What VCs Really Think About Personal AI Agents | E2347

This Week In Startups is made possible by: Plaud: http://Plaud.ai/twist Odoo: http://Odoo.com/twist Northwest Registered Agent: http://www.northwestregisteredagent.com/twistdomain Quo: https://www.quo.com/perks/twist Today’s show: A personal AI agent that’s still invite-only just hit a $10 billion valuation. Guest host Deedy Das of Menlo Ventures is joined by Kanu Gulati of Khosla Ventures, Chris Farmer of SignalFire, and Sheel Mohnot of Better Tomorrow Ventures to debate whether Instinct can beat Meta’s Muse, what they actually use their own agents for, and what breaks when everyone’s agent makes the same move. Plus we’re also talking about OpenAI’s math breakthroughs, the Factory vs. Cognition fight, and whether SaaS is still worth funding. Catch the whole debate on this week’s VC roundtable. Guests: Deedy Das on X: https://x.com/deedydas Menlo Ventures: https://menlovc.com Kanu Gulati on X: https://x.com/KanuGulati Khosla Ventures: https://www.khoslaventures.com Chris Farmer on X: https://x.com/chriswfarmer SignalFire: https://www.signalfire.com Sheel Mohnot on X: https://x.com/pitdesi Better Tomorrow Ventures: https://www.btv.vc Relevant Links: OpenAI’s 722 math manuscripts from an unreleased internal model → https://github.com/openai/math OpenAI’s earlier Navier–Stokes Millennium Prize result → https://openai.com/index/navier-stokes-solution/ Pramaana Labs, the Khosla-backed startup turning tax rules into provable logic → https://pramaanalabs.ai Logos Research, the Imperial College spinout verifying AI-written financial code → https://www.imperial.ac.uk/news/articles/2026/logos-research-plans-to-make-ai-reasoning-trustworthy-in-high-stakes-environments/ EvenUp, SignalFire’s AI company for personal injury law → https://www.evenuplaw.com/ WindBorne Systems, the Khosla-backed weather balloon company Kanu mentioned → https://windbornesystems.com Gokul Rajaram’s X Post: https://x.com/gokulr/status/2107851356019290569?s=20 micro1, the AI training data company Chris and LAUNCH backs → https://www.micro1.ai Vercel’s data on companies shifting to open-weight models → https://vercel.com/i/open-weight-models Instinct raises $1B at a $10B valuation (SiliconANGLE) → https://siliconangle.com/2026/09/28/everyday-personal-ai-assistant-startup-instinct-raises-1b-at-10b-valuation/ Meta introduces Muse, its personal AI agent → https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/ Wajo, maker of the Fo agent Kanu uses → https://wajo.ai Factory’s CEO accuses his board adviser of spying for Cognition (TechCrunch) → https://techcrunch.com/2026/09/30/factory-ceo-just-accused-his-vc-board-advisor-of-spying-for-cognition/ xAI rents its Colossus 1 data center to Anthropic (Data Center Dynamics) → https://www.datacenterdynamics.com/en/news/musk-spacex-xai-is-actively-seeking-more-ai-compute-customers-following-anthropic-deal/ Bending Spoons agrees to buy Miro, weeks after Airtable → https://investors.bendingspoons.com/newsroom/bending-spoons-agrees-to-acquire-miro The FCC’s new rule on foreign-made robots → https://www.fcc.gov/covered-list-faqs-robots-inverters Timestamps: 0:00 VC roundtable with guest host Deedy Das 1:23 If your work depends on conversations - interviews, meetings, calls - you need a Plaud NotePin S. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off! 2:11 OpenAI's math results and what's left to invest in 5:10 Formalizing taxes and code so AI can prove its work 6:37 Why science breakthroughs still need the real world 9:44 Odoo: The all-in-one business platform. Your first app is free! Get started today at https://Odoo.com/twist 10:54 Can startups compete with the labs on compute? 13:02 Private data and vertical AI as the moat 18:54 Would you invest in AI training data companies? 20:34 Got a new business idea? Northwest Registered Agent helps you bring it to life. Get a free domain, email, phone number, and more - with no purchase required! - Learn more at www.northwestregisteredagent.com/twistdomain 26:29 What a great founder looks like in the AI era 29:13 Quo (formerly OpenPhone) gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at https://quo.com/TWiST 34:35 Is Instinct worth $10 billion? 41:31 Can Muse reach a billion daily users? 44:49 Favorite personal agent use cases 49:06 What happens when everyone's agent makes the same call 53:17 Factory vs. Cognition and VCs backing rivals 1:03:49 Is SaaS still worth venture money? 1:09:49 Where the panel gets its information 1:14:45 What a seed fund means in 2026 Subscribe to our newsletters on Substack: TWiST: https://twistartups.substack.com/ TWiAI: https://www.thisweekinai.ai/ TWiVC: https://thisweekinvc.substack.com/ Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank you to our partners: Check out all our partner offers: https://partners.launch.co/

• 0:00Speaker 00

  Hey everybody, welcome back to This Week in Startups. 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 Deedy Das, our special moderator of the week. Deedy, thanks so much for being here.

