20VC: Why Foundation Model Performance is Not Diminishing But Models Are Commoditising, Why Nvidia Will Enter the Model Space and Models Will Enter the Chip Space & The Right Business Model for AI Software with David Luan, Co-Founder @ Adept

23:17Let's look at the dynamics involved. It's one where as these models get smarter and smarter They kind of become the base computing primitive today the base computing primitive is like nodes on EC2 or like storage, right? But in the future when more and more software is just like the logic of software is actually just handled by by an LLM, nobody cares anymore about the base computing primitive as all you need to do is access these models and compose these models to go solve things for customers. So then whoever controls the model layer controls all of the underlying compute. And so like right now what's happening right is like if you don't have an offering here that is state of the art then you're just going to be cut out of this particular game.

23:57I think it's also actually an area where I think it's really important for companies like Nvidia to go up the stack, right? Like Nvidia clearly killing it right now on chips, but what's happening is every one of the major clouds and every major LLM provider is working on a strategy to have their in -house chips because that way they have better margins. And so then at the end of the day, If you're like a developer or you're like an end user talking to chat GPTN from one of the different providers Do you care whether the backend is an Nvidia chip or an AMD chip or an in -house chip from Google right you don't really care And so therefore like there's like a really key point of the interface of the LLM gives you tremendous leverage on everything downstream So do you think we'll see the ownership of the vertical stack?

24:43We saw Apple talk about their own ships being a prominent part of their new releases Do you think we'll see Nvidia really move into model layer with prominence, but also the model layer move into the chip layer with prominence and both try and eat each other's lunch from different ends of this spectrum? That's my expectation. To me, what's interesting about AI from a business side is it forces the question of what companies or offerings are going to be bundled or integrated and which ones are going to get unbundled. I actually think that there's going to be a really strong vertical integration pressure between model builders and chip makers.

25:15Can you just unpack that for me? Keep going with that thought. I love the topic of chips. We can stay here for a while. It's just, it's so much fun. It's like the most interesting thing that's happened in some time for that industry. So we were just talking a minute ago about how important it is for model makers to control their chips. Because that way, if it really is a scale in resources game, if company A, let's say, choose Google with TPU, TPUs are great, right? With TPU has a 20 % cost advantage compared to company B using chip Y, then Google will just be able to just the better cost of model training will let them go bigger, let them invest more in post training tricks like the ones we were talking about earlier and have an advantage.

25:56And so then company B is like going to be really pressured to go find some way to go do that themselves. Similarly, if you're a chip maker, it's just too easy to be to be commoditized by these in -house efforts if you don't also own something at the model later. So I think that's like vertical and And I think it is it easy. Like when you think about Nvidia and what they do, is it easy for them to be from all of the sites? Is there not such sophistication that actually it's incredibly hard for these people to move into the chip player and take that line? OK, I think I should think we're saying the same thing.

26:27It is incredibly hard. Like Nvidia is killing it. It is incredibly hard, but it is possible. And if the economic returns are high enough, people will do it. So I just think Google TPU is a great example. I'm a massive Nvidia fanboy. So it's incredible. And I think Nvidia has executed so well here. I think we also have the gift props, though, too. I think the TPU team, when I was at Google, was like sub 500 people. And their budget was a shoe -strain budget. And yet, somehow, every generation, they taped out quite good chips that were then used to train Gemini and Paul, and are used by third parties now and all that stuff.

27:00And there is such a strong will to ensure that Google has its own first -party chip. That's the counter example to the perpetual chip dominance I had someone say to me that actually Apple are the kind of the dark horse in the race because they own obviously the consumer and the end device and they can actually run models offline on everyone's device without Reliance on anyone. How do you think and feel about that? Okay, I like to think about AI capabilities in terms of actually to start from the top on this one thinking about the Apple think about like the Apple advantages in this particular space.

