Betting on the Future of AI – WIth Sarah Guo

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1:31Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. Today, we're going to talk about generative AI, where the opportunity is for the next generation of startups, whether this is actually investable or not, and plenty more, including are these fears of AI wiping out the world actually grounded in anything reality? All right. We're joined here by an amazing guest. Sarah Goa is here. She's the founder of the VC firm Conviction, and she's the co-host of the No Priors podcast. Sarah, welcome to the show. Alex, thanks for having me. Oh, it's great to have you here.

2:04So a little background for listeners. We were both at CEO Summit that CNBC put on a few months ago. And I thought you were just far and away the best speaker there. Just like speaking to this like really important new mode of technology with like actual examples and like, you know, substance as opposed to a lot of the fluff that I've seen out there before. So it was like, I basically ran after you. I was like, Sarah, like we kind of know each other from Twitter, but please come on the show. So it's great to have you here. Yeah, I appreciate that. It was a fun event. So let's start with the possibilities and which fields this technology might end up changing.

2:40One area that you're looking at is the creative field. I'm curious how it plays there. Yeah, I think the creative field will both change from a just like a tools perspective and then also from a consumer experiences and social perspective. Right. So the tools one is pretty direct in that everybody recognizes that these language models can generate like written content that is at least an interesting starting point. And the and I think like Dali to and Mid Journey and like other products have really shown image generation rapidly increasing quality. But if you think about generating audio and video and imagery that like complies with brand guidelines that you could use in advertising that you have more control over that even, you know, surpassing some of the basic research constraints today around resolution and length, like we're going to see a lot more of that.

3:40And I think having more powerful tools is going to make anybody capable of creating much richer content. So you could have not just your podcast, but an entire movie studio someday. And so I think that's something we think will both be really valuable and be just massively changing for society. Because there's more creatives than there are people who know how to, for example, use Premiere. So do you remember the photo app Prisma? It basically allowed you to put like an AI layered painting style on top of any photo. But it really didn't take off because I think people respect like the amount of work that artists and creatives put into their creations.

4:24And that's sort of what makes it unique. And AI has a way of homogenizing that stuff. So eventually every Prisma photo looked the same. Why isn't that going to happen again, even though we're talking about much more ambitious projects like AI assisted movies? Yeah. So I think, you know, in any creative field, you have tools that artists use to create something new. Right. And like I have liking respect for the Prisma founders, but I think if something is a specific filter where the aesthetic is defined by, you know, a product team or a founding team, there's only so much creative variation you can have.

5:05Right. And so I think we're going to get to a point with these tools where it's not so product defined and there's room for expression of creativity. And so I think we're going to be like amazed by the number of things people create. And it's just going to become part of the artistic suite. I definitely don't think it replaces like creative direction and artists, video makers, any of it. Yeah, it would be cool, for instance, to just have like the podcast production side of things. taken over by AI so I don't have to spend time editing, you know, and then just being able to interview more or spend more time on prep.

5:42That would be ideal. I feel that way too. But I think one of the things is like, you know, you can, you can have taste and you can have an opinion, but not have the technical skills. Right. And so I'm sure you have like podcasts where you're like, oh, well, this is, that's not how I want, especially if somebody gives you the tools today, there's no capability, but I want it to look this way. Right. I want it to sound this way. And so I think giving people those opinions, they won't naturally all aggregate to the same place. Right. Take an example like, I mean, maybe something even analogous to the thing you described with Prisma.

6:14Instagram has inbuilt filters. People still have a huge variation of the types of things that they want to capture in their photos and the way they frame and like all of that creativity, including the use of like much more high resolution tools has not gone away. Yeah, well, we could do a full episode on basically the fact that filters have faded away on Instagram. Again, going back to this idea of like, well, they made everything look good, but in the same way. Anyway, we could spend all day talking about this. Another area you're looking at is like assistive agents, which I think is really interesting, especially because, you know, I think people have this idea of an assistive agent as, you know, just like buying movie tickets for you, booking a hotel.

6:56But it actually can be industry specific as well. You want to say a little bit more about that? Yeah, I think you are going to see like as a sort of first generation assistant products for lots of different workflows, both consumer and enterprise. Right. So today, like the experience that the most people have had is an assistant experience, but like where it's just giving you information back. Chat GPT. Right. And that's first of all, not up to date. and second, not personalized. And then third is not doing anything for you. But from the consumer perspective, like your ability to do things on the web or take action in real life, like that's a big difference, right?

