Nathan Labenz and Erik Torenberg sat down with Sarah Guo and Elad Gil, notable investors and co-hosts of the AI-focused No Priors podcast.
Sarah is the founder of $100M AI-focused venture fund Conviction VC, which she launched last fall. She was previously General Partner at Greylock. Elad is a serial entrepreneur and a startup investor. He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Below is a slightly condensed transcript of the discussion:
TCR - Sarah & Elad
Elad Gil: [00:00:00] And I started digging around. I tried to find people to build an OpenAI competitor and I couldn't convince anybody to do it. everybody said, well, it's not that interesting of a business and you know, are these APIs that good and all this other stuff? And I, pitched person after person and nobody was willing to try it.
Sarah Guo: Within consumer, Character is one of the most interesting companies. Replika is one of the most interesting companies. A lot of people don't like this. Even though you see like a decade or more than a decade for a lot of looking at consumer company metrics, you're like, Shit. Right? I'm gonna pay attention if people are spending hours a day on this service because it's so rare.
Elad Gil: You know, there's base models and there's based models with a D, right? What kind of model do you want your kid to interact with, and what do you want them to learn over time? And, you know, how does that get selected and who adjudicates what that selection process is? Or what's the ethical framework?
Elad Gil: Based on your location around the world that should be applied or shouldn't be applied. And so I think there's lots and lots of interesting questions here.
Erik Torenberg: Hello and welcome to [00:01:00] the Cognitive Revolution where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence.
Erik Torenberg: Each week we'll explore their revolutionary ideas and together we'll build a picture of how AI technology will transform work life and society in the coming years. I'm Nathan Labenz, joined by my co-host, Eric Torenberg.
Elad Gil: Before we dive into The Cognitive Revolution, I want to tell you about my new interview show Upstream.
Erik Torenberg: Upstream is where I go deeper with some of the world's most interesting thinkers to map the constellation of ideas that matter. On the first season of Upstream, you'll hear from Mark and Dreesen, David Sacks, Balaji, Ezra Klein, Joe Lonsdale, and more. Make sure to subscribe and check out the first episode with a16Z's Marc Andreesen. The link is in the description.
Hi everyone, and welcome back to the Cognitive Revolution. After taking a deep dive into prompting process automation and jailbreaking over several recent episodes, we're now zooming out of it and talking to some fellow AI scouts. These are people who are not only working overtime to understand everything that's going on in ai, but also creating thought leadership and educational content meant to help others get up to speed as well.
Today our guests are investors, Sarah Guo, and Elad Gil, co-hosts of the AI focused No Priors podcast. Sarah was previously a partner at Greylock and is now the founder of the 100 million AI focused venture fund conviction vc, which she launched last fall. She blogs on her website, sarahguo.com. Elad Gil is a notable angel investor with investments that include Airbnb, Coinbase, Figma, square, Stripe, and many more, including recent AI companies such as Character AI, Harvey AI, and Perplexity Ai, whose CEO Arvas, you may remember, was a guest on The Cognitive Revolution back in episode seven. He also blogs at blog.eladgill.com.
We spoke about how they are approaching AI investment opportunities right now, how that does or doesn't differ from how they've thought about investing in the past, where in the stack from hardware to applications that they expect to see the most value accrue, what modes of human-AI interaction they're most interested in developing and plenty, plenty more.
I hope you enjoyed this conversation with Sarah Guo and Elad Gil. Welcome to the Cognitive Revolution. Thanks for having us. Yeah. Very excited to, uh, have this conversation with you guys. We are in such a moment right now of just the whole world kind of turning attention to ai, and that's something that, you know, I think we're probably four or five months, uh, into since the release of Chat-GPT.
Nathan Labenz: You guys have each been thinking about AI very seriously and, and, uh, both independently and together. I think, um, well before that, [00:04:00] So I thought we would maybe just start by kind of revisiting a couple things that you guys had published about six months ago, and then ask you to just kind of take us through this period of time where, you know, new models are being released, new tools, new paradigms, just, you know, attention piling in investment, you know, dollars, I'm sure, um, piling in from all directions as well. And, uh, then we'll then we'll take it from there. So, Sarah, starting with you, you announced, uh, about six months ago this a hundred million Conviction Fund where you are investing in Software 3.0. So I thought for just, uh, a start, you know, can you set a foundation for us and tell us how you think about Software 3.0 and what that means?
Sarah Guo: Yeah, I think it's, it's shorthand for, um, uh, just believing that there's a very unexpected new set of software businesses emerging that can be very important, right? So everybody knows, like, you know, you have this exponential creation of capabilities in machine learning.
Um, I remember a [00:05:00] lot and I actually had a debate like six plus months ago, like, Hey, there've been a lot of ML first companies that haven't worked in the past and what changes. But I think the, these, these, the sort of exponential creation of new capabilities is the thing that got me really excited for the fund. And when we think about like, what's so different about the software, it's what it does, right? I think it attacks lots of categories of, um, services or like areas that were not, uh, like big software markets before.
Sarah Guo: Copywriting like illustration law, right? Um, what the companies look like. So this could be, um, how many people does it take to create a one or 10 billion company? Like now we have empirical proof, like 20, right? Um, and like that was not true, um, in previous generations of software. Uh, and then I, I think like one that is still unexplored is just like how the product should work from a UX and a human interaction perspective.
