Generative AI Moats in B2B with Emergence Capital’s Jake Saper

25:10That is data that the LLMs, no matter which off -the -shelf LLM using, are never going to have, no matter how much data they train on, because that is proprietary to you, and you've gathered that data through the workflow. And so, architecturally, then, to these models, sort of let you create feedback loops with your own data to create a better outcomes version of them. It depends how you define these models. Like if you're just using a call, an API call to GPT -4, no. There is an infrastructure layer that's being built that allows you to store and integrate your own proprietary data in things like vector databases so that you can maintain that knowledge.

25:49In addition, and you guys referenced this earlier, there is a growing ecosystem of open source models and open source stacks that you could use to customize the models with this information. This kind of closed versus open ecosystem debate is really fascinating right now. And my guess is there will be some kind of hard lines drawn in the sand over time on this. But back to the Ironclad example, ultimately the defensibility lies in the fact that they have all of this outcomes data because it's a broader workflow tool that has the full job to be done. And just zooming out for a second, Ben, you asked the question, what else do you need?

26:21what they're scaffolding do you need. It's all the boringsass1 .0 stuff. It's robust permissionings and approvals because you need to do that to make sure the contract has approval from different people within the company. It's a native text editor, it's data integrations, and the other formulations, eSign, all the other things that you need to actually complete the full job to be done. Audit trails and. Yeah, it's all that stuff. It's all that stuff that I think in the history of Gen AI, we've lost sight of the fact that like, like, oh, you need all this stuff to actually make software work well, but you do.

26:53And so the point I would make is that there are a lot of companies that are either solving jobs to be done that can primarily be done within the LLMs and therefore not durable and or the job to be that they've chosen is such a narrow slice that there's not enough of this basic scaffolding you need to build something that's ultimately defensible and has workflow that gathers proprietary outcomes data on this ironclad example, which is such a great one. I want to clarify something for me and hopefully for listeners too. It sounded like you're not that excited as an investor about this feature, but I wonder if that might be mischaracterizing how you feel about it.

27:34Is that true or is it that you're not excited about if a competitor were to launch with this feature as like a new startup built around just this feature that they would have a very tough time competing with Ironclad? Which of those do you say or something else? I'm super excited about the feature in the sense that when I first saw it, I was also blown away. I think it has tremendous potential for the platform. The point I would make is, if you were just developing this on a stand -alone basis, I would be less excited. If a company can't even pitch me with a Genai -enabled contract redlining tool, set aside my conflict because I'm invested in Ironclad, I wouldn't be excited because to me, the majority of that job to be done could be done within the context of the LLM.

28:15maybe not as well, and certainly not with proprietary data that you'd be gathering over time. But if you're doing this within the broader context of a well -formed job to be done, that's where this thing gets interesting and defensible. Right, right, right. I would imagine as a board member of Ironclad, you're very excited about the future of all of the new Genai enabled product features that Ironclad can ship over the coming years because they've already built this robust framework to get the job done and a venue where it is happening. And it's also why I don't have as much fear about the countless redlining Gen AI enabled startups that have popped over up over the past three days.

28:57I thought you were going to say months, but there probably have been countless over the last three days. Then math on this is crazy, Ben. Like, if you look at the growth of chat GPT, it took chat GPT two months from launch to get to 100 million users. It took Instagram three years. It took Netflix 10 years. And I've heard this stat cited a few times before. I have no doubt that it is unbelievably fast, but I don't think this is apples to apples because I think OpenAI is counting registered users, not active users. The hard thing about this is typically you would look at a monthly active user, but in this case, because it's only been a month or two, registered an active or basically the same thing.

29:38So I think what you're getting to, which is a good question, is what's the retention curve going to look like? And TBD, what this shows is just the massive mainstream interests that this technology has, which is a big part of the reason why anyone can code is why there has been so much activity around new startup creation here. I mean, there was a South Park episode on it already two months ago. Yeah. And they use the words chat GPT and OpenAI 50 times during the episode. So it's not like in the abstract, like mainstream America, mainstream world is already like, oh cool, this is the product and this is the company.

30:13I'm trying to think, is this can happen before that like all of that, where it's like this being on South Park is the consumer use case for OpenAI's chat GPT product. But what we're talking about here is like, how can this technology be used in the enterprise? It's a totally different thing, but it's like the same company and technology being used in both ways. Part of the reason is so exciting is because the extensibility of this stuff is effectively endless. You can start to see why people use this analogy, but it's kind of like when the internet was created, kind of the same thing happened.

30:50and people went nuts when Netscape came out on the consumer side. But also a lot of Silicon Valley B2V technology companies were like, oh, we need to understand what this means for our side of the house. And that led to the cloud. It's a good point, David. I guess, and that's a good transition to think about how do we contextualize this in the broader context of B2V software or revolutions that have happened before? How does this compare to the on -prem -to -cloud revolution? How does this compare to mobile? So on -prem -to -cloud, incumbents had a really tough time adopting and adapting in that moment because it's really hard to rewrite your entire code base.

31:32Most of the incumbents from the on -prem era are dead. Sebel is dead, Salesforce became the cloud -based CRM and took off. And it's not just rewriting your code base. like it is a physical change of how your entire company operates. So of course, there's the like, we should make data centers and we should stream the bits down to customers and we should deliver it through the web and blah, blah, blah. But there's also like, we should completely change the nature of our relationship with customers such that they buy a different product from us, such that they're buying access to software rather than like the truck of our stuff by our employee that arrives there to install it and charges service fees for installing it.

32:17Like, of course all those companies are going to die. Rewriting code, it's like firing everyone and hiring a whole new set of people to do a completely different set of things, such that the job to be done for the customer is the same, but, you know, it's like the tip of the iceberg looks the same, but what's under the water is actually a different iceberg. That's exactly right. Yeah. And so unsurprisingly, there was a dinosaur like asteroid that hit and the incumbents died and life was born in the cloud. Mobile was a little different in B2B. It did require some replatforming, but there have been a number of incumbents who have adapted with some success, certainly in the consumer space, Facebook, very notably.