27:36I think there's like two areas of like extreme power and leverage that you get in machine learning right now. One is the ability to run smart models for free at the edge and the other one is to have the absolute smartest models possible. And so I think Apple has a massive advantage on the former. And when we go think about like whether that'll be enough, I think that's a really hard question to think through because I kind of think about as like concentric rings of model capability. to give some concrete examples. Like a 1 billion parameter model that is otherwise trained to be a state of the art kind of has like this set of capabilities that it's like perfect at and then it's like kind of okay at the next round up.

28:14So like maybe the very minimal set of capabilities is like is this tweet positive or negative, right? Like you don't need, you don't need GPT 10 to go tell you whether this tweet is positive or negative. Like a pretty small model can do a perfect job at that. And so things like that will always run on the edge. And so then if you're a giant frontier model provider, like you're just not gonna be able to monetize these like tiny skills that are just going to live at the edge. But then conversely, a billion parameter model is probably for some time not going to solve like, be able to create a 3D part for me for my car.

28:44That's probably going to be a bit like GPT -10 problem. And so I think as a result, I think Apple is just going to completely crush at everything that looks like something that's really private, something that's fine tune on your own particular data, but doesn't require massive reasoning capability. And that will all run at the edge. Can I ask you, I was quite shocked by Apple's partnership with OpenAI in terms of the looseness that they tied to it. They continuously said, oh, but we'll actually maintain relationships with others, and they very much left the door open to switching between different providers.

29:15I almost thought it was a negative when I heard it. I'm just intrigued. How did you interpret that when you heard it? I am extremely impressed with OpenAI. I think in terms of their technical delivery, I think the degree to which GP240 was like, I think relatively underhyped, relative to what I think the true scientific improvements have been in that model. That's a pretty big gap. Like I think like we're moving towards a world where we're gonna be training these like universal models that take any input in, right? Audio text, video, you name it, and then generate any output out. And all of humanity's knowledge will be encoded in one of these models.

29:52I need to do 4 .0 is a much bigger step towards that than people realize. So I think that Apple cutting that deal with OpenAI, I think at least part of it is a recognition that I think OpenAI is on a different trajectory compared to others on actual model progress. But at the same time, it also really strongly hints at a commoditized future. To the same extent today, as a consumer, I no longer care whether my computer, my desktop at home is powered by an AMD or Intel CPU, trying to create a way in which Apple owns the interface and Apple owns the end customer and then the like big brain LLM smarts is just like one hot swatable thing is brilliant for them.

30:31What do you think the NSA does for the foundational model there before we move on to the application layer? Like did they just get bought you know you've got a couple of cool ones which is really anthropic, Mr. Rale who have raised a lot of money billions and will not have the resources to compete continuously in the tens of billions of dollars needed. What happens to this layer? I think what happens is all of the tier one clouds will have their own effort that will do well because it has to do well And they will do whatever it takes to go and sure that they have the capital and Data flywheel and talent to go do that then I think for the independent companies and I would say that Adept is very different because we're what we do is we sell an actual and user facing agent to enterprises which is a very different business model than selling models to developers, but companies that sell models to developers will either need to effectively be the first -party effort of one of these big clouds, or they have a short window between now and quantization to build such a big economic flywheel that they can afford to stay independent.

31:35How could you build such a big economic flywheel, just so I understand, like an amazing enterprise go -to -market that generates 5 billion of free cashflow? I think you would have to look like something like that. I think that right now that's why I think of the companies of the independent foundation model companies besides adept I'm more excited about places like open AI because they have chat GPT to help do that Whereas I think if you're a pure play model seller. I think it's very difficult Would you say adept is a foundation model company or would you say that actually you are not? How do you think about the positioning?

32:09Yes, we're really really focused on this particular problem that we're trying to solve We're trying to build an AI agent that you can delegate arbitrary work tasks to and so then everything we do stems from that So what we are not doing is we are not trying to just train foundation models to sell them to other people We're doing is we're building like a very vertically integrated stack going back to our previous discussion of where well vertical integration happened versus not I do think that in the agent space It's extremely important that you own the entire stack from what is the end user interface?