Sarah Guo: Um, I think we're gonna get a lot more than just like the single chat box. [00:06:00]
Erik Torenberg: Yeah, I definitely look forward to unpacking that cuz I just see so many different possibilities and it feels like we're so early in kind of exploring what the modes of human-AI interaction are going to be. No. One thing that jumped out to me about your announcement was.
Erik Torenberg: Just, you know, the, there's the AI focus, but then beyond that, just like extremely broad, um, investment thesis. Right? Totally. Up and down the stack, all the different verticals. Um, how has, if, if at all, how has your thinking evolved in terms of like which parts of the stack or which verticals are kind of most interesting over the last six months?
Sarah Guo: Some of the areas that are, um, really attractive from a demand, like a value for end users or for customers perspective are like obvious in hindsight, but somewhat unexpected, right? And so anybody who has, you know, heard of Babelfish or like interact with somebody who speaks a different language, like I think they intuitively understand that like translation.
Sarah Guo: Is interesting and the idea of like dubbing of the service is interesting. Um, but I think if you zoom out more broadly, I, I think the question around synthetic voice and the ability to take one form of media and translate it to others cheaply and easily obvious. In hindsight, I think I've been somewhat surprised by the demand on that side from across a range of use cases.
Sarah Guo: In terms of things that we, you know, are interested in, but like the bar's just very high because the, um, the cost to build a company and the advantage of the incumbents are so high is like, we haven't done a chip company, well, we've done companies that are, um, up and down the stack otherwise
Erik Torenberg:. Cool. Well, I wanna ask you guys also about, you know, some specific, uh, portfolio companies that you've invested in, that you're excited about and, and get a little tour of, kind of some of the use cases and some of the things that will be coming at us, um, you know, from a consumer or or business standpoint in the not too distant future.
But I also wanna kind of do the same thing, uh, for you Elad cuz about six months [00:08:00] ago you published this essay, “AI: Startup versus Incumbent Value”. And that hit me at a pretty opportune moment. I was just at the period, uh, the end of a period of 60 days of super intensive red teaming on G P T four.
Erik Torenberg: And I was basically not, I hadn't even really tried to synthesize what I had seen at that point. I was really just scouting, you know, all the different use cases and, and everything I could think of to test and try to understand what this thing could do. And right as that kind of closed, you published your essay.
Erik Torenberg: And so I read that and I was thinking, for me, it seemed like, boy, this G p T four is incredibly powerful. And, you know, the, the conclusion that I started to leap to is I think that the enterprises are largely gonna be able to apply this technology fast enough that they largely won't get disrupted by somebody who's starting, you know, with a language model and then thinking like, how do I build [00:09:00] all this other stuff around that?
Erik Torenberg: How would you grade my intuition, you know, from six months ago? Uh, how has your thinking evolved on that question now that you've had the, the advantage of seeing GPT-4 launched and the, you know, the deal flow that you're seeing downstream of that?
Elad Gil: So, let's see. So, you know, I got really interested in generative AI, um, uh, uh, bunch of years ago, probably four or five years ago as all the stuff was happening, simply because I thought that, um, you know, the based art stuff was super interesting.
Elad Gil: And before that I'd been investing in AI, uh, and also worked on ai, uh, related products myself directly for like 10, 15 years. You know, so when. Mobile and ads targeting. And ads targeting were big ML systems. And then I sold the company to Twitter. And at Twitter, one of the teams that worked for me was search, and that was all ML and ai.
Elad Gil: And then I invested in the area for about a decade and for about a decade, nothing worked right? Um, or I should say a lot of things worked for incumbents, but it didn't work for startups. And so you had the Facebook newsfeed and you had Alexa from Amazon and you had all these really big products. But the startup ecosystem in terms of companies that were started to specifically do ML, just really didn't seem to go anywhere in terms of, you know, building really massive companies and then this, this generative AI wave hit.
Elad Gil: And I think things started to get really interesting around GPT-2 and then maybe as GPT-3 came out with a big step function and functionality, you realized how compelling it was. And I remember, um, I went in on a recent podcast and we talked about it specifically, and I think at the time a lot of people were ignoring it.
Elad Gil: And I started digging around. I tried to find people to build an open eye competitor, and I couldn't convince anybody to do it. Um, everybody said, well, it's not that interesting of a business and, you know, is, are these APIs that good and all this other stuff? And I, I pitched person after person and nobody was willing to try it.
Elad Gil: But a lot of people who'd worked in the area before wanted to build applications. And so I started investing in companies like Character. You know, Noam Shazeer was one of the main authors on the, on the transformer paper. Um, I helped out some of the early team that was working at Adept, although I never got involved there as an investor.
Elad Gil: I got involved with things like Perplexity and Harvey and a variety of companies that basically, I think ended up [00:11:00] forming, you know, some of them were interesting companies, now, a year or two later, in hindsight, in terms of this wave of AI stuff. And a lot of the, the question in my mind is, if you look at the history of technology waves, there tends to be differential capture between incumbents and startups.
Elad Gil: And each technology wave is different. And so if you look at, um, you know, the first internet wave, it was like 80% startups. It was Google and it was Amazon and all these new companies. And then people like Microsoft benefited too. And then you look at, um, you know, mobile and that was 80% incumbent value.