32:54To think about B2B, I would argue like Salesforce in the mobile revolution has done an okay job of adapting to mobile, but if anyone's used the product, The most product is still not awesome. There's now a bunch of use -case -specific mobile CRM applications that have been built to fill in the gaps were invested in one called ViMo focused on financial services. So in that era, the incumbents, it was difficult, but not impossible for them to adapt. And I think that's what's happened. Generative AI is a completely different ballgame. It's as easy as an API call. And so Salesforce has already integrated this technology.

33:33Now, how much success they have, how well they've integrated, how other incumbents do, et cetera, we're all still very early in figuring that out. But this is a different ballgame and has meaningfully different implications for the startup opportunity. Yeah, we're just talking about the Ironclad example, right? Like, they are the incumbent in the space, and they are the best, you know, probably the best actual product version of generative AI in the space as well. I'm obviously biased in saying that, but I think that's true. So there's interesting implications for, Or if you think about them as the incumbent, if a startup is trying to pick off little parts of their job to be done, and you can do most of those within the LLM, it's gonna be less durable.

34:11What types of incumbents do you think are the most at risk from complete disruption, like their business going to zero from the fact that this technology exists? It's a good question. I think that there are incumbents for whom the current UX or UI paradigm is not one that will sustain effectively in the new environment. So let's get into UX UI. So as everyone knows, a chat interface, hence chat GPT, is a common way to interact with these LLMs. There are going to be some B2B use cases, and certainly many more, I would say B2C use cases, where a chat interface is superior to a point and click interface.

34:55And if you are an incumbent who has built their entire stack on a point and click interface, you're going to have an innovator still in the problem. So I don't know if this is true, but let's imagine a world where a chat first interface is the best way to build a CRM. If that's the case, it's going to be really hard for Salesforce. It's not that Salesforce can't afford to hire great product people to build that. It's that they have an install base of millions of daily active users. They're used to the point and click interface, and they can't disrupt that business. So there's an innovator's dilemma issue that could arise from this UX paradigm shift.

35:32One working hypothesis I've been sort of nuling on ever since we interviewed Avloch, the CEO of Angelist on our last ACQ2 episode is the more services oriented a firm is the more at risk they are of LLM disruption. He was pointing out that for all of everything that Angelist does, all the tens of thousands of portfolio companies sort of managed by Angelist and hundreds, maybe thousands of VCs that have a back office and I can't remember what he said, but like hundreds of thousands of K1s, there's 170 human team that works at the company like inclusive of their software engineers and management, like everyone, designers to perform all of that activity and they do a lot of AI behind the scenes for operational efficiency.

36:23His point was that 170 people relative to, they put joking on the episode, 170 people at Angel List is like, probably roughly the same amount of people that Andresan itself has in their back office. And Angel List has scaled to support nothing against Andresan, right, but like they support just like orders of magnitude, more funds, portfolio companies, K1s, and they use LLM and Gen -Ai technology to get that leverage. So it basically provides operating leverage for businesses that that use case didn't use to have operating leverage. Like you can have high gross margins in what we used to think was an exclusively low gross margin industry.

37:04Yeah, I think it's also an opportunity to think about not just internally, but externally, what product could I build that was once a service? An example of that, and I don't know if anyone's working on this yet, but pricing strategy is something our portfolio company spends so much time and money trying to figure out. It requires trying to estimate a price elasticity curve. How much are people willing to pay for this product, different types of people, what should the right packaging be, et cetera. That type of thing today is the domain of consultants and guessing, largely, but you can imagine a world where you could input all of your historical data on this and a model could spit out, this is how much you should charge for this, this is how much you should package it, and then it could be updating that real time as it's getting data from how people are purchasing.

37:48That is not an existing category. So this isn't a question of like disrupting an incumbent. It's about creating an entirely new category. And also to your point about UI -UX, that is a wholesale different UI -UX from the current way that job is done now, which is by people in consulting and steak dinners. If you productize it, that's gonna be very different. So one way to think about this is what jobs to be done couldn't have been done with previous technology. And that's often going to be a thought exercise there is what is the domain of consultants today. My partner Gordon Ritter in 2013 wrote an article called the death of McKinsey with sort of this spirit in mind, but I think the death of perhaps not McKinsey, but it was just a few years too early.

38:29Yeah, I think many of these consulting efforts if they don't adapt this technology themselves are likely to be productized away. Then there's another category to think about if you're a startup thinking about how do I play in the incumbent landscape, which is what entirely new jobs to be done will be created by this technology. And there's the obvious answers here around infrastructure, right? There's a whole layer of companies that are being built today that are doing vector databases that are doing prompt engineering that are doing model chaining, all these model training, etc. that exists as this technology rises.

39:04So that will be obviously opportunity for startups. There's going to be a massive opportunity around compliance in generative AI. And there's a bunch of stuff happening, you know, whispers of what's happening on Capitol Hill right now in terms of potential regulation around generative AI. But my view is that regardless of what happens in DC, enterprises themselves are going to demand that their vendors have some form of compliance on this front. And that compliance will likely entail something around, hey, what data did you train on? Is this even legal for me to use this product? How do I ensure that you're not taking my data and using it in the model?

39:43Or if you are, I'm aware of that and getting paid for it, et cetera. How do I ensure that there are proper guardrails around the technology such that, you know, the thing doesn't go haywire and screw up my business? All of these types of things, there will be companies built to do this. You put this very elegantly in our little outline we were working on before, the episode for this, a Vanta for AI, which I love to do. I was a sponsor. Shout out to you. It's not a great partner of ours, but yeah, totally. It's a super exciting and I think really challenging space to build in, but one that I think will become important and it's an opportunity for startup because there's not a space today.

40:18Well we're incomprehensely and it's kind of an interesting thing to note that Sock 2 is not a regulatory framework that has nothing to do with anyone on Capitol Hill. So will AI be the same way where it's not legally enforced, but it's sort of a set of standards that gets adopted? That's kind of in my current framing. In general, I like to invest in stuff that has business tailwinds and not just regulatory tailwinds. And my sense is there's enough here that people are gonna care. You guys probably read last week, Samsung discovered that three of its employees had uploaded proprietary data to ChatGPT.

