This Week in Startups - What VCs Really Think About Personal AI Agents | E2347

Starting point is 00:00:00 Hey, everybody. Welcome back to this week in Startus. We're doing a VC roundtable, and you may notice this guy doesn't usually do the VC roundtables. I know Jason is traveling. He is abroad this week. He is not here, but I'm not doing this by myself. That would be ridiculous. We've got an amazing guest host stepping in. He is a partner at Menlo Ventures. Give it up for Didi Das, our special moderator of the week. Didi, thanks so much for being here. Thank you so much for having me, Lon. These are quite big shoes to fill. As you know, I talk. I talk. I am not the world's greatest moderator, Jason, J. Calais, but I will try my best to entertain all the lovely folk on stream here today. We certainly appreciate your expertise. Stepping in as a guest host. That's going to be amazing. And we've got an all-star panel for Didi's big week here. First up, Canu Galati, she's a partner at Kostla Ventures. She's led investments in Pali, AI, Wabi, Sarvam, so many others. Kanu, thanks so much for being here. Very excited to be here. Awesome. And we've got Chris Farmer. He is the founder and CEO.

Starting point is 00:01:00 of Signal Fire, grammarly and alchemy among the notable investments I see here in my notes. So Chris, awesome to have you. Thanks for having me. And finally, we've got Sheel Monat.

Starting point is 00:01:09 He's the co-founder, a general partner of Better Tomorrow Ventures, Seed Stage Firm. They're investing in fintech and vertical AI startups and a wonderful returning panelists

Starting point is 00:01:18 to the show, one of our favorite. Sheel, thanks for coming back to the show. Glad to be here. Before we go into our big stories, I do want to take

Starting point is 00:01:24 just a moment to thank our friends at Plod. You may notice I am wearing my Plod note pin right now. It helps we keep track of everything we talk about on the show. Also, just all the conversations happening around the office, anything that I need to keep in mind. It is constantly listening in only when I tell it to. I can turn it off if I don't want to share what I'm doing. And then it turns everything into these amazing AI generated reports and transcripts,

Starting point is 00:01:48 whatever you need. You never have to like, oh, what was that person saying? I forgot to do the thing. It's just recorded. It's saved. It's in your notes. It's ready to go. So you definitely want to check it out at plod.a.a.a. And if you use the code twist, they're going to give you 10% off of your Plaud note pin. There was a look at one of those amazing transcript. So thank you to our friends at Plod. And now, finally, we could begin the VC roundtable. Dede, where do you want to go first?

Starting point is 00:02:15 I think, you know, the biggest news of today and of, you know, maybe I would reckon maybe the biggest news of many decades, for me at least, is this unbelievable math result from Open AI. You know, am I qualified to talk about math? No. However, you know, there's this meme that goes around which says, hey, did you read the latest Ryman hypothesis result? And everyone's like, yes, I did. Did you?

Starting point is 00:02:41 It's like, yes, I did. Did you understand any of it? No, I did not. Did you? No, not really. But it's cool. And it's a big deal. And this is why I wanted to talk to, you know, the trial, Chris and Kanu about is we're looking at these math results

Starting point is 00:02:56 So that, let me just start with some headline stuff. Seven millennium prizes, price problems exist. One of them was solved 20 plus years ago. And if you look at this result combined with, say, the Navier-Stokes result that Open AI published a while ago, along with some other researchers, you're looking at major mathematical progress in four of the seven, four of the six remaining unsolved,

Starting point is 00:03:25 biggest unsolved problems in mathematics with AI. And there's many ways we could sort of take this discussion, but maybe I'll just start with Kanu, who's, you know, at Kossela, I don't know, an open AI investor. Like, when you see results like this, how do you think about investing in anything? Or how do you think about startups and innovation and what continues to have value in a year or two years

Starting point is 00:03:54 when we're looking at, hey, the smartest minds of the world can't do what AI can do today? We at Coastal Ventures are very proud, early investors in Open AI, and I'm very excited about all the results. But as a former scientist, it does make me uneasy because it is moving at such a fast pace. There's a amount of, just the pace of advancements in all of these different sciences is going to continue to be mind-boggling. I think, you know, in the past, you know, Sam have identified that, hey, people are usually not great at thinking how when things compound. And we're already at that inflection point where AI and AI's capability and generalization and being able to solve some of these unseen, unopened math problems is this compounding.

Starting point is 00:04:46 To your question is very fair. You know, from an investment point of view, we often think about, you know, what can we invest in where the ultimate value, is not just flowing into the token generators today. What is the next capability that we can invest in? And I can talk about some of the things that I have been investing in which I feel could be interesting, but it is a very fair question. For example, even within related to the math area, right? So math happens to be a domain that is very formal.

Starting point is 00:05:17 And for AI to continue to build proofs on top of proofs is you can see why it's working. There are domains which are very unformal, which are, you know, connected, written in very natural language with the intent hopefully captured well, but it is not 100% correct. And so some of the companies I've been investing in are trying to take real world, very complex domains and formalize it such that you can do this advanced math. And so you always think about what are we investing in and will it continue to get better because of the underlying models getting better and all of the capabilities that they are producing.

Starting point is 00:05:54 So, you know, Pramana Labs would be one of them that is going after taxes. Logos Research out of Imperial College in London is another one that's going after financial code. But the intent of the math or intent of the code, that once it's captured in a formal domain can go move much, much faster. But I see, I think, similarly in chemistry and physics, just in the software or digital land will move a lot faster. And now we're seeing a lot more companies trying to close the loop with real world experiments, with wet lab experiments. It's robotic experiments, and we're seeing more of that. But it is a very, very fair question about what can we invest in early stage that will not ultimately flow into the token generators,

Starting point is 00:06:33 and it is getting harder and harder for all of us. Chris, where would you agree, disagree, continue that, or say, hey, here are some areas that I look at or I think are still valuable, or, you know, it's a free for all. Who knows what we're all doing and we'll see? What's the take? We spent a lot of time as in healthcare, right? I think there's going to be just unbelievable breakthroughs in that ecosystem.

Starting point is 00:06:54 system of understanding a lot of, you know, parts of the human body and life that we still don't understand well today, like the brain and complex systems and cancers and all these types of things. And I think as to Kano's point, as, you know, it's hard to anticipate like how powerful this gets as they compound. But as you see a convergence of all the different investments that have been made across life sciences, wet labs that can test experiments, really quickly and run through it, you know, capturing all of the data that you wouldn't have historically kept in a human context because you only put something to clinical trials if it's been successful, whereas in an AI world, you want to capture all the failures as well in order to

Starting point is 00:07:40 train and learn from those data sets. There's a lot on the data capture and reinforcement learning and what on all these categories. And as these things converge, where we get better at understanding complex systems, but at the same time I get better understanding biology or, or, you or chemistry or all these things as these different types of models converge and can collaborate, the breakthroughs that I think we're going to be able to have is going to be super powerful. And I think a lot of what I'm most excited about is a potential impact on human health. Right. And then also planetary health and other super complex systems that, you know, there's a lot of debate on what's real and what is climate change and all this type of stuff.

