Inside the $100M Anthropic Fund, Menlo Partner Deedy Das on AI’s Next Wave

28:48So 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 all of these novel technologies to the industries that need it the most? 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.

29:30A lot of people, you know, I think and sometimes are 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. And you grew up with it, you know it, maybe your mom or your dad worked in it. And you're like, I know this area so well. I know that there's so many efficiencies. I also happen to be really good at this 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. And number two, I think I care a lot about, you know, we're at this point where research is not happening in academic institutions anymore.

30:08They just don't have the money to fund frontier research. So how do you underwrite high technical risk and what research problems could unlock economic value? And how do you find those teams? That's an area that I care a lot about, like fundamentally interesting research. search. And I would say the last, the third thing that I personally like care a lot about is I look at these companies that are, how do I say it? That are just beautiful product builders. I think that art is a lost art. People don't give it enough credit. A very overused example in venture also, but like, I love the example of, of granola and, and I'll give you why I like this example.

30:56No one thinks meeting note takers is fundamentally interesting, right? No one is waking up every morning. 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 outcomes granola. And you use this product and you're like, that's beautiful. It just works. I don't really have to think about it. It's just kind of nice. 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.

31:31What is it solving? So I think product 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 been for many products here's a here's a theory if your enterprise product can be plg then it must be otherwise a plg company will absolutely eat your lunch and destroy you um and it kind of ties back to a lot of venture capitalists these days are talking about like momentum and whether three three two two two 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 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.

32:22So not only in two years is your revenue weak, but your product is shittier because you've done all these shitty things to repese enterprise plans, 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. Like classic rag solutions have now become agentic solutions. Those actually mean something technically, but people couldn't evolve their product. 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.

32:58And 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. Turing builds realistic reinforcement learning environments and data systems based on real operational traces, the kind of infrastructure frontier labs need to train superintelligence.

33:35Visit Turing.com slash S-O-U-R-C-E-R-Y. Speaking of one that was recorded in this room was Cluelly. 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, Cluelly. A lot of people have a lot of opinions on Cluelly. um i i actually think that you know i i find myself having to defend roy a lot um not because i think everything he does is right but i actually think he's a very thoughtful guy in a very not you know brash exterior shall i say and the my story is when interview coder became a thing prior to Cooley.

34:25That was what Cooley was, I think, prior. It was called Interview Coder. It was like this tool that was cheating for your tech interviews. And I remember seeing this somewhere. I forget where, small post. And I thought, 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 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 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 you are not good at your job and somehow you are so senior and the reason is because you could game tech interviews.

35:20Yeah. I think it's not representative of your skills. So I see this product and I'm like, I like it. I like it. I like what it stands for, which is these interviews suck and they have to go. And so I tweeted it quite like, I still remember kind of the tweet where I said, you know, cheating is really bad. 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. Oh my God. Something to that. and uh and and that blew up uh 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 to get out of the way so like kudos to you for doing that and that i think was the first time that anything related to clearly or roy went super viral that post had several million views on on on twitter um but then roy took a took it on his own and he's he's sort of uh he's sort of a force of nature before I knew it, like in a month, I was asking him for allocation.

36:19How are you? No, not allocation. How do you do all of this? Yeah. Um, that's funny. So Roy, I think he started interview coder in founders Inc. That's where we're recording. Um, I 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? 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.

36:56AI 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. I can't wait until the next report comes out to see where that like the Delta is or like how much it's changed. But if everybody, if every company is AI, how are we measuring this? Every company is AI. Should be. Well, I think the stat that he said was what, 30 % premium, right? You said 30 % premium. I'll tell you that maybe I take 10 to 20 pitches every week.

37:38And 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 and silence you know it's reinforcement learning what you know 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 look 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 um I would say that like one of the reasons that when when we look at deals me say tim we're both very technical we both code with the stuff all the time um 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 actually understand what they're talking about too in the tech side what i can ask them like hey you know when you say ro do you mean like this like do you call this library or What are you actually doing?

