Inside $6.8B+ AUM Fund Behind 80+ Public Companies, 165+ M&A

[00:00] Almost no iconic company in the history of venture capital has come from anyone's thesis area. [00:08] No one was sitting on OpenAI when like Khosla invested in OpenAI before really Chachapiti was a thing going like, hey, my thesis is that LLM chatbots are going to be a thing. No one's saying that. The investment is these guys are really smart and they're working on something that could be pretty valuable. The number one thing I'm looking for when it comes to the founder is, are you just doing this because you think being a founder is cool? [00:30] Everyone thinks being a founder is cool and high status. I don't care about that kind of founder. There is information asymmetry between what most people in AI know, like the Venn diagram of things people in AI know and care about, and things that matters in the world. And who is at the very edge of the little tiny intersection between that, where they're able to capture a little bit of that, is pretty important. I run such a cool company right now. If I get acquired, I will no longer run that cool company. Why would I want that? [01:00] so celine welcome to sorcery sorry dd welcome to sorcery so great to be here molly [01:14] Congrats on the big promotion. I know you're just promoted a partner at Menlo. Thank you. This is really cool. I want to know, between it all, you were a founding team member at Glean. You're now at Menlo. You are following the dream. You're becoming an investor in Silicon Valley. What is your biggest bet right now? When it comes to big bets that we make right now, I'll tell you one company that I'm super excited about. We're investors. But one company I'm super excited about and then just an area that I think doesn't get enough attention

[01:44] excited about. The one company I think is [01:47] There's a company called Good Fire. Not enough people know about it. [01:51] That will probably change hopefully soon. Goodfire was this company that does, you know, I like to call it brain surgery for AI models. But the technical term is mechanistic interpretability for AI models. So it's these researchers who sort of found their respective teams or worked with the very early teams of Interp in Anthropic, DeepMind, OpenAI, who came together to sort of say, hey, I think... [02:19] If we have AI everywhere in the world today, there is no future that we want to live in where this is a black box and we don't understand what's going on. All explainable techniques for AI today are kind of not are they're empirical. So they're looking at the results. They're evaluating it. [02:37] That's not fundamentally explaining why a model does what it does. [02:42] A good example is when, you know, ChatGPT, GPT-4O specifically became super sycophantic. That's not something they really caught in eval. I mean, that's something they found out much later. What if you could peek into the brain of a model and find out what it was actually thinking? [02:58] why it was actually saying the things it said. And I think they've come up with some really, really interesting novel discoveries that still... [03:06] pretty secretive to them that I think could really change the game of how we think about AI in the future. So I'm really excited for that company in the future that it's looking at. And the name of that company was? Goodfire. Goodfire? That's correct. I wonder what they found.

[03:21] I can't say. You can't say? Can't say. Tell me. It's a super niche scientific breakthrough. I think it's quite interesting. Okay. You know, one of the interesting thing about when you back labs that can be, you know, quite anxiety inducing is, you know, they're probably not on this like, revenue train of the only thing that matters to me is get deals through the door and get money. [03:51] productionize them into [03:53] a go-to-market product. I think I would say they have done a big part of one pillar of the five things that they're looking to solve. And unfortunately, that's all I can share for now. Classified. Bummer. We've only run into that when talking to Palantir, but this is another classified conversation, I guess. Okay. So you're really into this company and then also area. [04:23] And [04:23] I still think, I know this is a very cliche thing to say, everyone says it, every single VC says it. But when I mean a very specific thing, when I say this, when I say we're in the very early innings of AI, what I really mean by that is, and I know it's the most cliche thing in the world. What I really mean by that is, [04:42] If you really look at [04:45] birth rates in the world. There are just a bunch of jobs that

[04:52] people can't hire for anymore. [04:54] And it's not just in America and in Europe and in parts of the Western world, even in other places. If you look at what young kids want to do and have wanted to do for the last 10 years, they want to be – [05:07] youtubers [05:08] Coders, desk jobs, right? Primarily, like people want to do desk jobs or positions of power maybe or that. No one does blue collar labor. No one wants to do, say, like a tax job. Like how many no kid is dreaming about being an accountant? Mm hmm. [05:26] 10 years before that, I think there were some kids being like, look, if I get a nice, stable job, that's all that... [05:31] I don't think kids that way think that think like this anymore. What happens to the industries? [05:37] They actually can't hire. And I talk about this a lot, but the gap between like [05:43] what the valley thinks about tech and AI and focuses on and what the world cares about. It's pretty vast. And [05:52] I think that cap is like not even close to being filled. So when it comes to things like insurance, we talk about what? The professions we know. Here's the most common thing that happens in the Valley. Talk about finance and legal because that's where other Ivy League school kids go and work. Therefore, there's an AI for finance and there's an AI for legal. People don't even know what else exists, really. [06:12] Tax, okay, we know tax, but... [06:15] Think about insurance brokerages. Think about trucking. Think about logistics. People do a lot of things in the economy, in the world that are not finance and legal. Just because you know a couple of private equity guys and a couple of lawyers doesn't mean that's all everybody does. So I think there's a huge, huge gap to be solved there. And we're only scratching the surface on what is really important there. Because if you can't hire these people, you don't,

[06:41] You rely on the technology to be able to fill the gap. You depend on it. And so you really hope it works. You're not betting on some future where AI can solve math or coding in a specific way. [06:53] You need this now. So that's an area that I'm really excited about. [06:56] Wow. Well, I have longer term fertility efforts, I think, generally are pretty interesting. Interesting. What do you mean? Humanity has, in the modern day, has never seen an extended period of time where the population has fallen. [07:09] systematically, systemically, I should say. And we don't know. And I know I want to go on this pro-natalist agenda. It's not like that. [07:20] The real point is we do not know what the repercussions of that to the economy are. You know, there are some very smart people, much smarter than me, who would say a lot of our economic growth is built on the fact that consumption continues to grow. Consumption continues to grow for a large part because the population in the world continues to grow. [07:39] What happens when the population starts shrinking? You're not going to buy like double the furniture and double everything else, right? Those people, like consumption is somewhat limited in terms of variance. So if the population is not growing, where is the economy going to come from? Where is spend going to come from? What does that do to the world? What does that mean for humans if populations start declining? So I'm kind of curious about that. Like what I know there's a lot of sub issues that... [08:07] are underpinning why that happens. But that's also, if I take a...

[08:12] 10 to 50 year time period. That's one of the things that I think is pretty interesting to look at. [08:17] So we'll need furniture that breaks easier and we'll need iPhone releases faster with more complicated adapters. Seven cameras and then maybe people will buy the iPhone 20. Why just have three go to seven? We should really be pushing the limits here. Technology. Apple's not thinking hard enough. They're really – they're not. Let's be real. [08:39] Before we go way too far, because I want to go into the portfolio and your thesis and everything because you have a really great technical background. [08:47] Um, [08:48] We need to address something. [08:50] You are ridiculously famous on X. Yeah. [08:54] What is going on here? How did you become so famous? I wish I really knew the answer. But like I was saying before, I can give you some theories on how this happened. I mean, let me just talk about what's your strategy. Okay, of X. I'll talk about strategy to I'm like very open about this. Okay. The first thing I was telling a friend this actually just the other day. You know, a lot of people now will come up to me and say, Hey, man, great Twitter game. How do [09:21] It's concerning. Like there are people in positions of power where I'm like, you really shouldn't be the one asking me this question. You're like a billionaire. Why are you asking me about my Twitter game? But people care about this stuff. And I always say that there's two main things that I think people do wrong. [09:37] I'll start here and then I'll tell you my journey. Two main things is one... [09:41] I think people are in it for the wrong reasons. And it's so easy to fuck it up if you're in it for the wrong reasons. Most people care about Twitter game in so much that they will like other people hearing the sound of their own voice.