• 0:24Speaker 01

  Thank you so much for having me, Lon. These are quite big shoes to fill, as you know. I talk, I am not the world's greatest moderator. Jason, J Cal is. But I will try my best to entertain all the lovely folk on stream here today.

• 0:37Speaker 00

  We certainly appreciate your expertise stepping in as a guest host. That's gonna be amazing. And we've got an all-star panel for Deedy's big week here. First up, Kanu Gulati. She's a partner at Coastal Ventures. She's led investments in PolyAI, Wabi, Sarvam, so many others. Kanu, thanks so much for being here.

• 0:55Speaker 02

  Very excited to be here.

• 0:56Speaker 00

  Awesome. And we've got Chris Farmer. He is the founder and CEO of Signalfire, Grammarly, and Alchemy, among the notable investments I see here in my notes. So, Chris, awesome to have you.

• 1:06Speaker 03

  Thanks for having me.

• 1:07Speaker 00

  And finally, we've got Shil Monat. He's the co-founder and general partner of Better Tomorrow Ventures, seed-stage firm. They're investing in fintech and vertical AI startups. And a wonderful returning panelist to the show, one of our favorites. Shil, thanks for coming back to the show.

• 1:21Speaker 04

  Glad to be here.

• 1:22Speaker 00

  Before we go into our big stories, I do want to take just a moment to thank our friends at Plod. You may notice I am wearing my Plod note pin right now. It helps me 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, 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 plaud.ai/twist. And if you use the code TWIST, they're gonna give you 10% off of your Plod note pin.

• 2:06Speaker 00

  There was a look at one of those amazing transcripts. So thank you to our friends at Plod. And now, finally, we can begin the VC roundtable. DeeDee, where do you want to go first?

• 2:15Speaker 01

  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 OpenAI. 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 Riemann hypothesis result? And everyone's like, yes, I did. Did you? It's like, yes, I did. Did you understand any of it? No, I did not. Did you? No, not really. And, but it's cool and it's a big deal. And this is what I wanted to talk to, you know, the Shil, Chris, and Kanu about is, we're looking at these math results that, let me just start with some headline stuff.

• 3:01Speaker 01

  Seven millennium prizes, prize problems exist. One of them was solved, you know, 20-plus years ago. And if you look at this result combined with, say, the Navier-Stokes result that OpenAI 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, biggest unsolved problems in mathematics with AI. And there's many ways we could sort of take this discussion, but, you know, maybe I'll just start with Kanu, who's a, you know, at Coastal, an OpenAI investor.

• 3:39Speaker 01

  Like, when you see results like this, what, 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 when we're looking at, hey, the smartest minds of the world can't do what AI can do today?

• 4:00Speaker 02

  We at Coastal Ventures are very proud early investors in OpenAI, and I'm very excited about all the results. But as a former scientist, you know, it does make me uneasy because it is moving at such a fast pace. There's an amount of, this is 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, when 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

• 4:41Speaker 02

  and being able to solve some of these unseen, unopened math problems is just compounding. So 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.

• 5:09Speaker 02

  The, you know, for example, even within, related to the math area, right? So math happens to be a domain that is very formal. 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 informal, 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 the, we 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?

• 5:55Speaker 02

  So, you know, Pramaana 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, with 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? And it is harder and getting harder and harder for all of us.

• 6:35Speaker 01

  Shil, 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?

• 6:48Speaker 03

  We spent a lot of time as in healthcare, right? I think there's gonna be just unbelievable breakthroughs in that ecosystem 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 Kanu'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 are being 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

• 7:32Speaker 03

  because you only put something in clinical trials if it's been successful. Whereas in an AI world, you want to capture all the failures as well in order to train and learn from that, those data sets. So 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 get better at understanding biology or chemistry or all these things as these different types of models converge and can collaborate, the breakthroughs that I think we're gonna be able to have is gonna 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,

• 8:14Speaker 03

  there's a lot of debate on what's real and what is climate change and all this type of stuff. 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 gonna 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.

• 8:42Speaker 00

  Yeah, I completely agree. I feel like it's actually, the math stuff is obviously 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 Kanu and Chris said, I think there's gonna be a lot of implications in other sciences. I think, you know, cancer therapies, stuff like that, but I think...

• 9:18Speaker 04

  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.

• 9:43Speaker 05

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• 10:53Speaker 01

  You know, maybe one thing that I struggle with, and I agree with all of you guys, right? Like, so this is, this is true. And there's all these other domains that are non verifiable or domains where, you know, you kind of have to go the additional last mile that maybe you don't think a big lab will do. But the thing that I struggle maybe, to be very honest with you, the most with is you look at these big labs and well, compute is clearly very constrained in the world. And it is, you know, it's a, it's a rich person's game. I mean, there are certain companies who are able to commit billions and billions and dollars of dollars to compute and their compute efficiency defined by, you know,

• 11:34Speaker 01

  the revenue they can generate for every incremental dollar of compute that they purchase is, is probably the best in, 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 OpenAI or an Anthropic who has, you know, hundreds and thousands of thousands of GPUs with, you know, a paltry maybe 5,000, 10,000. How does one, how do you think about that? Like, is, does that really sit with you? Or you still believe, no, you don't actually have to have the most compute to be able to, to do this?