32:40I think like we're talking about the Apple example earlier owning the interface gives you tremendous leverage in this era of AI to How do you make agents that are reliable enough to be used at work all the way down to what needs to happen to the foundation modeling later to enable this whole end -to -end system to be maximum performance? That's what we do. It's this vertical slice. How do you think about the variation of agent requirements based on a power industry basis? Do you know what I mean? That's so varying. It's completely different. Yes, that's what our advantage is is like You know, we get this question all the time, right?

33:08With the depth trying to, like, we want to be the system of record for workflows and enterprises. Like any employee at any large companies, we've got to teach a depth, hey, like, here's how I do this particular thing, right? Like, here's how I handle fetching all the data for an insurance claim, right? And this should be to show a depth that and then a depth should be to do it for them. That generalization, all of those edge cases and variability is why the only way to solve that is to have vertical integration of model with use case. And it's also why I think we'll do better than companies that are just focused on a vertical, like a particular narrow problem, because I was talking to Parag who used to be the CEO of Twitter, we were just hanging out the other day and he's like, dude, every enterprise workflow is an edge case and he's absolutely right.

33:49And that's why you need to do the chosen. What does he mean by that? Can you unpack that for me? I mean, just even looking at something as simple as I want to add a new lead to Salesforce, right? Let's go outside and find 10 different companies who all use Salesforce and look at how they've got it configured and it all looks completely different from each other. Is this not what RPA was always meant to be? I'm friends with Daniel Dynes from UI Path. Wonderful dude. I was like, this is what RPA was. So can you help me understand the distinction between traditional RPA, which is what we've seen with UI Path in this new era of agent that we see today?

34:23Yeah, totally. I mean, there's a good question. It's actually a question that used to cause me a lot of heartburn because I found it so hard to explain to people why agents were going to be different than RPA. Best analogy I've got is RPA is very useful for high volume tasks that always look the same. The analogy that I would give would be RPA is a little bit like, you know, when you go to a factory floor and there's robots roaming around everywhere, what those robots do is there's like a literally yellow line painted on the floor. And the robots like follow that line, they go from cell to cell and station to station and they pick up stuff.

34:54But what agents are, agents are meant to be constantly thinking and re -evaluating and planning at every step to solve your goal, and it's much more like full self -driving. The difference in utility between those two things is fairly large. Of course, there's many areas where you don't want something that can have variability, and therefore you should use RPA. I just think in five to ten years, people are going to use their computers by giving them high -level goals. They will, the largest enterprises in the world, run RPA and agent -based systems alongside each other. I think so. Yeah. But why are PA players not best placed to provide an agent solution to existing customers?

35:30I think it's just really fundamentally disruptive to their business model. The way that a big corporation uses UI Path, right, is like there's a big plans where in a process transformation, they need to be done. Sometimes like an in -accenture or something comes in and then maps out what the processes are like. Sometimes what the process discovery thing. And then our PA engineers go and build those workflows and then six to nine months later, you hit play on the stain that then automates I mean voice processing every night or something like that. Like this new model of like you just put an agent in there and the agent observes what the end user does to go do that job and then that becomes like a thing that you can then just invoke with natural language is like really disruptive to the business model.

36:11I think that the best way to run circles around and convinces to do something that's a different business model than what they have. How's your business model different? Well, so basically the way that we're doing things is we are addressing use cases initially that are really painful to get our foot in the door, but we're really focused on how do we make the end user be able to teach at any new capability. Like, I should be able to dump in my standard operating procedure for this new thing my team does, or I should be able to show a depth like 10 times and give it corrections on how I enroll a new nurse into a healthcare portal in the US, and then the model should be able to do that for me.

36:46And basically, we're working on something that's ultimately very self -serve over time. Everyone speaks about kind of we're going to sell the work and not the tools and the end of price per seat and we're moving to a consumption based pricing model. Do you agree with that statement? Do you think we're all slightly over -amp sizing like the end of price per seat and how do you feel about this kind of fundamental shift in business model and pricing that AI could bring about? I think in places where definitely we're going to see that become true but I actually think knowledge work, the most valuable things to do will not be priced that way.