40:54They had a bunch of secret meetings and they wanted someone to summarize or something to summarize the takeaways from the meetings. so they just put in Chats GPT without thinking like, oh, that data now belongs to OpenAI and Microsoft are a potential rival. More and more of that is going to happen. Right now, what's happening is companies are just saying, don't use Chats GPT. If you're firewalling this technology completely, then you're going to get left behind. So you've got to find a way to integrate this technology in a way that is enterprise compliant. And I think that will be driven by the enterprises not necessarily the government.

41:27Is that how Chats GPT's terms of service work? any text that you upload here, we can read any of our employees. There are a lot of look at this in plain text. So I don't know if it's any employees can read it. It's not like Uber, Godmode. No, I don't think it's Godmode. I hope not, God. Just thinking about some of the things I put in chat GPC. I believe they changed their terms of service recently where it's either the default or easier to default into them not being able to access that data. But the broader point remains that there's a level of fear, and I think justified fear around sharing particularly sensitive and proprietary data with third parties.

42:01Part of this will get addressed with this open versus closed ecosystem conversation we're having before. If you are super, super nervous about your data leaving your premises, you're likely are to opt into the open source and open ecosystem and kind of build your own, which is now easier to do than it traditionally was because of some of these breakthroughs. But I think there are also going to be a bunch of privacy and compliance related technology that's necessary to ensure even in that world that the data that you're using is legally kosher and not leaving your promises and the thing doesn't go off the rails, etc.

42:32Well, this is such, historically, has been such a huge opportunity for startups like I'm thinking of Zoom. And I remember when we were talking about right after taking our business school classmates and you joined Emergence right after we graduated and talking about the Zoom investment as you guys were making it. And at the time, you know, Facetime I'm, was really good, still is really good. The more directly competitive product with Google and Hangouts was pretty good. Zoom's probably still is better, but still. I'm biased, but I think so. The obvious at the time, Zoom became much, much, much bigger and more consumery over time, but like, was, hey, enterprises aren't gonna use FaceTime for a whole bunch of reasons, and they also don't like Hangouts for a lot of the same reasons.

43:15Yeah, the enterpriseification of new technology be create massive businesses, both underlying application businesses as well as the derivative technologies that are compliance and regulatory and everything else to make sure that they succeed. It's even more important in this world than I think it was in previous worlds because the risk of things going wrong is so much worse. I assume you guys are familiar with it. If you're not, I'll share a bit about this concept of auto GPT. Are you guys familiar with this? I like literally just started reading about it last night And listeners, this is going to date how long we wait between recording and releasing episodes.

43:51But AutoGPT is still a pretty new thing as we're recording this. It's possible that we won't exist by the time that this episode comes out, because AutoGPT will have taken us over. AutoGPT is effectively an AI agent that you can give a goal in natural language, and it can attempt to achieve it by breaking the goal into sub -tasks, and then using the internet and a bunch of other tools you can plug into it in an automatic loop to solve it. So basically, you give it agency to solve problems. It's implemented as a browser plugin, right? So it can do things like type in websites, go to them, fill out forms, download files.

44:26It can be, there's a bunch of formats, but that is one of them. So basically, you can tell it like, hey, and there's been a bunch of examples of folks doing this, create a business. Like, I'm gonna give you $100, create a business, and try to make it as profitable as possible. And it can go and, I'm gonna give you $100, turn it into $200, as quickly as inhumidately possible. It can start a company with Stripe Atlas, it can go to Shopify, it can spin up a shirt store, it can do all of those things. It doesn't take much imagination to understand how sci -fi nightmares become real. Not just from a consumer perspective, but from a B2B perspective.

44:57If your well -meaning employee goes to AutoGPT and says, do a bunch of scouting for prospects and draft a bunch of emails and send a bunch of emails, you can imagine a world where some information that is shared in those emails is probably not what you want shared. Or, hey, do a bunch of research on this supply chain opportunity and reach out to these vendors and get some price quotes and come to us with the right. You can imagine a world where a bunch of stuff is purchased that you don't mean to purchase. There's all sorts of ways in which I think that we could be trending towards our own FTX moment in gender AI.

45:32What do you mean by that? So, what I mean by that is things are evolving so quickly that people are building first and thinking about oversight and guardrail second. And so it's likely that there will be some sort of catastrophic issue in a company in the coming quarters that is driven by someone building an agent that goes wrong. And the reason I call it the FTX moment is because it will likely be similar to what happened there in that there will be a very famous blow -up. And the issue won't necessarily be because there was a flaw in the underlying technology. The FTX didn't blow up because crypto was bad.

46:10FTX blew up because there was poor oversight. And I think the same thing could be true here. It's not that the underlying technology is bad. The underlying technology has a lot of limitations that you have to build around. But if you have proper oversight, I think it can be really helpful. But people aren't thinking about that right now. There are a bunch of Genai enabled hammers just looking for a nail. Another good analogy could potentially be the Sony hack, if folks remember that. That was such a key point of like companies even big enterprises like Sony didn't think about cybersecurity in anyway the same form until that happened and like the incredible disaster not just for Sony but for so many other companies that got caught up in that because of their emails with Sony people like Snapchat was caught up in that and that was a watershed moment for enterprise cybersecurity.

46:54Yeah, so I think we will have something similar to that which I think ultimately will be healthy. We will likely go through some sort of trough of disillusionment with this as is true with almost all new technology innovations and it's possible that the Apex will look like one of these catastrophic moments and a lot of enterprises will pull back and say whoa whoa is this ready for prime time what do I need to do to make sure it is and that will be kind of the healthy growth moment it'll be an opportunity both for the derivative companies the Vante for AI to come out and help make that a reality But it also goes back to UX design.

47:27I think we will learn a lesson in how we build these technologies to effectively include the human in the loop. It's pretty interesting the trough of disillusionment because normally when I look at this Gartner hype -seical graph, it's like about VR or about some technology that we hoped would reach mass scale and mass utility, but we all got too excited and it didn't and then over time slowly over the next 5 -10 years it did. In this situation, there's obviously an insane amount of utility for hundreds of millions of people, and that is already true. And so our sort of like fall from grace here when the hype gets ahead of the utility isn't going to be that there's not utility, it's going to be that we're not ready to embrace everything that comes along with that utility.

