we noticed was is that we were having like these very, very long threads, sometimes with like 60 messages, like 100 messages. And like those become really, really challenging to manage the appropriate context window of what should go inside of the context and how to preserve the context so that the model can continue or the product can continue giving good responses, even if you're like 60 messages deep in a conversation. So that's where the prioritized user messages like comes from. It's like people have asked us to just like let them pin messages that they want to be left in the conversation. And yeah, and then that seems to have like really gone a long way towards solving that problem, yeah. [00:41:54]Alessio: And then you have a run on Replit thing. Are you planning to build your own repl? Like learning some people trying to run the wrong code, unsafe code? [00:42:03]Michael: Yes. Yes. So I think like in the long term vision of like being a place where people can go from like idea to like fully working code, having a code sandbox, like a natively integrated code sandbox makes a lot of sense. And replit is great and people use that feature. But yeah, I think there's more we can do in terms of like having something a bit closer to code interpreter where it's able to run the code and then like recursively iterate on it. Exactly. [00:42:31]Swyx: So you're working on APIs to enable you to do that? Yep. So Amjad has specifically told me in person that he wants to enable that for people at the same time. He's also working on his own models, and Ghostwriter and you know, all the other stuff. So it's going to get interesting. Like he wants to power you, but also compete with you. Yeah. [00:42:47]Michael: And like, and we love replit. I think that a lot of the companies in our space, like we're all going to converge to solving a very similar problem, but from a different angle. So like replit approaches this problem from the IDE side. Like they started as like this IDE that you can run in the browser. And they started from that side, making coding just like more accessible. And we're approaching it from the side of like an LLM that's just like connected to everything that it needs to be connected to, which includes your code context. So that's why we're kind of making inroads into IDEs, but we're kind of, we're approaching this problem from different sides. And I think it'll be interesting to see where things end up. But I think that in the long, long term, we have an opportunity to also just have like this general technical reasoning engine product that's potentially also not just for, not just for programmers. It's also powered in this web interface, like where there's potential, I think other things that we will build that eventually might go beyond like our current scope. [00:43:49]Swyx: Exciting. We'll look forward to that. We're going to zoom out a little bit into sort of AI ecosystem stories, but first we got to get the Paul Graham, Ron Conway story. [00:43:59]Alessio: Yeah. [00:44:00]Michael: So flashback to last summer, we're in the YC batch. We're doing the summer batch, summer 22. So the summer batch runs from June to September, approximately. And so this was late July, early August, right around the time that many like YC startups start like going out, like during up, here's how we're going to pitch investors and everything. And at the same time, me and my co-founder, Justin, we were planning on moving to New York. So for a long time, actually, we were thinking about building this company in New York, mainly for personal reasons, actually, because like during the pandemic, pre-ChatGPT, pre last year, pre the AI boom, SF unfortunately really kind of, you know, like lost its luster. Yeah. Like no one was here. It was far from clear, like if there would be an AI boom, if like SF would be like... [00:44:49]Alessio: Back. [00:44:50]Michael: Yeah, exactly. Back. As everyone is saying these days, it was far from clear. And so, and all of our friends, we were graduating college because like we happened to just graduate college and immediately start YC, like we didn't even have, I think we had a week in between. [00:45:06]Swyx: You didn't bother looking for jobs. You were just like, this is what we want to do. [00:45:08]Michael: Well, actually both me and my co-founder, we had jobs that we secured in 2021 from previous internships, but we both, funny enough, when I spoke to my boss's boss at the company at where I reneged my offer, I told him we got into YC, they actually said, yeah, you should do YC. [00:45:27]Swyx: Wow. [00:45:28]Alessio: That's very selfless. [00:45:29]Swyx: That was really great that they did that. But in San Francisco, they would have offered to invest as well. [00:45:33]Michael: Yes, they would have. But yeah, but we were both planning to be in New York and all of our friends were there from college at this point, like we have this whole plan where like on August 1st, we're going to move to New York and we had like this Airbnb for the month of New York. We're going to stay there and we're going to work and like all of that. The day before we go to New York, I called Justin and I just, I tell him like, why are we doing this? Because in our batch, by the time August 1st rolled around, all of our mentors at YC were saying like, hey, like you should really consider staying in SF. [00:46:03]Swyx: It's the hybrid batch, right? [00:46:04]Michael: Yeah, it was the hybrid batch, but like there were already signs that like something was kind of like afoot in SF, even if like we didn't fully want to admit it yet. And so we were like, I don't know, I don't know. Something kind of clicked when the rubber met the road and it was time to go to New York. We're like, why are we doing this? And like, we didn't have any good reasons for staying in New York at that point beyond like our friends are there. So we still go to New York because like we have the Airbnb, like we don't have any other kind of place to go for the next few weeks. We're in New York and New York is just unfortunately too much fun. Like all of my other friends from college who are just, you know, basically starting their jobs, starting their lives as adults. They just stepped into these jobs, they're making all this money and they're like partying and like all these things are happening. And like, yeah, it's just a very distracting place to be. And so we were just like sitting in this like small, you know, like cramped apartment, terrible posture, trying to get as much work done as we can, too many distractions. And then we get this email from YC saying that Paul Graham is in town in SF and he is doing office hours with a certain number of startups in the current batch. And whoever signs up first gets it. And I happen to be super lucky. I was about to go for a run, but I just, I saw the email notification come across the street. I immediately clicked on the link and like immediately, like half the spots were gone, but somehow the very last spot was still available. And so I picked the very, very last time slot at 7 p.m. semi-strategically, you know, so we would have like time to go over. And also because I didn't really know how we're going to get to SF yet. And so we made a plan that we're going to fly from New York to SF and back to New York in one day and do like the full round trip. And we're going to meet with PG at the YC Mountain View office. And so we go there, we do that, we meet PG, we tell him about the startup. And one thing I love about PG is that he gets like, he gets so excited. Like when he gets excited about something, like you can see his eyes like really light up. And he'll just start asking you questions. In fact, it's a little challenging sometimes to like finish kind of like the rest of like the description of your pitch because like, he'll just like asking all these questions about how it works. And I'm like, you know, what's going on? [00:48:19]Swyx: What was the most challenging question that he asked you? [00:48:21]Michael: I think that like really how it worked. Because like as soon as like we told him like, hey, like we think that the future of search is answers, not links. Like we could really see like the gears turning in his head. I think we were like the first demo of that. [00:48:35]Swyx: And you're like 10 minutes with him, right? [00:48:37]Michael: We had like 45, yeah, we had a decent chunk of time. And so we tell him how it works. Like he's very excited about it. And I just like, I just blurted out, I just like asked him to invest and he hasn't even seen the product yet. We just asked him to invest and he says, yeah. And like, we're super excited about that. [00:48:55]Swyx: You haven't started your batch. [00:48:56]Michael: No, no, no. This is about halfway through the batch or two, two, no, two thirds of the batch. [00:49:02]Swyx: And you're like not technically fundraising yet. We're about to start fundraising. Yeah. [00:49:06]Michael: So we have like this demo and like we showed him and like there was still a lot of issues with the product, but I think like it must have like still kind of like blown his mind in some way. So like we're having fun. He's having fun. We have this dinner planned with this other friend that we had in SF because we were only there for that one day. So we thought, okay, you know, after an hour we'll be done, you know, we'll grab dinner with our friend and we'll fly back to New York. But PG was like, like, I'm having so much fun. Do you want to have dinner? Yeah. Come to my house. Or he's like, I gotta go have dinner with my wife, Jessica, who's also awesome, by the way. [00:49:40]Swyx: She's like the heart of YC. Yeah. [00:49:42]Michael: Jessica does not get enough credit as an aside for her role. [00:49:46]Swyx: He tries. [00:49:47]Michael: He understands like the technical side and she understands people and together they're just like a phenomenal team. But he's like, yeah, I got to go see Jessica, but you guys are welcome to come with. Do you want to come with? And we're like, we have this friend who's like right now outside of like literally outside the door who like we also promised to get dinner with. It's like, we'd love to, but like, I don't know if we can. He's like, oh, he's welcome to come too. So all of us just like hop in his car and we go to his house and we just like have this like we have dinner and we have this just chat about the future of search. Like I remember him telling Jessica distinctly, like our kids as kids are not going to know what like a search result is. Like they're just going to like have answers. That was really like a mind blowing, like inflection point moment for sure. [00:50:34]Swyx: Wow, that email changed your life. [00:50:35]Michael: Absolutely. [00:50:36]Swyx: And you also just spoiled the booking system for PG because now everyone's just going to go after the last slot. Oh man. [00:50:42]Michael: Yeah. But like, I don't know if he even does that anymore. [00:50:46]Swyx: He does. He does. Yeah. I've met other founders that he did it this year. [00:50:49]Michael: This year. Gotcha. But when we told him about how we did it, he was like, I am like frankly shocked that YC just did like a random like scheduling system. [00:50:55]Alessio: They didn't like do anything else. But, um. [00:50:58]Swyx: Okay. And then he introduces Duron Conway. Yes. Who is one of the most legendary angels in Silicon Valley. [00:51:04]Michael: Yes.So after PG invested, the rest of our round came together pretty quickly. [00:51:10]Swyx: I'm, by the way, I'm surprised. Like it's, it might feel like playing favorites right within the current batch to be like, yo, PG invested in this one. Right. [00:51:17]Alessio: Too bad for the others. [00:51:18]Swyx: Too bad for the others, I guess. [00:51:19]Michael: I think this is a bigger point about YC and like these accelerators in general is like YC gets like a lot of criticism from founders who feel like they didn't get value out of it. But like, in my view, YC is what you make of it. And YC tells you this. They're like, you really got to grab this opportunity, like buy the balls and make the most of it. And if you do, then it could be the best thing in the world. And if you don't, and if you're just kind of like a passive, even like an average founder in YC, you're still going to fail. And they tell you that. They're like, if you're average in your batch, you're going to fail. Like you have to just be exceptional in every way. With that in mind, perhaps that's even part of the reason why we asked PG to invest. And so yeah, after PG invested, the rest of our round came together pretty quickly, which I'm very fortunate for. And yeah, he introduced us to Ron. And after he did, I get a call from Ron. And then Ron says like, hey, like PG tells me what you're working on. I'd love to come meet you guys. And I'm like, wait, no way. And then we're just holed up in this like little house in San Mateo, which is a little small, but you know, it had a nice patio. In fact, we had like a monitor set up outside on the deck out there. And so Ron Conway comes over, we go over to the patio where like our workstation is. And Ron Conway, he's known for having like this notebook that he goes around with where he like sits down with the notebook and like takes very, very detailed notes. So he never like forgets anything. So he sits down with his notebook and he asks us like, hey guys, like, what do you need? And we're like, oh, we need GPUs. Back then, the GPU shortage wasn't even nearly as bad as it is now. But like even then, it was still challenging to get like the quota that we needed. And he's like, okay, no problem. And then like he leaves a couple hours later, we get an email and we're CC'd on an email that Ron wrote to Jensen, the CEO of Nvidia, saying like, hey, these guys need GPUs. [00:53:02]Swyx: You didn't say how much? It was just like, just give them GPUs. [00:53:04]Alessio: Basically, yeah. [00:53:05]Michael: Ron is known for writing these like one-liner emails that are like very short, but very to the point. And I think that's why like everyone responds to Ron. Everyone loves Ron. And so Jensen responds. He responds quickly, like tagging this VP of AI at Nvidia. And we start working with Nvidia, which is great. And something that I love about Nvidia, by the way, is that after that intro, we got matched with like a dedicated team. And at Nvidia, they know that they're going to win regardless. So they don't care where you get the GPUs from. They're like, they're truly neutral, unlike various sales reps that you might encounter at various like clouds and, you know, hardware companies, et cetera. They actually just want to help you because they know they don't care. Like regardless, they know that if you're getting Nvidia GPUs, they're still winning. So I guess that's a tip is that like if you're looking for GPUs like Nvidia, they'll help you do it. [00:53:54]Swyx: So just to tie up this thing, because so first of all, that's a fantastic story. And I just wanted to let you tell that because it's special. That is a strategic shift, right? That you already decided to make by the time you met Ron, which is we are going to have our own hardware. We're going to rack him in a data center somewhere. [00:54:11]Michael: Well, not even that we need our own hardware because actually we don't. Right. But we just we just need GPUs, period. And like every cloud loves like they have their own sales tactics and like they want to make you commit to long terms and like very non-flexible terms. And like there's a web of different things that you kind of have to navigate. Nvidia will kind of be to the point like, OK, you can do this on this cloud, this on this cloud. Like this is your budget. Maybe you want to consider buying as well. Like they'll help you walk through what the options are. And the reason why they're helpful is because like they look at the full picture. So they'll help you with the hardware. And in terms of software, they actually implemented a custom feature for us in Faster Transformer, which is one of their libraries.Swyx: For you? [00:54:53]Michael: For us. Yeah. Which is wild. I don't think they would have done it otherwise. They implemented streaming generation for T5 based models, which we were running at the time up until we switched to GPT in February, March of this year. So they implemented that just for us, actually, in Faster Transformer. And so like they'll help you like look at the complete picture and then just help you get done what you need to get done. I know one of your interests is also local models, open source models and hardware kind of goes hand in hand.Alessio: Any fun projects, explorations in the space that you want to share with local llamas and stuff? [00:55:27]Michael: Yeah, it's something that we're very interested in because something that kind of we're hearing a lot about is like people want something like find, especially comp