37:20And here's why. I think that the definition of price per work assumes repetiveness, commoditization, cookie cutter, no creativity. I think what these AI systems are going to do, especially the AI agents are going to do, we are basically going to give people the ability to go do new things and have way more leverage on their time and give them more opportunities to be creative. And so then ultimately what we're building is like a co -pilot or a teammate and co -pilots and teammates don't charge you price for work They like you really pay them based on their ability to augment your ability to go do new things, right?

37:51Like I'm just saying that you mentioned that the kind of co -pilot approach I add the guests on the show say that the co -pilot and I think it was miles from to a benchmark or whatnot thrive So that she co -pilot says an incumbent strategy is leveraging existing distribution and it's an incumbent strategy Is that fair or do you think that she's not giving due credit to the co -pilot approach? I think both these two things can be true. I think the co -pilot are a great incumbent strategy because it lets them morph their existing software business model and something that kind of looks the same while getting in on the AI thing.

38:22But even separately from that, I just think where are these systems going to be most useful? I just feel like everybody in this field has this vision that AI is going to take all jobs. The pricing by work thing is just a corollary of AI is going to take all jobs. right? Because then it's like, all right, maybe you price by work on invoices. And then next month you price by work on like consulting decks. And then before you know it, you price by work on like being AI CEO of like David Co or something like that, right? Like that's not, I don't think that this is how this is going to play out. I think the way this is going to play out is that where we're going to have is we're going to have humans fundamentally be the drivers of these agentic systems that basically give everybody a tremendous amount of leverage on their own creativity.

39:03And how can that be built without a co -pilot style approach? It's like my question. What does that do to the org structures of teams, David? Do you think, does this mean much, much smaller companies? How do you think that actually plays out? Actually, this is something I'm going to steal from our angel investor, Scott Delsky, who just thought about this so much. He always calls it like this collapsing the talent stack thing. And the idea is basically that projects in teams where the same person is simultaneously the PM and the designer and or the engineer or the go -to -market person or the marketer or whatever.

39:33The more that those different skill sets are smushed in the same person, the faster that thing moves and the more effective the thing becomes. So I think what it's going to do is it's going to make people, humans at work, much more like generalists. And it's going to have like, giving people a larger and larger scope over various different areas or different functions today, while they ultimately supervise a cohort of AI co -pilots that are the specialists. One thing I did want to touch on was when we think about the rollout, I think we overestimate enterprise adoption. One, are we still in experimental enterprise adoption budgets or do you think we are moving into core enterprise adoption budgets?

40:09This is a very, very good question and I'm, Harry, we should rewatch this podcast in 10 years and see how we feel. But I think, you know, when we talk about, yeah, I'm so freaking broad. It's a little bit like us asking maybe in the early days of the internet, that generalized thing about the internet as well. Like it's just I think there are some use cases that are clearly hitting PMF within an enterprise But for the most part like just when we go to enterprises to go to go to go sell them stuff Like they've got so much stuff that's still on prem They've still got workflows running on mainframes and it's it's 2024 and so I think even if technologies like cloud Which we probably look at as it from startup lenses being so freaking mature still doesn't have full Full adoption and enterprises is like I think that stuff is really is really interesting And I think as a result, we're going to be on this adoption curve for enterprise AI for a very, very long time.

40:58So we are still in the experimental budget phase. I think the majority of it is extremely experimental. One of the things we do, for example, is we really try to not sign deals that are coming out of experiment budget because we want quality remedial basically. Do you think we gross the overestimate enterprise adoption in the short term and underestimate it in the long term? Yeah, I think it's true for most new technology, but definitely here. I like and it shorts the C .M .a. Meta told me in recently released a piece about kind of hype cycles in new technology and actually he states is kind of concern that AI will Replicate autonomous driving in the way that we got so excited about 10 years ago.

41:35Everyone's gonna be unemployed 8 million truck drivers We're all fucked and then you've got to love your mother when she messes and you don't have it on do not disturb Yeah, yeah You're never too cool for your mother. But my question to you is, are we gonna see that similar plateauing where for 10 years actually, kind of autonomous cars didn't feel like it was progressing? How do you think about that? So I feel like in self -driving, what happened was there was an aha moment where you could get the thing to work at all. And then you're like, okay, well now it works 60 % of the time. How do we get this in 99 .999, 99, 99 % of the time?