7:36You have an executive assistant, somebody managing your life, like they can be a lot more useful to you. And so the idea that you have agents that can not just retrieve information, but be like, okay, these are the personalized, right? These are Alex's priorities. These are things he hasn't even thought about, but based on what I know would help him. I can write code. I can take actions on the web. I can interact with other things. I think we're at the very beginning of what a consumer assistant looks like. And we're investors in inflection. I think they're taking a big step on the personalization front there.

8:10And on the enterprise side, this I think is actually going to be a little bit slower because you really need to fit these intelligences to workflows, right? And so we've made some investments here already, but you kind of need these two things to meet like somebody with deep understanding of the domain, right? Be that podcasting or legal or enterprise analytics. We're investors in Harvey, which is like legal co-pilot or seek, you know, talk to your enterprise analytical data. And I think there's a lot of nuance in, you know, how you actually make somebody with that role in a company more productive?

8:48Right. So first of all, I cannot wait to get to the point where I basically type a sentence and, hey, I'm going on vacation for this week. Book some hotels for me. And this is my price range and this is what I want. I mean, we've been talking about that for a while, but it does finally seem like that's coming into focus in a way that's like, oh, this might actually happen as opposed to like me typing that request. And there's a bunch of humans on the other side doing that. But the enterprise thing to me is really fascinating. I mean, Harvey, I've spoken to at least one law firm that has that in action.

9:21First of all, this is the case that all of these doomsayers have been talking about of a bot that can scour documents and summarize them and you can ask questions to it and it can respond to you about what you're looking at. And they said, well, paralegals are gone and junior associates are going right to the bread We just wrote about this in the Boston Globe, actually, that's going to be syndicated on big technology. But it is just so interesting that like the firms that have this implemented, A, find it extremely useful already. And B, they haven't fired a single person because of it. Yeah, I think that at least coincides with my understanding of like what happens historically when you make people more efficient, dramatically more efficient.

10:10Right. If there was like all markets are dynamic. if, you know, there was so much demand for the law when the law cost$1 ,000 an hour, like if you reduce the cost of producing that legal task to$100 an hour, I think people just do more law, right? And so I actually think like, yes, like tools like Harvey are extremely capable and they will do a lot of the work that a first year associate or a paralegal does. But I think what happens is those people do different things for law firms and you take on tasks of scales and costs that you couldn't as a law firm before. Right. And so one of the examples I like with Harvey is, you know, they'll have customers whose end clients want to look at twenty five thousand sales contracts.

11:01tax, but that's not a tenable thing if, you know, the firm, junior people at the firm cost $800 an hour, right? And so either that's like, oh, like we're actually going to do deeper M &A due diligence. We're going to do things we never did before. And like the end client gets more value for the same dollar. I think that is much more likely and more in line with everything we've seen so far in the broad arc of technology history. But I, you know, I think it's unavoidable and true that it may cause like short-term dislocations in what people are doing. Right. And it's so interesting because a company like Harvey, like at once makes you think, well, these generalized bots aren't going to be able to handle everything because Harvey trains on a specific data set on legal, legal files.

11:45It might even be personalized law firm to law firm. So you can have, you know, upload or have it trained on your discovery and then be able to go ahead and take a look at it. But on the other hand, it does seem like a capability that like a more generalized bot should be able to handle just maybe not now, but eventually like you just train it on the specific thing. And then it doesn't have to be like a legal bot or a medical bot just knows. So what's your thought about like whether these specialized bots with specialized data sets are actually that defensible? Yeah, I'm a pretty strong believer in this, right?

12:17I'm biased. My money is where my mouth is. But I think like one of the things that you discover is like the tasks are not as straightforward and the data is not necessarily in the internet data set, right? Like if you're looking at like the vault top 10 law firms where, you know, somebody like you're, you're dealing with litigation and you want the very best lawyers in the world. Like, unfortunately the content of how to be the best lawyer in the world to deal with your case is not deeply represented on the internet. Right. Like, and so when you say like, eventually it seems like these bots may train against this data, I'd say one, it doesn't exist.

12:58Right. Companies have the opportunity to go collect a lot of the expertise in different domains. And like the firms who are doing this will like sort of keep that, you know, for their end customers, I think. And two, it's also like, you know, one of the ways these models work is it's next token prediction today, right? It's like, what is the most likely output given all of the data I have seen with some instruction control? And you don't want what like the internet says is the next step in litigation. You want what the best lawyer in the world says, right? So then what are these naysayers saying?