• 12:14Speaker 01

  How bitter lesson pilled do you, do you think you are when it comes to, to that sort of thing?

• 12:20Speaker 03

  I, I, I'm not at all a believer that the models are just going to own everything. You know, I think that's intelligence, right? If you'd isolated that, that's great. 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 deploys 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, 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.

• 13:06Speaker 03

  We do a lot in vertical AI. We, we used to be probably 80% horizontal SAS and, you know, with, with a heavy ML element. And then we flipped to 80% vertical AI 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, you're going to outperform dramatically and that can take many shapes. Like, for example, we have a company called EvenUp in the personal injury law 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 insurers. And 99% of that data is in the private domain. You know, the, the, the, the big models, like, what are they going to do against that

• 13:52Speaker 03

  with somebody who has so much dominance in a vertical? Or for example, a company like all, all dot health, 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, or, or whatever, that, that you, 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, the context, permission systems, guardrails, security, all of the things that

• 14:37Speaker 03

  need to be wrapped around it to, to actually get that horsepower to the ground.

• 14:40Speaker 02

  There are a few different things I'd point out. Like, I am obviously a believer in large language models. I believe 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. 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, you know, 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.

• 15:24Speaker 02

  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, maybe, you know, maybe the, 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'll be a ton of different algorithmic approaches that will improve. And it might come out of labs outside of, you know, the, the labs you mentioned. I also think, you know, already because of the current lack of HBM or current lack of resources, there is innovation happening from open labs. And it's partly driven because of the constraints in the current ecosystem, but there is innovation.

• 16:08Speaker 02

  So there's a ton of innovation that's going to come out.

• 16:12Speaker 02

  I also don't believe that LLMs and transformers will be the only way for us to get to, you know, whatever we consider AGI or, you know, SI, et cetera, whatever the, the current term for it is. It's, it's going to be different types of intelligence, different types of approaches for artificial intelligence. And so 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, in, in those areas because you're collecting the data. You know, for, for example, we have a, a, a weather modeling company called Windborne that collects intuitive data from different points in the atmosphere.

• 16:56Speaker 02

  And they will just be ahead of anybody else collecting those data points. So there is a, a physical world data flywheel opportunities as well. And we've been investing in those and I can go on and on about that. But I think, yeah, I, 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 shot, the constraints in compute and other components is, may not be, it's, it's not like five, seven years from today and probably for the next two to three years, yes.

• 17:26Speaker 04

  Yeah, I think at some point compute stops being the bottleneck in, in all of these things. Like, like we talked about, like, you can create a bunch of promising molecules and, you know, right now, something that costs $50 million 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 lab capacity, the ability to do clinical trials, manufacturing patents, all that kind of stuff might be the constraint. So I, I, 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.

• 18:03Speaker 05

  Can we just double click on that real quick? What do you think Shiel is going to change that's going to make? Are, 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 sets? Or what's going to happen that's going to make compute become so much more widely available?

• 18:19Speaker 04

  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. You, it, it need not be 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.

• 18:36Speaker 01

  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 AI, there's data, there's compute, there's maybe algorithmic improvements, there's a couple of other things here and there. There was this tweet that I saw this morning that I thought was, was, was interesting. It's a tweet, maybe we can pull this up. Gokul says, many of us may know Gokul already, says, you know, the, 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 company. And, you know, Shiel, Chris, Kano, you, you guys are all seed investors. You guys see seed investments all the time.

• 19:20Speaker 01

  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. Hundred million dollars in revenue or, you know, an enormous amount of revenue in a very quick amount of time, most of which is typically non-recurring. But how do you, 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?

• 20:03Speaker 01

  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, how do you guys assess this? Like, would you guys invest in this kind of stuff? And if so, what type of data companies would you invest in?

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• 21:23Speaker 03

  We're investors in companies like MicroOne, you know, that are doing a lot of training reinforcement, learning that type of thing.

• 21:34Speaker 03

  There's just, there's just so much capacity to do that. I think you have to get to the frontier. 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, 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, it's going to move from the labs to a broader set of end customers.

• 22:12Speaker 03

  You know, and so we have, we have a number of companies doing this in different verticals and whatnot. And yeah, the growth is explosive and it's 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 Vercel data that was one of the sort of stunning shifts from proprietary models to open weight models. It went from like 80/20 to 20/80 in a matter of months. Right? And as these corporations start to get more adept at this, build the skillsets in-house to personalize, you know, for their own use cases and build their own defensibility over time versus outsource it all to, to, to proprietary models.

• 22:57Speaker 03

  You know, 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 huge go-to-market challenges and professional services type challenges. But I, 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, as rapidly as it went up. And so it's going to be a matter of, of, you know, how adept these entrepreneurs in, in building lock-in and, and landing and expanding, just like the SaaS days, right? With these customers where they find the pain points and then they keep serving them and until they become, you know, more recurring and deeply embedded.

• 23:41Speaker 02

  I disagree with the, with the sentiment about data will always be needed because as you'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 the, how, how well the open weight models 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, it's the terminal value seems not exist. But I think Chris's point on, at the end of the day, it is the, you know, the founder that they're backing.