42:13Every day you show up to work and just play whack -a -mole on what's not working. and you just like hope and pray that this converges to that like 99 .9999. That's not true for AI right now. That's not true for specifically what I'm about to say is only applicable to building smarter and smarter models and agentic systems that ultimately help you do work. That's the thing that I'm trying to talk about. For building that, that's not how the underlying dynamics are right now. Like every day we go to work and there's like actually brand new scientific things we want to try that just dramatically improve the performance of the model.

42:45Some of those bets don't work, and some of those bets really work. The reasoning that we talked about earlier is an example of one. I think another example of one is this universal multi -modality that you put you for O is those breakthroughs are visible. And as a result, what I think has a hope of preventing this from just being a hype cycle that falls flat like AB is that those shoes are yet to drop. And as they do, the capabilities of these models are going to continue to improve. And on top of that, there's not a technology that you don't, that you need to get to that level reliability before it can be deployed.

43:15It's already deployed today. Speaking of deployment, I think, and the kind of enterprise adoption, I tweeted actually that you would see AI services companies, people who help in the implementation of AI and large enterprises, be bigger than the model providers themselves in terms of revenue. And we've seen that actually come out as being true. I had some famous people call me an idiot, which actually made me quite pleased when those revenue numbers were revealed. How do you feel about implementation provide us, AI services providers being bigger than the actual providers in that's five years.

43:44Do you think that it's right that the biggest players to come out of this cycle will be the AI services providers? You know, I don't think so because I think the third bucket of like economic upside is still early and I think that bucket is the companies that then turn the use cases that have product market fit into repeatable products. Right now, right, imagine you're very large company X, right, and you need capability Y, and then you've got the base model over here, right? That's pretty darn smart. GPD4 or Gemini or whatever, right? This is giant golf in the middle. In every one of these cases, the first people that go fill that golf are like sort of consulting e -service providers, right?

44:21But then the moment that golf starts getting filled, you start seeing, ah, okay, like this is the really useful thing for an enterprise, then people just go productize that thing and that becomes a startup. And so then that becomes eventually a company that's a conduit between the base intelligence and the customer. So today that might be true for services, but I feel like a lot of these things will be turned into generalizable products. And then when they do, those companies will then be the real economic winners. One of the concerns that I have, I have two other concerns, but I want to touch on both of them with you because they keep me up at night and I have enough wrinkles, David.

44:49Regulation, Europe, you know, specializes in it. One concern that I have is that we could regulate ourselves into oblivion around data usage, data collection, and actually we don't see the progression of these models in the AI in a way that we want to. How do you feel about that? How likely is that? What would you like to see happen in regulatory environments? I think my main concern right now is actually one of regulatory capture. In the same vein we were talking about earlier about how there will be only a few sort of frontier model companies that can exist at a state. I think the move to go pull up the ladder behind the Ms.

45:21Arready beginning. A lot of makers don't really understand this technology at all, and so their default instinct is sort of listen to the most credible source, and usually those credible sources have alternate ulterior motives here. So what happens in that case? I think that what happens is that it becomes harder for the general field to go build on open source. It will become harder for new companies to get started that have new AI ideas they want to go train and scale up. I think what really happens is just another concentration of power moment. You mentioned kind of the ability to build on open.

45:51The other concern that I have is that we had Alex Wang on the show and his statement was AI is more powerful than nuclear weapons and in the hands of the wrong people, especially AGI, it could be probably the most lethal weapon used ever, and for that reason we should probably have more closed systems. How do you think about that debate of open versus closed and whether for some of the most crucial decision making AI systems it should be closed? I think two things. One, I think the broader set of concerns about use and misuse and safety are extremely important. And I think that like what was a good thing about all of this is that people are having these discussions more openly, which I really strongly appreciate.

