Ep 666: Shuo Chen — Fractional Founders, Frontier Firms and Why Enterprise Data Is AI's Gold

Oftentimes Silicon Valley describes it as like, I just quit my job, I just dropped out of school, and I go do this thing full-time and keep on working on it until it works out. That's actually not what I've observed firsthand. Oftentimes what I see is someone who's working in a job has had, you know, 5, 10+ years of work experience where they've really deeply gotten to know that field, gotten to understand the problems, and oftentimes experience that as a personal pain point. Or have served customers who are underserved and they wish they could build a better solution. And then instead of quitting their jobs cold turkey, they took the time to really understand what the pain points are and how to fix it. And then they kind of side hustled to building a solution. Oftentimes many of these entrepreneurs try to pitch this idea to their boss and, you know, all the way up the command chain and try to do this in-house. But oftentimes, as we were talking about earlier, Aidan, you know, the organism rejects the organ and They can't do it in-house, and so they have no choice but to go and do it as a startup. And so I oftentimes see some of the smartest entrepreneurs being reluctant because they wish they could have just done it in-house. I've done a lot of work with EY, and one of the conversations I've consistently heard is, hey, if now we don't necessarily need entry-level employees to do very basic things like update spreadsheets and do more tedious work, then we can actually bring them along to client meetings and build relationships earlier on, have them do more at a younger age. So that's one side of the coin. As most folks know by now, Spirit Airlines had declared bankruptcy. And what became somewhat surprising to people was that their most valuable asset wasn't just like what terminals they had access to and what airports, but rather their literal, literally decades of operational data. This included things like employees' emails, anonymized like customer data, operational data, literally how they were running processes. So if you think about the world, um, 95% plus of the world's data is not available on the internet because it's private. It's stored on within enterprise databases. And that is gold for these AI companies, because if you think about how AI works—

Before we start, I want to thank our sponsor, Kyndryl, who runs and reimagines the technology systems that drive advantage for the world's leading businesses. With a unique blend of AI-powered consulting built on unmatched managed service capability, Kyndryl helps leaders harness the power of technology for smarter decisions, faster innovation, and a lasting competitive edge. You can find out more about Kyndryl at kyn-dryl.com. Thanks as always to our sponsor Kyndryl, who runs and reimagines the technology systems that drive advantage for the world's leading businesses. With a unique blend of AI-powered consulting built on unmatched managed service capability, Kyndryl helps leaders harness the power of technology for smarter decisions, faster innovation, and a lasting competitive edge. You can find out more about Kyndryl at kynryl.com. The top 1% of startups spend 8.3 times more on AI per employee than the average. It's a startling fact and it leads to extraordinary outcomes. It comes from a newsletter from a lady who I worked with last week when we were in Vancouver, and I had to have her on the show. She has a fantastic newsletter where she explores 3-2-1, 3 trends in startups, tech, and venture, 2 theses on what comes next in the world, and 1 tool that she loves. She is an investor with iovc.io Ventures, where she works in the Bay Area, and she's a professor with Stanford and indeed Singularity University. She works for corporate all over the world. She keynotes, she emcees, she interviews. She is Shuo Chen. Welcome to the show.

Thank you so, so much for having me, Aidan.

I probably didn't do you justice there. So maybe you'll give an explanation of the type of work you do, especially because of the work you do with VCs. It's really interesting and you have a unique view of how you do that work.

Really appreciate that. So to give everyone a brief context to what I do, I guess entrepreneurship has been the connecting dot across all of these different experiences. I was a former entrepreneur after selling the company, came back to invest in early stage entrepreneurs. And now, of course, I also teach entrepreneurship at Stanford and Berkeley. And so you can see that I very much caught the Silicon Valley startup bug, to say the least.

And one of the things that I found so interesting was when we, when we worked together, you talked about this concept of a fractional founder. And it reminded me so much of so many listeners to The Innovation Show. And that's what really caught my attention was it's very similar to somebody who works inside an organization, an intrapreneur who's coming up against change, coming up with the corporate resistance, coming up against the politics of an organization. And you talk about this concept of a fractional founder, and it really has so many common traits to that intrapreneur inside an organization. I'd love you to share. who this person is and what the role is evolving to become.