[09:51] That's the primary reason people want to be on Twitter or big on anything. They're like, I am... [09:58] I want to be heard. [09:59] Essentially. I think that's the wrong motivation. Um... [10:03] And it doesn't work. That's one. Number two, most people who fall into that first bucket [10:08] This is true for Twitter especially, not for other forms of social media. I just don't think they have thick enough skin to endure it. I know plenty of people who are sort of the, I don't want to say cookie cutter, but kind of cookie cutter, sort of the overachiever archetype who are like, you know, I studied all my life and I went to Stanford and I did all this stuff. Like that archetype of person usually does not like can't take Twitter when it gets real. [10:38] a few times. You can't do Twitter without getting hate a few times, without people saying the meanest things about you for the world to see. You just can't do it without it. And most people don't have the thick enough skin to be able to endure that long term. So I say that. [10:53] I'll pause and then I'll talk about what I did and my sort of journey. [10:58] I always like writing from a very young age. [11:02] I joke that if I wasn't in tech, I would probably be a writer or a journalist. [11:09] And I've always been writing since I was in high school and college. Some of my pieces that I wrote were usually like data explorations. It was like, oh, I found this interesting data. This is interesting. And then other people liked it. So the first big one was called Hacking the Indian Education System. This was in – I was a freshman in college.

[11:32] what is the equivalent of like an AP or the SAT in America for India. And I found that a bunch of these exams that are taken by a million people every year were flawed in their grading. Like there were statistical anomalies and all of this other stuff. So that went viral. [11:47] And it got sort of the feedback loop going of like, hey, maybe I should write a little bit more. [11:52] So I did a bunch of these little things over time. The second big reason which really convinced me that this is something to take seriously was it's kind of a touching anecdote. I love it so much. [12:03] But I didn't only write publicly. There was this Facebook group of a bunch of like high school kids applying to college, usually like international students like coming to America for college. And they just have a bunch of questions, right? It's like not everything is clear or online and very not. [12:21] um, [12:22] you know, crystal clear to their understanding. So I would go and offer to do like an AMA every year. One of the people who ran the group was like, hey, maybe you should come do this. I think you have been helpful to me personally. So I would love for you to do that for the community. So I did that for four years straight. [12:39] In the first year, there was, you know, usually the way these things go are hilarious because always these guys asking the douchiest questions publicly because guys have no shame sometimes. And then it's sad because the group is not gendered. I guess men and women. And this one girl reached out to me on DM and it was just essay. I'm like, holy shit, like what's going on? So I read this whole thing and she basically said, look, I got into, I applied to college in America.

[13:09] don't want to stay in my country. I just don't see, she was in India, so I don't want to stay in India because I just don't see a world for women here. It's so hard to do XYZ. I'm going to get married off at a young age. I don't like this. And I'm like, okay, what college did you get into? [13:23] She's like, I applied to one, I got into one. It was Penn MNT. [13:27] And I'm like, dude. [13:29] You should absolutely do that. Why is this a decision? Like you have the financial aid. You have everything you need to do that. Why aren't you doing it? And she's like, my dad doesn't want to send me. [13:39] And I'm like... [13:41] That's insane. He probably has no idea what he's talking about. You know, what can I do to help? Like, how would you like me to help? And she said, can you talk to my dad? And I was like, I was a 19 year old kid. I'm not very at that time. I wasn't very comfortable talking to parents in general. And I sort of froze up. But I'm like, sure. I mean, if that's what it takes, I'll talk to him. And I swear to God, Molly, like I got on the phone with his her dad for five minutes. [14:09] That's all. And her dad was like, I think you know what you're talking about. I'll send her to Penn. Wow. So she went to Penn. She graduated. She works in private equity right now. Great. Great firm. It was a great story. But, you know, what that really taught me in the moment was – [14:24] you know, [14:25] The asymmetry in influence, in true influence you can add on people's lives by just sending a text is so high that, you know, it's insane that more people don't do it.

[14:39] Long story short, I couldn't do it when I was in big tech because I got a lot of pushback. They threatened to fire me a couple of times for brand reasons. Yeah. But at Glean, I didn't. [14:49] have that constraint anymore. And I was like, well, I'm definitely going to write more publicly because I think it can help people. And so I made it a thing. I would every day go and write a thing that I thought was somewhat helpful. [15:01] To someone. [15:03] And in the beginning, there was like zero views, zero likes, you know, I didn't really care about that. But over time, there are people who are like, dude, thanks for sharing that. That was super helpful. I didn't know. [15:12] Um, so that's kind of the long and short of it is today when I go and I try to think of a tweet, the only question I'm asking is, is this helpful for somebody? Is somebody going to read this and go like, that's kind of useful? That's all. And everything that doesn't meet that bar, I usually don't tweet. There are obvious exceptions. People can like pounce on me and find some examples where I kind of just like knee jerk did something. [15:42] maybe has driven... [15:44] whatever has happened online. Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture-backed startups in the U.S. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, chipping away at it with fees, Brex is designed to help you spend smarter and move faster. Their all-in-one solution combines

[16:14] protection into one powerful account. You can send and receive money globally at lightning speeds, get 20 times the standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMSS, Anthropic, Flexport, Robinhood, and Plaid trust and use Brex. [16:44] e-x.com slash sorcery. Most of the ones I see in my feed, because I'm also like... [16:51] definitely active on x but i like a lot of data driven content is the data driven content that you put out whether it's charts or like macro analyses and that kind of thing so like what is your thinking behind those i have a [17:04] 10 commandments type list of when I started writing on Twitter specifically, I told myself, it's so easy to knee jerk do stuff, right? Like, you see, you scroll for a bit, you're like, Oh, man, this is a thing everyone's talking about. I need to have my opinion. I need to put a take out there. I found that. [17:23] paradigm to be not evergreen. This is just not a systematic way to do things. So I told myself, okay, what is my thing? [17:29] And so I have four or five areas of competency. [17:33] Two, I try to stick to facts and not opinions as often as possible. No political commentary. No, like, you know, try to minimize negativity in any way and not offend people for no reason, even though sometimes I clearly have missed that goal.