Starting point is 00:08:19 And it's because there's so many, you know, inputs into these systems that it's hard to sort of really understand the butterfly effect of different things. And so now, I think as we get better, these models get better and can collaborate across different disciplines. There's going to be a lot of exciting, I think, breakthroughs and better understanding of the ramifications of some of the choices we're making in these domains. Yeah, I completely agree. I feel like it's actually the Matt stuff is obviously the first, the first, the first, results we're seeing, but that's just because it's the place where it's easy to verify that the AI discovered something actually new. But I think what's very exciting is just extrapolating that to all of science and making invention itself cheaper. And I think like Ghanu and Chris said,

Starting point is 00:09:07 I think there's going to be a lot of implications in other sciences. I think, you know, cancer therapy, stuff like that. But I think the implication is that AI can create a cancer therapy quickly, but I think there's still a lot in proving that it works safely in humans. And I think that's where we've done some investing. I think similarly in chemistry, you know, you can design a better battery chemistry, but someone still has to manufacture that battery and test the battery. And I think that's why you're seeing so much in physical AI. is the all-in-one management software that brings everything you need

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Starting point is 00:11:14 look at these big labs and, well, compute is clearly very constrained in the world and it is, you know, it's a rich person's game. I mean, there are certain companies who are able to commit billions and billions of dollars to compute and their compute efficiency defined by, you know, the revenue they can generate for every incremental dollar of compute that they purchase is, is probably the best in the market versus any other. kind of startup that tries to compete. So it's almost an unfair balance of intelligence in many ways where how could a company possibly compete with an open AI or an anthropic who has, you know, hundreds and thousands

Starting point is 00:11:59 of thousands of GPUs with, you know, a paltry maybe 5,000, 10,000. How does one, how do you think about that? Like, does that sit with you or you still believe, no, you don't actually have to have the most compute to be able to do this? How bitter, lessened-pilled do you think you are when it comes to that sort of thing? I'm not at all a believer that the models are just going to own everything. I think that's intelligence, right? If you isolated that, that's great.

Starting point is 00:12:29 And it's obviously a critical foundational input and incredibly valuable. But how you apply that intelligence and how you give it context and permissioning is going to be critical. Like, you know, there's a reason why Ford Deployees engineers have become such a thing. in recent years there's a lot of change management a lot of guardrails a lot of you know trust building that needs to happen that a lot of companies are going to have to wrap this you know the intelligence in context and permissions in order to actually do anything agentic right and so the change management piece is non-trivial then there's tons of proprietary data sets we do a lot in vertically i we used to be probably 80 percent or you know horizontal sass and or you know with a heavy m l

Starting point is 00:13:14 element and then we flip to 80% vertically I because I think you can build data density in lots of different use cases where the model may have intelligence but it doesn't have anywhere near the context that you have because you've you've built that up and you're going to outperform dramatically and that can take many shapes like for example we've a company called even up in the personal injury loss space I mean they're the dominant player in that domain because they've seen all the different cases and back and forth with negotiations with insurance and 99% of that data is in the private domain. You know, the big models, like, what are they going to do against that with somebody who has so much dominance in a vertical? Or, for example, a company like Alldot Health,

Starting point is 00:13:57 which is a clinical grade wearable, right, that has all of your personalized context on your, you know, biofeedback and whatnot. Like that, you know, the models help unlock that data, but it's that personalized data for early detection of, you know, issues that you may have on heart or whatever, that you need to augment the intelligence with context on a person or an industry or whatever it is. And so I think there's lots of opportunities to organize and unlock intelligence through the workflows, context, permission systems, guardrails, security, all of the things that need to be wrapped around it to actually get that horsepower to the ground. There are a few different things I'd point out.

Starting point is 00:14:43 Like, I am obviously a believer in large language models and maybe the transformers are built today and they're scaling laws because, you know, those have worked and all of the large labs are going down that path. I do think there is a ton of opportunity to improve them. You're still in the very early innings. Like, these models are very inefficient.

Starting point is 00:15:01 The amount of data they need to train, the amount of compute they need to train, the amount of compute they need to serve. They're all very, very inefficient. and there are tons of very interesting work happening outside of these labs in solving some of these problems. I think some of the opportunity that Chris mentioned is very true today because there is limited context that these models can work because the underlying compute infrastructure is still very expensive. If there were a way to have very, very long context possible, so it's not limited to 1 million tokens as of today, but maybe 100 million,

Starting point is 00:15:33 and maybe how you surface that information and what you really need to retrieve ahead of time, et cetera, all of that changes. So I think there will be a ton of different algorithmic approaches that will improve, and it might come out of labs outside of, you know, the labs you mentioned. I also think already because of the current lack of HBM or current lack of resources,

Starting point is 00:16:00 there is innovation happening from open labs. And it's partly driven because of the constraints in the current ecosystem, but there is innovation. So there's a ton of innovation that's going to come out. I also don't believe that LLMs and Transformers would be the only way for us to get to whatever we consider AGI or, you know, SI, et cetera, whatever the current term for it is.

Starting point is 00:16:24 It's going to be different types of intelligence, different types of approaches for artificial intelligence. as I'm very bullish on, like, there will be a ton of opportunity. And then there's this whole other thing about, you know, like maybe in specific areas where you need data collection from the physical world, labs may not think about it going early, but there is a flywheel to be had in those areas because you're collecting the data. You know, for example, we have a weather modeling company called windburn that collects in situ data from different points in the atmosphere. And they will just be ahead of anybody else collecting those data. point. So there is a physical world data flywheel opportunities as well, and we've been investing

Starting point is 00:17:03 in those, and I can go on and on about that. But I think, yeah, I don't believe that just scaling up will be the only way for us to continue winning the ultimate challenge for the next couple of years, yes. And I also believe that the constraints in compute and other components may not be, it's not like five, seven years from trade, and probably for the next two to three years, yes. Yeah, I think at some point compute stops being the bottleneck in all of these. things like we talked about like you can create a bunch of promising molecules and you know right now something that costs 50 million dollars to create you know a few years from now might be in the thousands of dollars and it might not be that compute is the constraint actually it might be

Starting point is 00:17:46 lab capacity the ability to do clinical trials manufacturing patents all that kind of stuff might be the constraint so I think right now it certainly feels like the big labs are where this kind of research gets done, but I don't, I don't know that that's necessarily needs to be the case in the future. Can we just double click on that real quick? What do you think, Sheila, is going to change that's going to make, are we just going to be able to make a lot more chips? Is it going to be the chips get way more efficient? Is it just we're going to build a lot more data? What's going to happen that's going to make compute become so much more widely available? Yeah, I think it's all of the above and sort of like the, the frontier model production is like what we're relying on today. It need not be

Starting point is 00:18:28 that like it may be that today's model today's model a year from now is going to be much cheaper and you still may get output from it that that is relevant there's so much to be said about compute there's another but so when i look at labs i think of or generally let's talking about a i i there's data there's compute there's maybe algorithmic improvements there's a couple of other things here and there there's this tweet that i saw this morning that i thought was was interesting it's a tweet maybe we can pull this up uh gokel says um many of us may know know, Gokul already says, you know, the fastest company to go from zero to a billion in revenue run rate is actually not any of these guys that you've heard of. It is actually a data

Starting point is 00:19:12 company. And, you know, Shio, Chris, Kano, you guys are all seed investors. You guys see seed investments all the time. I am sure that you have seen a 101 different RL environment slash data companies that sell to labs. And I'm sure you've also seen companies that come and say, hey, you know, we're at $100 million in revenue or, you know, an enormous amount of revenue in a very quick amount of time, most of which is typically non-recurring.

Starting point is 00:19:45 But how do you assess this? Like, do you invest in those kinds of companies? I've heard a mix of different opinions from sort of my investor friends and various different people. Some people are like, look, I don't touch that kind of stuff. I don't believe it has long-term value. Some people are like, who knows what has long-term value?