46:29I mean with a lot of these systems you can already see like clearer ways to go to go misuse them right like Spend up a bunch of servers take the best code model you have out there Use them to go try to find vulnerabilities and software systems like that's if that's already happening It's gonna really start kicking in a gear. So like things like that. I think make me very concerned at the same time I think that like a GI is just a really difficult thing to reason about because the way that many people define it is like almost defining it as in infinity. And like, reasoning about infinity is this really hard, because you multiply infinity by 0 .000001%, and that's still infinity.

47:03And so I think it's a very brain breaking thing. And so I think a better way to go look at it is to look at the path dependence, like how will this technology actually be developed in the next five years? And I think in the next five years, open will always lag closed. And because open will always lag closed, because open just as fewer resources behind them and fewer incentives for people to go make things to be open as these things become more and more expensive. I view open really as a way for the rest of the field to keep up with the biggest incumbents, and therefore I think it's actually pretty darn important.

47:33You mentioned kind of AGI being kind of infinity there in people's minds. You said before to me that the last step is human computer interaction and that's the last ingredient to AGI. Before we do a quick file, what did you mean by that? I didn't get that one me, though. Okay. So what I mean by that is that I personally find a world in which increasingly generally intelligent systems have run around with their own agency and goals and not involve what humans most care about to be not a world that I really want to live in. And I think because of that, this goes back to what you were saying about like selling AI by work versus as a as a software tool, right?

48:08Like I would much rather live in a world where we have sort of these like AI teammates and assistants that we interact with instead. And then I think the question becomes how do you find the right interface between smarter and smarter AI systems and people and How that interface is defined actually changes a lot about what training data you collect How can humans align these systems towards the preferences of what humans want also ultimately like how these models are even built and what their architectures are and so in a Weird way the way the field is moving is let's make model smarter and let's make use cases started and then let's go put them in people's hands And then let's figure out what this means for people It's kind of this waterfall sequential method, which I don't think is a very good way to develop the technology I think it should start back from ultimately how do how should humans use these things and then create the whole solution And to end that way and so that's why the HCI problem by people Disarranged spending enough time thinking about like chat is obviously not is obviously not it You mentioned that kind of the wrong way to think about it What questions do you think people are not asking enough that they should be asking more?

49:06questions along the lines of like as these models get smarter and smarter and they sort of know more and more about the world and have more and more ability to do things in the world. How do you interact with them? How do you supervise them? How do you give them corrections and teach them to be more aligned with what you want? Questions like that. Listen, I've peppered you with questions. I want to move into a quick fire. So I say a short statement. You give me your immediate thoughts. Does that sound okay? Yeah, sounds great. So what if you changed your mind on most in the last 12 months. It's actually a little bit of what we were talking about earlier.

49:37I actually think agents and chat pods are going to speciate and turn into two different products. How does that look? I think it's going to look like you're going to have these rich interactions with these increasingly smart systems that can do things on your behalf. And then you're going to go have other systems that you talk to for therapeutic or fun use cases. What's the biggest misconception people have today of the next 10 years of AI? Biggest misconception is that this is just going to be something that at every step takes another human capability and fully automates it. Like the implicit goal of AGI right now is replace human work, but like so much of human work, like I just don't think it will be neatly captured by AI, and instead it's going to be, it's just like AI would be a tool to level up human intelligence.

50:18What's your vision for the future of agents? If everything goes to plan, agents in five years time are dot dot dot. Agents in five years time, I mean, it's kind of going to be like a non -invasive of like brain computer interface, basically. I think that's when an agent will be. All of us are gonna be up leveled. It's gonna feel like the same transition from like, DOS slash command line to the GUI, but from GUI to agents. We're gonna interact with them at a high level at the level of goals. And they're basically just gonna let us, I think, have like, basically like, new kinds of thoughts, like the ability to go, like, reason at one level of strash and beyond what we all do today.

50:52You're writing in pre -mortem on why that does not happen. Why agents are not that in five years time? What is the most probable reason why that does not happen? I think one way it won't happen is that fundamentally agents are a reframing of where, of how software is bundled. Today we bundle software in these functional ways. You've got notion or Google docs for your docs, and then you've got Salesforce for Sales, and then you've got Workday for HR and all of the stuff. right but the work that we do fundamentally spans all these different domains and an agency should bridge those domains otherwise you can't become a higher level thing.