[17:50] And so, you know, things like that. I have a bunch of list of commandments. The reason the data thing always stands out to me is because I'm like, well, it is literally fact. [18:03] the fact and more people should know that this is what the reality or is what the data says on something. That's why I do a lot of data driven stuff. What are your favorite charts? [18:13] Like in time of charts? Name them. Yes. Tell me all your favorite types of charts right now. Oh my God. I don't know if I have. You guys are investors in Carta. [18:23] They put out lots of charts. What are your favorite charts? Peter is so good at this stuff. He's so good. I actually met Peter in person recently and he told me he literally just took the job at Carta because of the data access that he would get. It's amazing. And I'm like, you do such a great job. Yeah. I am so fortunate to have like Claude Code be able to do all my charts right now. So I write myself. I've written a big skill for how I like things to be graphed. And I'm very clearly opinionated about that, like exact color and what the themes have to be and things like that. [18:53] put them into a skill. That's how I do my charts. [18:56] I don't know if I have a favorite shirt. Can you send that to me so I can use it? Yeah, absolutely. [19:01] And I use really rudimentary tools. The other tool I use is the... [19:05] You know the new thing on the Macs these days? Freeform. Freeform. It's that new app that no one really knows about. Okay. It's kind of like a canvas where you can drag and drop stuff. It's kind of like Microsoft Paint without the paint. So you just drag pictures and text and stuff like that. So I use that. Like I take the pictures from different places and I put them together. And yeah. I use Google Slides a lot.

[19:28] It's a pro tool. That's how I make most of my graphics. And then I toggle between Figma and Canva. [19:35] Figma, admittedly, and Canva. So I know some people are really good at that. It's a good skill. Okay, to shift this back to investing, you spend 50% of your time on mostly Menlo portfolio investments, and then also on the Anthropic Anthology Fund. So can you just explain the structure between those two? And then we'll go into one. Right. So let's talk about the Anthology Fund first. The Anthology Fund is a fund that we do [20:05] So the goal of this fund, it was set up like a decade ago in the AI world, but like the beginning of last year. Oh, my God. Anthropic was a no-name company that no one really cared about. And the goal was like, hey... [20:18] let's invest in companies around the ecosystem without making this a corporate venture fund. And instead of having Anthropic do it, let's have Menlo do it. Because we were the biggest investor in a bunch of those rounds. And so we chose to do that fund as a way to say, we just want [20:37] There's three kinds of companies that meet that criteria. [20:40] One is a fantastic early seed team trying to build an AI. Let's get them early and let's back them. It can either be a lead or a small follow check. [20:50] We do usually the minimum we do is 100K, but we go all the way up to leading those rounds. Either that. Number two, something of strategic importance to...

[21:00] Claude or to Anthropik, essentially. So things like, you know, we talked about Turing, talked about things like a Mercore, various other companies that are just extremely important to the ecosystem of Anthropik and maybe other companies as well. And the third thing is just iconic companies that are building on Claude. [21:17] whether it's seed or not. So those are the three types of companies that we like. And that's how we invest out of the Anthology Fund. Now, [21:27] The Menlo Fund, the way we do our deals is Menlo is generally a fairly low volume shop. [21:34] And what I mean by that is, we're not trying to do [21:38] like even five deals a partner a year we actually have quite low volume and we like to pick and choose the companies we work with very carefully um but when we do pick the reason we keep the volume so low is because when we do pick we actually want to work with those companies to drive real outcomes we don't have the time to be able to work with our portfolio companies and if you're when we think if you're doing five plus deals per partner a year definitely five but [22:08] six companies that you're heavily involved with and you're not really gonna have time to do work for any of them. [22:14] And we like to do that. The reason we like to do that is because we actually think it makes a huge difference when it comes to not just the classic venture stuff, but exiting those companies. Like who is working on, hey, like let's say two, three years later, this company is not going so well. Who's going to help you do an M&A motion? Most founders haven't done that ever. They haven't seen that. They can't learn that on the fly. These are time bound things. That's when we come in.

[22:44] So that's, that's sort of our main fund strategy. And recently we've hired a, a bunch of like tenured operators to such as myself, less tenured, but such as myself, but also, you know, there's a Tim Tully who comes from Splunk and he was CTO there, uh, [23:00] a Joff who is the chief product officer at Atlassian, or Matt Craning who started and sold one of the few cyber security unicorns that have been sold for above a billion dollars. [23:12] in the last 10-15 years. So these are people who've built stuff, who know how to hire, who know how companies operate. And we think that's a big differentiator for how we view and want to work with companies. We're not just investors, we [23:27] we could build this company with you. [23:28] So, [23:29] That's the long and short of it. You mentioned one of your portfolio companies at the start, but could you just break down your thesis and what you focus on? Two things. The first thing is I have this huge gripe when people ask VCs their thesis because, you know, honestly, I think all of them are semi-lying. Okay. Say more. This is a hot take, right? Okay. [23:50] All smart VCs know this. So it's not like they're... [23:55] they don't know this, but almost no iconic company [23:59] in the history of venture capital has come from anyone's thesis area. Right? No one was sitting on OpenAI when like Khosla invested in OpenAI before really ChatDBD was a thing going like, hey, my thesis is that LLM chatbots are going to be a thing. No one's saying that. Right? The investment is these guys are really smart. And they're working on something that could be

[24:18] pretty valuable. [24:20] That's your thesis. The same thing with like Facebook. No one had a thesis on social networks going like, hey, guys, we looked at Hi5, we looked at Orca, we looked at like, I don't know, Google Plus, and we really decided you guys were the one. No, like it was a ripping product with a fantastic founder that had a lot of ambition and people like, okay, this could go somewhere. [24:41] So that's the part I think where most people are semi-lying. [24:45] Not every VC really has a strong... They have a thesis and I do have a thesis. But most of the investment we do is not just bound to that thesis. It's bound to like, are there incredibly... And people use the word smart all the time. But there's founders who... [25:02] Many smart people in this world, but there's founders who can run through walls with conviction on something. Like, imagine this. Here's what I'm trying to get at. [25:09] If you're a founder who you've done all this stuff in tech, you've had a 10 year career, you [25:17] could work at OpenAI and make millions of dollars. You can work at Anthropic and make millions of dollars anywhere. [25:23] And you choose to be a founder in something that has been your life's mission for 10 years. [25:29] If it was really your life's mission for 10 years, like I take that seriously. I'm like, okay, well, why do you care about this? Why is it meaningful for you? And why do you think this would be a big thing? [25:39] That's my bet on teams. The number one thing I'm looking for when it comes to the founder is, are you just doing this because you think being a founder is cool? Because everyone thinks being a founder is cool and high status?

[25:51] I don't care about that kind of founder. And there are some sniff tests to sort of figure out. It's not easy, but to sort of figure out who they are. And it's not like all founders who do it for status are bad. They often do really well. But my belief is... [26:05] The most important thing is I want you to wake up five years later and be like, I would be doing nothing else except this. Whether your company works, doesn't work, I need to believe that you believe that. [26:14] to bet on you. [26:17] Not for the least of reasons, is... [26:20] What if you just run with the money? I don't know. Like, what if you're like a bad founder, you just like blow something up into the into the sky and blow it on this, you blow it on that doesn't work, you're like, whatever, like, I don't know what to do. We're trying to avoid that kind of founder, right? We want a founder that really, really genuinely believes in why. So that's number one. [26:37] on thesis, I would say. But then I can get into areas that I think are interesting also. [26:44] I want to get into areas, but I want to understand that a little bit more. So what do you... [26:48] How do you pressure test those types of founders? What are the traits you're looking for other than like really wants to build a company? One thing I can easily say that we're not looking for is [26:59] It is very hard for us to get to conviction on a founder or a company when we haven't known them for a while. [27:07] I think, you know, [27:09] personality, like with, you know, VCs love using the word shotgun wedding. I don't know. But it kind of does matter. You just can't trust the nature of a founder when they're [27:20] at like pitching to you right before a raise. That's a really hard, like they're selling to you, right? Like it's really hard to get through that. People can be great salespeople. What I really care, and I can't speak for the firm, I can say most of Menlo operates this way.