48:14So we're going to be like reigning or attempting to rein in some technology that clearly is useful. And that basically never goes well. Like you kind of can't tell people stop using that super hammer that's cheaper, faster, stronger, better than your existing hammer. It's like, I'm pretty sure I'm going to keep using the super hammer. Otherwise, I'm not going to come work at your company. People do what they want, where they find value. That is true. And I think that will particularly be true like on the consumer side of things. I think the genies out of the box, when you're thinking about selling into a mid market or enterprise company, there is a level of conservatism that exists there and should exist, which I think will rein in some of these behaviors.

48:50But it's the opportunity, right? If you're an application builder or anyone in the stack that's building to try to sell into a proper B2B company, you have to keep this stuff top of mind. I think one of the most interesting things to pay attention to in B2B software over the coming year or two is how user interfaces and user experience do evolve. We talked a bit before about the Salesforce innovators to LEM issue if chat interfaces become popular. There are some situations where chat interfaces will become more popular and B2B, although the infinite landscape, the infinite canvas that that presents, I think, does have limitations.

49:23So I think it won't be ubiquitous necessarily. But in general, how do you figure out how to effectively build a co -pilot or a coach? What are the best UX best practices to do so to ensure that you're both getting the best of the human, as well as ensuring accuracy, and that the thing you build doesn't go off the rails? Like, the companies that do that best will likely be the winners of this next generation. And it feels like it's already here, right, in that the UX for some of those things you just mentioned, but I haven't actually tried Copilot, but like I'm thinking of Notions AI feature. It's not really a chat interface.

49:58Like it's baked into the workflow that already exists in the platform. I think a lot of these UXs will obviously in the back end they're conversational. You're having kind of a conversation with the LLM, but the front end will likely include, depending on the use case, some elements that are more conversational and something aren't. I think notions are good example of that. In the sense that you can type a phrase and the notion can tell you like, hey, you can offer you, do you want to make it more professional? Do you want to make it more casual? Etc. So it's bounding the canvas and telling you what is possible.

50:29The scary thing with the chat is, you just don't even know what's possible. And there are lots of limitations within that, you know, and scary things that could happen. But I think a lot of these interfaces will take the best of that and say, hey, here's some suggestions on what you could do, press this button and see what happens. And then the the best of them will learn what the human does with that suggestion, what edits they make, et cetera, and then tie it to a business outcome so that every time a new suggestion is being made, it's improved by the historical data. – Ben and I, we were just texting yesterday, I think, right, about a piece of data that we've had for a long time on acquired episodes that we basically completely ignored is the graph of listener engagement through the course of an episode.

51:11like when do listeners stop listening? And we kind of have never done anything with it, but like you can imagine. If we had though, it would have changed our behavior. We would have been like, oh, don't make episodes too long, because it drops your completion percentage. Right. So like we might have optimized on the wrong things, but also just completely ignoring it is like, probably not the right answer. This is why the human in the loop matters, just like that discussion you guys just had. Surfacing that data is gonna be really important, and then you as the human have to figure out what do I do with that.

51:40That's where the interpretation and frankly, like the reason you get paid exists. Cause otherwise, like, auto GPT generated an all -in podcast script. And so you guys need to figure out how you can keep up in your game, which is also going to come down from inside. It's like, what's your share? Well, that's kind of where I was going with the, like, when we were talking about the Xerox risk earlier, like, at least in our world, people will engage with what is compelling. If a Xerox world is not compelling, people won't engage with it. and then somebody will come along and tweak it as a human and they will again.

52:13I think that's right. There is likely to be an interstitial period though, where we build these technologies, these suggestion -based technologies, and the humans don't stay engaged. Do you can imagine a world where, like, let's say people, you know, start building what I'm describing, which is they have kind of a smart coach, co -pilot UX, which helps hopefully mitigate some of the accuracy risks and tries to get some of the brilliance, but what ends up happening is the human gets used to it and just clicks except, except, except, except, except, except, it isn't actually engaging their brain at all.

52:44The Dacu sign problem. Yeah, exactly, which is, yes, yes, there's a real problem there. But I saw like a quote in the journal like last week, the woman who runs HR at Heinz, a craft Heinz company said like, the thing that's keeping me awake at night right now is how to use these AIs as a co -pilot, not an autopilot. It's a succinct way to summarize this problem. There are too many business use cases where accuracy is critical and having the pilot fall asleep could crash the plane. And so back to the UX question, I think the best companies will find ways to keep users actively engaged. And part of that may be things like the way you actually designed a suggestion.

53:23So we work with a Seattle based company called TechSDO, which has been doing augmented writing since like 2015, I think. And their user interface is really clever. They're focused on HR writing. So if you have a job post, they will go through and help you highlight, they will highlight phrases and say if you change this to this, you're 12 % more likely to attract whatever type of candidate you want to attract and eliminate bias in that process. Most of their suggestions, if you hover over them, will tell you, hey, change this to this. Every once in a while, they highlight a phrase and they say, change this.

53:55They don't give you any suggestion. Oh, just so you don't get in the habit of like click, click, click, click, click. There's two reasons why they do it. The first is so you don't click, click, click, click. But the second is because they're actually trying to generate new data, new what you could call mutations for the system. So it's not the Xerox problem. Interesting. And if the user base is wide enough, even if I only get one out of 100 times, and most of the time it's really speedy, you still create a very large new corpus of data. Exactly. And like if you take this even further, you could think about almost incentivizing or gamifying this process for the user.

54:31If I innovate on the data set by coming up with a new way to answer this problem or phrase this thing in a job post, and that has a positive business outcome, it helps close the job post faster, whatever the outcome is, I should get paid. If the inside I came up with in conjunction with this AI really helps the whole system evolve or mutate to a better business outcome. I as a new value should get paid. And so there's gonna be new like compensation models that I think could arise with this stuff. The Patreon of AI. Yeah, perhaps the Patreon is an interesting concept. By the way, I went to a scary pockets show a couple weeks ago at the Film War.