Elad Gil: It was, the big platforms were Apple and Google, which were already incumbents. You know, people were talking then about what, what is Salesforce on your phone gonna be and who's gonna build it? And it turned out to be Salesforce, built Salesforce for your iPhone, right? Or search on your phone was Google.
Elad Gil: But there was new things like Uber and Instacart and Instagram. Basically anything with Insta in it, you should have just invested in. And then you had other types of platforms that emerge. You know, for crypto it was a hundred percent startup value. There's basically no incumbent capture of crypto, right?
Elad Gil: And so for the first decade of AI with all the CNN and r and n, again, related, um, approaches, all the value went to incumbents. That first wave of AI was an incumbent wave. And now we're seeing something really interesting where I think it's gonna be a differential split. And maybe it's 80-20, right? 80% incumbent, 20% startup, but 20% is a lot.
Elad Gil: For what I think is probably the biggest platform shift in, you know, a decade plus, maybe two decades, maybe longer, because to Sarah's point, you're changing a few things in a massive way, in an underlying way. You're changing the computer model and how to write code. You're changing the user interface, but you're also changing the baseline functionalities and what this wave of computing can actually do in terms of both applications.
Elad Gil: Um, as well as sort of other deep areas. And so, you know, there's tons and tons of, of, of places that I think is gonna impact. Sarah mentioned Voice and Wing and Texas Beach and, you know, I think those are super interesting areas and she and I have talked about those in the past a bit. Um, there's tons of room, I think for social products, and I'm really interested in things like, what is, what does a generative social product look like?
Elad Gil: There's lots of apps on the B2B side, there's lots of tooling, like a Lang chain or LAMA Index or other things like that. And then obviously there's the base LLM layer, so there's, there's just a ton, right? And. A bunch of that stuff will go to incumbents. You know, probably the base models are largely incumbents with maybe Anthropic and one or two others being the, the counter examples, right?
Elad Gil: OpenAI, Microsoft, Google, et cetera. But there's lots and lots of room for people to build, you know, brand new de novo things. That'll be super exciting.
Erik Torenberg: Just, just on that for a moment, you know, you mentioned that you tried to get people to build open AI competitors and uh, they, you know, you couldn't.
Erik Torenberg: Couldn't get people to bite. What are you guys trying to get people to build today? Uh, for the talented people listening to this podcast, who wanna do something and what are the things that people aren't spending as much time as they should, perhaps, or maybe overlooked opportunities within space?
Elad Gil: I would say voice applications, social, certain big B2B applications, and then certain types of infrastructure.
I know what Sarah, what you think, but those, those would be kinda the four quick ones. Yeah. Um,
Sarah Guo: I'd add there's this idea of tool use. Right. So, you know, an LM can be a reasoning engine against, uh, [00:14:00] knowledge that it holds in the model itself or some database or, you know, some repository of information, but it can also take action now, right?
Sarah Guo: So if you think about tool form and automation in the previous generation of products in the sort of RPA (Robotic Process Automation) category, I think that's going to get a lot more interesting when the approaches get more robust. Um, there's a lot of workflows in. Every part of the enterprise, but especially the back office and some verticals like healthcare where like we have a lot of people moving data around between systems or filling out forms based on some policy and like, we've been unable to do that flexibly today, but it is a, you know, basic tool used in natural language tasks.
Sarah Guo: So I think that's really interesting. One, like there are some areas that we think are just going to get more. Important from a core architecture, uh, um, perspective over time. So, um, the idea of retrieval and just like, how do you guarantee like retrieval and memory are two concepts that I think are really interesting in research that people can't figure out how to use in like actual enterprise applications.
Sarah Guo: And [00:15:00] then, you know, I, I'm really interested in some of the more emergent stuff. Like if you look at companies like Midjourney. Uh, the idea of democratizing capabilities like that people didn't have before, if that's illustration, which I promise you not a lot of VCs were focused on before, but generally, like I think media creation is, um, is really interesting.
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Elad, can you, can you say just a little bit more about what AI social could look like?
Elad Gil: You know, I actually have a lot of ideas, um, but they're probably really bad ideas, right? I think with social products the key thing is you wanna launch things and then quickly iterate and sort of see what gets adopted and you know, I know people doing a range of things, but I also don't wanna kind of like dox them or something in terms of what they're doing.
Elad Gil: But, you know, over the last couple months, I think I've heard a couple [00:16:00] really interesting ideas on the social side. And, um, you know, it, it's a variety of different formats and approaches and everything else, and I, I just think there's, there's a ton to do there. And I think the issue is, when I look at social products today, people are basically constantly trying to rebuild Twitter for some reason.
Elad Gil: You know, every, every month there's a new, Hey, we're doing Twitter, but just decentralized, we're doing Twitter, but it's whatever. Not with generative ai, right. Um, or people are trying to do like, you know, Facebook throwbacks or, and you're like, what's, what's, where is the technology heading and what does that mean in terms of entirely new types of interactions where you're still taking advantage of core social behaviors, right?
Elad Gil: Rudolph has this seven deadly sins, right? Every social product. Basically it is like gluttony or lust or, you know, one of those seven deadly sins. And I think if you think of, of that through a generative aspect, there's really interesting ideas that you can start coming up with versus saying, I'm just gonna throw things back.