Starting point is 00:20:03 If anything, I think data will always be something that labs need. So, you know, this is as good of a bet as any. And then yet others caveat it by saying, yes, but I don't know what value I would pay for these things because they definitely don't trade like software businesses. They're non-recurring. The margins vary. to say the least, how do you guys assess this? Like, would you guys invest in this kind of stuff?

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Starting point is 00:21:38 You've got to be at the front lines, understand the nature of this data, what's working, what's not working, how to apply it. And then, you know, great entrepreneurs at the end of the day is what we're backing to to figure this out, right? And to see where they can, you know, migrate to corporations to help them adjust and tweak their data in order to apply it in their particular personalized use case. You know, I think it's going to move from the labs to a broader set of end customers. You know, and so we have a number of companies doing this in different verticals and whatnot.

Starting point is 00:22:16 And yeah, the growth is explosive. and is very concentrated in a handful of labs today. But all of them have a thesis of how it will be applied to broader enterprise, because I do think that as you move to more open weight models, you've already seen a massive migration per the Purcell data. That was one of the stunning shifts from proprietary models to open weight models. It went from like 80-20 to 2080 in a matter of months. And as these corporations start to get more adept at this,

Starting point is 00:22:46 build the skill sets in-house to personally, for their own use cases and build their own defensibility over time versus outsource it all to proprietary models. I think there's going to be a lot of need for this across any large business. And so, you know, how they do that bridge, there's a huge go-to-market challenges and professional services type challenges. But I think there, you know, some of this will be enduring, but some of it will also, you know, come back down. pretty dramatically, maybe as rapidly as it went up. And so it's going to be a matter of how adept these entrepreneurs in building lock-in

Starting point is 00:23:28 and landing and expanding just like the SaaS days with these customers where they find the pain points and then they keep serving them until they become more recurring and deeply embedded. I disagree with the sentiment about data will always be needed because as we've seen, you know, some of these larger model companies, they're good at taking this data and then at some point they don't need that data, right? Because a model is just inherently able to solve those things. You know, we don't know how well the open weight models

Starting point is 00:24:03 are distilling from the top labs, right? So the data, if the top labs get it, the open weight models also get it. So the enterprises or this existing data is your currency, then you're going to, the terminal value seems not exist. But I think Chris's point on, at the end of the day, it is the founder that they're backing. That it's very hard to argue because I have seen founders may have started somewhere and the story started to work. And then they were ahead of anybody else in figuring out what the labs need next.

Starting point is 00:24:34 And they're just ahead. And they know how to continue to build a business and continue to be one of the most wanted data or data type. What Apple is built on top of that data commodity. So I think the right entrepreneur can still solve it. We have, as a fund, close of engines, we've stayed away from that category, which we believe, like, the terminal value might not be there. So for, yeah, we haven't invested in anything in the data category for that reason. And, you know, but there are founders that has been very hard to be like, oh, are we not

Starting point is 00:25:03 going to invest in that founder based on everything we know about them? That's the hard discussion. So I think they're super interesting companies, but I think, like, even thinking about headline revenue is kind of like the wrong way to think about these businesses, right? But you have to think about it from a gross profit basis. And then even then, there are a lot of questions. We mentioned customer concentration. We mentioned, is the demand structural or is it temporary?

Starting point is 00:25:29 And I don't have a good answer for that. I would say, like, the models obviously continue to need better post-training data and evals. But so far, it feels like as models have improved, actually, the, the amount of expertise actually has risen. I don't know if that asymptotes at some point. I think there's a question of like, what is proprietary in any of these businesses? And in many cases, there isn't much,

Starting point is 00:26:02 but there's a great opportunity right now to generate cash. And they actually don't require a whole lot of fixed capital. So it's a very interesting business. I haven't invested in any of them. Interesting. You know, I like, there's a couple of threads that. all of you touched on, which I thought was interesting, which we talk about this a lot as,

Starting point is 00:26:19 as investors, which is ultimately a large part of every investment we make does boil down to, you know, how talented this founder is. I sometimes wonder, and I've had, you know, varying thoughts about this over time, is we live in a very different era today than maybe five to 10 years ago, namely, you know, five to 10 years ago, you might have been, I don't know, 10 years at a great SaaS company and then you leave and you say, hey, I'm going to start basically another SaaS company. I'm going to do some top-down enterprise sales. I'm going to hire a bunch of people. There was usually a little bit less competition and you could be quite successful sort of running several kinds of playbooks back in the day. A big constraint for many founders.

Starting point is 00:27:10 was just being able to hire software engineers at scale that actually could build, build out the software. Maybe you disagree with parts of that. So if you do, feel free to tell me. And that's just a part of businesses. Obviously, many other great companies in that era existed too. Whereas today, all of a sudden, you know, at least when I talk to young founders,

Starting point is 00:27:31 you know, the way they work is just extremely different. I mean, the size of teams versus the kind of capacity of output that they have, you know, it's heaven and hell. You have teams of five people who are running thousands of agents in the background. You do a bunch of different things. They say yes to this, no to this. It sort of seems like a wholly different skill set, at least when it comes to software. Leaving aside maybe some healthcare specific or fintech specific stuff where, of course, I know you need some niche skill set.

Starting point is 00:28:03 Do you agree with that? And how does that change how you think of what a good founder looks like today? Is it fundamentally the same and you're looking for the same stuff? or are the skill sets actually changing a little bit today than it was 10 years ago? Yeah, it's a good question. I think like the broad skill sets of setting a vision, selling that vision, recruiting people are all the same as they ever were. And that's what it takes.

Starting point is 00:28:28 But there is this new class of founder that didn't exist before. And I think that they, I would say like in our experience, at least skew younger. And you have very folks in their early 20s building amazing businesses scaling very quickly in categories that you would have thought required expertise in accounting legal stuff like that. People without backgrounds in those places are doing quite well. But I think at the end of the day, what have they done well? They set a vision for their company.

Starting point is 00:29:05 They recruited people against that vision. and they sold that vision to their customers. And so I think those things are evergreen. You've heard me say it many times now in common. If you're an early stage startup or small business, every single new potential customer is extremely valuable. You can't afford to have a team that's missing calls. If your communication is inconsistent, your customers will notice.

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Starting point is 00:30:03 Plus, get 20% off your first six months. when you go to quo.com slash twist. That's QUO.com slash twist. This is one of my favorite products in the world. It gets my highest endorsement. We take a very bimodal view of how we invest in some of this. In some areas, you go with super deep domain experts that understand the workflows, the politics, the incentives, the data sets, etc.

Starting point is 00:30:29 And they have the credibility to get the partners they need to design and build systems to attack problems in particular verticals. But we're also very extensive investors in what Shield, you know, described as these young, this is their first job. And they're just sort of hacker type personas that have no idea how an industry works. Because we also believe when you have these tectonic shifts and nobody knows where it's going, you need heat-seeking missiles that, you know, are just insatiably curious, moves super quickly and iterate.

Starting point is 00:31:03 And it's somewhat Darwinian. and that a lot of these things don't work. But when they do work, they reinvent industries to an even greater degree that anyone from the industry might do. So, like, someone from Telecom wasn't going to invent Snapchat or someone from the hotel industry wasn't going to invent Airbnb, right? So you look at it from a completely new paradigm and a blank sheet of paper. You have some of the most revolutionary, you know, advancements because you're not burdened by sort of the prior logic and maybe constraints that the industry. had faced historically. So I think it's a really interesting time for sort of young founders because you can also do so much with so little money, much like what cloud computing did

Starting point is 00:31:46 for people to sort of not need all the sort of CAPEX in order to get off the ground. I mean, what you can do to spin up and iterate quickly with these AI tools is pretty incredible unlock. I think how AI pill the founders are has an impact on how we think about them because they're going to take the bets that they feel will be the right best. for the company in the coming years. They're making an informed approach about, you know, what the underlying capability of the large frontier models will be. And then they're basing the bet on the company on that.