Jennifer Stojkovic is a General Partner at Joyful Ventures, an early-stage VC fund focused on disrupting the $1.4 trillion meat and dairy industry with sustainable protein. She's also the founder of Vegan Women Summit (VWS), a platform of over 60,000 women professionals in the future of food, and author of the award-winning book, The Future of Food is Female. Prior to her career in food technology, Jennifer built her career in Silicon Valley under Ron Conway, Founder of SV Angel, and worked with the world's largest tech companies including Google, Meta, and Microsoft.

Jennifer Stojkovic is a General Partner at Joyful Ventures, an early-stage VC fund focused on disrupting the $1.4 trillion meat and dairy industry with sustainable protein. She's also the founder of Vegan Women Summit (VWS), a platform of over 60,000 women professionals in the future of food, and author of the award-winning book, The Future of Food is Female. Prior to her career in food technology, Jennifer built her career in Silicon Valley under Ron Conway, Founder of SV Angel, and worked with the world's largest tech companies including Google, Meta, and Microsoft.

Tara Palmeri joins Peter to discuss A.O.C.'s new role in Congress and address all the speculation about her future plans. Then Teddy Schleifer and Ben Landy dig into how Ron Conway helped save SVB. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Episode #69, Martin Tobias, founder, Incisive Ventures in Seattle on Fireside with a VC discussing: · How is Seattle different from Silicon Valley, Austin, or New York startup VC ecosystems? · Getting away the very last IPO as CEO before the dot-com crash and lessons learned and how these apply to today's climate. · Old days of Microsoft and becoming a super angel and launching a Seed VC fund. · Investing in Google, DocuSign, Ron Conway's SV Angel I and II Funds · Figuring out which of his 200 angel investments were the best and developing an investment thesis around this. · Top meta themes & screening process YouTube: https://youtu.be/iCl0_4i_rqM Spotify: https://rb.gy/chjrsf All podcast platforms: https://anchor.fm/FiresideVC Martin writes notes to himself on how to live the good life at DeepGreenCrystals.com. Registration is open for 7BC's in-person VC insight panels, startup presentations & networking receptions New York City, March 7, 2023 – https://7BC-neonVest-NYC.eventbrite.com. Seattle, April 4, 2023 – https://7BC-neonVest-Seattle.eventbrite.com Planning events in cities in key tech corridors across the U.S. Get in touch if you want to host or sponsor any of our events. andrew@7bc.vc https://www.linkedin.com/in/romans/ --- Send in a voice message: https://podcasters.spotify.com/pod/show/firesidevc/message

As a food-tech leader, author of The Future of Food is Female, and the founder of the Vegan Women Summit, Jennifer Stojkovic is an amazing woman who continues to push boundaries. She began her career working with renowned Silicon Valley investor and founder of SV Angel, Ron Conway. Following her time there, she entered the food-tech market. Throughout her career she has worked alongside some of tech's most well-known CEOs holding positions at Google, Microsoft and Facebook. In addition, she has also partnered with a number of fast-growing startups such as WeWork, Cruise and Postmates. Jennifer shares her career journey, what she has learned and the experiences she's had along the way, all of which have shaped her into the food industry leader she is today.Today's episode is hosted by Diana Fryc of Retail Voodoo, connect with her on LinkedIn: https://www.linkedin.com/in/dianafryc/KEY TAKEAWAYS-The Vegan Women Summit and its goals-The motivation behind writing her book “The Future of Food is Female”-The lessons and unforgettable experiences from her careerQUOTES"My views changed when I learned how Americans manage to put food on the table. Knowing the situation and wanting to change it motivated me." - Jennifer"Every single community deserves culturally specific food products that are healthier and natural and plant-based and better for them." - Jennifer"We must ensure that more people with more products and ideas that address more issues in more places are financially supported." - JenniferABOUT THE GUESTJennifer Stojkovic Author, Advisor, Founder of Vegan Women SummitLinkedIn: https://www.linkedin.com/in/jenniferstojkovicWebsite: https://www.jenniferstojkovic.comCHAPTERS00:00 | Introduction03:08 | The Vegan Women Summit06:48 | Getting Attention09:53 | Motivation to be a Part of the Movement14:21 | The Future of Food is Female25:52 | Exciting Experiences30:49 | Moments That Stand Out33:14 | On To The Next36:04 | Women To Look Up To37:39 | What's Next For JenniferThis episode is brought to you by Retail Voodoo. A brand consultancy focused on building, growing and revitalizing brands in the food, beverage, health and wellness industries. If you are ready to find a partner that will help your business create a high-impact strategy that gives your brand an advantage, please visit http://retail-voodoo.com/contact to set up a discovery call today.Produced by Heartcast Media.https://www.heartcastmedia.com/