Elad Gil: It reminds me a lot actually, when I left Google, it was a long time ago. Either way. Uh, whenever I left Google, whatever year that may have been, um, [00:17:00] the, uh, a lot of people were building things that they shouldn't have been building because they were building for the past. So they're like, oh, I'm gonna build this SEOable thing and get traffic that way and blah, blah, blah.
Elad Gil: Instead of saying, Hey, I'm gonna build a developer tool, which is a new thing, or I'm gonna build a mobile company, which is a new thing. And so I think, um, a lot of the social products I see are reflections of the past versus the future. And, uh, that may work. They may actually create really big companies.
Elad Gil: But the flip side of it is there's probably some really interesting things. That we can all kind of squint and imagine that's coming. I think
Sarah Guo: that there is a lot of open-mindedness required for some of the consumer stuff in, in that if you like, consume, like within consumer character is one of the most interesting companies.
Sarah Guo: Replika is one of the most interesting companies. A lot of people don't like this. Even though you see like, you know, a decade or more than a decade for a lot of looking at consumer company metrics, you're like, Shit. Right? Like, I'm gonna pay attention if people are [00:18:00] spending hours a day on this service because it is so rare.
Sarah Guo: I think it's very easy not to like it because it's a weird thing that people want to have these parasocial relationships and, um, and they're, because there's demand for NSF W use cases, but like, that's how a lot of things on the internet start, right?
Erik Torenberg: Yeah. We had Eugenia from Replika on as a guest, and it was certainly one of the more fascinating conversations that we've had to understand.
Erik Torenberg: First of all, just like what the user base is today and has been historically, while the models have been so limited, frankly. And then to kind of extrapolate that into, you know, the present and the future where it's like this was not honestly super compelling to me. But I see how it could easily become, you know, much more compelling.
Erik Torenberg: It's, there is a, a phase change or kind of a threshold that we've hit. I think that is going to kind of take replica 1.0 and make it look pretty quaint as we hit 3.0. Well, I
Elad Gil: think it's gonna be deeper than a lot of people imagine.
Sarah Guo: Seven, eight years ago, we started investing in what [00:19:00] you'd think of as like mobile coaching, like marketplace applications for different areas.
Sarah Guo: So that could be like something in health, like nutrition or um, like people are doing like fitness training and such. And um, as you might imagine, like having an accountability partner or somebody you're building an emotional relationship with means you can affect behavior change, which is really hard for humans.
Sarah Guo: Um, and one of the most interesting things I've seen recently is, um, bots can, in, they can convince and like coax people to do things right, plan their days, change behaviors. And so, um, I think that's something we're gonna see a lot of.
Elad Gil: Yeah, I think the applications of it are gonna be broader than anybody thinks.
Elad Gil: Like if you look at it, um, you know, if you think about education and how do you revamp education and everybody's gonna have like a bot. Is there a kid growing up that's gonna teach 'em things and help them with stuff and maybe it becomes their best friend? Right? And there's very positive and very negative implications of that, right?
Elad Gil: And so I do think, um, people are dramatically [00:20:00] underestimating the degree to which, on the one hand there's a bunch of lonely people or people who wanna interact online more and they don't have the capacity to do it otherwise. And then on the other hand, there are these really deep fundamental societal use cases that are coming through the generation of these agents that interact with you like a real person.
And in some cases, I mean, every parent is gonna want the thing that's gonna educate their kid in a hyper customized way. And that's gonna be both very powerful. But which company's gonna control that and what does that mean for our kids and how they're taught and raised and all the rest of it, right?
Elad Gil: So I, I think there's some very deep fundamental things here that people are just barely touching the surface on. And some of it's an old sci-fi literature, like the Diamond Age, right? It's the Young Lady's Illustrated primer, but, In some cases, I think people haven't really thought about it very deeply, or there's another book called Lady Amazes, where every time there's a sufficiently large block of people that believe something that substantiates into an AI agent that represents them in Congress.
Elad Gil: And so why even vote when you can have a perfect [00:21:00] representative? You suddenly appear and actually, you know, fight for and adjudicate the things that you truly care about. And so I think there's all sorts of crazy things that are coming on, on a more
Erik Torenberg: Tian day-to-day level. I want an agent that just helps me maximize my productivity, uh, that, that's watching me at all times, watching all my interactions with people and, you know, Tells me when I'm acting out of line or says it.
Erik Torenberg: No, say this. This will be better thing to say. Kind of like a personal trainer for all things
Elad Gil: like coach, you know, that's watching me at all time. Yeah. I just want a sycophantic ai. Let's be like you. So good. That was such a good joke. You know, pretend amazing. Amazing.
Erik Torenberg: There's a couple really interesting.
Erik Torenberg: Questions here that I think these examples get at. It's funny you mentioned the training one. Going back to my GPT-4 experimentation, one of the things that I tried to do is just see like how many sort of chat, you know, specialized chat agents, could this thing sort of play? I did the physical, you know, kind of exercise coach [00:22:00] one.
Erik Torenberg: Um, and I also did one simulating tech support for my 90 year old grandmother, uh, which was even maybe more eye opening to me cause I like really spoke to a pain point that we have in my family. But how do you guys think about that as investors? Right? Because I'm, I'm sitting there using base model GPT-4 and it's basically working, and then I'm kind of like, this feels like it sort of hits the threshold.