38:54What are you actually using for that? And if you prod deep enough, 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, I think the premium 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 say the vc genuinely believes you're working towards an economically viable direction and that's another one like i would say at least 70 percent of ai companies are like fall off because the tldr of their pitches i just want to build models i'm not sure why it's useful.

39:55You know, that's kind of a bunch of pitches that we hear too. A 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? 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. Yeah. My steel man to this would be that the premium for actually good companies is much, much higher than that.

40:28Because, 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 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, you know, billion dollar seed. What's your like, 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. At the same time, even despite those rough parameters, it's so hard to justify a billion.

41:08I 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, right? It's one of those SPVs type 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. 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, you know, 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 could justify putting it out of your own fund.

41:47It doesn't make that much sense. I don't want to put a hard number on the seed that we'd go up to because sometimes we break that. Sometimes we do exceptional stuff, but definitely not a billion. Even a hundred million seed. Yeah, I can see that. I can see that in some cases. So that's how I would put it. 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.

42:30But you're a software company. What are you going to do with 400 years of runway? Like, 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, okay, we raised a boatload of money at a crazy valuation. Now we've got to back up into it. Yeah. Yeah. I mean, there's not much rocket science to be said. I think more capital can sometimes unlock velocity. 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 % of it for an early company.

43:13If you raise 100, you're getting maybe 90%. 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. 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 fill? Right? Like that's one. Number two, they start, like I call this the$50 lunch problem. 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.

43:43You'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. 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. 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 i can name a few i guess but there are companies that have hundreds of millions of arr that most people in like core Twitter tech.

44:35It'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. And it was just a boring company. So 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. 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. So maybe you've gone to an RSU model, you're a billion dollar company, you're losing people to Anthropic because Anthropic is paying whatever, four million dollars 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.

45:29and it's kind of competitive. So those are some good reasons, but there's too many companies that I've overraised in my opinion. Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cashflow or defense tech companies growing revenue over 25 % year over year. Public's AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis.

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46:36I 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 triple double? Like it doesn't make sense. And training just, it's, yeah, it's, 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, cause I guess off topic, but I can, And maybe I don't know how much they're raising. I also don't know 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 founders get secondary when they overraise.

47:14So 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. that's really bad because the founder just basically exits and goes like, well, 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. I see more logic for one of the prediction markets over, say, hey, I'm a seed stage AI company.

47:54and I just want to train or whatever. So that's just my view. To go back into the infrastructure 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. So I know the AI research accelerator place and 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. 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? Most of this, I think most people know I'm not repeating that much interesting stuff, but we had, we trained on all of internet data.

48:42We trained large language models on all of internet data and we were like, okay, turns out this can actually do interesting things. And it sort of generalizes outside the training obvious training set that's interesting that was gpd3 then people realized okay well we can rlhf this data which is a form of reinforcement learning and make it like a chatbot 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 let's like what if we trade a bigger model and there was a big model phase right so people were like okay gpt3 kind of small in the grand scheme of things let's go big um so scaling laws were invented or slash empirical study was done how how does size affect quality and people scale 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 uh 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 uh 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 right rl world was maybe I get proprietary data by paying for it on very specific things.

50:22And 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? So then you see companies like a Mercore, 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, well, how far does RL scale?

50:59Like 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. 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 RL is kind of a shitty paradigm to learn. Karpathy obviously talks about this a lot. 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 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 um if i lost so how can we get that improvement mechanism to be better that's one the second thing is data.

52:04How do we get better, more interesting data? 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, 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. 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.

52:49Models 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. 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. They're very undeterministic because they have no core structural understanding of things. And how do we fix that? 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.

53:26And I like Anthropics framing of like sort of the end goal. One of the nice end goals to have that is quite cool to measure, easy to measure and interesting to follow is the economic tiering test. So 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 tiering test. And can I drive that value of X higher and higher and higher? That's an interesting framing, which I think is like what the actual goal, one of the goals might be here. How does the evolution of this and trying to think of what comes next inform your investment decisions?

54:05It'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, people have different opinions, right? So a common opinion, I think this is the most sensible take in many ways is, hey, we have this graph of amount of hours of work that a model can do in some structure. And we see that graph kind of go up and to the right like this and some like log linear curve, but whatever, up and to the right like this. Maybe that can keep happening. It's been happening for a while.