Tarek Mansour is the Founder and CEO @ Kalshi, the first regulated exchange where you can trade directly on the outcome of events. Tarek has raised funding from some of the best including Alfred Lin @ Sequoia, Ali Partovi @ Neo, Ron Conway, Charles Schwab and Henry Kravis. Before founding Kalshi, Tarek worked at the likes of Citadel, Palantir and Goldman Sachs, in various different roles. In Today's Episode with Tarek Mansour We Discuss: 1.) Entry into Startups: How did Tarek make his way from a nerdy kid in Lebanon to having his first startup funded by Sequoia and billionaires likes Charles Schwab and Henry Kravis? How did his mother's continuous desire for excellence change Tarek's mindset? What are the biggest lessons that Tarek took from his mother's strict parenting and how did he apply them to how he manages the team at Kalshi today? 2.) What it Takes to Succeed: Why does Tarek believe that the world does not want your startup to exist? In that case, what are the core traits that founders need to fight this headwind? Does Tarek believe in work-life balance? What are some of the struggles of this? Does Tarek believe you should work on your weaknesses or double down on your strengths? 3.) Building the Team: Does Tarek believe that naivete is a strength or a weakness? At what point does it change between being a strength to being a weakness? Does Tarek prefer to hire more senior experienced people or younger hustlers with more energy? What have been the single biggest hiring mistakes that Tarek has made? How has it changed his approach to team building? What is the one single trait that if Tarek sees, he will not hire? How does Tarek make the interview process both fun but different and challenging? Where do so many founders make mistakes in how they construct the hiring process? 4.) Tarek Mansour: AMA: What have been the single biggest lessons from working with Ron Conway and Alfred Lin? What are some of Tarek's biggest insecurities in leadership today? What does Tarek know now that he wishes he had known at the beginning of his time with Kalshi? What would Tarek most like to change about the world of startups? Items Mentioned in Today's Show: Tarek's Favourite Book: Never Split the Difference: Negotiating as If Your Life Depended on It 

One in seven California tenants are behind on their rent. So, Attorney General Rob Bonta is issuing instructions to sheriff and police departments across the state on how to respond when someone reports an illegal eviction.  Reporter Erin Baldassari, KQED  Overall, 91 percent of jobs at daycare centers in California have come back.  That might sound good, but the state lags behind the rest of the U.S. economy.  Reporter Daisy Nguyen, KQED  Starting January 1, the University of California and Cal State school systems will offer *all* students medication abortions through their student health centers. Thanks to a law that passed in 2019, the new policy will connect more than 62-hundred students statewide with those services. And for some, including those in the UC system, student insurance plans will cover all associated costs. Reporter Danielle Chiriguayo, KCRW Big money donors supporting Proposition 30 include San Francisco venture capitalist Ron Conway and former Presidential Candidate Tom Steyer. But Lyft has contributed by far the most -- more than seven million dollars.  Last year California approved a mandate for ride hailing companies: 90 percent of their miles logged must be with electric cars by 2030.  Climate Editor Kevin Stark, KQED The statue of Frank Bogert was removed from the front of Palm Springs' City Hall this week. The former actor and rodeo announcer was Mayor of Palm Springs in the 1950s and 60s and oversaw much of the desert city's growth. What's raising controversy is that he also authorized the bulldozing of homes of poor Native American, Black and Latino families from an area of the city called Section 14, back in the 1960s.  Reporter Saul Gonzalez, The California Report

Ron Conway and Topher Conway of SV Angel speak with Upfront managing partner Mark Suster about the firm's investing strategy, learnings from being one of the OG angel investors, as well as the announcement of their first-ever growth fund, SV Angel Growth.

They call him The SuperAngel. The Godfather of Silicon Valley - I think that's a bit unfair since he's not really that old - anyway, I just call him Ron. I've known him for years. Back when he was running a startup (Personal Training Systems) and not just investing in them (he's run SV Angel since Internet 1.0). We had a great chat that covered all of this and much more. Please give a listen, share with a fellow entrepreneur or investor and subscribe: The Look Back with Ron Conway….

David is the head of Samsung NEXT and EVP at Samsung David started as an early employee at Google, then lead SV Angel with Ron Conway, was in the Forbes Midas List, and is now leading Samsung's NEXT fund. We got to discuss with him about his early days, SV Angel, Samsung NEXT, Wearables and Data, investing in startups, company culture and Terra - the API that makes it easy for apps to connect to wearables. The Site ► https://tryterra.co More Podcasts ►https://blog.tryterra.co Twitter ► https://twitter.com/terraapi Linkedin ► https://www.linkedin.com/company/terraapi

We’ve got a new show and we’re in the swing of things. Today on the show, CNBC’s Carl Quintanilla, Jon Fortt and Deirdre Bosa take a deep dive into the long-awaited Coinbase IPO and talk the future of crypto with Coinbase investor Ron Conway. Plus, we’ve got an exclusive interview with Slack CEO Stewart Butterfield, and Julia Boorstin joins to break down a CNBC.com scoop on the recent GOP retreat that brought together major donors and leaders at Trump’s Mar-a-Lago resort.

We've got a new show and we're in the swing of things. Today on the show, CNBC's Carl Quintanilla, Jon Fortt and Deirdre Bosa take a deep dive into the long-awaited Coinbase IPO and talk the future of crypto with Coinbase investor Ron Conway. Plus, we've got an exclusive interview with Slack CEO Stewart Butterfield, and Julia Boorstin joins to break down a CNBC.com scoop on the recent GOP retreat that brought together major donors and leaders at Trump's Mar-a-Lago resort.

We’ve got a new show and we’re in the swing of things. Today on the show, CNBC’s Carl Quintanilla, Jon Fortt and Deirdre Bosa take a deep dive into the long-awaited Coinbase IPO and talk the future of crypto with Coinbase investor Ron Conway. Plus, we’ve got an exclusive interview with Slack CEO Stewart Butterfield, and Julia Boorstin joins to break down a CNBC.com scoop on the recent GOP retreat that brought together major donors and leaders at Trump’s Mar-a-Lago resort.

We’ve got a new show and we’re in the swing of things. Today on the show, CNBC’s Carl Quintanilla, Jon Fortt and Deirdre Bosa take a deep dive into the long-awaited Coinbase IPO and talk the future of crypto with Coinbase investor Ron Conway. Plus, we’ve got an exclusive interview with Slack CEO Stewart Butterfield, and Julia Boorstin joins to break down a CNBC.com scoop on the recent GOP retreat that brought together major donors and leaders at Trump’s Mar-a-Lago resort.

In this episode we talk to Maria Salamanca about her journey into politics and investing, her views on diversity at the funding and portfolio level, innovation trends that excite her the most and the women who inspire her.Maria is a Principal at Unshackled Ventures, a fund that fills a unique space in the entrepreneurial ecosystem, funding teams with immigrant founders at pre-seed stage. During her time there, she has been involved in 40+ investments and seen ~1,500 deals a year. She was the first Latina named Forbes 30 Under 30 for Venture Capital and Business Insider’s Under 30 Rising Stars. Maria started her career at FWD.us a bipartisan political organization focused on immigration and criminal justice reform. The group was founded by Mark Zuckerberg, Bill Gates, Ron Conway, Reid Hoffman and other tech leaders. She was also the founding COO at Swing Left and supported Higher Ground Labs with the first-ever political technology landscape.

Alex Pall and Drew Taggart are the Founders of The Chainsmokers and Mantis. The Chainsmokers are one of the most sought after musical acts of our time. As for Mantis, just last week they announced their first $35M venture fund and have backing from Ron Conway, Mark Cuban and Keith Rabois. They have already invested in hotly contested rounds for Fiton and Loansnap. Drew and Alex also own a production studio, are stakeholders in the spirit brand JaJa Tequila and last year co-founded the anti-scalper ticketing platform Yellowheart. In Today’s Episode You Will Learn: 1.) How Drew and Alex made their way into the world of tech and startups and how they came to found a venture firm with Mantis? 2.) Why did Alex and Drew decide now was the right time for the fund? What did they look for in their LPs? How do they use their LPs to strategically help their companies? What is their preferred stage, sector? Do they have ownership requirements? 3.) Are Alex and Drew nervous about making the move into venture? If everyone has a chip on their shoulder, where does the chip on their shoulder come from? How do they think about their own vulnerabilities? How do they manage them? What works? What does not? 4.) What ways do Alex and Drew most like to work with their founders? Where do they provide outsized value? What are some examples of this? How do they think about working with VCs to get into the best rounds? How do they want to position Mantis in the ecosystem? 5.) With the tequila brand, the film production company and now the venture fund, how do they think about the expansion of "The Chainsmokers Empire"? What does this look like in 10 years? How would they like it to expand and grow? Items Mentioned In Todays Episode Drew's Favourite Book: The Unbearable Lightness of Being As always you can follow Harry and The Twenty Minute VC on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