Erik Torenberg: I can certainly, you know, wrap this up into an app or somebody can, but at that level of like, I used to go hire a human to do this, now I can maybe slot in an AI to kind of play that role. Are there businesses there or is that all kind of, is all that value accrue to. OpenAI in your minds or foundation model providers in general?
Elad Gil: Yeah, I mean, I think there's tons of room for, um, standalone applications and I think a lot of them will be building workflow against it or some form of like storage or history or memory or something else that is sort of associates with the, the chain of stuff that you did relative to that. Um, you know, it's kind of funny, [00:23:00] I'm gonna give an extreme example, which doesn't quite apply, but in the nineties everybody thought that, um, everybody was gonna set up their own email servers.
Elad Gil: Right. Oh, email's a protocol and everybody's gonna use it. It's so easy to use. And then obviously everything just centralized to like Gmail and Yahoo Mail and whatever your corporate server was. And I think the same thing happens with a lot of these things where, you know, there may be interfaces like chat, pt or the like and things like memory and some of the other things that, you know, some sort of recursive interaction across a.
Elad Gil: Language model will come into play and chaining and all sorts of things, it'll be more complicated. But I think fundamentally, um, people will need very specific workflow for very specific applications in many cases. And in some cases you'll have a general purpose tool where Chat-GPT will be good enough to just do a bunch of stuff for you, right?
Elad Gil: Or whatever, whatever version of an agent you're using in the future. So, um, I do think we're gonna end up in a multi-agent world, but there may be like specific things that are dominant for specific use cases, just like everything else that exists today. You know, I feel like the best indicator of how things will evolve is kind of like, how [00:24:00] do, how do market structures evolve in the past?
Elad Gil: And I think it's gonna be kinda the same thing.
Nathan Labenz: So how about like se cases, or not use cases exactly, but modes of interaction? This is something I've really been trying to organize my thinking around. Eric's got this vision for the, you know, AI that kind of rides shotgun all the time and like, you know, helps him, uh, maintain his social graces.
Nathan Labenz: And you can kind of envision, you know, that's kind of the Reed Hoffman vision, I would say is like the co-pilot for every profession, co-pilot for every phase of life. And then you're speaking to also on the other end, like people are gonna need specific workflows. I kind of think of that and, and Sarah you mentioned like rpa, like there's this sort of process automation.
Nathan Labenz: Context where it's like I'm a big corporation, you know, I have like these cost centers, which are humans that have to do these tasks. I've never had any way to even think about automating these tasks in the past, but now I sort of have that. And then there's kind of this third way that's emerging that's like the agent model, which I kind of think is bridging.
Nathan Labenz: [00:25:00] Think of it as bridging those two. Cuz I can like, Talk to it in a sort of ad hoc, realtime way, but I can also kind of send it off and say like, you go figure out the plumbing and like how things connect together. You know, so even get me outta the business of having to design your architect the workflow, I guess, you know, that's enough for me.
Erik Torenberg: How does that framework resonate for you? Do you have like your, a different one that you kind of bump company, you know, deal flow up against, and what modes of interaction do you think are ultimately gonna predominate and is that the same as those that give you the best return on your investment?
Sarah Guo: I think when you start to.
Sarah Guo: Actually, like look at, um, the tools that have succeeded at scale. There's like a whole range of ways that users want to interact with the stuff, depending on the task. So like prompting is not an easy thing for like 99.99% of humans today. So just because you enjoy, like Nathan messing around with GPT-4 and lots of users of your podcast might, it's hard to ask a good question.
Sarah Guo: And I, I think like one [00:26:00] of the things that I've seen in companies that I think will just become more common is like, Multimodal input passively using context. Right. I think there are a lot of companies that figured out, like giving end users in a particular category, there's 20 prompt templates that made sense for their use case and an easy button so they don't have to figure out how to engineer a good output.
Sarah Guo: Like that's a company right now. Right. Um, and so it's not clear to me that we're gonna have, you know, generic interfaces for all the different use cases. One of the things that I think will happen to the point of like, do we. Like, I think search is going to Right. And I'd love to get, you know, Elad’s point of view on this since he actually worked on search.
Sarah Guo: But I, having been invested in search companies prior and having friends, starting them now, still search as many use cases. It is weird to me that like getting information from the internet has fallen into one box at Google. And I imagine that many of the use cases like the, um, stereotypical one being like travel planning or buy something like, [00:27:00] That's something, uh, I think an agent should be able to much better do for you in the future.
Sarah Guo: So I think there's like certain things that will fragment from a market perspective and every slice of that market is like plenty valuable to go after for a new company. So one of the basic frameworks we use, I don't know if I've got like the um, sort of overarching unification right now. The ground is doing stable.
Elad Gil: Yeah, I kinda have two answers to it. I think there's almost like a two by two matrix of like, is a person busier? Do they have a lot of free time? And then as a context, um, you know, the context kind of, maybe it's not two by two, it's like three by two or four by two or something, right? It's like busy versus free time.