55:13Scary pockets is an amazing funk band they cover music and the CEO of Patreon is in the band amazing amazing Jack Conti right exactly Jenny my wife went to high school with him. Oh, that's so cool as a laps musician myself I find like his ability to combine his day life and his night life so inspiring. Is Carlos still a Patreon Carlos? Still a Patreon yeah I picked him to see if you wanted to come to the show, but he moved to Montana. Oh wow nice This is our other DSP classmate who is also an accomplished musician. He was the, is the CFO, I think? I believe he is. The CFO, yeah. Back to the UI and UX interface.

55:49Like there's a framework that actually this guy James Cham at Bloomberg Beta helps me think through, which is thinking about the AI and human relationship as an intern to manager relationship. So if you think about the AI as the intern and their job is to help provide leverage to the manager and the manager's job is to review their work, improve upon it, and then press submit, the human gets leverage from the AI, but the accountability stops with the human. That's what people are going to pay for, I think. Right. The intern screws up, the manager's going to get fired. Yeah, exactly. Exactly. And so we need to be building with this, the use cases where accuracy is important, and it's tied to important business outcomes, building with this framework in mind I think is likely to create positive outcomes.

56:45But right now, so much to the conversation is the opposite, which is the AI is gonna be the boss, can take every job. And the reality is there's a ton of jobs where accuracy may not be as critical, where the human's input doesn't have as much input or effect on the business outcome, where the task is super static, it doesn't change very frequently, where those will be in our being automated away. But the use cases with the Reopuset, you need the human effectively involved. It's so hard for a human to stay in the loop if the suggestions are good enough. This is why Tesla makes you grab the steering wheel every X seconds, because you will just tune out.

57:19And it is your fault if you crash the car. You have a big counter incentive, including your own life, to not tuning out, but you're still All those YouTube videos of people, you know, going to sleep in the back and like all, you know, insane stuff. David, you and I got a docu -sign with like a lot of pages and a few things that we needed to sign maybe three, four days ago. I don't think you read that whole document. I think you... I think you... I know what you're talking. I'm pretty sure that end most cases these days. I'm just like, oh Ben's gonna do this for me. I already have one. That's why I read it.

57:53I was like, there's no chance to even do this. I need to do it. It's called parenting. There are AI -enabled therapists if you guys are looking for some intervention here. A business marriage counselor. Let's take the Tesla example for a second, and even the DocuSign example for a second. I think one framing to use here that hasn't been discussed much is how much influence does variation in human behavior have on outcome. What is the variance in outcomes in general? So in the case of driving the car, the variance in outcomes is it's kind of binary. Like you get to a crash or you don't. But in a lot of business contexts like sales for example, there's like a top 10 % that perform way, way, way better than everyone else.

58:38And that is larger driven based upon delta's in their behavior. There's uncapped upside and downside. Exactly. And so in that world, I think you can build user interfaces and potentially compensation schemes that keep the human being engaged. And you make it to a world where just the very top performers are the ones that stay engaged. And then you fiber when else, because the system can do the lower stuff. I read a paper that my colleague Jess shared with me a few weeks ago about analysis of co -pilots in example scenarios. And it found that the biggest impact they had was on people who are already high performers.

59:16Biggest positive impact that they had. It helped the high performers perform even more, which is both like a heartening and also a little bit disheartening Example perhaps in the future the copials can be built to actually up -level Folks who aren't you know operating at a high level already. I just think about that like What are high -performing people who I've worked with? Like they're people who are engaged. Well, I thought you were gonna name names. I was excited to hear you Participants on this podcast are you know at the very top here? I'll leave it to you guys to debate who's higher. But, but yeah, it's people who aren't doing what I do with Oliver.

59:52Back off his stuff for a choir to click, click, click, click. I'm a low performer there. But I'm a, hopefully a high performer when it comes to writing scripts. Like I'm highly engaged. Like that's the point, David. Forgive me for like zoom out for a life philosophy for a second. My guiding life philosophy is understanding what gives you as an individual energy. and then pursuing your life to maximize those things and minimize everything else. And it's possible that writing scripts and maybe playing with Nell are the things that give you the most life energy, maybe not. They do. Doing docutized interviews is not something that gives you energy, but it's possible that Ben actually gets energy from it or someone else does, et cetera.

1:00:33The process of self -actualization is to get yourself into the zone of energy creation. Here we go. Jennie is pushing everybody to the top of Maslow's hierarchy. That's the positive spin on this, right? If we designed the systems correctly, you get to a place where it's like, you know what? The person who was like the lower performing sales rep, that never really gave him her energy anyway. And so they can be replaced and the high performers can be coached and they can go and do the thing that gives them more energy. Doesn't this argument also require universal basic income though? Because at some point, there's not enough income producing jobs for everyone to do the thing that gives them the most flow.

1:01:10I think that is probably true, but I think one thing that is worth keeping in mind as we talk about all the bots that are coming for our jobs is that After ATMs there were more bank tellers. The fear was that like ATMs come bank tellers are gonna be gone And now there are more bank tellers today than what is that population growth thing? That's stupid. Why would there be more bank tellers? The bank tellers are doing other things Yeah, the point humans like interacting with humans. Yeah, before bank tellers were just like giving you cash and now bank tellers can do like a vast array of things that they could never do before.

1:01:42Manage your relationship with the bank. I get the new jobs thesis generally, but the more bank tellers thing, always smells funny to me. I haven't dived in deeply on it. I know that Ezra Klein goes back to it frequently, and so I'm kind of trading on Ezra here. I don't mean to invite you on my podcast and then tell you you're wrong, but like. What I'm gonna do is put Ezra in my place, since that's where I stole that one from, and you can debate with me with him. I think that the broader point though, is absolutely right, which is like, like you have no idea all the new jobs that get created by this new boom.