Yeah, thank you for that because first of all, if there is another name that listeners to the show identify by, please reach out and tell me cuz I'm looking for a better name. But the closest I've gotten to describing this phenomenon I've just observed out in the wild, having done early stage venture investing for the past 10+ years, is fractional founders, which is, there's multiple ways to think about it, but the way I think about it is I think fractional founders are essentially entrepreneurs in the earliest stages. Of starting something where they're still side hustling. So the best entrepreneurs we've invested in for the past decade plus, I've just observed this trend. And for context, I've done a hun— over 150 early stage investments in the past 10 years. Um, we've had about a dozen successful exits in the form of IPOs and acquisitions by Fortune 50 companies. We were one of the earliest investors into Instacart. We, uh, have sold companies to Goldman Sachs, to Ford, to Caterpillar. You know, you name your Fortune 50 company. And the trend that I see over and over again across these entrepreneurs is oftentimes the smartest and most thoughtful ones don't just quit their job cold turkey and go explore full-time, contrary to what you see in pop culture and how they describe Silicon Valley. Oftentimes Silicon Valley describes it as like a I just quit my job. I just dropped outta school and I go do this thing full-time and keep on working on it until it works out. That's actually not what I've observed firsthand. Oftentimes what I see is someone who's working in a job has had, you know, 5, 10+ years of work experience where they've really deeply gotten to know that field, gotten to understand the problems, and oftentimes experience that as a personal pain point or have served customers who are underserved and they wish they could build a better solution. And then instead of quitting their jobs cold turkey, they took the time to really understand what the pain points are and how to fix it. And then they kind of side hustled to building a solution. Oftentimes many of these intrapreneurs try to pitch this idea to their boss and, you know, all the way up the command chain and try to do this in-house. But oftentimes, as we were talking about earlier, Aidan, you know, the organism rejects the organ and they can't do it in-house. And so they have no choice but to go and do it as a startup. And so I oftentimes see some of the smartest entrepreneurs being reluctant because they wish they could have just done it in-house. Most classic example of this is Marc Benioff, who was working at Oracle before he had come up with the idea of doing something like a Salesforce, because he wished that he could have used that as a solution himself. He could have presented that to underserved customers that he was dealing with at the time. He didn't have the right infrastructure to do so, but when he left, he actually got a check from the founder of Oracle to go do Salesforce. So many other stories like this. Jensen Huang, another classic example. He and his 2 other co-founders were working full-time in the semiconductor industry, understood all of the pain points, and then they would meet up after work to just brainstorm, how might we do another chip company better, faster, cheaper, you know, in every single way? And they had done substantial amounts of the work before they decided to quit their full-time jobs and make NVIDIA their full-time job. And so we just see this over and over and over again. And so after observing this from a startup perspective, I decided that I wanted to do more systematic research to figure out, you know, is this true in the broader world beyond the anecdotal case studies that I was seeing as an investor? And we ended up doing this research and it turns out in the world there are only 12 companies to ever be worth more than a trillion dollars. 2 of them are non-American, 10 of them are American. The 10 American trillion-dollar companies, 8 out of the 10 of them were all started fractionally. So it was only 2 where the founders were like, let's explore full-time, and they luckily landed on something. But 8 out of the 10 were all where it tied directly to what they were doing before, whether they were in school or they were working full-time, and they had built substantial portions of things before they left and, and did it full-time. And then we expanded this research to looking at Fortune 500 companies. And it turns out that a quarter of all of them, so 1 in 4 Fortune 500 companies, they were all started fractionally. And so I've, I guess over time developed a taste for fractional founders and now I love working with folks early on to just be a thought partner and figure out, is this the thing to work on? What is the most efficient way to test this hypothesis and run these experiments? And like, is this the right thing that you should go full in on and all in on, or You know, should we test out other things before you decide to quit your job and drop out of school?

You're going to have an influx of messages from people who listen to this show. For people who do want to get in touch, where is it? LinkedIn the best place?

LinkedIn works well. Um, I actually put all my emails on LinkedIn as well, so I make it easy.