Starting point is 00:32:21 So being AI-pilled is probably, I guess, very, very important access now. Age-wise, I've seen all various ages. Yes, you know, they're the younger founders, but also, you know, folks who were like, you know, clearly like in their 30s and 40s and 50s. But because they are able to take the ambitious bets and they think about, you know, you know, like we're investing in a chip design company that is going to release a chip with like 20 folks

Starting point is 00:32:44 because they have so many more agents that can do a lot of design and exploration and all of the hard work of generating the RTF and also all the way to physical design. I think it's just a new world now. And so I think we all have to reassess what the high quality founder looks like, but we're all learning. there is no pre-made template like, okay, thou shall look like this or thou shall be at this age. I think we all have to just fundamentally question our known beliefs here. Yeah, I mean, my only thing to add is like for me, you know, taking the sort of venture hat off, I just think it's spectacular how every, you know, generation of technology seems to exacerbate the skew of outcomes of every young generation.

Starting point is 00:33:32 So, you know, one way to say it is, it seems like the media gets slightly worse, but the extremes get immensely powerful. And so when I see some, you know, the other day, I was spending some time with this 19-year-old. He wasn't a founder or anything. And he just showed, he was just showing me, he was introduced to me by a founder. He said, hey, this guy's really smart. You should chat with him. And he was showing me his flow of how he did things. And to me, it was just remarkable.

Starting point is 00:34:00 He had, you know, this tab of, hey, here's a bunch of my 50 agents that are running, and it's just trying to break different websites at once. And then it'll alert me when it breaks a website, and then I get to do something with it. And I can just collect bounties off of this. And I'm like, how are you doing this at 19 just boggles my mind? I wouldn't have even thought to think about this. So it is pretty exceptional how much it can amplify talent at that level. And I do want to touch on another topic which comes up all the time. I was recently out of my tech bubble and I was at a friend's wedding.

Starting point is 00:34:39 And the only AI topic that seemed to come up at this wedding was how are you using your muse or instinct? And I thought that was quite funny because they talked about nothing else model related. It was just muse instinct. And then some of the hedge fund guys were like, our venture capitalists absolutely bonkers. why would you fund this for $10 billion? It doesn't make money. Please explain this to me. And so let's let's just start with that.

Starting point is 00:35:09 How do you, you know, when people ask you, and I'm sure maybe some people have outside of the tech bubble, what is up with these valuations for something like an instinct? Is your instinct to defend it and say, hey, you know, actually there's a world where this might actually be an amazing company for these, these reasons, or are you sort of like, you know, I don't really know. How do you guys think about it?

Starting point is 00:35:33 I'm a bear. Oh, I look, I think these personal agents, I think this is actually going to be an unlock period for personal agents and it's going to be super exciting in the next few years. I think a lot of what I've seen with other people and certainly applies to myself is there's only so many points of entry into my private data that I want. And, you know, my Gmail has, you know, social security numbers, taxes, like all sorts of things that, you know, it just gets very dangerous at some point to have too many, like, things plug into that. And obviously, like, Facebook has just massive distribution.

Starting point is 00:36:15 I was actually looking this up with a founder friend of mine. And it's something like, you know, three and a half billion people on the platform, you know, and Google with YouTube and everything. everything is similar, right? I think it's even greater. It's like four billion people. It's like half the people on the planet. And it's like something like 70, 75% of people on that are connected to the internet. Right.

Starting point is 00:36:37 And so, you know, the distribution reach they have, the, the intimacy they already have through Gmail or Apple or, you know, I think is not, is a very, very difficult thing to get over. And so, you know, I think either a more specialized model versus a general sort of personal assistant. I just think it's a it's very non-triggled to overcome getting people to give you access to their most intimate information that is dangerous from a cyber threat type of standpoint. And I think people are going to really want high levels of trust and security. And I, and I think this is an area that could favor incumbents unless you're highly specialized like travel or something like that where you just have real advantage in a particular domain. But we'll see. So I think,

Starting point is 00:37:25 paying that kind of valuation, even if you have a super slick product, that versus distribution and the ad networks and everything that the huge platforms have, I think is a, that's a big uphill battle from my standpoint. So instinct, you don't think this will overcome distribution. That makes sense. What about Muse at large? Like, do you think Muse is a billion user product? Billion Dow product.

Starting point is 00:37:50 I mean, it could be. I mean, look, I think opening I ran into the situation, right? They, they, they, they're, if you look at what they're building relative to like, I mean, it's Googles to lose in a lot of ways and they seem to be foot faulting again. They seem to be on, you know, the, the balls of their feet for a while. And now they're, now that, now they seem to be a little off. But, you know, opening eye, you know, needed to build monetization, right, to bring the cost down for a lot of folks.

Starting point is 00:38:18 And that's going to be, you know, some sort of where the, where the consumer is the product, right? And the payer is someone who has a commercial interest in selling something to or showing something to that consumer. Right. So they had to build an ad network. Then they're also trying to build hardware devices and sort of the whole stack that like decades of work have gone in at Apple and Google and whatnot. And so, you know, I think it's not just distribution. It's distribution and the ad ecosystem and the wallets and the pay. I mean, like all of the different things that you need in order to do.

Starting point is 00:38:53 everything to commercial transactions to advertising and the network of of commercial relationships that you need in order to monetize this in order to subsidize it for the consumer to make these agents you know free or or very low cost for them and so i think it's there's there's lots of layers of the stack that the incumbents have and look startups beat incumbents all the time but i don't think you know you know paying that kind of entry valuation to me the the risks are significant, even if the reward is significant and it's hard to justify that risk reward sort of trade off in my mind. I think it was a bit of like tail wagging the dog.

Starting point is 00:39:32 The company had to raise so much money because compute is expensive right now. And I think you have to believe, if you're investing at that price, you have to believe that this is going to be at least one of the major consumer interfaces. And so if personal agents are a major consumer interface that we use for travel purchases, restaurants, those are the things people are you're seeing for right now, insurance, a bunch of others. You can certainly make money. I don't doubt that you can make money doing it. I think where I struggle is that instinct will be the winner rather than OpenAI, Google,

Starting point is 00:40:11 meta, maybe Apple. And of course, we've seen since the time that Instinct came out, a couple months ago, we've seen a whole host of other agent companies come along. And obviously, not every one of them will be able to raise the amount of money you need on compute to be successful. I feel like Instinct did a tremendous job at their launch. And at least four VCs, got a lot of hype early. They were out a little bit before Muse, and that was great. Muse is a phenomenal product. You've seen it in meta stock price. But instinct, you know, I personally use instinct all the time.

Starting point is 00:40:52 And I've tried it out. There's Hark came out yesterday. Sesame has a product. There's several other products that I saw just launching today. People are constantly messaging me, hey, try this one. I actually have personal agent fatigue where I'm pretty reluctant to try another one. And again, input all of my data. So it'll be interesting to see what happens.

Starting point is 00:41:13 I personally think that the large companies have the huge benefit here. but I can understand why you would underwrite instinct to a $10 billion, which is like, what are the odds that this becomes the major consumer interface? That's worth a lot of money. And if the odds of happening, you know, you sort of like build some distribution on the likelihood of that happening. I think it's tough, though.