Elad Gil: And then one of a series of contexts around B2B use cases, commerce slash action-based use cases, et cetera. And then based on that, you're gonna have a different modality. And so I think you can almost come up with a map on it. And it reminds me a little bit of like, if you work at a big company, the way that you interact with the CEO is different from how the CEO interacts with you.
Elad Gil: You'll write this long email of like multiple paragraphs and the CEO will send you Yep. Like a single word or whatever. Or you know, execs tend to [00:28:00] leave a lot of voicemails or they used to, right? Um, because it was a more performant way to communicate and so that's kind of busy versus not and all the rest.
Elad Gil: And so I think there'll be all these modalities. I think the other answer is in some sense it kind of doesn't matter. Um, it's funny, I met with this hedge fund guy who's really sharp, like, you know, amazing investor. And we were talking about AI, and you know, a lot of his questions tended to center on this kind of stuff.
Elad Gil: He's like, well, will you just talk into your phone? And I was like, who cares? It doesn't matter. That's missing the main point, which is what does this technology fundamentally enable? And we'll figure out the interface and we'll iterate on it, and it'll be one of a series of interfaces that we use today, right?
Elad Gil: I mean, fundamentally we have like N senses and we'll have different modalities that match with different senses depending on the use case. But it's clear that, you know, say that you use Alexa, right? People with kids love using Alexa because it's voice-based and the kid can yell at the thing and it'll reply, but you're not gonna have a lengthy information extraction conversation with it unless you're like a three-year-old, right?
Elad Gil: And so I think it, it [00:29:00] kind of maps to the, you know, what are you trying to accomplish? And I think the most interesting aspect of all this stuff is just like, what are the fundamentally new capabilities that all this enables? And what does that mean in terms of the applications that can be built, in terms of how it reshapes our lives?
Elad Gil: You know, like, I think there, there's really, I'll give you an example. Um, you know, obviously these models now perform better than many doctors on standardized medical exams or other types of tasks. And you can imagine a world where you start having models that are basically available to anybody in the world, which allows you to upload an image and, you know, describe some symptoms and then you end up with medical care that's, you know, in some senses on par with what you get at Stanford or whatever top, um, you know, uh, medical association.
Elad Gil: And you can do that anywhere in the world as long as you have a phone that has your own characteristics. That's really, really, really powerful. And to some extent, the interface is secondary to that impact. And so I, I'm not trying to denigrate the inter interface question. I think it's really important stuff.
Elad Gil: I just think like fundamentally, the capabilities are [00:30:00] so rich. That it's almost like, okay, where do the capabilities take us? And then based on that, what happens as, as the output, there won't be multiple types of outputs.
Erik Torenberg: Interface is definitely interesting. There's all, you know, I'd be interested to hear if you guys have seen any really creative ones that, you know, you would recommend that, uh, people check out.
Erik Torenberg: But I'm also kind of thinking even a little bit more. Like big picture than that. Like just how do we relate to these damn things in the first place? You know? Are the, is the co the co-pilot feels kind of like a peer, you know, or some, like a, a real time collaborator and that could be an audio interface or a text or, you know, UI or whatever.
Erik Torenberg: But then there's like, you know, the agent, you're kind of delegating to it and then there's the sort of, you know, supervision mode perhaps where like you largely trust it, but you kind of, you know, maybe trust but verify and hopefully you like actually do the verification and don't just, you know, start rubber stamping everything.
Erik Torenberg: What about on that level? Do you have a sense of where this is going? Like how, another way maybe to ask this question is like, how weird do you think. [00:31:00] thing are going to get as these tools.
Elad Gil: I think eventually we're gonna call all these things your highness, as they sort of take over the world. I love my boss, the ai.
Yeah. No, I think the world is, is gonna get really interesting and weird. And I think that's back to the education point. For example, like if you have a bot helping raise your kids, what does that mean? Right? Or for one of the primary sources of information no longer becomes YouTube. It becomes some agent.
Elad Gil: That's not only working them through a math and history and other curriculum, but potentially choosing which form of history gets presented to the kid. You talked about base model, and I, I can't remember its biology or something else. It sounds like something you'd say, but I dunno if you said it, you know, there's base models and there's based models with a D, right?
Elad Gil: Like what, uh, what kind of model do you want your kid to interact with and what do you want them to learn over time? And, you know, how did that, how does that get selected and who, who adjudicates what that selection process is? And so I do think there's a lot of these, um, really interesting things that are coming because [00:32:00] you're RLHF (reinforcement learning from human feedback), some effing something, and who, who's that cohort of people?
Elad Gil: Who's training the thing that is providing the human feedback, and how do you select who those people are or what's the, the ethical framework based on your location around the world that should be applied or shouldn't be applied? Right. Should the. Western viewpoint be applied to somebody in another country that may have very different values and mores relative to the model and its output.
Elad Gil: And so I think there's lots and lots and lots of interesting questions here.
Sarah Guo: You know, I, I think it's useful to like try to imagine the interactions we have with agents in a few different ways, right? And so like a lot was saying like, your kid or you, like today we are at the mercy of, uh, a bunch of algorithms that control our information flow.
Sarah Guo: Right. And we can lightly curate them by like swiping correctly on TikTok or Twitter or whatever. But if you can instruct your, your bot more directly for yourself or your kids or whatever, like, that's quite interesting. I think like a [00:33:00] fun interaction to imagine is, um, in like the thing about the enterprise side, you have.