I'll link to that in the show notes as well, just to put that straight up, because you're going to be exciting a lot of people over here. But I wanted to come back to that. So that idea of that was really caught my attention when you were giving a talk for Mastercard with me. And I really dived into that and I went back into through all the research and you saw so many people, Steve Wozniak came out of HP, they rejected his ideas. He wanted to build for them. And you know, one of the things I see, and I'm sure you see this is It's like, I always think of that scene from Shawshank Redemption where he tunnels out the wall. So the character who escapes, he tunnels out the wall, Andy Dufresne, and then he puts the poster behind him. And I always think that when, when somebody leaves like that from an organization, the organization badmouths them. So they're like going, oh, well, you know what, Aidan was, he was crazy anyway, and he wasn't good for the company, et cetera. And, and I often think it's because they don't want anybody else to find your little hole and tunnel out after you because they don't want everybody leaving the organization. But therefore they should actually harness that energy, that thinking inside the organization. I, I don't know what thoughts you might have on that because like, it's just such a shame. I'm sure companies when they see that go, oh my God, we had that guy working for us. We could have done it. Yeah. You know, Eric Yuan in Zoom, working inside Cisco. There's so many of these people.

I, I feel like talent is becoming increasingly a scarce resource these days, where I think what you're describing is so spot on that in the past, people would, you know, put that poster back up, like, let's block the hole, prevent this leakage. Whereas now I almost feel like I'm starting to see culture evolve around this topic. So, as an example, we talked about Marc Benioff earlier. I think that's a great example where obviously Larry Ellison, who founded Oracle, took Marc Benioff under his wing and said, look, I think this is a really talented individual. If, you know, we're bound to, to lose him, let's make sure we have an equity upside in what he's about to build next. And I think the reverse is also true for intrapreneurs. The way I would think about it is like you have all this talent within your organization. If you don't give them a way to build inside, they'll leave. So you should find a way to stay in touch. But if you empower them and inspire them within your own organization, then they get to help you build incredible things that perhaps you don't necessarily have the internal forces, motivation currently to do so. And so I, I don't see it as like there's a hole and, you know, all of the talent leaks out. Maybe I keep on thinking about it in terms of like, This actually attracts some of the best talent to your organization when they could see that, hey, if I want to do something that's ambitious, that there is a way and a platform for me to do that.

There is a whole, there was like, don't tell a lot of people about this, but we have a place for you in here. It's like this sandbox where you can play with your ideas, but don't tell everybody. We don't, we can't let everybody work there. So I'm not going to get through your work, your newsletter, so I'm going to move on to the opening line that I talked about, this idea that, and this was data from OpenAI, that frontier firms, which you might give us a definition of, they have so much, almost 10 times the use of tokens as a typical firm. And we saw this thing of token maxing where organizations had encouraged their own people to use the tokens. They ended up using them to tick the box. Then they were using them to do menial tasks that they shouldn't have been using and using up all their tokens. But maybe you'll give us a view of this part of your newsletter.