Starting point is 00:41:38 And then just to follow up, do you think Muse is going to be that? Say, say instinct maybe struggles to compete on distribution. Do you think Muse actually hits a billion daily users who continue to use personal agents. And I ask this question in the vein of like, or is this sort of a first world type problem of like people have to book flights and restaurants in the Bay Area,

Starting point is 00:41:58 but most of the world really doesn't care that much about this. Where do you stand on that? Yeah, I think a billion users is a big number. And I don't think that there's a billion DA use for this product. But there are a lot of use cases out there. There are a lot of like in a lot of the world, you still have to like enter. information into a shitty system. Like, I've used it for a lot of medical things.

Starting point is 00:42:22 And actually, interestingly enough, like, I used it to pay a healthcare bill recently. And Muse wouldn't do it because it only has my tokenized credit card data. Instinct stored my credit card data in plain text, which is not great, but it actually was able to pay my medical bill in a way that Muse was not able to. But I think there are certainly hundreds of millions of DAUs that this is a product people can use. People talk about it as if only VCs have this problem. And it's true, people don't travel as much as we do. But people do make purchases.

Starting point is 00:43:01 I over the weekend was at a bachelor party. And I actually used it instead of split-wise. It actually automatically knew what purchases I'd made. And it was just like, and it had a spreadsheet of everybody who was there. there and what times they were coming in. So it actually automatically split everything amongst everyone instantly, pretty useful thing. Again, not something that happens every day, but it happens often enough for everyone.

Starting point is 00:43:26 I think the category can become really, really large, right? This could become the way that people interface with anything digital, right? Like, you know, if I just work with my agent, I happen to be trying out Lajo, which is a KV portfolio company, their agent four. I don't need to call another physical business to, I don't know, organize my eight-year-olds, birthday party, I am happy, right? Like, all the way, you know, Bado's take is they can go even recruit the human they would need to go carry out a task.

Starting point is 00:43:53 If somebody else can just take over all that from my plate, I think the value I would pay could be very high. So I do think that if their company gets the trust, gets into the workflow of our day-to-day lives, and like she said, you know, we have the fatigue. Like, we don't want to continue to train different agents and get them connected on how we think about and what our preferences and how we would like to do. works. There is stickiness possible here. So I do think the category can be very, very large. I am not a consumer investor. And so coming in at a 10 billion kind of a price would be like just

Starting point is 00:44:24 something I would not think of. So very early bets on trying out something very different approach. You know, I did make an investment in a company called Rabbit that got a lot of flag because they promised you much. But if you look back, like this is what they had promised. Like you would have a device. You would just talk to it. It would do the task that you need and not worry about individual apps. But coming in at a very early price to say like, hey, this is a bet worth taking, I would do that. A $10 billion price as an entry point, I would be worried about that. I have to go around the table and ask everybody what their favorite use case for themselves. I know, Shield, you already had a great one for a personal assistant was because everyone keeps asking me this.

Starting point is 00:45:02 And they're like, look, I can only think of flights and, you know, these really stupid sort of basic things I could already click buttons to do. My personal favorite one so far has been, and I guess it's with an asterisk as it's still yet to be completely done, is I was flying back from this wedding, from the EU, and two of my flights back to back were delayed. So I ended up being six hours late, and I was just frustrated. I find out, thank you, I'm not going to say, which I use instinct. Okay, so I as thank you instinct for telling me that I am eligible for a 600 euro, refund because this is how the EU works. So I said, great, I don't, I don't want to fill any forms. Can you go and figure out how to get me this, this amazing refund?

Starting point is 00:45:50 Because my flight's six hours late. And it went ahead and did a bunch of stuff and chatted with an agent and tried to escalate to a real person and then did that and then asked for their manager. And it was, it was just phenomenal. And they sent a few emails and it said, look, here's your case number. We're going to get it for you. I was very impressed. I said, look, if you can automatically get me the money that I clearly rightfully deserve

Starting point is 00:46:17 for my flight being late, this is a good use case of an agent. So with that, you know, Chris, Gunow, feel anything exciting that you have used instinct or muse for... There's a lot of stuff like that. You know, there's a lot of stuff where there's like a little bit of friction and using an agent makes it easier. You do wonder, like, so many business models are built on that little bit of friction preventing people from doing it. So you do wonder what changes. I do a lot in fintech, and I think a lot about finances. And, you know, credit cards, they charge you more interest.

Starting point is 00:46:55 If an agent can automatically move your debt from a credit card somewhere else, what happens? There's a bunch of things like that. So I think that's another interesting thing is like, how do businesses change when friction is reduced? Yeah, I haven't used instinct. for the reasons I mentioned, mostly because I didn't want to give it access to all of the data. But I have built personal operating systems and Claude and whatnot that I find sort of really helpful as like a chief of staff for big complex projects that sort of take all the inputs from, you know, all the different, you know, whether it's granola or, you know, calendar and email and all this type of stuff with all the research I do around a project on top of it.

Starting point is 00:47:37 I'm also really bullish on personal finance use cases because it's like the bane of my existence of like got to aggregate all this stuff and then give it to an accountant or whatever it is, like just the paperwork on the health care side, like that type of stuff is the stuff that not only is it time consuming and requires some level of expertise and there's tons of intentional bureaucracy and friction in that, but it's also like very, very, very important. very painful, like, sort of time on things that I don't enjoy doing, that I would, you know, have a lot of value for those types of use cases. I'm most bullish and excited about sort of personal finance use cases and just all the administrativeia that we deal with in our lives

Starting point is 00:48:20 that hopefully we can agentify and not have to deal with insurers that deny things for stupid reasons when they really shouldn't. I haven't used them for any of cases where I would have to share any personal information like my credit card or SSN. I mean, there was an incident yesterday. Somebody reported that, you know, their agents shared their personnel bank account information on a company's group, slack, et cetera. So I don't trust it. But for, you know, like I mentioned, organizing my eight-year-old's birthday party, calling up different places who can accommodate. You know, these businesses are open between nine and five. I have other things to do between nine and five Monday to Friday. I don't want to be on a phone call, calling up who I called,

Starting point is 00:48:55 what information I gave them, what was the next follow-up step, what I need to fire up a court, like all of those very annoying tasks can be now taken care by my foe, I'd pay for that, right? So, yeah, I've been using it for very lightweight, but just stuff or areas which are not exciting for me. Fascinating, fascinating. I guess one thing that I was thinking about hypothetically,

Starting point is 00:49:17 kind of to Sheel, your point is there was this really viral Twitter post that I saw maybe about a week ago where it was some senior banker, believe who kind of made this hypothetical, which I kind of thought was a little bit absurd, but he was like, well, if everyone's going to use a personal agent and everyone says, hey, help me with my finances, then what if we have a bank run? And because everyone's agents tell them, hey, you ought to take all your money out of the bank and put it in XYZ assets.