Sahil Lavingia is the Founder and CEO @ Gumroad, the company that helps creators do more of what they love. With Gumroad, Sahil has raised funding from an all-star list of investors including Accel, Kleiner Perkins, First Round and then Max Levchin, Chris Sacca, Ron Conway and Naval Ravikant on the individual side. However, most recently Sahil has made waves launching one of the first rolling funds on AngelList with his being $6M per year. In the past, Sahil has backed the likes of Lambda School, Figma, HelloSign and Haus to name a few. In Today’s Episode You Will Learn: 1.) How did Sahil make his way into the world of startups and angel investing? What were his biggest takeaways from being employee #2 at Pinterest? How did that experience impact his mindset? 2.) Why did Sahil decide to make his new fund an AngelList rolling fund? How is it structured? Does Sahil think this will represent a seismic shift in early stage investing? Is this a game of the 1%? Why does Sahil think early-stage remains so undervalued? How will this impact Series A pricing? 3.) How does Sahil assess his own price sensitivity today? How does Sahil think about the right way to turn down a founder? Where do many go wrong? How does Sahil feel about the rise of pre-empted rounds? How does Sahil advise seed founders with offers from multi-stage firms? 4.) What does Sahil believe founders care most about today in their investors base? How does Sahil think about investor brand and distribution? How does Sahil analyse the pros and cons of party rounds? How does Sahil advise founders on constructing their early cap table? 5.) How does Sahil think about his relationship to risk and to money? How did Sahil deal with it when his investors wrote off his company? How did Sahil feel about the weight of expectation placed on his shoulders at such a young age? How did he deal with this? Items Mentioned In Today’s Show: Sahil’s Fave Book: How to Win Friends and Influence People As always you can follow Harry and The Twenty Minute VC on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

Manny Fernandez is an angel investor, serial entrepreneur, bestselling author, and TV personality of CNBC's Make Me A Millionaire Inventor. He has been featured on CNBC Squawk Box, Wall Street Journal, NBC, CNN Latino, Forbes, and Fox News, among others. Named by Inc. Magazine as one of the top 33 Entrepreneurs to Watch of 2016, he has been successful in investing in his own ideas as well as taking several companies from startup to exit. Some of his investment success stories include TaskRabbit, which was recently acquired by IKEA. Fernandez has been an angel investor with TiE Angels since 2012. In 2013 he founded SF Angels Group, a network of angel investors to help tech entrepreneurs in the San Francisco Bay Area. Founding SF Angels members included early investors in Google and Paypal and a former partner of Ron Conway. In 2014, Fernandez was named SF Angel Investor of the Year and received the Equity Crowdfunding Leadership Award. In 2016, after founding DreamFunded, he was named Hispanic Shark of the Year by the California Hispanic Chamber of Commerce, Hispanic Angel Investor of the Year by California State Controller John Chiang, Silicon Valley Equity CrowdFunding Pioneer by Menlo College, and was invited to the White House by the Obama administration. Fernandez's energetic and inspiring presence has led him to become a frequent judge, international keynote speaker, and panelist for Silicon Valley corporations, startup demo days, and universities. He has been a featured guest speaker for events at Stanford University, UC Berkeley, Harvard University, Angel Capital Association Summit (ACA), TiE New York Conference, University of San Francisco (USF), Pepperdine University, Draper University, Plug and Play, Yahoo!, USAWeek in Europe, Qianhai Equity Exchange in China, Intel, California Hispanic Chamber of Commerce's (CHCC), Shark Tank, Startup Grind, AngelHack Global Demo, Slush Singapore, Slush Shanghai, Slush Finland, Startup Weekend, SXSW, and many more.

Hear what one of the world's most renowned motivational speakers, Les Brown and Manny Fernandez say about mindset during a Zoom call.  About Les Brown As one of the world's most renowned motivational speakers, Les Brown is a dynamic personality and highly-sought-after resource in business and professional circles for Fortune 500 CEOs, small business owners, non-profit and community leaders from all sectors of society looking to expand opportunity. For three decades he has not only studied the science of achievement, he's mastered it by interviewing hundreds of successful business leaders and collaborating with them in the boardroom translating theory into bottom-line results for his clients. About Manny Fernandez Manny Fernandez is an angel investor, serial entrepreneur, bestselling author, and TV personality of CNBC's Make Me A Millionaire Inventor. He has been featured on CNBC Squawk Box, Wall Street Journal, NBC, CNN Latino, Forbes, and Fox News, among others. Named by Inc. Magazine as one of the top 33 Entrepreneurs to Watch of 2016, he has been successful in investing in his own ideas as well as taking several companies from startup to exit. Some of his investment success stories include TaskRabbit, which was recently acquired by IKEA. Fernandez has been an angel investor with TiE Angels since 2012. In 2013 he founded SF Angels Group, a network of angel investors to help tech entrepreneurs in the San Francisco Bay Area. Founding SF Angels members included early investors in Google and Paypal and a former partner of Ron Conway. In 2014, Fernandez was named SF Angel Investor of the Year and received the Equity Crowdfunding Leadership Award. In 2016, after founding DreamFunded, he was named Hispanic Shark of the Year by the California Hispanic Chamber of Commerce, Hispanic Angel Investor of the Year by California State Controller John Chiang, Silicon Valley Equity CrowdFunding Pioneer by Menlo College, and was invited to the White House by the Obama administration. Fernandez's energetic and inspiring presence has led him to become a frequent judge, international keynote speaker, and panelist for Silicon Valley corporations, startup demo days, and universities. He has been a featured guest speaker for events at Stanford University, UC Berkeley, Harvard University, Angel Capital Association Summit (ACA), TiE New York Conference, University of San Francisco (USF), Pepperdine University, Draper University, Plug and Play, Yahoo!, USAWeek in Europe, Qianhai Equity Exchange in China, Intel, California Hispanic Chamber of Commerce's (CHCC), Shark Tank, Startup Grind, AngelHack Global Demo, Slush Singapore, Slush Shanghai, Slush Finland, Startup Weekend, SXSW, and many more.

Season finale. Spoken word poetry dealing with pain. Features pieces by Ann Jarmolowicz, JRay, Adrian Ruth, Helene Burke, Stephanie Christy, and Ron Conway. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Today we talk about one of the most prolific investors in Silicon Valley, Ron Conway. Why it is a good idea to be a nice person, authentically.

Steve Huffman is the Co-Founder & CEO @ Reddit, home to thousands of communities, endless conversation, and authentic human connection. To date, Reddit has raised over $550m in funding from some of the world's leading investors including Sequoia Capital, Marc Andreesen, Peter Thiel, Ron Conway, Sam Altman, Josh Kushner, Alfred Lin and Tencent, just to name a few. Steve started his career at Y Combinator as one of their first alumni back in 2005. At YC, Steve co-founded Reddit with Alexis Ohanian, which they sold in 2006 to Conde Naste Publications. In 2010, Steve co-founded Hipmunk, making business travel seamless and easy. Then in 2015, Steve re-joined Reddit as their CEO. In Today’s Episode You Will Learn: 1.) How Steve made his way into the world of startups and came to be one of the very first ever entrants in the now hailed Y Combinator? How did that lead to the founding of Reddit? Why did Steve return to Reddit, the company he founded, in 2015? 2.) What were Steve's biggest lessons from his journey with Hipmunk when it came to product feedback and iteration? How does Steve assess people's reliance on data today to drive product decisions? Why does he believe 3 criteria must be considered? What are the other two? What time did Steve see the confidence of his own intuition really increase? 3.) How does Steve think about stress management today? What was he like when he was younger in his relationship to stress? What did he actively do to change his relationship to stress? How has Steve seen himself change and develop as a CEO? What have been the inflection points? What has he struggled and also made mistakes in the journey? 4.) What have been Steve's biggest lessons when it comes to hiring truly A* talent at scale? What are the commonalities in the very best hires Steve has made? In the cases of it not working, what does Steve advise founders on the right way to let someone go? How does one do it with efficiency and compassion? 5.) Why does Steve believe that in dense cities, self-driving cars will not be that useful? How does Steve envisage the future of consumer transportation? What does he believe are the alternatives to self-driving cars? How does Steve see the future for the unbundling of social networks? Will they be unbundled into specific communities? How will this look? Items Mentioned In Today’s Show: Steve’s Fave Book: Shogun: The First Novel of the Asian saga: A Novel of Japan As always you can follow Harry and The Twenty Minute VC on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

Dave King is the Head of Marketing at Asana, the work management platform that teams use to stay focused on the goals that grow their business. To date, Asana has raised over $210m from some of the biggest names in tech including Mark Zuckerberg, Peter Thiel, Marc Andreesen, Ben Horowitz, Sean Parker, Ron Conway, Benchmark, Founders Fund and more incredible names. As for Dave, prior to joining Asana, he led the marketing teams at Percolate, Highfive, and Salesforce Community Cloud. In Today’s Episode We Discuss: How did Dave make his way into the world of SaaS and startups? When did he realise his love of marketing SaaS companies? What does Dave mean when he says, “we are entering the 3rd wave of marketing”? What were the 1st and 2nd chapters? What does the “3rd wave” of marketing mean for marketers today? How does it change what marketing should be focusing on? How does it change how marketing works with sales and customer success?  What does Dave mean when he says, “offsites serve as a crutch for 2 core elements of the marketer's role”? How does Dave advise marketers on crafting their playbook? What are the core questions to ask? Where does Dave see many going wrong here? How does one turn a playbook into a repeatable, measurable process? With channel volatility being so high, is it possible to have a repeatable and predictable process?   What are Dave’s biggest observations on what B2B marketers can learn from B2C? How does that change how Dave thinks about new campaigns and community building with Asana today? Who does Dave think has done this particularly well in the world of enterprise? Are there any challenges to trying to carry over B2C into the world of B2B?  Dave’s 60 Second SaaStr: What does Dave know now that he wishes he had known at the beginning of his career in marketing?  Biggest breakdown in the working of an efficient funnel? A moment in Dave’s life that has served as an inflection point and changed the way he thinks?  Read the 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 Dave King