• 24:24Speaker 02

  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. 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 or apps on top of that data commodity. So I think the right entrepreneur can still solve it. We have, as a fund, Cozaventures, 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, but there are founders that it's been very hard to be like, oh, are we not going to invest in that founder based on everything we know about them? That's, that's the hard discussion. So.

• 25:08Speaker 04

  I think they're super interesting companies, but I, I think like, even thinking about headline revenue is kind of like the wrong way to think about these businesses. Right? You, you have to think about it from a, 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? And I don't have a good answer for that. I would say like, the models obviously continue, continue to need better post-training data and, and evals. But so far, it feels like as models have improved, actually, the, the amount of expertise actually has, has risen. I don't know if that asymptotes at some point.

• 25:54Speaker 04

  I, I think there's a question of like, what is proprietary in any of these businesses? And in many cases, there isn't much, 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.

• 26:13Speaker 01

  Interesting. Yeah, I like, there's a couple of threads that all of you touched on, which I thought was interesting, which, you know, we talk about this a lot as, as investors, which is ultimately a large part of every investment we make does boil down to, you know, how talented this, this founder is.

• 26:29Speaker 01

  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, you, you, 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, 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 was just being able to hire software engineers at scale

• 27:14Speaker 01

  that actually could build, build out the software. Maybe you disagree with parts of that. So if you do, feel free to, 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, to young founders, 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 who do a bunch of different things. They say yes to this, no to this. It, it sort of seems like a wholly different skillset, at least when it comes to software. You know, leaving aside maybe some healthcare specific or, or fintech specific stuff,

• 27:59Speaker 01

  where of course I know you need some, some niche skillset. 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 skillsets actually changing a little bit? Today than it was 10 years ago.

• 28:15Speaker 04

  Yeah, it's a good question. Um, I, 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, and that's what it takes. But, um, there is this new class of founder that, that didn't exist before. And I, I think that they, I would say like, in our experience at least, skew younger. And you have very folks in their early twenties, building amazing businesses, scaling very quickly. In, in categories that you would have thought, um, required expertise. In accounting, legal, stuff like that, people without backgrounds in those places are, are doing quite well.

• 29:00Speaker 04

  But I think at the end of the day, what have they done well? They, they like, set a vision for their company, they recruited people against that vision and they, they sold that vision to their customers. And so I think those things are, are evergreen.

• 29:12Speaker 05

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• 30:14Speaker 03

  I, we, we take a very bimodal, uh, view of, 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, et cetera. And they have the credibility to get the partners they need to design and build systems to attack problems in, 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, um,

• 31:00Speaker 03

  you know, are just insatiably curious, move super quickly and iterate. And it, it's somewhat Darwinian and 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, um, you know, advancements because you're not burdened by sort of the, the, the prior logic and, and, um, maybe constraints that the industry had faced historically. So I, I, I think it's a really interesting time for sort of, sort of young founders.

• 31:42Speaker 03

  Because you can also do so much with so little money, much like what cloud computing did. 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, is pretty incredible unlock.

• 31:57Speaker 02

  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 bets for the company in the coming years. They're making an informed, uh, 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. So being AI pill 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 are like, you know, clearly like in their thirties and forties and fifties. But because they are able to take the ambitious bets and they think about, you know, you know, like,

• 32:40Speaker 02

  we are investing in a chip design company that is going to release a chip with like 20 folks. 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 RTL and, and also all the way to physical design. I, I think it's just a new world now. And so, um, I think we all have to reassess what the, but the, but the high quality founder looks like, but we're all learning. There is no, uh, pre-made template like, okay, Tao shall look like this or Tao shall be at this age. I, I think we all have to just fundamentally question our known beliefs here.

• 33:14Speaker 01

  Yeah. I mean, I, my, 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. So, you know, uh, I mean, one way to say it is, it seems like the median 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, this 19 year old, he wasn't a founder or anything. And he, 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.

• 33:57Speaker 01

  And, um, and to me it was just remarkable. 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, at 19? Just boggles my mind. I wouldn't have even thought to, to, to, to think about this. So it is, it is pretty exceptional how much it can amplify, um, talent at that, at that level. And you, I do want to touch on another topic which comes up all the time. I was recently out of my tech bubble and, uh, I was at a friend's wedding.

• 34:39Speaker 01

  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. Um, and, and so, uh, let's, let's be just start with that, like, eh, 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, is your instinct to, to defend it and say, hey, you know, actually there's a world

• 35:24Speaker 01

  where this might actually be an amazing company for these, these, these reasons? Or are you sort of like, you know, I, I don't, I don't really know. Um, how do you guys think about it?

• 35:33Speaker 03

  I'm a bear.

• 35:36Speaker 03

  I, 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, you know, it, my, 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. I, I was actually looking this up with a, a founder friend of mine and it's something like, you know,

• 36:19Speaker 03

  three and a half billion people on the platform, you know, and, and, and Google with YouTube and 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? 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, I think either a more specialized model versus a general sort of personal assistant, I, I just think it's, it's very non-trivial to overcome getting people to give you access to their most intimate, uh, information

• 37:04Speaker 03

  that is dangerous from a cyber threat type of standpoint. And, and I think people are going to really want high levels of trust and security. And, 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. Um, but, but we'll see. So I think paying that kind of valuation, even if you have a super slick product, that versus distribution and the ad networks and everything that the, the huge platforms have, I think is, it's a, it's a big uphill battle from my standpoint.