Starting point is 00:49:51 Do you know, I guess how do people feel, I mean, not specifically about that, but this general high-level idea, which is the more that, the more diffusion that happens with these AI tools within humanity, more people are going to outsource their decision-making, perhaps, to this homogenous sort of entity that makes decisions in, you know, surprisingly similar ways across different groups of people. And what are the implications of that for the systems that weren't built for this sort of, you know, mass homogenous behavior to happen at once. Whether it's something as simple as everyone now wants to have reservations to the top hotel, a top restaurant in San Francisco, and so they can't accommodate anything, or whether

Starting point is 00:50:42 it's a bank run, or whether it's one of like a hundred different things that could go wrong because systems weren't really designed to handle that sort of behavior. Do you think that's a real risk and what happens? And how do we control for something like that? I think there's a lot of systems in the world that are. built on breakage, right? The very sophisticated that are at scale that can hire people to help them navigate, whether it's, you know, you're fighting the airline to get, you know, your $600 back, or it's, you know, insurance is a very common one, obviously, like where they deny things that they should really do and put hoops. But there's always been like rebates, all, you know, all sorts of

Starting point is 00:51:20 things that are, you know, that make you feel like you're getting a good deal on something, but then it makes it very painful to actually execute, you know, and get what you're due. I think agents will hopefully reduce some of that friction, right? And it'll become more transparent and more rules-based. Like, you know, banks will have to become more competitive on the interest rates that they pay, right, for being your bank account. Otherwise, you'll move it to somewhere that pays a higher interest because the friction is removed for doing that. And so I think it will force some leveling of the playing field. think in certain areas. And there'll certainly be companies that try to to fight back against that,

Starting point is 00:51:59 but at some point when you have a relentless agent on your side, I think, you know, businesses will have to clean up some of those elements. We are not going back to a world where we don't know this capability of AI exists or, you know, the fact that an agent can take care of the things that I don't want to do. And if businesses on their side don't keep up, like if they can't accommodate an agent calling and taking the order, etc or like doing the number of orders they can do or trust issues or you know mentioned bank because like the businesses will have to solve it at their end and more opportunities for startups to go solve that and think differently what could be the right way what could be the long-term

Starting point is 00:52:38 correct way of having this system work we're already seeing it in searches right like how google search was designed for humans and you would go down like one page two pages and that's it like that was a search. Agents don't search like that. Agents can go to 800 of those links and try to surface our best information. This is, you know, high-level Parag's company,

Starting point is 00:53:01 Prado Systems is working on. Because we have to rethink how the world is going to exist, given now that we know AI and agents are not going away. And I think this is going to be opportunities for businesses to, you know, get more AI build and serve their customers

Starting point is 00:53:14 with matching capability. There's one thing that I can't get away with this podcast. not talking about, which is, man, I woke up, I think it was Wednesday last week to the funniest drama that I have seen on Twitter in a while. And this was Factory V. Cognition. And I happened to be on a plane with a bunch of people who happened to be in venture. And we were just smiling to ourselves going like, I'm not sure why this is such a big deal, but I'm glad it happened because it's so entertaining. And we happen to have, you know, Ganu, obviously, you're at Kossla, who was at the center of this spat.

Starting point is 00:54:02 I'm not really sure what the spat really even was besides, you know, of course there was a, there was somebody, like, I think on the board of factory who went to cognition and that's how it started. And then, which, you know, I mean, I would say this sort of stuff happens. and usually it's a small thing, but it really, really blew up. Matan posted this tweet. Matan is the CEO of founder of Factory. Scott, the founder of Cognition, responded to it.

Starting point is 00:54:36 And then at some point, Vinod says, you know, Factory is a second-tier competitor, despite being investors in Factory. And I think, you know, kind of like everyone just wanted to know, like, you know, did Vinod forget that he was an investor? Is that was his true opinion?

Starting point is 00:54:55 What happened? Honestly, I know what you guys know, right? Because it's, I'm not in boardrooms of any of those companies. I do like that the MDs in the firm are going to stand for their founders. But that's all I get to say about it. Like, that's all I know, honestly. I was not in boardrooms or the conversations with Chris or any of the founders. I'm a huge fan of both Madan and Scott.

Starting point is 00:55:19 So yeah, I know what you guys know. I think there's a more, there's an interesting question here around should, should investors be investing in competitive companies? And that, you know, really for a long time, it wasn't a thing. And then in this cycle, it's super accelerated. And most of the, the multi-stage funds have multiple investments in competing companies. And I think what I've seen is, people, People tend to take favorites, and it's the company that's doing well. I think in this case, both they're doing fairly well.

Starting point is 00:55:56 And you have just different partners defending their company. I wonder if at some point founders say, hey, this is bullshit. Like, we're not going to talk to you. That was the case like 10 years ago. And then it became not the case more recently. But I wonder if that changes. Look, I think a couple things. First off is one of the greatest strengths of Silicon Valley.

Starting point is 00:56:19 But is also a weakness is that the amount of, you know, basically sharing that happens across the Valley, founder to founder on like best practice and what's going on. And it's an incredibly open place from that standpoint, which is wonderful. So tons of people are investors and all sorts of other companies, you know, in adjacent domains and all these types of things is, I think, really wonderful to pay it forward. the flip side is you also gain a lot of intelligence and like where are the lines on you know crossing something that should be proprietary and whatnot and where can you use them in what context and people are going to disagree and there's obviously going to be you know two perspectives on that and sometimes people cross into the unethical like i'm not saying i have no sense of what happened in this scenario but um you know just it's it's very inherent that like you know that when we have this co-opetition is you know look at micro-examination as you know look at micro-examination as you know look at microcontest Microsoft and opening eye or all these types of things. Their partners one minute and then their enemies and then their friend of me is another. You know, you just have a lot of this dynamic or Nvidia, right, is the, you know, selling to all the big labs. Now they're, you know, competing on the open weight side, just like, you know, many of the cloud guys are now competing on the chip side.

Starting point is 00:57:31 And so you've got just sort of this fierce competition and it's going to spill out and get ugly at times when people feel the pressure. Right. And so I think overall, it's generally a net positive because it drives competition and innovation and, you know, acceleration of this and transferance of knowledge across the whole ecosystem is what makes Silicon Valley so unique is that you've got so many people competing and collaborating and learning from each other. But it has these ramifications. And then there's some big personalities and, you know, no, you know, PR, I guess is, you know, no PR is bad PR, I guess. I don't know. We'll see. But it's certainly a fight for attention as well in the mix. Deity, we should mention Menlo put some money into factory this week since the story broke. So was this part of the discussion at all behind the scenes or is just like you're talking about the company and the fundamentals and not what's going on on X?

Starting point is 00:58:30 No, you know, look, as you know, these decisions are typically like made before they're announced. so they were there were two separate decisions and and you know I think one of the things it was funny because after that was announced that that we were you know invested in factory maton gave us a shout out Sean gave us a shout out I had my cognition friends text me saying hey like do you guys like do you guys not like cognition and and I'm like look man I'm firms invests like I think both of them are great companies I don't know why you're making it seem like I have to pick aside um like we invested in in one of them but you know I think I think both of them are fantastic companies and I think there's a bunch of room for both uh it is interesting to be a part of um you know a vocal public spat in some way though so um I I wasn't involved personally in the investment so I I don't know kind of waded in there just at the last second, so I had to ask. I think the question about, you know, our funds investing in competing companies and

Starting point is 00:59:44 is, you know, how does that work? I don't think we, at the outset, we are trying to find because we obviously don't want them to fight or want to go after the same talent or the same customer. Like, those are hard problems. But companies pivot and sometimes these markets are so large and, you know, the starting point and the starting wedge could be very different, how the company's grow can be very different, especially given how quickly AI agents the approach and how enterprises are going to think about this capability is changing so rapidly. It'll be silly to say, like,

Starting point is 01:00:15 hey, we have one robotic investment and that's it and we won't make another one. Or we have like one investment in agents and we won't make another one. I mean, the world's going to move really quickly. And if you find interesting wedges, and yes, you know, if companies do land going after similar talent or similar customers, then, you know, what we do at close our lunches, we have a very crisp, a very clear Chinese wall between those two partners that are working with those companies and none of the information ever gets shared. And we take that very, very strictly. And, you know, it's, again, we don't start like we want to invest in five of these companies, but companies pivot very quickly right now, especially when they're trying out different business