1:02:11Like all of the jobs that we all have, venture capitalist was a very niche thing, but not nonexistent. But like all the jobs we have podcaster didn't exist 30, 40 years ago. The job of program manager at technology company where I started my career basically didn't exist 30, 40 years ago. So most new jobs for college grads are ones that are new. Yeah. It's beyond my expertise to like really authentically prognosticate around where this is going to go from a broader jobs perspective. But I think that it is possible that the jobs that do remain, people who have genuine energy towards doing those jobs will be able to do them more effectively in conjunction with the help of a robot, as long as they think of themselves as a manager and they think of the intern as the helper.

1:02:57I'd have two areas I want to make sure we hit before we wrap up, Ben can jump in with others. One from our outline, we need to make sure we hit because we'll be doing people a great disservice, founders and investors if we don't. You haven't here how to think about pricing in Gen AI. I have heard nobody talk about this. So please give us your thoughts. This is evolving quickly and I'll explain mine a second, but one thing is pretty clear, which is that the current paradigm of per seat pricing is unlikely to be the future. And there's a bunch of reasons why that's true. One obvious reason is that there's a potential cannibalization effect.

1:03:41If this technology does make the user better faster, et cetera, you just need fewer users to get the same job done. And so if you succeed, presumably your contract size could actually shrink. And so there's some cannibalization. Now, you could argue like, if you make them more effective, you can hire more of them, et cetera. There will be situations where that's not quite one to one. But in general, the goal with pricing is to tie yourself to value creation. Like, that is the framework to think about when you're trying to figure out how do I price. And per seat made sense when you were selling a hammer.

1:04:13When you're selling a genitive AI -enabled hammer, it may not. There's other reasons why per seat pricing is potentially the ideal for AI. Another is, like, per se pricing can dissuade spreading across the company. And a lot of these products, we talked before, like, I believe that the medium term moat in this space is gonna be robust workflows around a complete job to be done. And I think the longer term moat around this space is gonna be proprietary outcomes data, business outcomes data. You're best suited to gather that proprietary business outcomes data when you have a lot of people that are using your product.

1:04:50And if you're pricing proceed, it just wades that from taking place. So I think there's also kind of, it just makes the product potentially worse over time if you price that way. Okay, so what do you do? So what's the answer? Yeah, so just to double click on the proceed, which has become, let me correct me if I'm wrong, you're the expert, but like the standard in SaaS, the reason I think it is is that like, it's really intimidating, you're gonna tell an enterprise to adopt a new tool, and you're like, oh, the way you do that is we sign a seven figure deal. versus the way we do that is like a small team of you guys who have sat a credit card, if we grow over time, that's a much better sales motion.

1:05:23It is although it can be hard to really get to the seven figure levels if you start that way. So it's a much broader conversation around go to market models for SaaS, which we can do an entire episode on if you guys want. The way to think about this is like, start with first principles, which is, do an ROI analysis to figure out how much value your widget is creating. And this gets back to the first thing we talked about around I'm like, what is the desperate need that you're solving? How big is that need and how effective are you at solving it? This is the honest, like, truth moment where it's like, okay, well, I'm solving it.

1:05:52It's this big and this is how much I'm solving it. And then you can ask yourself, okay, how much can I charge? So this is not how do I charge? It's how much can I charge? And in general, SaaS companies can charge roughly between 10 and 30 % of the value they create. In a more monopolistic setting, you can charge a lot more because there are other options in a more commoditized situation, you can you obviously charge less. But that like is a rough, a rough framework. And be really honest with yourself about this, obviously. Part of it is you have to gather data from customers and hopefully from the customer.

1:06:24This is hard when you're just starting as a state. You really can't do this because you don't know. It's true. It's, it's, it's, which is why like pricing is an iterative process, right? Like most companies, it's starting like it started phase. They either, if there's a really good comp, they just charge what the comp is doing and maybe a little bit less because they're the, in a startup so you need to get folks over, or they just go to a customer and they have five different pilot customers, they give five wildly different prices and just do price discovery that way. So like this is more of the academic framing to think about, and certainly as you scale, this should be the framing you're using, but the process of getting there, it can be a messy one, but you need to be keeping it like proactively top of mind.

1:07:03So let's say you figure out how much to charge using some combination of what we just described. then the question is how do you actually charge? And as I said before, like the goal here is to tie your pricing mechanism to value creation. That is where value is aligned, right? You did add value, therefore you should get paid, but you don't wanna disincentivize usage. And that's a really important point as we talked about before. So what's an example of that? So in the case of textio, the company we mentioned before, the classic way to charge there would have been per seat in terms of number of recruiters for their job post product.

1:07:37But number of recruiters may not actually correlate with how heavily use the product is, how much value it's creating, etc. They may not be hiring a ton of people right now, they may be hiring a lot of people, etc. You could also charge on a volume basis, which is how many contracts go through the system, but that dissuades usage. What Kieran, the CEO, decided to do, was to charge on the basis of how many jobs the customer expects to fill over the course of the coming year. So that basically creates like an unlimited all you can eat, you know, usage of the product. But if they're hiring a lot of people, then they should be paying more because they're getting more value out of it.

1:08:12If they're not hiring that many people, then you potentially charge, charge less. There's ways to deal with wrongness there, right? Like if you overestimate three quarters in a row, then like we should give you some kind of rebate or we should talk about how we give credits toward your next thing. Likewise, the opposite direction you can bring out a big hammer and say if you go over your limit, it gets much more expensive on a per higher basis so you should estimate correctly. Yeah, that's right. There's ways to validate. There's some work that goes into actually institutionalizing, operationalizing a model like this.

1:08:44They're also downsides because the customer is not used to buying in a model like this. They're much more used to buying per seed. As people start experimenting with new models for the reasons I talked about before, there'll be more openness to exploring approaches like this. Then the bonus way to think about on that, like how to charge thing in the world of AI, is how do you use pricing to actually improve your product? What's happened in the SaaS revolution? Yeah. Like how do you actually get people to submit their data to your system so that your product gets better for everyone else and allow them to use your data anonymously across the product?

1:09:20Preparatory outcomes data, we talked about, like I think that's the long -term vote for these businesses. It's more powerful if you're not building a model just for one company is proprietary outcomes data, but you have all of your customers proprietary outcomes data. Now, this is less likely to happen in data sets that are core to the company. So the way we think about this internally is what are critical non -core data sets? Data sets that are important, that people are willing to pay a lot of money for access to and anonymize way, but aren't necessarily core to your company. So hiring data is a good one.