13:40I mean, there are smart investors out there saying that seed and early stage AI companies are not investable. Can you like make their case for them? I mean, I'm curious what you would think is going through their head. Yeah. They should call me. I am interested because like I made a big bet on exactly the opposite. Right. But let me let me like try to find a way. With a lot of your own money too. Yes. Yes, I am an investor of the firm. I'm hoping this works. I think the fair version of the argument is general models will get only more capable over time. and like you just need to add more data sets, change the training mix, condition the models afterward to a particular use case and all the value is in the model.

14:34There's not product to be built on top. And there's like one company that rules it all. Right. Who has the foundation model, right? And everything else is a thin wrapper, I believe the term is. They love saying that. Yeah, I don't think that's true at all. I definitely think that like, if you just look at like volume of companies being started, I can see where this point of view comes from. Cause I'm like, man, there are hundreds of companies doing the same idea that is a thin wrapper idea. So like, I guess my view is like the viewpoint is not wrong because there are a lot of entrepreneurs that are perhaps not being sufficiently thoughtful about what it will take to succeed.

15:15because if you are producing a product surface that is nothing but the chat box and you don't own the model, like that's probably not a good path. If you've not, there's very little built IP or like depth of the idea. And shouldn't investors be able like have faith in their selves to be able to see that and actually say, okay, this is not that or this is that? I do, right? But I think the, like this is why I'm like trying to like genuinely like listen to the argument where I'm like, ah, from a numbers perspective, there's a lot of garbage. Right. But I think the, the thing that will be different is like, if you actually understand how these models work, if you're collecting unique data and I can give you a few examples here of like what I think has been interesting.

16:02And like, I don't actually think, I think natural language interfaces are incredibly powerful, but I don't think that the right interface for everything. Right. So like the end of software that people are saying, oh, it's, you know, software is just a bot. Now you're not really a believer in that, are you? Oh my gosh. I think we're going to have a lot more software because it's going to get cheaper to generate. Right. So like, I think about, we're just talking to a bot instead of using Excel. Is that your perspective? No, I think that there are general, like natural language interfaces that people will use and have a hugely valuable, but man, if people are not using core relational database based ERP systems like SAP five years from now, like I am shocked.

16:45I will bet my entire fund that that exists five years from now. Maybe like two examples of like why I think the like wrapper theory is not out, is not quite right for at least like some exceptional companies. We have a security company in the portfolio that is unannounced. And it's a former, like, you know, well-sighted open AI researcher that knows what they're talking about. And his original thesis was like, okay, I'm going to go partner with some existing security companies to collect the data I need to do the fine tuning on the model I want to do for a specific task. Right. And he goes to all the like companies that should have the data or like are most likely to have the data, the incumbents, and they don't have the data.

17:29right uh so like you know collecting data actually has cost you have to store it you probably want to try to get some value out of it otherwise why are you collecting it and it has like security and compliance implications and so a lot of the data that you might use to actually enable some of these experiences and make models better it has not been collected so i think people like totally over skew on the incumbents habit and so um if it hasn't been collected like where is the foundation model company going to get that data either, right? Like somebody has to collect it to create the model. It's like you have to do the work.

18:06You have to do the work and it doesn't exist yet, right? Which is expensive or hard. But if you are clever about it, like there's a reason that that works, that there is an opportunity. So your agent bot can do the work for you. So that's a great, great point, right? Which is that there is a pretty high labor load when it comes to creating any type of company. And it's not like you can have like one company just do all of them right away. Yeah, I actually think this is the more important point. Like you just said the thing that's more important than what I said, which is like, if you look at what's important, like all the parts of building a software company and what's hard.

18:42Right. Understanding customers is hard. Building the product that's really valuable is hard. Distributing the product is hard. Having it fit into customer workflows and making them happy and serving them is hard. And like, I would ask you like with love for foundation models and like friends with the big research labs, we've invested in these companies like they don't have all of that done. Right. It is unlikely that one company goes and understands all of the workflows in the world. Exactly. All right, Sarah, one minute before we're going to go to break. And then after the break, we're going to talk a little bit about how every big tech company stacks up in this battle.