Absolutely. Actually ties quite well with our earlier conversation because let me set some context. So you're right in that there are companies famously like Uber that literally burned up an entire year of budget within like less than a quarter. And so there was this phase in Silicon Valley where companies were encouraging their employees to token max, just, just use the tokens and show me that you can use it. But as is with life, most things, you know, you evolve as you become more sophisticated with usage of AI, you become smarter about how you actually use it. And so what we're starting to see at these frontier firms, and really there are different ways that we can define frontier firms, but usually when we talk about frontier labs, we're really just talking about the OpenAIs and Anthropics of the world. When we're talking about frontier firms, at least the research that I was citing is really firms that are in kind of the top 10% of usage and performance of being able to leverage AI. These frontier firms, we used to think of, is this company good at using AI? And we kind of look at their usage, like in terms of, do they have access to using these tools? For many firms, and it's still true that around the world, most of these firms don't even have access to like the OpenAIs and Anthropics of the world. And so we used to evaluate like, is this firm AI native? Is this firm AI empowered by looking at, do they give their employees access? And what this latest research is telling us is actually access is no longer enough. It's not just about giving employees access to the tools. It's actually about looking at usage. How deeply are they actually using these tools? So it's great if 100% of your employees have access to the latest tools from OpenAI and Anthropic and all the other AI companies. Great. But if they're not using it, then, you know, just giving them access really isn't enough. And what we're starting to see in this data is that these leading firms do have employees that actually just already have learned how to use AI efficiently and natively integrate that. And I think it has a lot to do with the earlier conversation we just had around scarcity of talent, because all of these firms are now looking at internal data points and saying, hey, we actually think revenue per employee is a really important metric to be tracking. And they're looking at it to say, well, how much revenue can each employee I hire into my organization help bring? And so the more AI and AI agents that they can use, the highest, higher leverage each employee has, the more revenue they can generate for us as a firm. And if my revenue per employee is high, that means my capital efficiency is high. It means literally for every single dollar I spend on hiring a human being, I'm getting max useful output. And so perhaps a broader context of this is we've moved on from just giving employees access to these tools. We're now looking at depth and quality of usage. So how much are they using these tools and are they using it in a very efficient way? In fact, something that I'll talk more deeply about in the next newsletter is this new metric that OpenAI very recently has started talking about. And I have a number of good friends who Are there leading the teams? And one of the things they start to look at is in terms of human to agent leverage ratio. So for every human that is working, how many AI agents are you running in the background to actually make this human more efficient? And of course, I think what is coming next—this is a prediction—the trend is we're seeing us evaluate companies going from access to usage. And the prediction is I think we're going to go from. Usage, actually looking at max output per dollar. So how do I maximize intelligence and useful output per dollar spent, whether it's on humans or on agents? But oftentimes the highest leverage is getting humans that also manage additional agents.

You were talking in the newsletter as well about an index where you also see this, the best performing startups tend to be the biggest and heaviest users of AI.

100%. Because the only, the only way that a startup has a chance at going up against an incumbent Is if they're somehow more agile, more adaptable, and also more efficient. Otherwise, there's just no chance that they have at competing against someone who is larger, who has had more time, who has more capital and resources. And so I see startups really not, not token maxing. Token maxing was just about using AI, but like really maximizing intelligence per dollar they put in.

The sponsor of the show is Kindle. I mentioned that to you beforehand. And the leader of that, the guy who actually looked at the innovation show first, is a brilliant guy, an author called Ismail Amla. And Ismail said to me before, he said, we're seeing this shift where having a big company and having lots and lots of people was the advantage before. And the startup didn't have that advantage. Now it's becoming a disadvantage because you have to almost upskill all those people or bring all those people with you on the transformation journey. While the startup, if they're using AI, they're way more nimble and they can actually be so much more efficient. If you even think about a meeting, like they can have a meeting with a small team, get stuff done so much faster now than ever before. What's, what are you seeing out there in that field?

I actually think that is so spot on because what I'm starting to see in the world of startups is a couple of things. For one, they definitely pay attention to that revenue employee per employee number Really upfront and every single time before they hire, the question they ask is, is this new employee I'm about to hire going to drag up my average or drag down my average? So they're super thoughtful about who they bring on board. So if I think this person is going to really substantially grow and scale the business, then it makes sense. But why would I hire a human who is less efficient than all the other people that I already have on my team? The second thing I'm starting to see that is kind of, uh, a borderline between a trend I'm observing versus like a prediction I'm making. Is I'm starting to see some early startups do this. I think it will become more of a trend going forward, but what I'm starting to see is some startups are saying, look, instead of having a job opening and then looking for candidates to fill that hole, let's do it the other way around, which is let's just find the smartest people out there that have connections that I want access to that can do things that we know our organization will benefit from. Let's just hire that person. Into a general title. Like, we are now starting to see this happen not only at OpenAI and Anthropic, but for example, Microsoft is starting to adopt this where they just call them member of the technical staff, right? So you just join the member— as a member of the technical staff, we plug you in and then we figure out how we're going to make most use of all this talent and resource and relationship and network that you bring. And so instead of plugging in like a missing hole, we actually bring something that's more additive to the organization. So I'm starting to see that conversation happen. And because talent at the end of the day is increasingly more scarce, especially people who can deploy AI agents effectively, startups are really starting to think about that question. Last thing I will say here is that I think when we look at any sort of situation for startups where they need to attract talent, it's not like a large organization where you're attracting them based off of You know, perhaps the compensation package that you could offer. Um, it's not just the, the salary, it's the future equity, it's the growth, the upside, the ambition that you now get to be a part of. And so there is somewhat of a self-selection effect there. But I think the way that I hear startup founders talking about this is they know very, very well that we are the last generation of leaders to be managing only people. Increasingly, they're seeing more and more employees manage people and AI agents, and they can just be faster at adapting to that than an organization that already has tens of thousands of people. At the same time, what I will say though, is there's a huge advantage to an organization that has already attracted tens of thousands of talent, because those are the people that startups are trying to tempt away from your organization. So if you find a way to inspire and empower those people that you already have within your organization, that's already a massive win. Because you're sitting on top, literally the most valuable resource of people that startups are trying to just build up.