“No one, VC or angel, has invested in more of the top startups than Ron Conway. He knows what happened in every deal in the Valley, half the time because he arranged it.” –Paul Graham on The Ronco Principle SV Angel is a San Francisco-based investment firm that is widely regarded as one of the most successful angel investing firms of all time. Kartik Talwar joined SV Angel in 2014 as an Insight Engineer while studying Astrophysics at the University of Waterloo. At SV Angel, Kartik ultimately built internal tools to track all investments, deals, financing, requests and relationships across 500 investments made and thousands considered ever year. How did they evaluate thousands of the top investment opportunities? How did they invest in the earliest rounds of Google, PayPal, Airbnb, Pinterest, Twitter, SnapChat, Square and other titans? What’s it take to launch a global engineering and education initiative like Hack The North and ETHGlobal? How are great teams and ideas brought to life? What does engineering excellence and the world’s top talent look like in the heart of American tech culture? Kartik Talwar, now a General Partner at A.Capital and Advisor at SV Angel, will walk us through these questions and more in our next Terminal Tech Talk.

ABOUT THE SPEAKER: Ron Conway is the man. He is THE investor. Google, Facebook, Twitter, Airbnb... He was an early backer of all of these companies and the list keeps going. Ron has been an active angel investor since the mid 90s and is the founder of SV Angel. He has invested in hundreds of companies & talked to thousands and thousands of startups in Silicon Valley. Angel and seed investments aren't hiding any secrets from him. ABOUT THE EPISODE: We're in love with what Ron did for the Internet industry. He truly is a rockstar and it was a honour having him talking at The Family for a unique fireside chat with Mathias Pastor, Director at The Family. They talked about which traits of greatness he observed in entrepreneurs, what makes an investment profitable + how he started and what he sees is coming next. *** Learn more about The Family and apply to join us here: thefamily.co Check out the video of the talk here: Ron Conway, the “Godfather of Silicon Valley”

Andrew Romans, based in Silicon Valley, is a successful VC-backed entrepreneur, author of two top-selling books on venture capital, former tech VC and M&A investment banker, cofounder of an angel group and General Partner of both Rubicon Venture Capital & 7BC which is a new fund focused on ai, blockchain, & fintech. In this episode we talk about (1) How he’s raised about $200M in funding as an entrepreneur and how fundraising has changed over the years (2)His relationship with Ron Conway & how things are done at SV Angel (3) Finding the best deal flow (4) Pivoting as an entrepreneur & fund manager music credits: Clouds by MBB | https://soundcloud.com/mbbofficial Music promoted by https://www.free-stock-music.com Creative Commons Attribution-ShareAlike 3.0 Unported https://creativecommons.org/licenses/by-sa/3.0/deed.en_US IMPORTANT NOTICE: This podcast is intended for informational purposes only. The views expressed in this podcast are not, and should not be construed as, investment advice or recommendations. Recipients of this document should do their own due diligence, taking into account their specific financial circumstances, investment objectives and risk tolerance (which are not considered in this podcast) before investing. This podcast is not an offer, nor the solicitation of an offer, to buy or sell any of the assets mentioned herein. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/joelpalathinkal/support

Roger Dickey is the founder of Gigster, a venture-backed start-up that gives clients a way to systematically outsource software development to a large network of freelance coders. Gigster has raised over $32 Million from high profile investors including Redpoint Ventures, Andreessen Horowtiz, Salesforce CEO Marc Benioff, former basketball player Michael Jordan, actor Ashton Kutcher, and "Super Angel" Ron Conway. The company has quickly become a Silicon Valley darling.

Roger Dickey is the founder of Gigster, a venture-backed start-up that gives clients a way to systematically outsource software development to a large network of freelance coders. Gigster has raised over $32 Million from high profile investors including Redpoint Ventures, Andreessen Horowtiz, Salesforce CEO Marc Benioff, former basketball player Michael Jordan, actor Ashton Kutcher, and "Super Angel" Ron Conway. The company has quickly become a Silicon Valley darling.

Ron Conway, the "Godfather of Silicon Valley" and Union Square Ventures' Fred Wilson discuss trends in venture capital beyond Silicon Valley and New York City.