51:31So if we're locked into like end walled gardens by incumbents and that vision will not happen. This sounds super awful VC mindset but when you look at like UI past today being like a six seven billion dollar company, do you not think there's bigger opportunities to go after? That took 17 year maybe more 19 years and it's a $7 billion company with billions in revenue. You're a super smart, super ambitious guy. That feels like a lot and a long time for actually a value capture that if done well. I think the question is, what percentage of work done today is addressable by RPA? It's very little, but what's the percentage of work done today that's addressable by agents?

52:10It's like 1 ,000X that, 10 ,000X that? I don't know, something in that order of magnitude. It's just that it's a very different market. It's like saying, should we work on self -driving, when self -driving didn't exist, and then looking at the market for those autonomous rovers and warehouses? Final one for you. What question are you never asked that you think you should be asked? I mean, I feel like you did a great job of covering so many interesting things. I think you've got all the good stuff. Does that mean I've done my research well enough? You've done your research very well. David, honestly, it shows like this and why I loved doing this.

52:41So thank you so much for being so brilliant. I appreciate you putting up with my lack of smiles but this has been fantastic. No, you're doing awesome. The fact that you're able to do this after a wisdom tooth removal is insane. I had so much fun. I thought you asked great questions across business and tech. And yeah, I'm excited to see how this whole place out from here. I have to say, I really do feel like I just have the best job in the world. I basically get paid to speak to the most smart and incredible people in their field, like David in that episode. And I get the credit for most of what they say, which is a brilliant business model.

53:13I highly recommend it to anyone who's contemplating it. If you want to see more, then you can check out the YouTube by searching for 20VC, that's 2 -0 VC on YouTube. I love to see you there, so let me know what you think there. But before we leave you today, we're all trying to grow our businesses here. So let's be real for a second. We all know that your website shouldn't be this static asset. It should be a dynamic part of your strategy that really drives conversions. That's marketing 101. But here's a number for you. 54 % of leaders say web updates take too long. That's over half of you listening right now, and that's where Webflow comes in.

53:49Their visual first platform allows you to build, launch and manage Web experiences fast. That means you can set ambitious marketing goals, and your site can rise to the challenge. Plus, Webflow allows your marketing team to scale without relying on engineering, freeing your dev team to focus on more fulfilling work. Learn where teams like Dropbox, IDO and Orange Theory, Trust Webflow to achieve their and most ambitious goals today at webflow .com. And speaking of incredible products that allows your team to do more, we need to talk about secure frame. Secure frame provides incredible levels of trust to your customers through automation.

54:27Secure frame empowers businesses to build trust with customers by simplifying information security and compliance through AI and automation. Thousands of fast -grown businesses, including NASDAQ, Angel List, Doodle and Coda, Truss Secure Frame to expedite their compliance journey for global security and privacy standards, such as SOC2, ISO 2701, HIPER, GDPR and more. Backed by top tier investors and corporations, such as Google, Client, and Perkins, the company is among the Forbes list of top 100 startup employers for 2023, and Business Insiders list of the 34 most promising AI startups of 2023.

55:05Learn more today at SecureFrame .com, it really is a must. And finally, a company is nothing without its people, and that's why you need Remote .com. Remote is the best choice for companies, expanding their global footprint where they don't already have legal entities. So you can effortlessly hire, manage and pay employees from around the world, all from one easy to use self -serve platform. Plus, you can streamline global employee management and cut HR costs with the remote's free HR And hey, even if you are not looking for full -time employees, remote has you covered, with contractor management, ensuring compliant contracts and on -time payments for global contractors.

55:44There's a reason companies like GitLab and DoorDash trust remote to handle their employees worldwide, go to remote .com now to get started and use the promo code 20VC to get 20 % off during your first year. Remote opportunity is wherever you are. As always, I so appreciate all your support and stay tuned for an incredible episode coming this Wednesday.