1:09:50It's critical data, but like Google's core proprietary, carry their code behind the search algorithm, they're never gonna give anyone else. But they're hiring data, they might, in like a very anonymized, protected, et cetera, away, if they get a pricing break, this gets back to the pricing thing. So you can think about saying, hey, you can have the single tenant version of my model that I built for you, and it's X, and then you can have the multi -tenant that's point A X. Not only is it cheaper, but you also get access to everyone else's insights. And so you have a twofold incentive to participate in this structure.

1:10:27That type of pricing thinking will become increasingly popular. Here's the part of my thinking on this that is still evolving because I think this whole space is still evolving, which is given that in many cases you're now renting a new piece of infrastructure that in and of itself has varying degrees of cost associated with it. How should you pass on that cost to your customer? depending on how much open AI is charging you for whatever thing you're using, that'll have meaningful implications on how you can charge. So you have to think a little bit. Traditionally software has not never had a cost plus pricing mentality.

1:11:01Like most stuff, most physical goods are cost plus. Like cost me this much to make it. I'm gonna charge 20 % top of it. Software obviously doesn't have that paradigm, which is part of the reason has such high gross margins. But this is a world where we're adding a new cog.

1:11:18We're adding a new you get better at the engineering problem. Like let's say a company sort of has a use case where there's AI magic that happens and customers are loving it and the company figures out, ooh, we can do 20 % less API calls if we cash in this layer and we figure out some efficiency there, that should accrue to you for figuring out that engineering efficiency and not to your customer. So you want to continue to get the operating leverage on new efficiencies you find in your company too. Exactly. This is all happening, evolving very quickly real time. There's ways to optimize if you're using third party models, like if you're using the OpenAI's suite of models.

1:11:56I know some folks, I won't name them publicly, but I know some folks that are using OpenAI's models to train their own proprietary models. So they're building a use case specific app, and they'll just do a bunch of calls on GPT4, and then they'll spend $50 ,000 training in their model off of that. There's interesting legal questions here, right? Is that legal? This is like the, I don't know. I did bootstrap on address books. It's AI laundering. But like isn't open AI doing some of that as well? Like the data set they've trained on is that. Let's close AI. We actually have no idea what they're doing.

1:12:28If it's dirty money all the way down, then like what's a few more germs? We've talked about the regulatory stuff here. Like one thing we didn't touch on is like, what will the legal implications of all this be? And I think it's gonna be years before this gets sorted out. And between now and then someone will make millions of dollars. It's just like the LinkedIn thing. Yeah, exactly. People are going to have to build it. I had someone ask me recently, should I just like not do anything for now until it gets figured out? And I was like, well, do you want to play in the next five, ten years? Like, no, you should probably go make a bunch of money right now before.

1:12:56To be clear, like, people should be behaving as an all -context with a high level of integrity. So like, I don't want to excuse like all this for lack of intention. When I say make a bunch of money, it's like, go create a bunch of value for customers and ask people to pay you for the value you created. Like that is the way to, yeah. That's exactly right. And the way people price, given what's happening on the underlying COGS layer, is going to have to evolve quickly. Because some people are training their models off open AI. Some people are just doing their own open source stack, which potentially could be cheaper for them to serve.

1:13:25And therefore, there could be some cost advantage against competitors here. So we talked about UX as a super fast evolving, exciting part of this that has a bunch of unanswered questions. I think the infra and the way the infra translates into application layer pricing is a really exciting space that like a lot of innovation is going to happen over the next year too. Awesome. Fricing discussion. Thank you for all of the alpha that you have just given us and founders listening to this right now. I said there was another topic I want to touch on before we wrap here. I just want to do a temperature check with you as a venture capitalist.

1:14:00list. Obviously this space and your corner of it in B2B is top of mind, something you're spending a lot of time and effort on. This is a really weird time in venture and startup investing. We just came off this 15 year boom with lots of mini -booms building it all up to a huge deflation that happened violently and rapidly. And now here we are with another. It's very disorienting, or at least I find it very disorienting. I'm curious how are you feeling? How is emergence feeling? What's going on? There's a level of schizophrenia for lack of a better word where you're pivoting between I mean, situations where companies may have been struggling to sell because sales cycles have gotten much worse or financing issues and certainly the SVP crisis didn't do anything for the good for the blood pressure of this whole ecosystem.

1:15:02And then you're also chatting with folks who are building extraordinarily exciting products, many of which are me two products, but some of which I think have the potential to really be enduring companies. And so I think it's always true in venture that the best of us find ways to control our emotions and kind of find a centered place. I think this time it's the hardest time I've ever experienced to try to do that. But it's also in some ways the most important because ultimately our job for the founders that we serve is to be emotional calibrants to them. As bad things or good things happen it's to help them calibrate their reaction and support them, particularly when things are challenging.

1:15:43So a lot of this time is about trying to emotionally regulate your excitement, your fear, your anxiety, your opportunity. This is a time also of great anxiety, not just for VCs, but more broadly. Like in the VC landscape or the startup landscape, there's a real fear of FOMO, right? Which is part of the reason why you see the horde of hammers. There's also a human anxiety level of like, what is the world going to be like for my kids? What should I be teaching my kids? like those are scary things to think through. All of that is very true, and I don't want to discount it. I think also the like one thing I'm struggling with is just like a capital allocation question, right?

1:16:22Like all of this is occurring when tech and venture, both public tech and private tech broadly defined, you know, like we said, just went through this massive deflationary cycle or price is crashed. Let's just put it that way. let's people want interest rates are like 5%, which turns out it's actually a very attractive place to park a lot of capital. And so risk appetite has gone way, way, way down. And all of a sudden, here is this new like incredible opportunity presented on a platter against the backdrop of an incredibly different macro environment from the last time. I'll make this a perhaps an easier your question to answer.