There's one thing that comes to mind. I was telling you, I was doing a 2-day, 4-session program the last 2 days with Enterprise Ireland here in Ireland. It's called Peer Circles, and it's groups of companies with similar challenges, similar opportunities. So for example, we had medtech and life sciences together. We had a CFO group. We had R&D and innovation, and we had a family business circles. We call them peer circles. But one of the, some of the questions that came up were around, for example, if I have my kids, it's like, what I'm fearful for my kids' future and why I think it's really interesting to throw this to you is because you lecture to, okay, I'm aging myself here, but 2 kids. So undergraduates as well as postgraduates. So you're not trying to, like, if you have a company that's like, okay, we have a revenue per employee metric, but they're going to have, they're going to, they're going to look for different skills from graduates, from, from new employees. And I'd love you to share that for those people who are thinking that way. What are you encouraging your own students to be doing?

100%. I'm glad you asked the question because I'm not only an investor and educator, I'm also a parent. And so when I think about what kid, what world my kid will grow up in, uh, I'm often brewing over, well, how does that then impact my role also as an investor? What am I investing in so that that future is something that I wanna be a part of? And what am I making an impact on as an educator so that again, that's shaping the world in a way that I think will be better for everyone? And so when I think about, you know, what changes are currently happening, they're kind of two sides of the coin. One side of the coin is, you know, that AI has been an incredible tool in terms of automating a lot of the repetitive and boring aspects of work so that kids nowadays graduating from, you know, college and graduate school, they are expected to do more at a relatively more junior role. For example, As we were talking about earlier, Aidan, I've done a lot of work with EY, and one of the conversations I've consistently heard is, hey, if now we don't necessarily need entry-level employees to do very basic things like update spreadsheets and do more tedious work, then we can actually bring them along to client meetings and build relationships earlier on, have them do more at a younger age. So that's one side of the coin. The other side of the coin is that it's easier than ever before to graduate from school, but harder than ever to find a job. Because the expectation for what you're able to bring to a job is that much higher. And so how I'm responding to that is that I try to give my students as many real-world exposure and experiences and project experience as early on as possible. So that's partly why I think the fractional founder concept is so empowering because oftentimes you find what you wanna do in life by working on side projects that you were genuinely interested in, where You weren't even compensated at the beginning, but it was just a problem that you obsessed over, or this thing that you really enjoyed building. And then that turns into, you know, what sparks the rest of your career. I, I oftentimes think that these projects are the best way to get you started. And so one of the top things I encourage my students to do is build side projects. It could be something that's not clearly what you're going to do for the rest of your life, but that's the best way to explore. So there are negative forces, you know, that make it harder for entry-level employees to get a job, um, that where the bar is higher for them to even be able to perform at that level. But I think there are now also more tools that they can play around earlier on and build side projects with that just make it that much more interesting for them. Um, and so I always encourage my students to, to try out as many side projects as much as they can earlier on in their journey. This is a computer science concept that I think is very true for careers as well. I think there are 2 phases to careers. There's the exploration phase and then the exploitation phase where, you know, you wanna make sure you explore the landscape, otherwise you're stuck on a local maximum. You explore so you can find what your global maximum looks like. So like, these are the things that I like, these are the things that I'm good at, and like, let's find where I'm the best at it. And then once you've done that exploration, then you can get really deep on that front and become genuinely one of the best at that. And most students now, I think, are under a lot of pressure to almost skip past the exploration phase, which is why there are people who go work at a job for 5 years, 10 years, and then realize, I, I don't wanna do this at all. I want a complete career change or a complete career pivot in their 40s. I'm seeing this very regularly happen for folks in their 30s, 40s, 50s even that come back to executive programs at Stanford and try to figure out like, how am I gonna do this career pivot? So I think there's a lot of merit to doing this exploration early on through side projects, even whether you're in school or you're already in a full-time job. Try to build these side projects, figure out what you like, what you don't like, what you're good at, what you're not good at. And I think that will be way more informative than, you know, anything else you can do. And so I tend to be an optimistic person by nature, as you can tell. Otherwise, I wouldn't be an investor because I would just think all startups would die if you look at the data. Um, so I try to take the more optimistic side when I look at things, which is what is something I can do that makes a positive impact? So as an investor, I'm trying to invest in companies that genuinely are built upon relationships, not just that it's a fundamentally better tool, but that there's a really important human element where it's rooted in humans and our connections to each other. And as an educator, I try to encourage my students to go build that human relationship, to try to build that larger network earlier on. I think oftentimes networking can come across the wrong way. Like sometimes people hear that and they just think, oh, that like, it feels kind of ick. Like that's not for me. Maybe the way I like to think about it is like, you're growing up with people around you, right? Like no matter what age you are, oftentimes I talk to people in their 60s and they're like, I'm still trying to figure out who I wanna be when I grow up. Right. So I feel like it's about surrounding yourself with people that inspire you, that like pique your interest, that you have interesting conversations with and you build deeper relationships with, get to know better. And then eventually you grow up together and, um, and then you expose yourself to a whole world of really cool things.