• 37:39Speaker 01

  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?

• 37:50Speaker 03

  I mean, it could be. I mean, look, I, I think, I think OpenAI ran into the situation, right? They, they, they're, if you look at what they're building relative to like, I mean, it's Google's 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 at their feet for a while. And now they're, now they're, now they're, they seem to be a little off. But, um,

• 38:12Speaker 03

  You know, OpenAI, you know, needed to build monetization, right? To bring the cost down for a lot of folks. And that's going to be, you know, some sort of where the where the consumer is the product, right? And the 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 out 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 and whatnot. And so, you know, I I think it's not just distribution. It's distribution and the 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 everything to

• 38:53Speaker 03

  commercial transactions, to advertising and the network of 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 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.

• 39:29Speaker 04

  I think it was a bit of like tail wagging the dog. 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 it if personal agents are a major consumer interface that we use for travel purchases, restaurants, those are the things people are using for right now, insurance, a bunch of others, you can certainly make money. I don't 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 Meta, maybe Apple.

• 40:14Speaker 04

  And of course, we've seen since the time that Instinct came out a couple of 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's stock price. But Instinct, you know, I I personally use Instinct all the time. And I've tried others. Hark came out yesterday. Sesame has a product.

• 40:58Speaker 04

  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. I personally think that the large companies have a 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 what how the likelihood of that happening. I think it's tough, though.

• 41:38Speaker 01

  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, you know, daily users who continue to use personal agents? Or I mean, and I asked this question in the vein of like, or, you know, is this sort of a first world type problem of like, people have to book flights and restaurants in the Bay Area, but most of the world really doesn't care that much about this? How do you where do you stand on that?

• 42:03Speaker 04

  Yeah, I think a billion users is a big number. And I don't think that there's a billion DAUs 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. And actually, interestingly enough, like, I 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.

• 42:44Speaker 04

  But I I think there are certainly hundreds of millions of DAUs that this is a product people can use. People talk about it as if like, only VCs have this problem. And it's true, people don't travel as much as we do, but people do make purchases. I, over the weekend, was at a bachelor party and I actually use it instead of Splitwise. It actually automatically knew what purchases I'd made. And it was just like, and it had a spreadsheet of everybody who was there and what times they were coming in. So it actually automatically split everything amongst everyone instantly, a pretty useful thing. Again, not something that happens every day, but happens often enough for everyone.

• 43:26Speaker 02

  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 Vajo, which is a KV portfolio company, their agent for. 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, Vajo's take is they can go even recruit the human they would need to go carry out a task. 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 if their company gets the trust, gets into the workflow of our day to day lives, and like Sheila said, you know, we have the fatigue, like, we don't

• 44:07Speaker 02

  want to continue to train different agents and get them connected on how we think about and what are preferences and how we would like to do work. 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 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 flack because they promised too 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. So the 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.

• 44:51Speaker 01

  I have to go around the table and ask everybody what their favorite use case for themselves. I know Sheila, you already had a great one for a personal assistant was because everyone keeps asking me this 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 from this wedding from 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, thanks, thank you, I'm not going to say which, I use Instinct, okay?

• 45:34Speaker 01

  So I thank you, Instinct, for telling me that I'm 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 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, your case, 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 that the money that I clearly rightfully

• 46:16Speaker 01

  deserve for my flight being late, this is a good use case of of an agent. So with that, you know, Chris, Ghanouk, Sheila, anything, anything exciting that you have used Instinct or Muse for?

• 46:30Speaker 04

  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 I do a lot in fintech and I think a lot about finances and, you know, credit cards, they charge you more interest. 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 because when friction is reduced?

• 47:06Speaker 03

  Yeah, I haven't used Instinct for the reasons I mentioned, mostly because I didn't want to give it access to, you know, 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 pro, 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, with, with all the research I do around a project. On top of it, I'm, 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.

• 47:49Speaker 03

  Health on the healthcare 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 painful, like sort of time on, on things that I don't enjoy doing. Um, that I would, you know, have a lot of value um for those types of use cases. I'm most bullish and excited about, sort of personal finance use cases and, and just all the administrative that we, that we deal with in our lives that, that hopefully, we can agendify and not have to deal with insurers that deny things for, for stupid reasons when they really shouldn't.

• 48:28Speaker 02

  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. Uh, you know, their agent shared their personal bank account information on a, 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. But 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, what information I gave them, what was the next follow up step, what's, I need to fight up a court, like all of those bloody annoying tasks can be now taken care of by my foe. I'd, I'd pay for that, right? So, um, yeah, I've been using it for very lightweight, but just stuff or areas which are not exciting for me.

• 49:11Speaker 01

  Fascinating, fascinating. I guess one thing that I, I was um, thinking about hypothetically, kind of to shield your point is there was this really viral um Twitter post that I saw maybe about a week ago. Where um it was, it was some senior banker, I 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 uh, because everyone's agents tell them, hey, you ought to take all your money out of the bank and um, put it in X, Y, Z.