[27:35] But for me especially is I'm looking to build that relationship way before you're you're raising. [27:41] And I want to understand like what makes you tick? [27:43] And half the time I'm not talking about work. I mean, work we have to sometimes talk about because it's weird. Otherwise, why is this VC talking to me about my personal life? But I kind of just want to understand what makes you tick? What are your motivations? How did you grow up? What drives you? And that gives me at least a lot of confidence on whether or not you would be long term successful. And then on areas, what are the areas you focus on? I'll say one last thing before. [28:13] that bucket. I think the [28:15] biggest bottleneck today is ability to hire. [28:19] um so i think yeah number one reason is there are plenty of really smart people who also care about something very deeply and then i mean the unfortunate truth is i look at them i'm like but who would work for you like i just don't see you being able to tap into a network or even be like super convincing when it comes to candidates and get people to buy into this mission you almost kind of need to be a little bit of a cult leader as a as a founder and not everyone is capable of a big cult leader um [28:46] People who do it well can hire well and retain well. So that's the second thing on the founder side. On the area side, one of the things I mentioned before is how can we apply that? [28:59] all of these novel technologies to the industries that need it the most. [29:04] The industries that no one talks about, the industries that are super, super boring, the industries where like there is information asymmetry between what most people in AI know, like the Venn diagram of things people in AI know and care about and read Twitter about and things the world really like matters in the world. And who is at the very edge of the little tiny intersection between that where they're able to capture a little bit of that is pretty important. A lot of people.

[29:31] You know, I think, and sometimes they're successful, but they kind of back into it. Like they do the top down research and find like, hey, this is an area that's important. Let me go attack it. Ideally, it's just an area you know for whatever reason. [29:44] and you grew up with it, you know it, maybe your mom, maybe your dad worked in it and you're like, I know this area so well and I know that there's so many efficiencies. I also happen to be really good at this AI stuff. Let me see how I can apply it. So that's one area. There's many, many such verticals like that. That's one. Number two, I think I care a lot about [30:04] You know, we're at this point where research is not happening in academic institutions anymore. They just don't have the money to fund frontier research. [30:13] do you underwrite high technical risk and what research problems could unlock economic value? [30:19] And how do you find those teams? That's an area that I care a lot about. Like fundamentally interesting research. And I would say the last, the third thing that I personally care a lot about is I look at these... [30:34] Companies that are... [30:37] How do I say it? [30:39] that are just beautiful product builders. [30:42] I think that art is a lost art. People don't give it enough credit. [30:47] I very overused example in venture also, but like I love the example of of granola. And I'll give you why I like this example. [30:56] And no one thinks meeting note takers is fundamentally interesting, right? No one is waking up every morning like, I need to fund a meeting note taker company. No, it's just not a thing you think. There's many of them that exist. There's nothing fundamentally novel about the technology. And out comes granola and you use this product and you're like...

[31:15] That's beautiful. It just works. I don't really have to think about it. It's just kind of nice. [31:22] that's a lost art. Not many products are like that. Most products are really like forced. It's AI for this. So therefore you should use it for this. I'm like, dude, I'm not going to use it. Like, what is it solving? Um, [31:34] So I think product... [31:36] beauty and taste, I know that's also overused, is kind of, that set of ideas is really interesting. Also, because I truly believe that you have, and for many products, here's a theory, you [31:49] If your enterprise product can be PLG, [31:53] then it must be. Otherwise, a PLG company will absolutely eat your lunch and destroy you. And it kind of ties back to a lot of venture capitalists these days are talking about like momentum and whether 3-3-2-2-2 is enough or you have to grow faster than that. I mean, I think the other way, I don't really have an opinion on that. I think the reality is [32:12] If you are going enterprise, you are held back by these insanely long sales cycles. And the industry and the technology moves faster than your sales cycle. So not only in two years is your revenue weak, but your product is shittier because you've done all these shitty things to appease enterprise clients, which we all have to do. But you've also not adapted typically to what's going on in the industry. You've made decisions that are irreversible on how the industry has moved. [32:40] like classic rag solutions have now become agentic solutions and those actually mean something technically but people couldn't evolve their product

[32:48] So I do think that for the set of ideas where you can go product led growth, they will absolutely destroy you in sales because you can do these long sales cycles and then a product goes viral. [32:58] And then though they come into your enterprise sales call and the buyer is like, yo, I've heard of this one. We should buy that one. That's essentially how decisions are made in many cases in the enterprise. So that's another area I like a lot. Tasteful products with PLG motion. Turing is training the next generation of AI with tasks that require real expertise and real world judgment. That's why companies like NVIDIA, Anthropic, Salesforce, and Gemini partner with Turing. [33:28] based on real operational traces, the kind of infrastructure frontier labs need to train superintelligence. Visit Turing.com slash S-O-U-R-C-E-R-Y. Speaking of one that was recorded in this room... [33:42] was Cluelly [33:43] You have a funny story about this? I do have a funny story about this. This is a hard pivot, but this just like reminded me. I'm like product viral. Okay, Cluey. A lot of people have a lot of opinions on Cluey. I actually think that [34:01] I find myself having to defend Roy a lot, not because I think everything he does is right, but... [34:09] I actually think he's a very thoughtful guy in a very not, you know, brash exterior, shall I say.

[34:18] My story is when Interview Coder became a thing... [34:24] Prior to Cooley, that was what Cooley was, I think, prior. It was called Interview Coder. It was like this tool that was cheating for your tech interviews. [34:33] I remember seeing this somewhere. I forget where, small post, and I thought, [34:40] You know, I broke a bunch of my Twitter rules and I'm like, dude, this is hilarious that this exists. I hate interviews. I hate tech interviews with a passion. And the reason I hate them is because I've seen so many people that are just... [34:51] Like I call them hardcore, like they're strivers. All they do is game the interview process and they've had fantastic tech careers. And I'm like, dude, you guys know nothing. [35:00] about building product. How are you a director at X company? There are hundreds of these people, especially like in my, I'm like 30, I can't remember, 30s. I'm in my 30s. I see people who are peers and some of them I'm like, [35:14] You are not good at your job and somehow you are so senior. And the reason is because you could game tech interviews. Yeah. I think it's not representative of your skills. So I see this product. I'm like, I like it. [35:24] I like it. I like what it stands for, which is these interviews suck and they have to go. And and so I tweeted it like like I still remember kind of the tweet where I said, you know, cheating is really bad. [35:37] Absolutely don't do it. Especially don't use this tool. It's really bad to use this tool. This is the tool. In case you ever use it by mistake, don't use it. [35:45] oh my god and that blew up clearly you know there are some people who are like oh my god you have no morals whatever and then some other people who are like yeah I mean I hate taking videos too so like kudos to you for doing that