Starting point is 01:00:54 models and different use cases. And if it happens that they're weighing up at the same cases, they don't share information. She or Chris, do you do, how do you feel about that? Would you invest in competing companies or does, has it happened that you have invested in companies that happen to pivot to being competitive and how do you deal with that? Yeah. At the outset, we don't. We have had it happen where companies sort of pivoted into a closer competitive nature

Starting point is 01:01:18 than we had originally planned and, you know, there's not much you could do about it. Yeah, I mean, you know, especially, I mean, you want to go on intentionally doing that. And I know some firms have like a mortal enemies list. And then like, you know, some founders are like, everything's a competitor. You know, yeah, you know, if I'm open AI, like everything from like Apple to instinct are all competitors, right? You know, and so, you know, I mean, I think you're going to, you're going to follow a lot of the same trends. Like, you sort of, you sort of find your wedge of entry, but then the world evolves the way it does. And everyone is reacting to those same developments in technology or business models.

Starting point is 01:01:57 or the landscape or whatever it is. And so they may converge at some points. But at the end of the day, most of these companies do not win and lose based on another competitor. Right. Most of it's execution and team and, you know, and they find different either geographies or segments or verticals or whatever they end up going. So they may compete for a while and then diverge again. And, you know, obviously sometimes they're direct competitors. But even ones that are direct competitors in one element often, you know,

Starting point is 01:02:27 don't overlap in others. So I mean, it's, it's, you just have to do your best to, to, to try to create Chinese walls when this happens and, you know, be very transparent with founders as much as you can to, to, to try to manage, uh, those dynamics. But it is, it's an inevitability regardless of where you start, you know, everyone's following the same trend. So they, they, they often end up overlapping at some point. And didn't like grubbot announced today that they will use underlying model, whichever is the best model for their use case. Um, I think that's, you just how quickly, you know, people's, what's important, like what is important for Grogbaud to win is getting access to the best model. And, you know, Elon announced like, yeah, this could be Claude Opus,

Starting point is 01:03:08 it could be Code X, image, journey, whichever is the best model, they would use that, right? So I think that is, it's partly in, like, yes, you know, you need to have the best experience for your customer, figure out what your flywheel, et cetera is, and just use the best stack that you can create for them. Another example with X is that, you know, they had, the capacity on the data center side and they ended up, you know, leasing it a very large lease to the tens of billions of dollars to Anthropic, right? So they're both competing with each other and supplying each other. I mean, it's just the nature of these things. I have, like, a couple of big things that I think are pretty interesting as well that to talk about that

Starting point is 01:03:46 are somewhat related to this. Let's maybe talk about like a somewhat easier one, which is we've seen what happened with what's happening with bending spoons. And, you know, there was this Miro acquisition. There was the Airtable acquisition. Some of the SaaS darlings from the 2010s are, you know, now not looking as hot as they once were. Yet, you know, like, I think, you know, some people like to be very alarmedist and say, you know, maybe SaaS is dead. And yes, there's some public market multiple compression on some of these SaaS businesses. But at the same time, like Figma continues to deliver phenomenal numbers.

Starting point is 01:04:25 quarter after quarter, there are a bunch of companies like that that continue to outperform in public markets. That being said, when we look at early stage companies, as you may see and may know, it is really hard to, I think Hayman from G.C.

Starting point is 01:04:46 says this in a bunch of his interviews, which is you look at a traditional SaaS business, which is a top-down sell, and it has these 3-3-2-2-2-d-d-d-d- dynamics where you grow 3x, 3x, 2x, 2x, 2x, year over year, typically because that is just the physics of the growth of the business and how fast you can hire sales reps and sell and quota, etc. And those are just not quick enough compared to how quickly some of, you know,

Starting point is 01:05:13 quote unquote, the AI businesses or any businesses that charge on consumption can grow. So, you know, what tends to happen is, you know, even if you have a very solid modern day enterprise SaaS business, the talent goes away, the salespeople are like, hey, why am I working here? I should be working somewhere that's going to pay me way, way more. How does that affect how you view top-down SaaS businesses? Is it a concern for you at all? Or is it, you know, this is a storm you have to weather and then you come out the back of 28 and everything's fine again?

Starting point is 01:05:49 Or, you know, what is your view? Is this dead? Is SaaS just dead forever? What's going to happen? Is it not venture backable anymore? I don't think that is ever true. All these things go through cycle. Semis was out of favor for a while.

Starting point is 01:06:02 Now it's the new hotness. Like, I mean, defense tech would have been like uninvestable for venture, literally by LPA at all for most venture funds. And now, you know, it's a hot domain. So I don't think in absolutes in any of these things. I mean, obviously the SaaS players have been selling sort of systems of record and workflows and tooling and whatnot. And the AI companies are often going after the labor on top of it. So it expands the TAM pretty significantly because the spend on labor tends to be or magnitude

Starting point is 01:06:36 bigger than the spend on tech budgets underneath them for worker productivity and whatnot. But as it said, it just will change those other dimensions. Right. So you move from, you know, the biggest enterprise companies were always horizontal because the Salesforce could sell to every industry, whereas somebody who was a vertical SaaS player could only sell into that market. But when you flip it in AI and say, all of a sudden, okay, you're going after the labor layer, so it's an order of magnitude bigger. And now you can build data density and specialization and whatnot to get to better outputs quicker in verticals than you can do horizontally.

Starting point is 01:07:12 You know, like vertically I became, you know, an area that became much more investable in AI than it was in the SaaS era. So I think these things all sort of migrate through. It's difficult to predict over the long horizon. But, you know, if SaaS enables the upstream workflows and you can monetize that, I think the historical, as you've said, physics of the growth rate of SaaS change. If you have to stay at the system of record level and can't move into, you know, the intelligence on top of it and the agency and execution, then you're probably more constrained. And it just has to be factored into the price. It doesn't mean you can't invest in it.

Starting point is 01:07:51 It's just the capital efficiency and the price. you enter, you know, has to, has to be, you know, adjusted accordingly. Yeah, I think it's interesting. I mean, I think like, some of these businesses are literally, I think of as like cigar butt businesses where like literally probably they're going to disappear over some amount of time because what you can do with these SaaS tools is not different than what you can do with the general tool. And we've seen that in like 1.0, you know, in the AI era, actually, we saw it with like the writing tools.

Starting point is 01:08:23 And obviously writing was something that was so simple that you don't need a specific tool for it. I think that's probably true of some of these SaaS businesses too. I think, you know, it doesn't happen overnight. I think what happens is seat-based pricing probably shrinks. Software creation gets easier. They're probably more competitors. You can do a lot more with the general models. The interface gets disintermediated.

Starting point is 01:08:46 And then I think one that is probably more true now than ever is like, switching costs have gone away. It's just so easy to switch from one product to another because AI can do the switching for you. Agents can do the migration. So I think that is a challenge. And you look at a bunch of these companies like the ones that bending spoons has acquired and they have to figure out something. And like what they're doing is dramatically taking out costs and increasing prices. They're kind of like a private equity firm in that way. And I think they're doing a good job with it. I don't know that I would want to be customer of those products going forward. But, you know, I think what they're doing makes sense. At the same time,

Starting point is 01:09:28 some of these companies that are trading down so poorly actually, you know, are delivering great results and they continue to deliver great results. But I think part of this is, look, we had a big overhang of COVID era 0% interest rates where like we thought those multiples made sense and held up and part of it is just like when that went away those multiples went away coupled with the AI disintermediation so it's a bunch of things happening at once that has led to this I look guys I know we're at 70 minutes in and so we should probably try to wrap up at some point but the last sort of set of questions I'd love to get from you is what what is top of mind for for all of you now.