Sarah Guo: Different influences in, uh, different teams because they are incentives for, for, for example, like compliance or security in an R&D team. Um, I think there's an, uh, a simple version of that, that's a bot that's like an early warning system. Like imagine in a FinTech company, like, oh, you're like hitting these.
Sarah Guo: You know, these merchant network rules, like the thing that you're trying to do is like a definite no-go. Um, but you can also imagine like a debate with that bot or, um, a, a fight with it as an extension of like that security champion or whatever. I think another really powerful one, um, that I like just looking at some of the examples of how people use like Chat-GPT and Co-Pilot is co-generation.
Sarah Guo: Right. I look, we're not that far off from a, like, especially in areas where there's just so much content online, like web development, junior web developer, like junior Python developer available to everyone, right? Not like [00:34:00] complete my code, which requires a bunch of previous knowledge, but write to a file run code, like deploy to the cloud, use APIs like that is really powerful.
Sarah Guo: And so I think, um, I think there's like, you know, I think of it as like there's new capabilities. Right that are like thought of as human capabilities. There's like interactions where I actually have to negotiate and then there's like the things that I can control that are like personal. So I, I think we're gonna have a lot of really weird interactions with agents.
Erik Torenberg: How does that sort of expectation of weirdness change how you guys are thinking about your role as investors or the investment decisions that you're making? Um, I would imagine that like, it would shift you more toward. Team relative to like current product, for example. But I'm wondering kind of what shifts you're finding compared to previous, you know, cohorts of companies.
Elad Gil: You know, it's interesting. I think it was Chris Dixon who said that the next great company starts off looking like a toy. And I think that's true both on consumer. That's true with crypto, that was true with [00:35:00] certain types of enterprise. And so I don't think it changes that much. I think the really weird stuff is often the most interesting stuff.
Elad Gil: And then there's gonna be the standard stuff that you just know is gonna work, right. And I think it's gonna be that same mix. I think social products in general tend to be weirder in terms of the things that actually work, or at least the behaviors team tend to break with other affordances kind of generationally.
Elad Gil: You know, snap, we're gonna make every image disappear and obviously that product morph quite a bit over time, but, At the time, everybody's like, what are they doing? You know, that's so weird. Um, my senses is just like Evan taking selfies of himself and then they would disappear. And that was the whole network for a while.
Elad Gil: You know? So I think that, um, I think behavioral will always start off seem strange. Um, I tend to be, and you know, Sarah and I don't have any like formal business relationship, right? We're just like collaborating on stuff and we have a podcast together and stuff. So I'm speaking for myself only, but like, I tend to be very much like a market driven investor, not a, a team driven investor, I should say.
Elad Gil: The team is incredibly important, right? I've started two companies myself, so if I didn't think teams were important, you [00:36:00] know, I, I never would've started a company. Um, but I think the markets are more important or the product market is the most important thing. And so often what I look for is like, what are early signs of product market?
Elad Gil: And then do I think that's in, do I think it's in a big TAM? Do I think the team is great? Do I think there's defense abilities or a why now statement? You know, there's all these other things around it, but. You know, fundamentally, I've always looked at it as product-market and the question is, if something's really weird, how can you tell if the product-market is there?
Elad Gil: And I think almost every great startup has to be non-obvious. Cuz if it was obvious, everybody, everybody would already be doing it, right? So definitionally, these things have to be somehow off, or there has to be some hurdle to overcome, otherwise it's not defensible.
Sarah Guo: I think it’s kind of funny that, um, some number of months ago, a lot couldn't convince anybody to start an open AI competitor, and now he probably can't convince anybody because they're like, oh, they're too far ahead. It's too big of an incumbent.
Um, so it is like a really, um, interesting pacing. Uh, related to that, I think one, one big [00:37:00] mistake investors make is they are, they're just kind of blind based on like their current view of the world, right? Like it's very easy to project. Your existing view of where the value is, especially if you like, are very focused on the more sophisticated customer.
Sarah Guo: Um, and you take that point of view and then you don't see the actual demand, which might start with people who don't have access to something or, um, where their use case is less sophisticated. And so I think it's, it's like really easy to see this in the media space. So like if you look at something like illustration, right?
Sarah Guo: There's like, there's this view of like, oh, like. You know, it's never gonna be good enough to like, make picture books or like do coke ads and like, pretty sure directionally we are going to get there sooner rather than later. But if you, if you take that point of view, like, oh, it's not gonna work for the EA games, right.
Sarah Guo: Or it's not gonna work for people who need Super Bowl quality video or, um, yeah, but I wouldn't like, you know, like use that [00:38:00] in my production code base. Like, you're just gonna miss a lot of like, well what are people actually using it for? And directionally where are we going? Yeah, I think people also over index on defensibility related to that.
Elad Gil: And so everybody at the beginning of a company asked too many questions around how does this become defensible? How is this defensible now? And can't people just build it? Cause two people built it in six months and that's sort of like every SaaS company. Right. What was defensible about retool in the early days or notion in the early days, or sort of choose your startup in the early days?