[36:00] And that I think was the first time that [36:03] anything related to Cooley or Roy went super viral. That post had several million views on on on Twitter. [36:10] But then Roy took it on his own and he's sort of a force of nature. Before I knew it, like in a month, I was asking him for... Allocation. How are you? No, not allocation. I was asking him, like, how do you do all of this? Yeah. That's funny. So Roy, I think he started Interview Coder in Founders, Inc. That's where we're recording. Wow. I... [36:32] try to keep that secret, but you know, I love this space, so got to give them some credit. Um, and then specifically on your areas of focus. So infrastructure, what else are you looking at? [36:43] Pretty broad. One of the many reasons I chose to join Menlo specifically was I wanted to keep the scope very broad. So I look at AI, SaaS, and infra, which people would argue is everything. Isn't AI the same thing as SaaS now? I guess. AI is the same thing as a lot of things. Yeah, isn't AI everything? Everyone's by default AI investors. Oh, this is a good point. So Carta put out a recent report that Series A's that are AI enabled or AI in their name are getting a 30% premium. [37:13] the delta is or like how much it's changed. [37:16] But if everybody, if every company is AI... [37:20] How are we measuring this? Every company is AI. [37:25] Should be. Well, I think the stat that he said was what, 30% premium, right? You said 30% premium.

[37:32] I'll tell you that maybe I take 10 to 20 pitches every week. [37:38] And in the last year, I have seen like the ups and downs of different buzzwords go in and out of flat fashion in a way which is hilarious. There was one time where every company was like, well, our differentiator is that we get data and then data flywheel feeds back into fine tuning. And that's why we're different. Same guys. One year later. Yeah, yeah. That same thing. But instead of fine tuning, it's RL. And I'm like, what do you know about RL? Tell me a little bit about it. [38:04] And silence. You know, it's reinforcement learning. You know, reinforcement learning? Yeah, I'm like, well, I don't know if I know, but what do you know? So, you know, what I'm trying to say is, [38:14] founders, many founders are coached to say the right things in a pitch. And maybe they should be like, maybe this is the game that they're playing. [38:22] I would say that like one of the reasons that [38:26] When we look at deals, me, say Tim, we're both very technical. We both code with this stuff all the time. [38:33] One of the powers of that is I don't think I like know much or anything or everything for sure. Right. But because we code with these things, we're not embarrassed to ask the dumb questions. A lot of VCs are because they don't they don't code. Right. They don't understand what they're talking about, too. Yeah. Tech side. What I can ask them like, hey, you know, when you say RL, do you mean like this? Like, do you call this library or what are you actually doing? What are you actually using for that? And if you prod deep enough at [38:59] I would say for at least 90% of companies, they're actually just like changing a prompt on LLM. Right. And then they're pitching all of this other stuff that they hope exists or want to believe could be the future of what they build. But that's not what they're building currently. If you ignore all of that and then look at the few companies that I think know what they're talking about and are very deep.

[39:22] I think the premium is is in some sense justifiable because it's such a constrained talent pool. Like the number of people who actually know what they're talking about when it comes to several parts of the AI stack, if you call it that, are so few that if you know what you're talking about, plus like we say, the VC genuinely believes you're working towards an economically viable direction. [39:45] And that's another one. I would say at least 70% of AI companies fall off because... [39:51] The TLDR of their pitches, I just want to build models. I'm not sure why it's useful. You know, that's kind of a bunch of pitches that we hear too. Few times you have the AI guys who know what they're doing. You know what they're, they know what they're going to apply it to and how this is economically valuable. And that to pay a 30% premium on, [40:08] Yeah, I mean, I do that. Like we're betting on an outcome that's a 10x, 100x, especially at the stage I'm looking at, right, which is primarily seed through B. But in this case, I'm thinking seed in A. Yeah, like I would 100% play that premium. [40:20] Yeah. My steel man to this would be that the premium for actually good companies is much, much higher than that. Because, you know, when competition comes to play, pricing goes out the door and you just want to pay. You want to pay whatever you can pay to win that deal. And hopefully it's somewhat... [40:40] responsible, but I would say that that's probably part of it as well. Yeah, we try to keep more discipline around, you know, I've seen some of these billion-dollar seed types. What's your investment range? Like, do you have parameters around check size, valuations? How do you think about that? I would say all parameters are guidance and they're rough.

[41:02] At the same time, even despite those rough parameters, it's so hard to justify a billion. I just don't see how people can write a billion dollar seed unless, and here's another hot take, unless like, you know, it's one of those situations where the money is not coming from the fund. [41:18] It's one of those SPV-stripe structures where it's like, okay, I'm kind of going to put in 1 million of 100 million round and raise 99 million from other people. [41:29] And I'll put my name on it, but I'm effectively taking none of the risk here. I'm just getting all the marketing value of being the big, bold VC who's backing these egregiously long-term bets. So that happens. But aside from that, I don't know how you can justify putting it out of your own fund. [41:48] I don't want to put a hard number on the seed that we'd go up to because... [41:52] Sometimes we break that. Sometimes we do exceptional stuff, but definitely not a billion, you know, like. [41:59] Even a hundred million seed. Yeah, I can see that. I can see that in some cases. So that's how I would. [42:06] but it. [42:06] I was having a really fun conversation with Anne who works at Elad's Fund. And we were talking about this because there are some companies that I am close to that are in very competitive markets and their competitors are raising billions and billions of dollars. And so now the competition and like the stroke of success and winning is who can raise the most money. But you're a software company. What are you going to do with 400 years of runway?

[42:36] I don't know how it ends because I think these momentum trains, they go for so long and then you're kind of left with, OK, we raised a boatload of money at a crazy valuation. Now we've got to back up into it. Yeah. [42:50] I mean, there's not much rocket science to be said. I think [42:55] More capital can sometimes unlock velocity. [42:59] And if you draw like a graph of like capital and velocity unlocked or acceleration unlocked, it starts looking like this for most companies. So if you raise 15, you get like 80 percent of it for early company. If you raise 100, you're getting maybe 90 percent. But honestly, maybe sometimes it even doesn't even look like that. It looks like this, because if you raise too much money, we've seen companies where this happens to. [43:23] A, there's an illusion of success. So the people aren't hungry anymore. We're already successful. We're a billion dollar company. Can we even fail? [43:29] Right. Like that's one. Number two, they start like I call this the $50 lunch problem. They start they start eating fancy. They start doing fancy stuff. They have money to blow. When you have money to blow like that, like, again, the hunger dies. You're not actually working that hard. You're kind of chilling. You're already a part of this like anointed company. So people don't work as hard. Number three, it's hard to recruit. [43:50] How do other people see the upside when you're already raising at whatever price without any product? Some people will believe it, but most people won't. [43:58] Um, so for those kinds of reasons, I actually think the curve kind of looks like that where you raise too much. It's actually pretty negative. You should raise the right amount. And history has told the story many, many, many, many times, right? Um, it's nice for a moment to be a part of a hot company that's raised a lot of money, but real businesses are, are built over a long period of time. And I think one problem with venture again is a very tech bubbly thing is we ignore a lot of amazing companies that just grew slowly over time.