54:38Hopefully 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. 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 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. 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.

55:18It'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. I don't know. 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. 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 pretty good at doing most of it, if not all of it already, so that's a problem to be solved.

55:54And I don't see a big lab coming in and solving that problem, so maybe invest in a company that does something like that. Or, you know, I talk about, say, the open router investment. It's not rocket science. I'm like, well, there's many models. 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 we look at that, I'm like, well, that's obviously something we need. Why not do an investment thing?

56:27So that's kind of how I would say I think about the investments. I just don't have a big long-term view that I'm confident about. Well, might I suggest you go to Calci and check out their prediction markets? 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, lots of layoffs.

57:05Like 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. 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. uh wait uh i'm curious what did uh what did michael say what was it what was his take i have my take already but what was his take so his take was there's there's about like there's two camps for this there's people that believe that ai will create more efficiencies and less jobs uh there's another camp that says 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 quote unquote, don't take this, not investment advice, anything like that.

58:12But his other take is that 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 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.

58:55Visit deel.com slash sorcery. That's deel.com slash S-O-U-R-C-E-R-Y. I'm smiling because I had this, I was going to say like 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 which is it was a banger it was a banger um 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 we know this I know them 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 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 uh early early days at Glean I remember I was talking to uh Arvind in the kitchen and and I was telling him like you know one of the things I just love about clean is like we're finally 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 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 arvin 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 ices that's just google i'm like so you know my point is not to pick on google or anything but this happens in plenty of other companies.

1:00:31It is very clear that, and my take on these companies is, sure, like in the tech sense, I think they're getting leaner and more efficient. 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 um and so in the big tech sense yeah i agree with you on on i agree with i guess michael on all of his takes but yeah coming coming back to the the actual question on like ai and i guess job layoffs um i i agree with him i think like there's some section of jobs that 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 a history with these things, right?

1:01:24There was a time where there were, most companies didn't need, didn't have a tech team. 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 So technological shifts do also create a lot of jobs. But on the same time, you could imagine one of those companies being like, but we have a robot for the factory.

1:02:01So, you know, maybe like less people in the factory. 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. Most people would argue that factory jobs are much harder and more annoying than a desk job where you write tweets. That is a good transition. 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?

1:02:42I 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 an LLM could clearly do better. And then you have robot factory, robotics, all this other kind of Waymo type AI companies. 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.

1:03:14so 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 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. I hate you. I hate robots. I hate the guys who make robots.

1:03:47That'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 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 in our life.

1:04:26That'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 and 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. Number one, I think, like I love the Japanese philosophy of, you know, respect your craft. You do work because that's what people do. 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.

1:05:05And 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, Didi, don't complain about hard work. Hard work is a gift. And if you wake up where you are doing the job that you do and you can work hard, you don't know how lucky you have it. most people can't do that. And at the time, I'm like, you're the 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.

1:05:45He works more than almost anybody I know at that level. He has no joy in ego stuff, 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 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. 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 um we would have all made out very well and i remember uh we were driving back to sf this is the kind of arvind the guy arvind is he could have done anything he was like dd will you drive me to the city 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 ask arvind And I'm like, man, like real talk, how are you feeling right now?

1:07:00We have this acquisition offer. This works. Like you have validation. You can be out with a billion dollars right now, liquid. 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, Didi, 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? Like, that was generally what was going through his mind. It's like, from a personal level, why would I not want this job?

1:07:35I 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. That's one lesson. Last lesson, I really like this one because I see this happen in so many other people. The very early stages of Glean again, I was kind of talking to him about, hey, man, like, have we thought about 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?

1:08:14Don'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 the 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. And he looks at me, obviously having done this way more than I have, and he goes like, Didi, you have a lot of questions. 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.

1:08:46That'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 is the answer yes or no i'm like no it's like that's all you need to work on that's all none of that stuff shit 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 in most like 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 like one or two things that really matter go to this one or two things so i love i love him for like those kinds of advice my god that is some wisdom 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.

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