I love it. I love it. And I just wanted to say one more thing on the education aspect, which is, I'm sure you see this and I know Stanford came out with a policy recently that many other organizations looking at it, which is Students using AI to write their papers, et cetera. And it's something that I see as a real threat to human cognition in general, as in I would say to many organizations, they're using AI to write policies, then they're getting AI to summarize those policies and give them questions to ask about those policies. The board then read, use AI to summarize the paper that came out of those policies. And they ratify it and no human has been in the loop at all. But beyond the human being in a loop, nobody has gone through the struggle that is absolutely required to learn or to write or to understand. And for me, I'd say this to my kids, like, for example, I was like to my older son, who was a bit of a rebel when it came to school. I was like, listen, it's not about going to school. It's about learning to be able to struggle through something, some type of content. And then deliver an answer on time to somebody who wants that answer. And it's that, that's a, that's a mechanic. That's a journey. That is life. And sometimes that means doing stuff that you don't want to do because everything, every job has aspects that it doesn't want, that you don't want to do. And I just wondered what your thoughts were on what your advice were to your students, because I have this myself and you can spot it a mile away. You can spot LinkedIn posts that are written by AI that clearly is like, It shouldn't deserve a LinkedIn post, but is made expand into a formula. And when people do that, they're not learning and they're not retaining knowledge.