I’m sure you’re aware of the talk and debate around the topic of diversity and inclusion. Maybe it’s left you feeling frustrated, tired, or downright apathetic…   I get it.   Much of the emotional rollercoaster stems from the challenges of navigating conversations with your teammates and peers on top of your day-to-day responsibilities.   Plus you’re probably wondering: Are these programs actually working?   You know how much I love busting myths! So in today’s Build episode, we’re going to talk about the issues specific to tech and provide you with some strategies for navigating those tricky conversations with your teammates and your peers. We’ll also dive into what isn’t working and why.   If you're curious about starting a diversity and inclusion initiative at your company or participating in another organization, then keep an eye out for the next episode where we'll do a deeper dive into a number of best practices.   To help us out I’ve invited Melinda Epler and Wayne Sutton, who are the founders of Change Catalyst and Tech Inclusion.   As you listen to this episode, you’ll learn the following:   Why we may shy away from talking about diversity and inclusion How shaming people and companies doesn’t help the cause Why awareness isn’t enough — how to shift to being more process oriented Why it’s hard to take action individually and how to get support How to know if diversity and inclusion are worth it   Build is produced as a partnership between Femgineer and Pivotal Tracker. San Francisco video production by StartMotionMEDIA.   ## Diversity and Inclusion: How To Navigate Conversations About Diversity And Inclusion in Tech Transcript   Poornima Vijayashanker:           There's been a lot of talk and debate around the topic of diversity and inclusion. And regardless of what side you are on, in today's episode, we're going to talk about the issues pertaining to tech, as well as how to navigate conversations with your teammates and peers. So stay tuned.   Welcome to *Build*, brought to you by Pivotal Tracker. I'm your host, Poornima Vijayashanker. In each episode of *Build*, innovators and I debunk a number of myths and misconceptions related to building products, companies, and your career in tech.   Now, as you can imagine, there are a lot of myths and misconceptions around diversity and inclusion. Conversations can be really hard to navigate. And that's why in today's episode, we're going to talk about the issues that pertain specifically to tech and help you navigate those conversations with teammates and peers. And if you're curious about starting a diversity and inclusion initiative at your company or participating in another organization, then stick around for the next episode where we'll do a deeper dive into those practices.   In today's episode, I invited Melinda Epler and Wayne Sutton, who are the founders of Change Catalyst and Tech Inclusion. Thanks you guys for joining us today.   Melinda Epler:            Thanks. Thanks for having us.   Wayne Sutton:              Thanks for having us.   What does diversity and inclusion mean?   Poornima Vijayashanker:           Yeah. So, let's start by talking about what exactly is diversity and inclusion?   Melinda Epler:            I'd like to say that diversity is about bringing diverse people to the table and inclusion is about inviting them to speak, encouraging them to lead, and supporting them in leadership. Diversity is really about the demographics and inclusion is about how people show up, how people thrive, and how people feel that they belong in their culture.   Poornima Vijayashanker:           Yeah. And how do you think it pertains to tech?   Wayne Sutton:              I like to say diversity includes everything, how it pertains to tech. Tech impacts the world, it impacts everything we do in our daily lives, from the time we wake up to the time we go to sleep. Tech creates wealth. Tech creates innovation. You look at all the products. They got some of the products living on your shelf. We're thinking about the customers. We're thinking about the followers. We're thinking about the equity or the inequity and all the opportunities that is connected to tech. And without diversity, right, you see problems being built by a homogeneous culture. You see solutions not brought to the table because it's being built by one or two mindsets.   You see a lot of disparity gaps in terms of access and wealth. So, without diversity, really, the tech industry is not as thriving as it should be or could be. And the impact is having, in a positive and a negative way, is not where it could be as a whole, as well.   Why Diversity And Inclusion Are Important                    At the same time, if the tech industry were more inclusive, I could imagine more innovative products. I can imagine an industry that is really identifying or being held accountable to its actions as current culture, where we wouldn't have issues that existed in the ‘60s, ‘70s, in terms of not having equal pay, not having women or other representative groups, African Americans in leadership positions, cultural and sexual harassment. If we had inclusion, these negative issues that is in the face of our society right now, in tech, wouldn't exist.   Poornima Vijayashanker:           So, let's talk about what drew you to focus on diversity and inclusion, because you both had different backgrounds before you were working on this.   Melinda Epler:            Yeah. My background actually started back in cultural anthropology and really looking at how individuals create change in societies, how culture changes. Then, I moved into the documentary filmmaking. I was a documentary filmmaker for about 10 years, became a consultant, and then, actually became an executive at an engineering firm. That experience of being a woman, and the only woman, in leadership in that engineering firm was what made me rethink what needs to change in society. I was not thriving in that culture and it was because it wasn't set up for me, it wasn't set up for my success. And so, I actually left that job as an executive to go into diversity and inclusion. I started Change Catalyst and Tech Inclusion programs, really addressing the inequities in our society. My whole life has been focused on creating social and environmental change.                    And I believe, now, that if we don't change leadership, if we don't change who can be a leader and who is leading our countries, who's leading our companies, who's leading our technologies, our stories, then we can't change what's really happening in the world. I am here because I believe that this is the most important thing for me to be working on to create positive social change in the world.   Early Attempts To Foster Diversity And Inclusion In Tech   Poornima Vijayashanker:           What about yourself, Wayne? Your background is also from a different angle.   Wayne Sutton:              Yeah, yeah. I started off doing design, graphic design—I'm showing my age some. Then, got into design UI/UX and computer graphic design. I did IT for about 8 years in North Carolina—Research Triangle Park—then became a tech founder. Had a startup back in 2007 or 8, and didn't realize that in North Carolina that I was one of very few unrepresented tech founders, a black tech founder. And then, experienced the challenges of growing and scaling a company. Then, fast-forward to 2011, the data from CB Insights said that 1% of the tech startups, that raise "angel" or VC are founded by African Americans and Latinos. They actually grouped that data together. So it really was less than 1% Black and Latino that received any angel or venture capital in 2011.   By that time, I knew various colleagues across the country that was also working on startups. I was basically like address the problem. What can we create a solution around? Some other colleagues and I, we decided to move to Mountain View and create the very first incubator and accelerator, focused on underrepresented tech founders. That led to a whole other window of opportunity. Moved to Silicon Valley, then moved to San Francisco. And then went through a period where, after that, I realized that the tech industry did not want to talk about diversity and inclusion, did not want to talk about the disparities of access and inequality for founders to receive capital. The tech industry was priding itself on being a meritocracy. It was like: Anyone can create a tech company, anyone can create a product, it doesn't matter.                     That may be true.   How The “Pattern Of Action” Created A Period Where People Didn’t Talk About Diversity And Inclusion   Poornima Vijayashanker:           But it wasn't happening.   Wayne Sutton:              No. It was not happening. And the data already showed that everyone was not raising angel capital. It wasn't because of lack of talent. It was not because the products were not as good. It was because what the VC industry would call "a pattern of action."   Poornima Vijayashanker:           Right.   Wayne Sutton:              And we went through a period where we’re not talking about diversity or inclusion at all. We're not talking about a lack of funding for underrepresented entrepreneurs. And then the tech industry released a diversity report, that was in 2014. And then email—and I want to say the phone, but more the email and Twitter DMs started come in. It was like, "Hey! We need to talk about diversity. I remember you used to talk about it in the day..." And Dan Blenman and I started collaborating. We met and started collaborating on solutions. We got invited to so many roundtables, kind of private conversations, one-on-one meetings around what can we do to fix this diversity problem.   Melinda Epler:            Yeah, those were from the White House, to the Small Business Administration, to the FTC, the FCC. And then local tech companies and local conversations as well all kind of talking about that.   Wayne Sutton:              That was at the White House during the Obama Administration.   Melinda Epler:            Yes, just so we're clear…   Why There Isn’t A Single Solution To Improving Diversity And Inclusion   Poornima Vijayashanker:           Yes, yes, of course. Well, we know who was in the Administration in 2014. So you already touched upon some of them in your intro, but what were some of the problems specific to tech that you continue to see in this phase and maybe even today?   Wayne Sutton:              I would say that the biggest problem is still that happened then that we see today is still the state of denial that there is an issue. There is also the problem that it's going to be one technology solution or you do one thing that's going to fix everything.                     And then—   Melinda Epler:            One cheap thing.   Poornima Vijayashanker:           Yeah.   Wayne Sutton:              And then one problem is still thinking that companies and individuals can focus on one demographic or one category. I don't want to say category. But one part of the conversation. And use it saying that it's solving all the problems.                     And I would probably say lastly, it's another problem in that I was shocked at how many companies when they started talking about wanting to do work around solutions around diversity and inclusion, they will not even connecting it to their business. Or their customers. Or their direct culture. They were just trying to say, "Hey, we're working on this. Get off our backs! Here's our solution. It's hard." And all those things combined is not really good for the goal we want to achieve.   Melinda Epler:            I think there's a few different reasons that this is happening. And this happened in tech differently than in other industries. It started back when Steve Jobs and some of the big, great tech CEOs became "the" story around tech. And the story revolved around that being the kind of...that is the tech founder: the white male CEO is the tech founder.                     And when those white male tech founders built their companies, they hired their friends. They just didn't think about it. They hired their friends and their friends hired their friends and the tech companies are still set up for referrals mostly. When you have the same people referring their friends, then you're going to have a homogeneous culture. And so the issue is, now that those companies are so big, it's really hard to create change in them.   Poornima Vijayashanker:           Yeah.   How Success Patterns Have Bred The Current Culture In Tech   Wayne Sutton:              That brings up another good point. Because the industry is manipulated by market trends and success patterns, right? So the VCs while they represent the founders, because they hadn't seen any. Or there hadn't been enough for them to say this is a good model, right? But in the culture of the tech industry, you look at your Microsofts, your Intels, your HPs. That's like one era of American business and society, how they grew and scaled companies.                     Then the second wave is the Googles, the Facebooks, the Twitter, the Snaps, the Instagrams, the Salesforce. Those companies basically, like Melinda said, they hired their friends when they started. Larry and Sergei were Stanford grads. And then they got funding from Ron Conway and the network. And there was a lot of luck involved, a lot of sweat, a lot of hard work, they built innovative products, but then because those companies made a lot of money, everybody just replicated that same pattern.                     I'm old enough to remember where it didn't matter what school we went to. You can code, you can build, you can design—it mattered. Let's get to work! Let's build something! But then because it became a new norm of like, "Let's copy Google, let's copy Facebook, let's copy other companies." Then that became the standard like, "We must do this." And if we look at that demographic data…   How to Measure Diversity And Inclusion In Tech   Poornima Vijayashanker:           So, obviously this isn't the first time that people are having this conversation. Like I mentioned in the intro, there have been many, many years that we've been having this. I know even for myself, moving out here in 2004, there were some rumblings, but not a whole lot, to the level it's at today. But obviously people have tried to fix this problem once before, and where have they fallen short? Like, what didn't work?   Melinda Epler:            Part of it is not measuring. And that changed in 2014. That was a big shift for everyone, where they really could see these are the diversity...these are the actual demographics of our companies. "Oh! Whoa! Those are really bad! We need to figure out how to solve them."   Why Measuring And Building Awareness For Diversity And Inclusion Isn’t Enough                     And then, once they said that, they kind of put those numbers out there. It hasn't changed all that much, because the first year was just about measuring. And then starting to hire diversity and inclusion managers and directors, but not putting a lot of money and resources into really...you can't hire one person to change a whole company with very little resources. That's just not possible.   So, as those diversity and inclusion managers have gained traction in their own companies, they're starting to get bigger budgets and things are starting to change a little bit faster in the companies. Now that does not discount the fact that people have been working on diversity and inclusion in some of the tech companies for quite some time. One of the issues is that many of the initiatives around diversity and inclusion in the past have focused on employee resource groups, community groups, and really helping support underrepresented people in the companies. And I think, on the other hand, some work has been done on education and awareness building. And this happens in every kind of major culture shift. It happened in the sustainability movement as well. There's an expectation that if you tell somebody there's something wrong, they'll do something about it.                     But the problem is, you actually have to help them change their behaviors. And really fundamentally think about how to shift culture. How to shift processes and systems that are ingrained and perpetuating the problem.   Why Shaming People And Companies Doesn’t Enact Change   Poornima Vijayashanker:           Yeah, talk is cheap.   Melinda Epler:            Yeah, and it doesn't really change anything. And the third component is really about shaming. There's also a really deep and disturbing trend around shaming people into changing. That doesn't really change people either! So, there's a new and growing movement and at least an understanding—I wouldn't say movement yet—and understanding that it takes more. It takes a bigger effort to really look at your culture. How do you change your culture? How do you change individual behavior? How do you fundamentally look at your recruiting process? And say, "Oh, wow!" From the very beginning. We need to change the way we're doing things. There are biases in the system, but also there are some mismatched systemic problems in the process.   Change Catalyst’s Approach To Helping Companies With Diversity And Inclusion   Poornima Vijayashanker:           So let's talk about Change Catalyst and how do you approach this differently?   Melinda Epler:            We have a few different things that we do. We have events, our Tech Inclusion events. And when we were talking earlier about having those roundtable discussions back in 2014, we started getting pretty frustrated that those roundtable discussions kept talking about the same things over and over. We had the same conversations over and over again. And they were really problems focused, which is important…   Is A Diversity And Inclusion Program Worth It?   Poornima Vijayashanker:           What's an example of that?   Wayne Sutton:              An example would be the question we get asked over and over again. "Who's doing it well?" And the reason why everybody wanted to know...   Poornima Vijayashanker:           Like they're a benchmark.   Wayne Sutton:             ...No, no, not that! The reason everybody wanted to know who's...everybody wanted to know what company was having any type of success around diversity and inclusion. Any type of success.   Poornima Vijayashanker:           Oh! To see if it was worth it.   Wayne Sutton:              They wanted to know if it was worth it, but also wanted to know if they could copy it. They wanted to replicate it. And that was really it. Because everybody wanted one moonshot idea to say, "We're implementing change." That they could say, "We're working on it." And that was a repetitive question across the board.                     At times, really, there wasn't a company that had all the answers or all the ideas or...   Poornima Vijayashanker:           You guys all suck!   Melinda Epler:            Exactly!   Poornima Vijayashanker:           Or it's not making an impact.   Melinda Epler:            I'd say it's still the case. There's no one company that's doing everything right.   Poornima Vijayashanker:           I'd agree!   What Diversity And Inclusion Training Looks Like   Melinda Epler:            Largely because the diversity and inclusion programs are under resourced. But there are gems. There are some people that are doing some really great programs that we can point to.   