• 49:50Speaker 01

  Assets. Do you, you know, I, I guess, how, how do people feel? I mean, not specifically about that. But this general high level idea, which is the more that, the, the more diffusion that happens with these AI tools within humanity. More people are going to outsource their decision making perhaps to, you know, 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 homogeneous behavior to happen at once. Um, whether it's something as simple as everyone,

• 50:35Speaker 01

  now wants to have reservations to the top hotel, a top restaurant in San Francisco, and so they can't accommodate anything. Um. Or whether 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, and what happens? And how do we, how do we control for something like that?

• 50:54Speaker 03

  I think there's a lot of systems in the world that are built on breakage. Right? The, the, the very sophisticated that can hire, that are at scale, that can hire people to help them navigate, whether it's, you know, you 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 in, but, but there's always been like rebates, all, you know, all sorts of 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, you're due. I, I think agents will hopefully reduce some of that friction.

• 51:34Speaker 03

  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, it will, it will force, some leveling of the playing field, I think in certain areas. And those certainly be companies that try to, to fight back against that. But at some point, when you have a relentless agent on your, you know, on your side, I, I think, you know, businesses will have to, to, to clean up some of those elements.

• 52:09Speaker 02

  We are not going back to a world where we don't know this capability of AI exist, or, you know, the, 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, et cetera, or like doing, you know, the number of orders they can do, or, trust issues, or, you know, mentioned bankers, like the, the businesses will have to solve it at their end. And it's more opportunities for startups to go solve that. Um, and, and think differently, like what could be the right way? What could be the long term um, correct way of having this? System work. Uh, 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.

• 52:53Speaker 02

  Agents can go to 800 of those links and try to surface their best information. Uh, this is, you know, high level of products company, uh, Paddle 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. Uh, and I think this is going to be opportunities for businesses to, you know, get more AI paired and, and serve their customers with matching capability.

• 53:16Speaker 01

  There's one, there's, okay, there's one thing that I can't get away with this uh, this podcast uh, not talking about, which is uh, 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. Um.

• 53:50Speaker 01

  And um, and then we, we, we, we happened to, we happened to have, you know, Gano, obviously, you're at Cosla, who was at the center of uh, of, of, of this. spat. I'm not really sure what the spat really even was besides, you know, of course, there was a, there, there was somebody, like, I think on the board of Factory, who, who went to cognition and that's how it started. And then, which, you know, I, I mean,

• 54:18Speaker 01

  I, I would say this sort of stuff happens and usually it's a small thing, but um, it really, really blew up. Uh, Matan posted this tweet. Matan is the CEO and founder of Factory. Uh, Scott, the founder of cognition, uh, responded to it. And um, and then at some point, uh, 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? What, what, what happened?

• 54:56Speaker 02

  Honestly, I know what you guys know, right? Because it's um, I'm, I'm not in boardrooms of any of those companies. Uh, I, I, I do like that the MDs in the firm are going to stand for their founders. Uh, but that's all I get to say about it. Like, that's all I know, honestly. I, I was not in boardrooms or the conversations with Chris or any of the founders. I, I'm a huge fan of both Matan and Scott. So, um, yeah, I, I know what you guys know.

• 55:22Speaker 04

  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, and, most of the, the multi-stage funds have multiple investments in, in competing companies. And I think um, what I've seen is people tend to take favorites and it's the company that's doing, that's doing well. I think in this case, both are doing fairly well. Um, and you have just different partners defending their company. Um, I, I wonder if at some point founders say, hey, this is bullshit. Like, we're not going to talk to you.

• 56:07Speaker 04

  That was the case like 10 years ago, and then it became not the case more recently. Um, but I, I wonder if that changes.

• 56:13Speaker 03

  Look, I, I think a couple of things. First off, it's one of the greatest strengths of Silicon Valley, um, 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 in 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

• 56:56Speaker 03

  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, you know, just it's very inherent that like, you know, that when we have this co opetition is, you know, look at Microsoft and OpenAI or all these types of things with their partners one minute and then their enemies and then their frenemies and now they're, 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 weights side, just like, you know, you know, many of the cloud guys are now competing on the chip side. And so you've got just sort of this fierce competition and it's going to spill

• 57:36Speaker 03

  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 transference 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, no, PR, I guess, is, you know, no, 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 there in the mix.

• 58:14Speaker 00

  Dee, we should mention, Menlo put some money into Factory this week since, since the story broke. So was this, 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?

• 58:30Speaker 01

  No, you know, look, as you know, these decisions are typically like made before they're announced. So they were, they were two separate decisions. And, 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, Matan 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 I'm like, look, man, I'm, firms invest, like, I think both of them are great companies. I don't know why you're making it seem like I have to pick

• 59:11Speaker 01

  a side. 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. It is interesting to be a part of, you know, a vocal public spat in some way, though. So I wasn't involved personally in the investment. So I don't know.

• 59:33Speaker 00

  Kind of waited in there just at the last second. So I had to ask.

• 59:39Speaker 02

  I think the question about, you know, our funds investing in competing companies and is, you know, how does that work? I don't think we, 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 companies 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, 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.

• 1:00:24Speaker 02

  And if you find interesting wedges, and yes, you know, if companies do land up going after similar talent or similar customers, then, you know, what we do at Coosolent is we have a very crisp, very clear Chinese wall between those two partners that are working with those companies and none of the information 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 models and different use cases. And if it happens that they're weighing up at the same cases, they don't share information.