It's so true. Um, uh, I, I think everything you described is true because when you have a lot of poor quality content flood the internet, and now we have more AI-generated content than human-generated content by like a wide multiple. That like it decreases the quality of the human-written content. For example, like I've been an avid user of em dashes before AI existed, and now that AI uses so many em dashes, like my messages look like they're written by AI even if I wrote them. So it decreases the quality of even the human-written posts cuz you look at it and you're like, wait, maybe this was AI generated. And then like even the human-written work doesn't get as much recognition or you know, uh, attention than before. And so I, I think it's very easy to end up in this kind of negative spiral where like there's no genuine human in the loop and it's just my agent will talk to your agent and, you know, that's it. And I think this is partly why I see so much of the value of education, of just going back to that in real life component, like in, like the whole movement in Silicon Valley around I, uh, IRL, in real life, I think is a thing because people are realizing like anything online That can all be AI, but when we show up in the same room, have a dinner discussion, uh, sharing, like breaking bread together, sharing a meal, and like that is not replaceable by AI. And so I think a huge value to the educational experience is partly what you described, like struggling through something, delivering an answer, and like understanding how to work under pressure and all of that. I do remember when Bringing calculators was controversial, right? Like that was a whole thing. And I think there are some ways that the current era of AI echoes that where like, hey, it can be a tool. At the same time, I think AI can automate things to a whole new level, unlike calculators where like humans can genuinely get away without much thinking at all. And so that's why on the other hand, I think it's so, so, so important that colleges Not just about learning and the process of learning and struggling, but also about like, these are all the people that are around you and building those relationships. I think honestly that is the most important thing that is not replaceable by any sort of technology. And if you look at most of the real world, most industries still thrive based off of relationships. So even though I invest in fractional founders who are building software startups that sell B2B, Very few of them are because like, hey, look, like you are the best, the only, the, you know, whatever. Like, of course those numbers matter, but oftentimes it's, I trust you. I am buying your software because I know when things go wrong that like I'm going to come to you and you're gonna help me out. You're not gonna leave me stranded in my job, in my role. Like you're gonna make sure that I'm okay. And I think that's how a lot of the world still functions. And I'd like to believe that even with AI, that that will still be very important. I do think we'll need better AI governance for sure to prevent a situation like you described where there's literally no human in the loop. But when I look at students, I always tell them like, there are students who prematurely drop outta school and there are absolute valid examples. Like I've had students whose startup, uh, was already making like $13 million in annual recurring revenue. And I'm like, of course you should do this full-time. Of course you should be dropping outta school. But then there are also other examples of students who want to drop out of school. And I always remind them like, hey, you know, AI can now tell you answers to everything. So you're not just in school for like the learning piece of it. It is being in this environment where you get to learn and grow with other people around you. Let's not forget the value of perhaps an acquaintance today that could become someone who's really important in your life later on. I am now working with a venture partner who I had met fleetingly back in undergrad when we were at the bookstore, just buying the same textbook. And we struck up a conversation, became good friends, and stayed in touch for literally well over 15 years before we ever did any work together. And so I think it's just the depth of relationships that compound over time.

I was mentioning about those groups that I worked with yesterday, and we mined them for their worries or their concerns when it came to usage of AI. And one of them was Some of the companies had genuine IP where they'd created really globally unique content or unique formulas for their, for their output, for their services, for the products. And some of them were like, well, if I put it into an AI like ChatGPT or Claude, where else is it going? Because I know it says it's unique to me and it won't go anywhere else, but do I really trust it? And it's a nice segue to something that you write about where you have the tech companies actually competing and bidding for proprietary data. And it wasn't data that is openly available. It was data that was from bankrupt companies. And in this case, you talked about Spirit Airlines. I'd love you to share those 2 flip sides of the coin, the story of Spirit Airlines and the bidding war, but then the concerns over enterprise data.