19:16But, you know, I'm hearing a lot about how like this type of technology is the end of the world. it's good but i'm not running for my life uh from chat gpt what do you make of all of it yeah i struggle to make the logical jump from um we have models that uh now do next token prediction on language tokens or we have diffusion models that can generate images to like um the the loose logic just to make sure we are understanding a reasonable version of it is like these models are so powerful that they give single individuals the ability to do things that are very risky that used to be very expensive right so this is like a computer virus that takes the grid down or a a biological virus that like kills people that used to be a very difficult look very difficult to design and then like even one step further the logic is something like, you know, like one engineer got overexcited and optimized the model against like an outcome of generating paperclips.

20:25And then like we all, you know, we're in resource competition with the paperclip generation AI. Right. And like, I can't draw any sort of exponential line from here to there, like where we would not understand. Like, I totally believe that we should be careful about what we can do with these models and have a democratic process around it. And like I'm trying, you know, I have deeply committed to AI, like trying to think exponentially. But I think that this is a mind virus. I think that there it's attractive to think about these risks because it's interesting. And I'm a little more cynical that, you know, calls for immediate regulation of a very nascent technology that has horizontal applications.

21:10I think of that as more of a competitive move. Yeah. Fascinating stuff. Let's go through the big tech companies when we come back right after the break. These days, it feels like every dollar should be working a little harder, but figuring out where to put your cash can be confusing. That's where Wealthfront comes in. Wealthfront is a tech-driven financial platform built to help you grow your savings into long-term wealth. Their high-yield cash account through program banks offers a 3.5 % APY on your uninvested cash as of November 7th, 2025. And there are no monthly fees, no minimum or maximum balance to earn that rate.

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23:27And we're back here on Big Technology Podcast with Sarah Goa. She is the founder of Conviction, VC firm that's investing actually in AI startups in the early stages. Believe it or not, it exists. She's also the co-host of the No Priors podcast. Can we go through quickly, like do a quick lightning round of like what you think of like the current efforts that the big tech companies are making in this field. You up for that? Yeah, sure. I guess I'll be honest. I don't know that I'm like super up to date informed about it. So I focus more on early stage innovation. Let's riff on it and see what happens.

24:02So Apple is rumored to make an Apple GPT. What do you think about that? I think it's no secret that the experiences around the Siri assistant have really lagged. In terms of what the expectation is now that we have this, you know, series of things that are really amazing from scaled up LLMs. Right. And Apple has quite literally the world's best platform for creating personalized assistant experiences. They have all of your data, they have user trust, and they have chips on device that are very capable of running increasingly efficient, highly capable models. So I would be really excited to see upgrades in this experience come.

24:51So Amazon has talked about how they want to be basically the distribution point for foundational models for companies that want to build upon them through AWS. And that's going to be their play. Is that smart? I think it really relates to the sort of overall Amazon attitude toward open source. Right. Like Amazon, you know, a friend of mine who's an executive at AWS used to say like, oh, we love open source. Right. Like, you know, somebody else builds a product and we get to monetize it. Exactly. Yeah. So, yeah. So I think the the knock on Amazon would be they are, you know, of the cloud like Google has a research team and a strategy that is very strong here.

25:41Right. They've got the DeepMind, now Google Brain DeepMind combined lab and, you know, big suite of Genesis products. Microsoft has, as you would well know, like executed extraordinarily well on a partner strategy with OpenAI and in terms of getting itself to be positioned as a partner for enterprises adopting AI. and so like lots of companies use Azure for AI in the way that it was not as competitive like a year ago in the cloud space. Amazon doesn't have a leading research lab in this domain, right? So I think one part of the strategy is very much like they don't have a choice right now, right?

26:25They would need to acquire the talent or develop more of a strength here. And the other is it fits with their strategy of like, if you, and I don't think this is wrong. I'm very excited for an increasing number of open source models that are really, really capable for lots of applications. But if you, if you think that's true, or you can work with all of the partners, like having Amazon be just the, the infrastructure target for these capabilities is a great play. Yeah. Okay. That's a great point about the research houses. So let's go into those. Google is interesting. It seems it's had a very weird year.

27:01We're going to talk about this a lot this month on the show, but like initially seemed caught off guard. Now it's marshalling all of its resources and starting to catch up. It has the products. It has the team. It seems like it has the will, but it's still kind of like when I think about Google, I'm just like, hmm. So what do you think? Yeah, I will not pretend to understand the things that hold very large companies back. And I have a ton of very good friends that are amazing leaders that work at Google today. And I see researchers that work at Google today or used to work at Google. So it's not like if they struggle, it will not be for lack of amazing talent and resources or even intellectual understanding around the importance of AI.