I think also, in 2014, there was a lot of talk amongst underrepresented people that were feeling disenfranchised. Feeling like the opportunities weren't there for them. Feeling, hearing over and over again that there are barriers, there are barriers, there are barriers. But less about solutions. How do we break down those barriers? What do we do? How do we solve that problem?   So that was one. We really wanted to focus on solutions, and that's what we have done. We've designed it to focus on solutions. The second is we really with our Tech Inclusion Programs...it's a systemic problem across the tech industry. So, it starts in education, and then there's huge problems in terms of entrepreneurship. Lack of investing. Lack of investors who are underrepresented. And then also lack of investing in underrepresented founders.   And then of course, the workplace. At the time, it was really focused on recruiting. As we talked about, you also have to change the culture. You can't just change recruiting, because you're bringing underrepresented people into a culture that's not creative for them. And they're going to leave! Like I left my position, right?   So workplace, and then policymakers as well. Policy and government agencies, and their power and wealth, and ability to create change in the system. And now we also focus on storytelling, like you do as well, to really help raise the underrepresented voices and perspectives, and have more diverse perspectives out there.   So for our events, that what we really focus across the tech ecosystem, bringing everybody together to focus on solutions.   For our consulting, training, and toolkits, we're also solutions focused. And focused on behavior and culture change, and really going beyond recruiting—recruiting is a part of that, but all the way through creating a culture of belonging.   Change Comes From Multiple Sources   Wayne Sutton:              Yeah, for us, it's that how change comes is different. We're not a "come in and look at one problem or one sector of the goal you want to accomplish around diversity and inclusion." We want to really discover and look at your entire company from a culture and a systematic perspective to help identify opportunities to create real change.                     What we've seen in the past is that a lot of companies contact us after taking unconscious bias training, and saying "We did that, now what? What do we do now? That has had some effectiveness, but we need more." And it's been an opportunity and a challenge aspect is that smaller companies—and even larger companies—they really have to be committed to put their resources in to explore what real change looks like. Whether you implement a new tool to remove names from resumes—that's just one thing. That's just one task you can do to help affect your recruiting process.                     But what about when you have your product team, your design team, your engineering team and there's different negative and positive behaviors in that one team dynamic. A software tool is not necessarily going to fix that. One unconscious bias training is not going to fix that. There needs to be a discovery and a real heart-to-heart conversation around employee behavior with accountability. And we come in and have those harder conversations, put together a report, talk with the executive team, and if they have a head of diversity officer, work with that individual to put together some strategies that can create change.   Poornima Vijayashanker:           Nice! So what's the impact that you've seen so far through your programs and your offerings?   Wayne Sutton:              We see impact across the board. There's impact from the consulting, has been from a company not having any strategy at all from everybody saying like, "Well, we care about this. We want to do something," to now that there is a board level, and executive-level type solution with a plan in place that they can measure and track results over time with some accountability involved, where there's an individual or team saying that, "This is the team that is working on creating inclusive culture." That's been in some of my trainings. A consultant impact.                     The other impact we've seen around our conferences and events that we've done now mostly across the globe. We've been overseas. Across the globe, has been everything from gender-neutral bathrooms to new jobs created.   How To Navigate Conversations About Diversity and Inclusion   Poornima Vijayashanker:           That's a great segue into my next question. I know a lot of people—especially in our audience—care deeply about diversity and inclusion. But they may find it hard to navigate those conversations or to even initiate them with their teams, with their bosses. So how have you kind of facilitated that?   Melinda Epler:            We start by asking everyone in the company, at least a broad set of people across the company, what diversity means to them. What inclusion means to them. Start to develop a company-wide definition of diversity and inclusion. And then, literally we talk to people across the company about the ideas that they have, the experiences that they've had, and really develop a strategy that includes all of those voices. I think you have to do that.                     So that's what we do at the kind of company-wide level, and including the executives all across the executive teams and the board as well. For an individual wanting to create individual change, who may not be an executive in a company, I think that there are some different resources for understanding the language around diversity and inclusion and there are some courses out there around allyship. And some information around allyship that I think can be really beneficial to really...there are so many different things that you can do.   Why It’s Hard To Take Action As An Individual Leading Diversity And Inclusion Initiatives   Poornima Vijayashanker:           Before we dive into that, because I want to talk about that in a future episode, maybe we can talk about why it's been hard for them to take action individually.   Wayne Sutton:              We have been contacted about a lot of individuals saying that they care about diversity and inclusion and they want to implement change in their company. They need help, right? And ultimately we go back and have a one-on-one call or face-to-face, and we say, “Well what does this conversation allow with the company values? What has been done? Have they discovered what has been done in the past?” And then question if they don't feel confident to have a conversation with their manager or someone, a colleague or someone in an executive role around diversity and inclusion, they need to see if this is a place they want to work. Because it can be a difficult conversation.                     I mean, an article just came out today where an individual, he quit a well-known company because his manager or executive said that, "Stop talking about diversity and inclusion!" Right? So this topic is sensitive to a lot of people. They're afraid of it, they don't want to talk about it. It creates a sense of fear and anger and frustration for others. So whenever people come to us and say, "I want to talk about it," a suggestion is approach it with a business case. That’s one. Approach it with an empathy case. Approach it with an idea versus, "Hey, I want to work on diversity and inclusion at my company."   That's how we get asked. It's like “diversity and inclusion” is such a big umbrella word. So for your organization...   #1 Reason That Keeps People For Taking Action   Poornima Vijayashanker:           Loaded   Wayne Sutton:              Loaded, right? Well a lot of emotions with a lot of history. So if you are an individual and you say you work in product and you want to work on diversity and inclusion at your SaaS company, right? So a suggestion would be to identify that you're going to talk to your manager. “I want to reach this audience that we haven't been talking with or connecting with. With this lens, how can we make that happen?”   That's gonna cost them a product. But from a cultural perspective, it could be "Have we measured?" Or "I noticed that I'm the only female or African American, Latino, LGBT. There are some issues to mean that are not being brought up." Or, "How can we have a dialog about it?"   Melinda Epler:            I mean, your question was "What keeps people from taking action?" I think, really. And the #1 thing is fear.   Poornima Vijayashanker:           Yeah. Losing their jobs.   Melinda Epler:            Losing their jobs, but also just fear of making a mistake. It can be hard to navigate. There are definitely people who are good at shaming, publicly shaming. And that doesn't make it easier to create change and to take action. So I think that inherent in what Wayne is saying is that just take the first step. Take one step. Try something new. Talk to someone. Understand basic things. Understand what their experience is. Listen. Those can be really powerful first steps.   Wayne Sutton:              It seems like the tech industry has forgotten that we are humans. We had a conversation as a team talking about...   Melinda Epler:            Human first.   Wayne Sutton:             ...Human first. Right? And just because I'm different. Just because I'm a black male from the South doesn't mean I can't have an intellectual conversation around topics that are passionate to me. That could be black man, STEM products, that could be how can we look at different demographics or location. Why can't we have a real conversation?                     If we can talk about growth. We can talk about APR. We could talk about growth hack and design thinking. Why can't we talk about working together as humans and expanding your mindset, opportunity, and behaviors for all humans? What's the problem?   Why People Are Reluctant To Talk About Diversity And Inclusion   Poornima Vijayashanker:           So maybe you can touch upon that. What's some of the resistance around the conversations?   Wayne Sutton:              It goes back to what Melinda says: Fear! It's fear. But it's also fear because the tech industry traditionally has been a Type A, god-like mentality. Where everybody has all the answers and so if you go to someone and say—talking about diversity and inclusion—you want to have all the answers, so there's it can create a sense of fear. And/or the tech industry we know today, right, is in Silicon Valley, it's in San Francisco, it's in the East Bay some, and the data just in terms of population, in terms of African American in San Francisco is like 6 percent. And if we know the numbers that at Google and at tech roles is average 2 percent within the entire organization. So the culture that these companies traditionally haven't been diverse. So now you want to take an individual, who maybe the one only diverse individual—African American, Latino, women, or Latino or on the team—they want to talk about a cultural topic that is relevant to them, to someone who doesn't have the same experiences, it could be sensing like fear and they don’t have all the answers and not understand why. And that right there creates tension.   Melinda Epler:            There are also studies that show that if a company is talking about diversity, then people within the company think it's changing. That is another aspect of this. That's just psychology involved in all of this. When you start talking about diversity, people think that it's changing.   Poornima Vijayashanker:           Changing for the better? Or like…   Diversity And Inclusion Is Not A Zero Sum Game   Melinda Epler:            Changing for the better. People think if you're talking about it, it's changing for the positive. And then there's also on the fear side, though, there's also a fear that if other people rise up, you'll fall down.   Poornima Vijayashanker:           A zero-sum game.   Melinda Epler:            Exactly. But it's not. This tech industry is growing rapidly. There aren't enough people to fill all the tech jobs. That's absolutely not the case. So we just need to change that perception   Poornima Vijayashanker:           So, in the next episode we're going to talk about best practices, but before we wrap up this one, I want to just address some of the objections that our audience may come across when trying to broach the topic. They might have somebody say, "Oh, we're not going to talk about it all, it's not a priority. Like ship product." Or, "Hey, we had lovely little meetup the other day, with some great female engineers. What's the problem? We're making incremental progress." Or, "Hey, we've got to move really fast and whatever you do, how do I know it's going to be a 10x impact?" Right?                     And that can be overwhelming for the person on the receiving end. So how do we deal with some of those objections?   Wayne Sutton:              Yeah.   First Step Is To Measure Your Diversity And Inclusion Efforts   Melinda Epler:            I think one of the things people in tech react really well to is data. So the first thing is measure. And find out that information. Find out the demographics of the company. Find out—if you can—the engagement metrics as well, because you can start to look at engagement metrics as it relates to race and gender and ethnicity. And that can...and people with disabilities. And you can really see something is not right.   And once you look at the data, then you can say, "Oh, wait! We have a really high turnover rate among women. That's a big..."   Poornima Vijayashanker:           Well, everyone's making babies!   Melinda Epler:            That's a problem! There are lots of data that shows...   Poornima Vijayashanker:           I know...   Melinda Epler:            An important part of society. But that is only one. Most of the women who leave tech just so we’re absolutely clear, most of the women that leave tech go to other industries and become leaders in those industries.   Poornima Vijayashanker:           So we're missing out on opportunities.   Melinda Epler:            We're missing out on opportunities. The cost of turnover is high in a company. You don't want to lose people. That's a huge cost.   Poornima Vijayashanker:           So that's just employment. What about with the product itself? Because you had been touching upon some of that.   Wayne Sutton:              Yeah, I want to say that for individuals who want to create change or they've experienced some—they're in a culture they want to make improvements in, you start with the data like Melinda articulated. But it's also starting documenting examples, right? Like we worked with one company where the CEO had heard some stories but it was coming second and third hand.   Poornima Vijayashanker:            Yeah, that's a challenge.   Document And Show Proof Around The Need For Diversity And Inclusion Efforts   Wayne Sutton:              So if you're an individual, you work on your product team, your engineering team, or you could be a product manager. And you constantly see these examples, these situations happening. Take note that this happened on this day. This was the experience. And therefore you are able to have proof. The opportunity to create change may come under the window or umbrella of diversity and inclusion, but it could be just how can we conduct an inclusive meeting? Just a better meeting? How could we make sure all the voices are heard?                   If you've got a product team, like seven guys, three women, and the women hardly ever speak up or talk, you have a communication and culture problem where the women either don't feel empowered or the men are being assholes. Or both, right?                     And so, it's like that is culture. So the change you want to make may not say…”I want to create diversity and inclusion strategies.” Or, “I want to increase #1 my product team.” Or, “I want to make sure the voices are heard.” Or, “I want to talk about how we can conduct a better meeting that benefits the company, and everyone.”   Poornima Vijayashanker:           So start small. And that makes a big difference compounded over time.   Wayne Sutton:              But track it! Because you've got to have real examples that's relative to the change you want to make.   Melinda Epler:            Yeah, it affects product design in a huge way. And I don't think we talk about it enough. That if you want to grow your customer base, and if that customer base is diverse, your designers and developers need to reflect that customer base. If you're designing for the wrong customer base, you're not going to have a success. And it has huge implications—I mean some really terrible ones out there. Even when the airbags were designed, for example, they were designed by men. And in the first rollout, several women died, children died, because they didn't test it out on women and children. That's just a really basic example.                     Then we see that in the tech industry a lot now, where especially when AI is being developed and things come out on Google search where black people are mislabeled. That is a really dreadful outcome of not having diverse people design your programs.   Diversity And Inclusion Doesn’t Just Impact A Single Group Or Criteria   Wayne Sutton:              I'll probably say here another problem is that—or opportunity—is that when people are talking about diversity and inclusion. You got to remember that if you're going to focus on inclusion, look at it from the perspective of everyone. Right? Think about diversity is beyond just a color of someone's skin.                     We were talking earlier about people who are hidden, invisible disabilities. Think about accessibility. Think about age. Think about class. You have people with different heritage.   Melinda Epler:            Veterans.   Wayne Sutton:              Veterans. It's not just black and white. It's not just gender. It's literally everyone. But the solution that may pertain to a company can be one thing—and that's OK if you're going to focus on, "OK, we're going to focus on STEM youth, kids' pipeline." That's OK.                     But identify, communicate that this is what we're doing as part of a solution, but not "the" solution. Or, “We're going to focus on college students.” That's OK. That's good. We need to do that. But identify and be clear and authentic about where your solution is around college students affects your business and your culture.                     Understand that if you're a person in that culture, say my company's all, "We've got a diversity and inclusion plan. We focus on college students." Yay! Great! But if most of your employees want to focus on a different demographic, want to do something around veterans, then as a team, as a company, depending on what size, you've got to understand why. OK, there's an opportunity and a need to focus on these areas as well.   Poornima Vijayashanker:           Well, thank you both for joining us today. And now for all of you out there in the audience, let us know in the comments below this video if your organization has put in place any diversity and inclusion initiatives. And what's been the impact?                     And be sure to subscribe to our YouTube channel to receive the next episode, where we'll dive a little bit deeper and share some of the best practices if you're thinking about putting in place a diversity and inclusion initiative at your organization, or want to join another one.                     Ciao for now!                     This episode of *Build* is brought to you by our sponsor, Pivotal Tracker.