[44:27] I can name a few, I guess, but there are companies that have hundreds of millions of ARR that most people in [44:33] like core Twitter tech. It's like, I don't know. I don't know what that company is. And there, they're just because it grew slowly. It grew over 10 years. Um, [44:41] And it was just a boring company. So... [44:45] Yeah, then the media obviously will disproportionately report on the crazy stuff. So that causes an issue. So, yeah, to summarize, I think it's generally bad. In some cases, I can see the logic for raising a large amount. [45:01] And that is when you feel like you need to make a splash in the market. One. Two, you need to compete on dollar value of equity offers. [45:10] So maybe you've gone to an RSU model, you're a billion dollar company, you are losing people to Anthropic because Anthropic is paying whatever, $4 million a year, whatever amount they want to pay. And it's hard to justify a future value of your equity to an employee. But if you raise at a future price, then you can be like, well, your paper value is actually worth this much. And it's kind of competitive. So those are some good reasons. [45:33] But there's too many companies that are full for raised, in my opinion.

[46:03] thesis. What really stands out is how clearly it explains why each stock is included. And before you invest, you can even backtest your idea against the S&P 500, so you're making decisions with real context, not just guessing. And beyond generated assets, Public lets you invest in stocks, bonds, options, crypto, all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com/sourcery. [46:32] Paid for by public investing. Full disclosures in the description. [46:36] Yeah. I just, I also don't know how they would hire out that fast. It's like, how, how are you going to hire out? Like what you're going to. [46:42] triple-double, like, it doesn't make sense. And training just... [46:46] It's really interesting to see that. And it's interesting to see that in non-AI companies as well. I'm actually curious, like... [46:55] I guess off topic, but I can maybe, I don't know how much they're raising. I also don't know like what they're going to do with it. But I do see a world where, well, there's two things to call out, not specifically for those companies, but some people also overraise because, you know, founders get secondary when they overraise. So that's just the whole other topic. Generally, really poor incentives in the Valley. We've seen that happen. We've heard rumors about all sorts of things where that happens. [47:25] really bad because the founder just get basically exits and goes like, well, [47:29] Who cares if I overraise? I'm out. I kind of got the bag. So that's bad. Second thing, I think for those specific companies, maybe they can use the money to blow it on ads and try to get users on board. So I see some logic maybe for consumer companies.

[47:45] I see more logic for a... [47:47] you know, one of the prediction markets over say, Hey, like I'm a seed stage AI company and I just want to train or whatever. So, yeah, [47:57] That's just my view. Mm-hmm. [47:59] To go back into the infrastructure, [48:02] piece of AI, where are we in the evolution of it? Can you like walk through it a bit? I know with Turing, I work closely with them. And like, so I know the like AI research accelerator place and [48:16] It went from like sweatshop data labeling, synthetic data. Now they're really focused on, to your point earlier, this is a thing, reinforcement learning. Like that is now the focus. So what comes after that and how did we get here? Let's talk about the easier one, which is how did we get here? [48:32] And most of this, I think most people know, I'm not repeating that much interesting stuff, but we had, we trained on all of the internet data. We trained large language models on all of the internet data and we were like, okay, okay. [48:46] Turns out this can actually do interesting things. [48:50] And he sort of generalizes outside the obvious training set. That's interesting. That was GPT-3. Then people realized, okay, well, we can RLHF this data, which is a form of reinforcement learning, and make it like a chatbot. [49:04] And so it just doesn't generate random text, but it actually can pretty easily generate text that makes sense in a chat conversation. And so we got one like, okay, we basically sort of solve the Turing test overnight. Amazing. Then the next step was, all right.

[49:18] Let's like... [49:19] What if we trade a bigger model? [49:21] And there was a big model phase. [49:23] So people were like, okay, GPT-3, kind of small in the grand scheme of things. Let's go big. So scaling laws were invented or slash empirical study was done. How does size affect quality? And people scaled this stuff up, right? So a lot of money was spent scaling the stuff up. Then people saw it and went like, well, it turns out that the scaling laws are proportional to the amount of data you can train on. So you can't just arbitrarily make the model super big if the data set is just the internet. [49:53] And yeah, maybe you can accentuate that with other kinds of like proprietary internet data and do all kinds of other stuff. But order of magnitude wise, you're not 10xing the data, right? You're kind of like stuck with that kind of magnitude of data. Therefore, you can't, doesn't it make sense to make the models bigger because you're not getting more performance on that data set? Then came RL world. [50:13] Right? RL World was [50:15] Maybe I get proprietary data by paying for it on very specific things. And I find interesting mechanisms to actually use a smaller amount of data to get more effectiveness and get domain expertise in a bunch of these different areas. So, hey, can I buy a bunch of coding data to get good at code? Can I buy a bunch of finance data to be good at Excel? Like, what are the things of value in the world and how do I get that data? [50:45] like a Turing come in and sort of fill that need, especially a scale left a big hole after the acquisition. So we're in that part of the cycle. And then the question really is, okay,

[50:57] Well, how far does RL scale? Like the scaling laws of RL are not intuitive because you're depending on this manual process of getting data from people. That's not like the Internet. Like you can't just go 10x that overnight. So that's one bottleneck. [51:12] The second thing people are thinking about is, well, okay, now we have these big models and we have RL. Here's a couple of things that are on people's minds, I would say. And this is not exhaustive. I'm just thinking about it out loud. One is, [51:24] RL is kind of a shitty paradigm to learn. [51:27] um karpathe obviously talks about this a lot uh it takes a lot of samples to learn some very basic stuff because you only get a reward at the end you don't actually understand things as it's happening um so uh like if you do rl for a game like chess it kind of pointless it's not how humans would even even close to working we sort of understand the incrementally good moves [51:47] And then we know we're closer or further from a result. RL is not a perfect analogy, but it's like, I only know at the end that I played a shitty game if I lost. [51:57] So how can we get that done? [51:59] improvement mechanism to be better. That's one. Second thing is data. How do we get better, more interesting data? [52:06] That's the second one or learn from fewer samples. So that's the sort of second piece that's interesting. And then third, I would say is... [52:16] Is there are there other ways to scale intelligence within like test time compute paradigms? And that's sort of the agent stuff that we're seeing, which is, I mean, I guess obvious now, not that obvious before that you can sort of chain these things together and make it call external tools. And that could be way more magical than just saying, hey, reason and come up with an answer.