• 1:00:59Speaker 01

  Sheel, Chris, do you, do, how do you feel about that? Would you invest in competing companies? Or has it happened that you have invested in companies that happen to pivot to being competitive? And how do you deal with that?

• 1:01:11Speaker 00

  Yeah, at the outset, we don't. We have had it happen where companies sort of pivoted into a closer competitive nature than we had originally planned. And, you know, there's not much you can do about it.

• 1:01:22Speaker 03

  Yeah, I mean, you know, especially, I mean, you want to go and 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?

• 1:01:34Speaker 01

  Yeah.

• 1:01:34Speaker 03

  You know, if I'm OpenAI, like, everything from like Apple to Instinct are all competitors, right? You know? And so, you know, I mean, and I think you're gonna, you're gonna follow a lot of the same trends. Like, you, you, 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 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, 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

• 1:02:19Speaker 03

  diverge again. And, you know, obviously, sometimes they're direct competitors, but even, even ones that are direct competitors in one element often, you know, don't overlap in others. So I mean, it's, you just have to do your best to try to create Chinese walls when this happens. And, you know, be very transparent with founders as much as you can to try to manage those dynamics. But it is, it's an inevitability, regardless of where you start, you know, everyone's following the same trends. So they often end up overlapping at some point.

• 1:02:50Speaker 02

  And didn't like Grokbot announce today that they will use underlying model, whichever is the best model for their use case? I think that's, you know, just how quickly, you know, people's, what's important, like, what is important for Grokbot to win is getting access to the best model. And, you know, Elon announced, like, yeah, this could be Claude, Opus, it could be Codex, Mid 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.

• 1:03:23Speaker 03

  Another, another example with X is that, you know, they, 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.

• 1:03:39Speaker 01

  I have like a couple of big things that I think are pretty interesting as well that to talk about that are somewhat related to this. Let's, let's maybe talk about like 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, you know, some of the, 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 alarmist and say, you know, maybe, maybe SaaS is dead. And yes, there's some public market multiple compression on some of these SaaS businesses. But at the

• 1:04:22Speaker 01

  same time, like, Sigma continues to deliver phenomenal numbers, quarter after quarter. There are a bunch of companies like that that continue to outperform in public markets.

• 1:04:35Speaker 01

  That being said, when, when we look at early stage companies, as you may see and may know, it is really hard to, I think, Heyman from, from GC says this a bunch 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 three, three, two, two, two dynamics where you grow three X, three X, two X, two X, two X year over year, typically because that is just the physics of the growth of the business and how, how fast you can hire sales reps and sell and quota, et cetera. And those are just not quick enough compared to how quickly some of, you know, quote unquote, the AI businesses or any businesses that charge on consumption

• 1:05:18Speaker 01

  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 2028 and everything's fine again? Or, you know, what is your, what is your view? Is this dead? Is SaaS just dead forever? What's going to happen? Is it not venture backable anymore?

• 1:05:56Speaker 03

  I don't think that is ever true. All these things go through cycles. Semis was out of favor for a while. Now it's the new hotness. Like, I mean, defense tech would have been like uninvestable for venture, literally by, by LPA for most venture funds. And now, you know, it's, 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, you know, 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, on labor tends to be or magnitude bigger than the spend on, on tech budgets underneath them for worker productivity and whatnot.

• 1:06:42Speaker 03

  You know, but as I said, it also changes other dimensions, right? So you move from, you know, the biggest enterprise companies were always horizontal because the sales force 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 go after the labor layer. So it's an order of magnitude bigger, some of these. And now you can build data density and specialization and whatnot to get to better outputs quicker in verticals than you can do horizontally. 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 and it's, it's difficult to predict over the long horizon.

• 1:07:26Speaker 03

  Um, but you know, if, if SaaS enables the upstream workflows and you can monetize that, I think the historical, as you 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 excellent agency and execution, then, 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. It's just the capital efficiency and the price that you enter, you know, has to, has to be, you know, adjusted accordingly.

• 1:07:57Speaker 04

  Yeah, I think it's interesting. I mean, I, 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 a general tool. And we've seen that in like 1.0, you know, in, in the AI era, actually, we saw it with like the writing tools and obviously writing was something that was so simple that you don't need a specific tool for it. Um, 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. Um, software creation gets easier. There are probably more competitors.

• 1:08:41Speaker 04

  You can do a lot more with the general models. Uh, the interface gets disintermediated. 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, 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. Um, but you know, I, I think what they're doing makes sense.

• 1:09:27Speaker 04

  At the same time, some of these companies that are trading down so poorly actually, you know, are, 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, zero percent 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.

• 1:09:58Speaker 01

  I, 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, um, what, what is top of mind for, for all of you now? Like what, what are you, you know, thinking about, worried about, trying to learn more about, um, when it comes to tech and startups today? And you know, maybe, and while you answer that, like where, where are you spending your time to learn about this kind of stuff? Is it, are you a call people and talk to them or read stuff online, read reports? Um, I think that's what a lot of people, uh, who probably watch this want to know, like where, where do we get our information from? So, and, and what sort of questions are you asking? So maybe I'll go around, Robin, and you just show, you just spoke.