Lyon Wong is the Founding General Partner @ Spectrum 28, a $170m Silicon Valley fund that invests in companies who leverage data and computation to create and transform large industries. Prior to Spectrum 28, Lyon was Partner @ Lightspeed Venture Partners, the $3bn VC fund with investments in the likes of Snapchat, Nest and Giphy, just to name a few. Before Lightspeed Lyon spent time with leading Silicon Valley seed fund, SV Angel, where he worked with the likes of Ron Conway and David Lee.    In Today’s Episode You Will Learn: 1.) How did Lyon come to be GP of Spectrum 28 with their recent $170m fund? 2.) Why does Lyon believe that you do not have to have operational experience to be a good investor? What are the core components to beeing a good investor and value add investor? 3.) Why does Lyon believe that there is no optimal stage to invest? How does he evaluate the efficiency curve and positioning on it? How does this affect his thesis? 4.) Why does Lyon believe that funds must focus on reputation instead of cash on cash? What is the most effective and scalable way to build a reputation in today's venture ecosystem? 5.) How does Lyon evaluate team building at Spectrum 28? What did he look for in potential partners? How does he look to implement generational transition into the fund? Items Mentioned In Today’s Episode:  Lyon’s Fave Blog: Medium, Quora Lyon’s Fave Book: Meditations by Marcus Aurelius Lyon’s Most Recent Investment: PatientBank As always you can follow The Twenty Minute VC, Harry and Lyon on Twitter here! Likewise, you can follow Harry on Snapchat here for mojito madness and all things 20VC. Angelloop is the leading post funding management platform for private market investors and their portfolio companies. They help investors manage and track their portfolio companies on the cloud while providing them with access to their investments performance data. Angelloop helps founders of startups track their performance, manage their cap table and keep their investors in the loop. Investors get free access while their portfolio companies pay only $49/Month. Use or share the promo-code 20MinVC to get your portfolio companies online with a two month trial. This episode was supported by Wunder Capital, the leading online investment platform that allows individuals to invest in large scale solar projects across the U.S. Wunder’s solar investment funds allow you to earn up to 11% annually, while diversifying your portfolio, curbing pollution and combating global climate change. Do well by doing good and sign up for a free account here and join the thousands of people that are already achieving their investment targets.

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Ron Conway, the Godfather of Angel Investing, who has been called "the man who has placed more bets on Internet start-ups than anyone else in Silicon Valley" got on his soapbox to share with us how he invests and what he looks for in his companies and entrepreneurs.

Ron Conway discusses his investment history in Silicon Valley. Conway has invested in more than 500 startups and gives great advice at picking companies that could be big winners. (January 18, 2011)

Experienced angel investors, Ron Conway, Founder of Angel Investors LP, and Mike Maples, Founder of Maples Investments, provide a rare look into the ins and outs of angel investing. Conway and Maples discuss how angel investors assess opportunities, provide assistance to entrepreneurs and transition start-ups to larger venture investments or exit. In addition, Conway and Maples provide advice to entrepreneurs about finding one's passion and developing that passion into new ventures, including insight into how much money to raise and how to manage that money after it is in the bank.