[52:38] So those are a couple. And then there's more fundamental work on, say, memory, understanding. Like when I say understanding, I mean reliability of models. [52:49] Mottos for humans, I hate using the human analogy, but it's easy to understand. If you ask a human a basic fact, if I ask you one plus one, you say two, you don't sometimes say four. [53:00] And that's a bad example, but there are many cases where models should be more deterministic if they understood the principle involved, but they're not. [53:09] They're very undeterministic because they have no core structural understanding of things. And how do we fix that? [53:16] So that's one. Context windows are another that people are working on expanding that. So this is just a bunch of these things that are pretty interesting that can unlock a lot. And I like Anthropics framing of like sort of the end goal. One of the nice end goals to have that is quite... [53:31] cool to measure, easy to measure and interesting to follow is the economic Turing test. So [53:38] Can I pay a AI X amount of money to do a task that I could pay a human as well? And I would not know the difference. That's the economic Turing test. And can I drive that value of X higher and higher and higher? That's an interesting framing, which I think is like, [53:54] what the actual goal, one of the goals might be here. [53:57] How does the evolution of this and trying to think of what comes next inform your investment decisions? It's a really tough one. I mean, this one I struggle with all the time, right? Because it's so hard to predict the future on where these models go. I mean, I don't think anybody even at the labs.

[54:14] People have different opinions, right? So a common opinion, I think this is the most [54:19] The most sensible take in many ways is, hey, we have this graph of amount of hours of work. [54:27] that a model can do in some structure, and we see that graph go up and to the right like this, and some log linear curve, but whatever, up and to the right like this, [54:36] Maybe that can keep happening. It's been happening for a while. Hopefully we can keep it going. That's one view, in which case, okay, if you keep extending that, then at some point, a lot of the work that humans do is automatable. [54:48] So that's one belief. The other belief is like, yeah, this doesn't really scale. So can we actually do real, and there are some novel discoveries that, [55:00] um, AIs can do today, but they're kind of pseudo novel in the sense that like, yeah, if you really put a human to go discover that and look at the data for that long, they'd probably discover it. It's just not that interesting type of discovery. Um... [55:14] And the way to think about it is like if the branch of knowledge is like this, it's like kind of a twig on one side. It's cool. But it's not like a branch. How do we get to like, hey, can we invent branches of knowledge? So some people think we can never get there. The current techniques are not good enough to get there. So my TLDR to that is I do not know the future. I do not know how to answer things like AGI 2027. People have so many views on this stuff. I just don't know. [55:40] So I can only reason from the information I have, which is, look, there's some jobs that are really important that people can't hire for, and the current techniques are getting better

[55:49] pretty good at doing most of it, if not all of it already. [55:52] So that's a problem to be solved. And I don't see a big lab coming in and solving that problem. So maybe invest in a company that does something like that. [56:00] Or, you know, I talk about, say, the open router investment. It's not rocket science. I'm like, well... [56:06] There's many models. [56:07] People want to use all of them. New ones come out. It's kind of frustrating to find a new one and figure out how to integrate with it. What if one API could give you access to all of them? It's not rocket science-y at all. It's not like the most insane novel technology, but... [56:22] We look at that and I'm like, well, that's obviously something we need. [56:25] Why not do an investment there? [56:27] So that's kind of how I would say I think about the investments. I just don't have a big long-term view of it. [56:33] that I'm confident about. Well, might I suggest you go to Calci and check out their prediction markets. [56:40] They have one for like the best model for the end of the year, the end of the month. You think it's Google? Oh, that's what the market says. That's what the market says? Gemini. Yeah. Yeah. Another one is jobs and like tech layoffs. That one was really interesting. I just had Michael Barton from KOTU on like a couple of weeks ago. And we were talking about that. And then, of course, like cascading events, like, [57:03] Lots of layoffs. Like Amazon is like planning on laying off up to like 30,000 or so, but I think it came out at like 16. But this is more in their like robotics division because they want to bring in robotics. But yeah, I mean, we're going to see like a shift in like any sort of technological wave of new jobs, old jobs, things just changing, rescaling, all that kind of stuff. And so maybe Coursera comes back.

[57:28] I would love that. You know, I was an intern at Coursera. Really? When I was a sophomore or a junior. I love that company. Maybe it does. Wait, I'm curious. What did Michael say? What was his take? I have my take already, but what was his take? So his take was there's... [57:46] There's about like there's two camps for this. There's people that believe that AI will create more efficiencies and less jobs. There's another camp that says... [57:57] AI will make your job much, much better. And so you'll hire out more because you can have more firepower. So there's two there's two things there. And then he also says, um, [58:07] quote unquote, uh, don't take, this is not investment advice, anything like that. But, um, [58:14] His other take is that [58:16] Layoffs are a positive indicator for the health of the business. I mean, we saw this with Facebook. This is something Brad Gerstner had talked about for a while. It's like getting fit. You know, all these years, like revenue and growth is going up 20% and employee count was going up with that. But now we're learning you don't need to scale employee count with that. The businesses are becoming more efficient. So the result will be... [58:40] much better looking companies with higher margins. So that's kind of where it landed. Founders ship faster on deal. Set up payroll for any country in minutes. Hire anyone anywhere. Get visas handled fast and get back to building. Visit D-E-E-L dot com slash sorcery. That's D-E-E-L dot com slash S-O-U-R-C-E-R-Y. I'm smiling because I had this, uh,

[59:06] I was going to say like, [59:07] It's surprising we're realizing that now. Isn't that crazy? I have this really like this thing I wrote that went pretty, again, this one was another viral one. A banger. It was a banger. It was a banger where I said something that I thought was the most obvious thing for most engineers in the Valley, which is most engineers don't do shit. [59:26] Thank you. [59:26] We know this. I know them. [59:28] Right. Like, we all know them. Yes. Like, we know the jobs, we know the companies they work at, and we know how many trips to Hawaii a year they go on. So obviously they don't do anything. Right. Like, so. [59:39] It's not like when people tell me I'm so surprised at the meta layoffs or the Google layoffs or whatever company layoffs. I'm like, well, do you know the people who work there? I'll tell you one other funny anecdote. Early, early days at Glean, I remember I was talking to Arvind in the kitchen and I was telling him like, you know, one of the things I just love about Glean is like we're finding like I feel like I'm working so like so hard. And like there's so many things on my mind. I'm like so in the zone. [1:00:09] And at Google, there were like people I knew just like didn't write code for an entire year and they're still there. And in classic Orvin style, he looks at me, he smiles and goes like, a year? I knew people who haven't written code for 10 years and their ICs. [1:00:23] That's just Google. So, you know, my point is not to pick on Google or anything, but this happens in plenty of other companies. It is very clear that my take on these companies is sure, like in the tech sense, I think they're getting leaner and more efficient.

[1:00:39] And Twitter sort of, Elon sort of proved this with Twitter. There are many bloated companies. There's like a power law of who gives value in terms of engineering. There's some people who do so much that keep the company alive in any good, successful big company. And there's a long tail of people who basically do nothing. And so in the big tech sense, yeah, I agree with you. I agree with, I guess, Michael on all of his takes. [1:01:09] um [1:01:11] I agree with him. I think, like, there's some section of jobs that... [1:01:17] have the pattern of they will be augmented, you can hire more. We see that in sort of when we look at, I always look at history with these things, right? There was a time where there were most companies didn't need [1:01:30] Didn't have a tech team. [1:01:31] There was nothing to do in tech. And then we had software and people were like, well, we clearly need a person to run, you know, whatever, call it the website, call it a couple of other things. And then we had a search engine. So people were like, oh, shit, we should do some marketing. So we need another marketing team to focus on search ads or whatever. So technological shifts do also create a lot of jobs. [1:01:55] But on the same time, you could imagine one of those companies being like, but we have a robot for the factory. So, you know, maybe maybe like less people in the factory. [1:02:05] This has happened through all big technical evolution. There has been some new jobs created and a lot of old jobs that go away. Broadly, I think no one wants to do the old jobs that go away usually. And then most people kind of want to do the new jobs that come like.