• 1:10:43Speaker 01

  So Kanu, why don't, why don't we start with, uh, start with you not to put you on the spot?

• 1:10:47Speaker 02

  Like, I, I mentioned, I do early stage investing in normally fairly like frontier tech, deep tech. So one of the things that I try to keep up with everything that's advancing in AI, right? So, um, and it's, it's, it's tough, right? Like even my friends at Open and Anthropic 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? I do spend a lot of time with universities, with professors, with some of their staff students, uh, to understand, you know, what can be demonstrable in the next two, three years versus what might still be hype. Uh, and having a sense of that as I make investment decisions becomes very important. Uh, it helps me assess technical talent from my portfolio, uh, helps me assess, uh, you know, what's really novel, what's defensible. Like, so I do spend a lot of time in universities.

• 1:11:31Speaker 02

  Um, I also have been, um, you know, helping leading some of the robotics investments at KV. Uh, I do line up spending time with their target customers, right? Like some factories and manufacturing plants. Um, I want to understand what is happening on those end sites. Like what is the challenge in scaling deployment? What can, what needs to exist? What, how it can be solved? Where is AI helping adoption? Um, and so, yeah, I think the, the physical world around us and you know, what needed to get past the, the known challenges of adoption in robotics. Uh, I've been spending a lot of time on 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, what is required for it to be compliant for robotic sale?

• 1:12:14Speaker 02

  Uh, you know, today it's like 65% of the bomb needs to be sourced or licensed from US, US 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? Uh, I'm trying to get smarter about that and talking to a ton of, um, companies, but also people who are coming up with these laws. Uh, 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, uh, trying to just have, um, firsthand knowledge on, you know, what, what should be investable.

• 1:12:44Speaker 03

  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. So that means the technologists at one level that are, are not just developing the technology, but applying it, right? It means the, you know, the labs and whatnot that are, that are, 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, and as Connor 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. Um, and there's, it's going to be very political, right? You see it in data center build outs, but I think you're going to see it in spades and

• 1:13:29Speaker 03

  in, um, and physical AI, you know, as you start to have humanoid robots, people are going to feel, it's hard to choke chat GPT, but you can definitely throw a beer bottle at a, at a, at a robot or, or a Waymo. So I, I think that physical incarnation will, 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, uh, all, all over the world who is, 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.

• 1:14:14Speaker 03

  It's the capital markets, it's financing, because the, because the capital intensity is so big now, right? Understanding that side of, 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 build outs for, you know, or chip buys, like it's, it's, 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, it's a multi-dimensional problem that's only gotten more complex.

• 1:14:44Speaker 04

  Yeah, I, 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, how does, what does a seed fund mean in 2026? And when, you know, when, when seed rounds are getting done at a hundred million dollar plus valuations at times, um, 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, 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.

• 1:15:26Speaker 04

  Um, 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. Um, and I wonder if that's even changed in the last couple of years. Um, in terms of like, you, you also asked 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. So 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, and they've all done quite well, uh, would do more in that category.

• 1:16:11Speaker 04

  Um, and so, you know, I, 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.

• 1:16:24Speaker 01

  Makes sense. I'll, I'll just close out by saying my, my answer to the above, and there's elements of what, you know, a, a, 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 are the gaps, where are the holes? It helps me do a lot of diligence because man, I, we see so many cloud decks and it's so easy to say, spew a jargon, um, 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, okay, what are you actually building here? What is actually hard to do here?

• 1:17:06Speaker 01

  Um, and so I do spend a lot of time there. And then of course, yes, universities and professors, um, as well, probably less with the regulators than, than, than some of you. But, um, yeah, the, but look, I mean, for, thank you guys for, uh, for coming. And, uh, I think we covered a lot of ground. Um, so I'm really happy with how, how, how this podcast went. I don't think I can take J Cal's job quite yet, but hopefully he, uh,

• 1:17:36Speaker 04

  You did a great job, man.

• 1:17:37Speaker 01

  You were right there.

• 1:17:39Speaker 02

  I think J Cal will be proud of me.

• 1:17:41Speaker 01

  Try to channel my inner J Cal. Um, and, uh, hopefully he watches this and says, uh, it's up to his bar.

• 1:17:49Speaker 04

  Yeah.

• 1:17:49Speaker 01

  So.

• 1:17:49Speaker 04

  I'm sure, I'm sure he will. Uh, yeah. So thank you to our whole panel. Kanu Gulati of Cozal Ventures, uh, Chris Farmer, he had to step out, but he's the founder and CEO of Signifier. Of course, Shil Monat, uh, from Better Tomorrow Ventures and Didi Das, our incredible fill in guest host from Menlo Ventures. Amazing job. Couldn't, couldn't have done it better myself. Legitimately, I could not have. Uh, 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 newsletters. This week in VC, it's going to feature VC roundtables, just like the one that you just

• 1:18:32Speaker 04

  watched and other informative ones like this, every week, plus fresh deals posted, more stuff. That's it. This week at thisweekinvc.substack.com. Again, thanks to our panel. Thanks to our guest host, Didi Das. We will see you next time on This Week in Startups.