Elad Gil: And in the very early days, nothing was defensible, right? It took two people three months to build a thing, you know? Um, and so I think that's kind of similar here, where there's a lot of questions around, okay, what's a defensible business model? And you wanna have defensibility over time. Absolutely. And if you look at it traditionally, there's all sorts of ways to do that, either in terms of platform effects or, um, certain aspects of sales or certain aspects of, you know, integrations or other things that you do over time.
Elad Gil: But fundamentally, I think. Network effects, right? There's all sorts of forms of defensibility, remotes. But I think, I think people, um, [00:39:00] really early, uh, tend to ask almost too much of the thing. And the real question is, does anybody care? And is anybody using it? And then I think later it becomes, okay, now that people are using it, how, how does it become defensible and not a commodity?
Elad Gil: And how does it scale and how much does it scale? And, you know, is this an n of one company or product?
Erik Torenberg: I'd love to kind of get a sense for the trends that you're seeing
Elad Gil: there. My favorite waitlist, example of all times was this early, um, AI company. I'm not gonna name which one it was. It was like 10 years. And, um, the founders of the company claimed it was an AI company, but in reality, they had a bunch of ops people like answering queries in the background.
Elad Gil: And the c the co-founder of a [00:40:00] very well known large tech company went onto it and anytime we'd go on, they'd ping all the ops people and they'd all jump on and answer all of the queries really fast for that one person as they end up getting bought by this like major tech company. And it was completely false.
Elad Gil: Like it really wasn't working the way that they were claiming it was. And they always were in private alpha. And they say, oh, we just, you know, so there's so much demand and look at this giant waitlist and all this other stuff. And they never actually, like really launched and, and they got bought for a bunch of money.
Elad Gil: And so I feel like often these infinitely closed wait lists are kind of, um, a negative sign that the traction may not be real. Now sometimes it's a real sign of demand and then there's some scalability issue in the background, or they wanna test it and all the rest of it. But I think if you have such raw organic adoption, You know, usually you just open the thing up because you know more and more people join unless, again, there's some constraint that prevents you from doing it.
Elad Gil: I think in general, the last decade has taught people some wrong lessons on how long they should take before launching a product. And [00:41:00] people point to Figma or they point to Notion, or they point to other companies where there is a longer period of time for development. And if you talk to the CEOs of those companies and say, I wish it was faster.
Elad Gil: We should have done certain things faster. Hey, it didn't work the first time, so we changed it and it worked the second time. But we tried, you know, we tried actually to get people fast. And so I think there's this whole like bespoke artisanal movement. Um, or similarly, one company I'm involved with was doing hand to onboarding of every company, superhuman style.
Elad Gil: And eventually one of their customers said, why are you getting in the way of your customers? I just wanna sign up and use the thing. Why are you onboarding me? And so they stopped doing it and they had a spike in usage. So in general, I think you wanna get out of the way of your users, and that means, you know, you don't necessarily need a wait list unless there's very specific reasons behind it, or you really need to test certain things.
Elad Gil: But after you've tested things enough, if you keep having a wait list two years later, it means the thing isn't working.
Nathan Labenz: So another kind of bit of the future that's starting to take shape is [00:51:00] the Neuralink uh, implants, which they've, you know, taken as far as, uh, the great apes. Yeah, I have two of them. Well, uh, then you've, you've answered that question already. I was just going to frame a hypothetical for you and say, let's imagine a near term future where say a million people have one of these things.
Erik Torenberg: And it's like broadly, you know, seen to be safe. Like they're walking around, you know, doing okay.
Sarah Guo: We’re a couple thousand dead monkeys away from that man. But I'm, I'm excited to imagine it.
Nathan Labenz: Yeah, we're not quite there. So anyway, would you guys be interested in getting one and being able to interface directly with the computing world with your thoughts?
Sarah Guo: Yeah, of course. Right. I, I think, um, like that's a, that's an easy yes, but, uh, I have a house full of Gen one. You know, broken consumer hardware and like here is one where probably not gonna be an alpha tester. Um, but if, if you just think of it, like actually looked, I've looked at a series of companies around this, um, [00:52:00] like, let's say like it's a, it's a nick for your, for your brain, right?
Sarah Guo: Um, and like there's a whole bunch of things that are still immature from a technology perspective, but I think it's very difficult to not, um, imagine that we'd want. Communication bandwidth to be higher, um, with like all of our devices and everyone else. And it's also hard for me to imagine that if like as a, as a like, as an input mechanism for if you can do, um, like knowledge capture in this way and it's an advantage for people, which it will be, that it won't become very popular if it works
Elad Gil: Yeah. I'm very skeptical on timeframe for this kind of stuff, so we'll see. I think, I think our understanding of the brain is so de minimus that, you know, with exception of a handful of systems that are easy to interrogate, like the, you know, visual system and things like that, it's actually, we are, I think the depth by which we understand how most of the stuff works is really shallow.
Elad Gil: And most of the deep brain stimulation stuff, which Neuralink is based on, has really been for treatments [00:53:00] of things like depression or a few other diseases. Um, so I, I'm quite skeptical about it, but we'll see. At least anytime soon. And by soon, I mean five, 10 years even. That might
Nathan Labenz: be the most bearish take we've had on, uh, on the neural in question.
Elad Gil: Should talk to neuroscientists.
Nathan Labenz: Well, thank you guys. This has been a lot of fun. Thank you
Sarah Guo: guys. Thanks for having us. Good to see you.