[1:02:21] Most people would argue that factory jobs are much harder and more annoying than, you know, a desk job where you write tweets. I think so. That is a good transition. [1:02:32] In this case, I think one of the fears of AI is what is the calculus on that balance? Is it really bad in the sense that we're going to take away too many jobs for the amount of jobs that we add? I don't think it really applies to tech. I know people say, hey, is AI taking tech jobs? I think that's more of a laziness, zero interest rate phenomenon, getting the business to be more efficient. But when you look at the rest of the economy, I'm like, oh, yeah, there are so many jobs where people don't do anything really. Or their jobs or whatever they do do is, [1:03:00] An LLM could clearly do better. And then you have Robot Factory Robotics, all this other kind of Waymo type AI companies. [1:03:08] On the net of it, population is also declining. So in some sense, we don't need as many jobs in the long, long run. So my prediction is, look, there's going to be some period of pain, as there always is with like a new technology. Some jobs will go away. People who have the jobs will be very upset. But in the long run, humanity finds a way typically and historically to find new things to do and new areas that they want to focus on. And that's where they're going to go. [1:03:32] But in the middle there is some pain. There's always, I'm sure, there was a factory worker in the industrial revolution who was like 35 with three kids and goes like, shit, now you have a robot. [1:03:43] I hate you. I hate robots. I hate the guys who make robots. That's fair for him to think. Eventually, I'm sure he found something to do. Maybe he had a little bit of a struggle. So I imagine something similar will happen in the AI world. And maybe less toxic fumes. And maybe less toxic fumes. Well, this is a good segue into performance and BREX. So BREX is all about spending smarter, moving faster, performance, intelligent, modern performance.

[1:04:10] Finance. Can you believe it? It's crazy. We have Rex cards. You have Rex? No way. Okay, that's great. I love spending my money on flights. Most of my Rex expenses are St. Frank's coffee. Really? St. Frank's? That's the best coffee shop. That's pretty good. That's pretty good. Well, I want to ask this question. So we went over like, you know, what you look for in investments and all that kind of stuff. But I want to know what informs you and who is someone that you admire most? [1:04:40] What have you learned from them? I think somebody I admire the most is probably like Arvind that glean. I'll tell you the reasons I admire him. [1:04:47] Number one, I think... [1:04:50] Like I love like I love the Japanese philosophy of, you know, respect your craft. You do work because that's what people do. [1:04:57] Um, you don't do work to win. You don't do work to be number one. You don't do work to have a high ego about what you do. And I think in a weird way, amongst people I've worked with, Arvind absolutely embodies that. He told me something which made no sense at the time. And I'm like, of course, you would say that in the early days. And it stuck with me. And he said, [1:05:19] Didi, don't complain about hard work. Hard work is a gift. And... [1:05:25] If you wake up where you are doing the job that you do and you can work hard, [1:05:30] You don't know how lucky you have it. Most people can't do that. And at that time, I'm like, [1:05:35] You're the boss. Of course you would say that. But he also works, despite having, you know, a paper net worth of multi billions of dollars, he works more than almost anybody I know at that level. He has no joy in ego stuff.

[1:05:51] events, podcasts. He hates them. I know he hates them. Like he goes to them because he has to, because he's a CEO in the face of the company. He doesn't like that stuff. He doesn't buy flamboyant things. He drives shitty cars. He lives in a house that, you know, we... [1:06:07] joke that you could have lived in 15 years ago in your career. He doesn't upgrade that. He doesn't care about any of that stuff. He cares about the fact that he can work hard every day and do what he does. He just loves it so much. [1:06:22] Another quick one on that is at some point he told me two other stories. I'll just because I'm going on an Arvind rant. Two other stories. At one point, we had a serious acquisition offer that we were considering. It was a big acquisition offer. We would have all made out very well. And I remember we were driving back to SF. This is the kind of Arvind, the guy Arvind is. He could have done anything. He was like, Didi, will you drive me to the city now? [1:06:46] Because I would rather like not spend extra money on an Uber or drive myself. And I'm like, okay, boss, like you got it. So we're driving up to the city. We're on the 280. And Arvind goes and I asked Arvind, I'm like, [1:06:57] man like real talk how are you feeling right now we have this acquisition offer this works like you have validation you can be out with a billion dollars right now liquid [1:07:06] And you've made it. You've done a second company that's a unicorn. Very few people have that. How do you feel? And he looks at me and I thought he was being pretty honest. Sometimes, you know, CEOs have to lie and just say what you want to hear. You have to hear. But he said, [1:07:20] Didi, I run such a cool company right now. If I get acquired, I will no longer run that cool company.

[1:07:28] Why would I want that? [1:07:30] Like, that was generally what was going through his mind. He's like, from a personal level, why would I not want this job? I don't want to be some exec schmuck at some company that I got bought at. Who wants to be that? And I'm like, wow. I don't know many people who could, like, seriously have that thought. And I would believe that they truly believe it, except Arvind. Love him for that. [1:07:52] Thank you. [1:07:53] that's one lesson last lesson i really i really like this one because i i see this happen in so many other people [1:08:01] the very early stages of Glean again, I was kind of talking to him about, hey, man, like, have we thought about [1:08:07] When we grow, what's our defensibility going to come from? What about like, you know, what if this company decides to compete with us? Don't you think they have a pretty good advantage? Or, you know, right now we care about like hiring, but are you thinking about ramping up the sales team? All of this stuff was always in my mind as kind of like I've read enough startup shit on the internet that I have views. This is how I like to see, like to think about it. [1:08:29] And he looks at me, obviously having done this way more than I have, and he goes like, [1:08:34] Didi, you have a lot of questions. [1:08:36] They're pretty good questions, but they're not the right question. At any given point, you have one question you're trying to answer and you want to go get that answer. That's all that you need to make something work. Don't overthink it. The one question we have right now is not any of that shit. All we need to know is do customers love this product? [1:08:55] Is the answer yes or no? I'm like, no. It's like, that's all you need to work on.

[1:09:00] That's all. None of that stuff matters. And I'm like, like the clarity you need to have to be that person is so hard. And I don't I don't see that kind of clarity. And most like, I forget, like founders for sure. But most people, like even in their like, day to day lives, I think we just like, jam our head with shit. Like, oh, my God, like all these things and concerns and questions and anxieties. And like, there's only one or two things that really matter. [1:09:25] go do this one or two things. [1:09:26] So I love I love him for like those kinds of advice. My God, that is some wisdom. [1:09:33] that's a great way to end it too thank you so much dd thank you molly this was really for having me hey it's molly if you enjoy our interviews check out our newsletter sorcery.bc where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews subscribe to sorcery today and don't forget to subscribe to the podcast on youtube spotify apple or wherever you listen link in description to sign up