Starting point is 01:10:10 Like, what are you, you know, thinking about, worried about, trying to learn more about when it comes to tech and startups today? And, you know, maybe, and while you answer that, like, where are you spending your time to learn about this kind of stuff? Is it, are you call people and talk to them or read stuff online, read reports? I think that's what a lot of people who probably watch this want to know, like, where do we get our information from? So and what sort of questions are you asking?

Starting point is 01:10:40 So maybe I'll go around Robin and, you know, Shil, you just spoke. So, Kanu, why don't we start with, start with you, not to put you on the spot. Like, you know, I mentioned I do early stage investing in normally fairly like frontier tag, deep tech. So one of the things that I try to keep up with everything that's advancing in AI, right? So, and it's tough, right? Like, even my friends at Open Aanthropic tell me, like, it's hard to keep up with everything that's happening in AI. So, you know, what shot do I have? But I try, right?

Starting point is 01:11:06 I do spend a lot of time with universities, with professors, with some of their staff students, to understand what can be demonstrable in the next two, three years versus what might still be high. And having a sense of that as I make investment decisions becomes very important. It helps me assess technical talent for my portfolio, helps me assess, you know, what's really novel, what's defensible. So I do spend a lot of time in universities. I also have been, you know, helping leading some of the robotics investments. at KV. I do line up spending time with their target customers, right, like some factories and manufacturing plans. I want to understand what is happening on those end sites. Like, what is the

Starting point is 01:11:48 challenge in scaling deployment? What can, what needs to exist? What, how it can be solved? Where is AI helping adoption? And so, yeah, the physical world around us and, you know, what needed to get past the known challenges of adoption in robotics. I've been spending a lot of time in that. And one of the things I'm trying to get smarter about is, you know, new rulings by the FCC on, you know, what needs, what is required for it to be compliant for a robotic sale. You know, today it's like 65% of the bomb needs to be sourced or licensed from U.S., U.S. friendly. Next year is going to be 75. And really, what is the intent behind it and what can companies do and what are current companies doing? I'm trying to get smarter about that and talking to a ton of companies, but also people who are coming up with these laws.

Starting point is 01:12:33 What is the intent? what are we trying to achieve and what can be done differently. So, yeah, all of those are areas where I spend time, trying to just have first-hand knowledge on, you know, what should be investable. You know, very similar. I mean, I think you've got to go to the source, right? I think it's very easy to be in the echo chamber.

Starting point is 01:12:51 So that means the technologists at one level that are not just developing the technology, but applying it, right? It means the, you know, the labs and whatnot that are working on the frontier. It means the end customers that are trying to sort of use it in their work case and justify an ROI and, you know, the change management and all that type of stuff. And as kind of said, also the regulators, right, which is a relatively new phenomenon. I think as you go after the labor layer, it looks a lot different than selling IT into a CTO. And it's going to be very political, right? You see it in data center buildouts, but I think you're going to see it in spades and physical AI.

Starting point is 01:13:32 You know, as you start to have humanoid robots, people are going to feel it's hard to choke check GPT, but you can definitely throw a beer bottle at a robot or a Waymo. So I think that physical incarnation will have a very visceral reaction for a lot of people. And so sort of understanding consumer psychology, you know, how enterprise is adopting. And I just try to do a listening tour of everyone all over the world who is trying to apply this stuff to understand and calibrate. you know, particularly as the head of the firm, you know, you know, all of the people who are doing deep dives in individual domains, how it fits into a broader context and dynamic that you're going to do because it's not just technology that's going to impact. It's the capital markets. It's financing because the capital intensity is so big now, right? Understanding that side of Wall Street and the credit side and things that, you know, have not historically been something that really mattered in venture, but when you've got multi-billion dollar data center buildouts for, you know, or. chip buys, like it's a different dynamic and you've got to understand at lots of altitudes and lots of horizontal, you know, so it's, it's a, it's a multi-dimensional problem that's only gotten more complex.

Starting point is 01:14:44 You know, I think your first question was like, what, what's on my mind? And it's really thinking about we're a seed fund. And it's like, how does, what does a seed fund mean in 26 and when you know when seed rounds are getting done at a hundred million plus valuations at times where do we invest are there still diamonds in the rough to be found or you know like are we okay investing in companies where the outcome is you know a billion dollars or less than a billion dollars and so it's like all things i'm thinking about um as we think about the future of our firm And it feels like as you know, up until now, it still feels like we've been able to find diamonds in the rough and come in at a reasonable price, own enough of the company that has done really well.

Starting point is 01:15:41 And I wonder if that's even changed in the last couple of years. In terms of like, you also ask like what, what do we, where do we read? How do we do research? I think we at times have had a thesis on a general area. a few years ago, we had thesis around accounting and did some homework, spoke to a bunch of execs in the accounting space, decided we want to invest there. We invested in three companies, and they've all done quite well, would do more in that category. And so, you know, I think we try to just have a prepared mind going into anything and the focus area for us in FinTech helps us do that. Makes sense. I'll just close up by saying my answer.

Starting point is 01:16:26 to the above and there's elements of what, you know, all of you said, and particularly I like trying to go to the source as much as possible for a lot of the what I invest in. That source happens to be playing with products and software and tools and actually seeing how these things function where the gaps, where are the holes. It helps me do a lot of diligence because, man, we see so many Claude decks and it's so easy to say, spew a jargon that a lot of investors sometimes don't understand. And so really understanding how the technology works, for me, has been very critical to be able to cut through some of that jargon and be like,

Starting point is 01:17:03 okay, what are you actually building here? What is actually hard to do here? And so I do spend a lot of time there. And then, of course, yes, universities and professors as well, probably less with regulators than some of you. But yeah, but look, I mean, thank you guys for coming. and I think we covered a lot of ground. So I'm really happy with how this podcast went.

Starting point is 01:17:31 I don't think I can take J-Cal's job quite yet. But hopefully he... You did a great job, man. You were right there. I think J-Cal will be proud of him. I try to channel my inner J-Cal. And hopefully he watches this and says it's up to his bar. I'm sure.

Starting point is 01:17:50 Thank you guys for time. I'm sure he will. Yeah, so thank you to our whole panel. Canugaladi of Coasel of Ventures. Chris Farmer, he had to step out, but he's the founder and CEO of Signal Fire. Of course, Shio Monat from Better Tomorrow Ventures. And Didi Das are incredible fill-in guest host from Menlo Ventures. Amazing job.

Starting point is 01:18:08 Couldn't have done it better myself. Legitimately, I could not have. Just as a quick before we dip out, we have lots of open launch roles here in Austin in Tokyo, in Saudi Arabia, and now in Calgary, Alberta. If you want to find out more about those, go to careers. launch.co. Finally, we have a new substack as part of our substack family of

Starting point is 01:18:27 newsletters. This week in VC, it's going to feature VC roundtables just like the one that you just watched and other informative ones like this. Every week plus fresh deals posted more stuff. That's it this week at this week in VC. dot substack.com. Again, thanks to our panel.

Starting point is 01:18:43 Thanks to our guest host, Didi Das. We will see you next time on this week at startups.

This Week in Startups - What VCs Really Think About Personal AI Agents | E2347

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