1:17:03What are your conversations with LPs like right now? Yeah, we just had our LP meeting, our annual LP meeting a couple weeks ago, so it was top of mind. And they asked a ton of questions about AI, because both as consumers and obviously as investors themselves, it's top of mind. I think that one of the key things that we talk about with them and that we've tried really hard to stay disciplined around is time averaging our deployment pacing. You can never time the market, both from an interest rate perspective and from a technology innovation perspective. And so one of the core lessons of investing over time is invest radically over time.

1:17:38Be disciplined about that. Do not deploy a ton of capital when things seem hot. Do not pull back when things seem bad. And so for us, like we've tried to hold this one of that. We're investing out of our sixth fund now. It will be a four year roughly fund cycle. Effectively all of our funds have had a three and a half to four year fund cycle, including those that happened in the 2021 build up. and we'll do that for fun seven as well. Yeah, one of a small number. Yeah, and so that is something we talked about with the RLPs. It seems like others did not follow that path. And we'll do the same thing now.

1:18:08Higher interest rates, obviously mean lower exit multiples, but we're investing for the future, not for now. The technology landscape, there'll be a bunch of stuff, including stuff that we invest in that blows up and goes nowhere. And hopefully, you know, there'll be stuff that goes somewhere. But we'll also continue to invest in this two, three, four years from now when some of the answers to the questions we've discussed today will become clearer. So like a lot of this comes down to discipline and thinking about this as an institutional practice where you're investing radically over time, understanding that most of your investments won't work, but if you invest with that discipline pace and you're able to you know spend time with the right people, one or two of them will hit and everything will work.

1:18:48Yeah, it's interesting. We could do a whole other discussion on this, but why brand power is so powerful adventure. And I think it's unlikely to change despite how much, you know, what venture invested and changes over time as we've been talking about this whole episode. But yeah, brand and institutional staying power to be able to do that. Because what's so hard about it, like everything you say is like, oh, yeah, of course, stuff. Like that's what you should always do in any type of investing. The problem is the game on the field changes. Yes, that's easy to say. But when you rewind two years from now and companies are raising series A's at 200 posts to 300 posts.

1:19:25Well, your options are don't deploy or play the game on the field. Well, that's true. So I got a really good question from one of our LPs that was good for me to wrestle with. We generally strive for healthy ownership percentages when we first and us because we spend so much time with our founders. We make one investment per partner per year. So we have the time to try to become the most important partner to our founders. That's our second core value. We take it really seriously. And the LPS me, how have we been able to do that? Like, why have we been able to maintain our high initial ownership when others haven't had the same ability?

1:20:01And after reflection, I think there are three potential answers. The first is we are able to make higher conviction bets earlier than other people are seeing it because we're so focused. Right? In the case of this coaching network stuff, we've been doing it for since 2015. And so we may see a company where they don't yet have of obvious product market fit, but we have been spending enough time thinking about this construct that we're willing to take the early bet and get paid for that bet if it pays off. There's a second category of companies, which you described before, that are obvious companies that are super hot, doing really well in high price.

1:20:35And we win those deals and we get less ownership generally. And in that case, that obviously lowers our average ownership. Correct me if I'm wrong. I think there's still a class though. That happens with those deals, but there are a class of firms of which I think you guys are one of them and there's a very few of them. That even in those cases, the ownership you're getting, it might be lower than that first class of deals, but it's not below a certain threshold. And that's a high threshold. That's true. And hopefully we've earned that through our founders' references. That's the core value we have in a process where a new founder is trying to decide if it's worth working with someone like us or someone that may be less expensive, so to speak.

1:21:18It's ultimately about the value that you're able to add. Ultimately, this is a services business. And perhaps to the earlier conversation, we may get disrupted by AI ourselves as we are a services company. But that's ultimately what it is. There's also a third category of companies where there's a negative sort where we're investing in stuff that we get the ownership, but it's not necessarily a great company. The reality is it's a portfolio. There's examples of all of that in our portfolio. If you invest with that mindset and you do it over time, and you do it in a discipline way without deploying too much too quickly or too little too quickly, then hopefully you win.

1:21:52I think you hit on the key point, which really is a theme of this whole episode. I think I didn't know going in, we would maybe tie these two things together, but it's value, right? Like, where's the value? Are you disciplined enough to invest in it? And do you not dilute your own value? Yeah, how do you charge for the value? Like if there's one key takeaway, I also hadn't really thought about that coming in, but the biggest issue with the Gen A .I. start -up landscape right now is that people are not thinking about the value they're creating. They're not focused on that. They're focused on the technology.

1:22:22And if you just start with the core principle of like, in my business, what is the desperate problem I'm solving and how much value am I creating and solving it? That helps clarify your thinking. If you're a gender, the I start up, if you're an incumbent, if you're a venture capital firm, Yeah, whatever it is you're doing. Yep. That feels like a great place to leave it for now. Jake, always pleasure. Thank you so much for coming back on Acquired, being such a good friend to us over the years in many, many ways. We're looking forward to seeing you next time. Thank you, David. So that will be my GPT for four and a half or five depending upon how we're counting the number of appearances I've had.

1:23:02I've had here. You may actually be an AI model at this point. I'm maybe an AI model. The crazy thing to think about is this conversation, maybe we do this on like an annual or semi -annual basis on this topic, because like the things that we will talk about will look so different. I'm doing this lecture that Ben mentioned at the GSB tomorrow, and I've been teaching guest lecturing in this class on business and AI for four years now. And for the past three years, I've been able to use basically the same presentation, because the space has evolved, but not all that much. I have literally rewritten every single slide for the conversation I'm having tomorrow because the space has changed so quickly and as a result I think like this conversation I know we try to keep this podcast evergreen and there'll be elements that are but there'll be elements that I think Fade quickly that's what's cool about this are Interviews and our conversations here at ACK2 is like that an explicit goal of The main acquired stories is evergreen these are timeless stories But like that's not all of the value in the world right?

1:24:03There's a lot of value to what's going on right now at exploring as things are changing. So that's the goal. So the answer is we just got to have you back in six months. I'm excited to do it. All right, Jake. Thank you, sir. Thanks, David. Jake, thank you so much. Thanks, Ben. And listeners, we'll see you next time.