Absolutely. So as most folks know by now, Spirit Airlines had declared bankruptcy. And what became somewhat surprising to people was that their most valuable asset wasn't just like what terminals they had access to and what airports, but rather their literal, literally decades of operational data. This included things like employees' emails, anonymized like customer data, operational data, literally how they were running processes. So if you think about the world. Um, 95% plus of the world's data is not available on the internet because it's private. It's stored on within enterprise databases. And that is gold for these AI companies because if you think about how AI works, and by the way, if this is too, too basic for you, like skip 10 seconds ahead, but like AI basically is made of 3 things, right? It's data, it is compute, and it's algorithms. So very analogous to the human body. Data is like all the knowledge that it learns. Compute is like the physical brain, like the actual hardware compute storage where things are stored on. And then algorithms is like literally what we were talking about, learning how to learn. So some people are more visual learners, they're better at visuals, and other people are like better with tac— like tactile, right? So it's like the different models, different models out there. There are some that are really good at images, some that are really good at videos, some that are really good at math. So exact same analogous, uh, situation to humans. And so when you think about AI, uh, you can teach an AI to be really good at, let's say, looking at a particular kind of data. So you're training its algorithm, you're storing it on the most efficient compute on state-of-the-art chips, but you can't do any of the training unless you have knowledge, right? This is what we go to school for. This is what we live for. We absorb a lot of knowledge, and AI precisely needs that kind of knowledge. So imagine trying to teach your kid to go to school, With just by watching stuff on the internet. Like, you're not going to have a very thoughtful, smart child, even though the internet is full of information. If you wanted to have, like, raise a kid or teach a kid, or in this case, teach an AI how to be a really thoughtful adult that can work in the enterprise environment, you need to have it learn in an enterprise setting. It needs to be able to have access to, like, how do people talk to each other in a corporate setting? Like, what data are they transferring? How do operations and procedures and workflows actually function inside a company. This is why this kind of data is so valuable to these AI companies. So Spirit Airlines declared bankruptcy. All these companies want to bid for the data. The latest that we're aware of is Google sent in the largest bid for that data because they want to be able to use that to train their AI so that the AI can do better in an enterprise setting. So that's the first side of the story. The second side that Aiden, you were talking about is Well, like there are all of these people that are sending their information off to AI and it's like, are they using it for their own training purposes, et cetera? First of all, if you're not paying for your ChatGPT, Claude, Gemini, whatever it is, they're definitely using your data because when you don't pay for it, you are the payment or your data is the payment right now. If you pay, there are more stipulations that you can read through. It doesn't fully. Protect you. So you need to read through all kind of the details to figure out what exactly are you trying to protect. Typically now, if you have an enterprise account, it's much more clearly laid out and you can negotiate upfront that, hey, like none of my proprietary enterprise data will be used to train models that then can benefit other companies, for example. But one thing to keep in mind is that this is actually an emerging conversation, maybe a topic that I'll cover in a future monthly note as well, where Increasingly, companies are saying, given that these proprietary workflows are my most valuable resource, why would I just give it away to someone else to train the data? Why don't I figure out how to train the data and actually own all of it? Because that's literally my most valuable thing, like how I do business, how I manage people, how my people talk to each other. So why would I just give it away to the equivalent of a Frontier Lab and have them own that? Why don't I try to do that in-house? Now, for anybody who's spent time in corporate, you'll know that sometimes it doesn't work for a lot of reasons due to, sometimes it's resource constraints, sometimes it's politics, sometimes it's lack of will, like tons of different reasons. But I definitely see an increasing move from companies that are saying, look, let's figure out how we can do a healthy balance where maybe we use one of the open weight models for just basic tasks, these are models that are cheaper and open sourced and there's no proprietary data for basic workflows there. But then for the more proprietary stuff, maybe we want to retain that because that's literally my most valuable resource. I want to keep all of that data within the company and then be able to train a super specialized model that is really good at what I do. And it could be in a specialized sector like legal or like financial services. And it could be even more niche, like just credit or just lending within financial services. And so I'm definitely starting to see a shift there, but that will require a lot of human capital, a lot of political capital, a lot of will to make this all happen within a company.

We are not going to get near the newsletter. I thought we'd get through it in half an hour. Not a chance. Maybe we'll just share it. There's stuff in there. There's your future-oriented trends, things that you see coming down the line, but let's share your tool. So the tool that you love, this is from the August newsletter and Hopefully we'll get together and do the September one, but let's share this one. This, I thought this was really cool.

I, in my monthly note, tried to share 3 trends, 2 thesis, which are kind of predictions of the world, and 1 tool. And, um, uh, I try to keep the newsletter really concise. So Aidan, thank you for being so patient and letting me ramble on about each of these points because a lot of these are things that I wish I could have included in the note, but I just don't have the space. to do so and had to keep it really concise. In the tool, I usually feature something that either I've worked with a founder or crossed paths with them somewhere and just found what they do to be really valuable. And the tool that I just talked about is this startup called Wolf. Joshua, the founder of Wolf, actually was a longtime professor, um, in the UK. He was at Oxford for the longest time, I believe, as a philosophy professor, and then also served on the board. Um, and he has done some fascinating work since he decided to leave and start the startup. Also somewhat of a fractional founder story where he had experienced this pain point working in the administration of the university and then said, look, I need to build a better tool. So what he started by building initially was just, look, um, the university administrator to student ratio is way off. I think in the US there's some even worse numbers. Don't quote me on this. I can Google and find out the exact ratio, but like, I think the Yale administrator to student ratio is double digit. It was like 30 administrators to a student or something like that. Uh, they do a lot of work. Administrators do. Like, there's all of this paperwork and reporting that you have to keep up to date with the regulators so that you can stay accredited as a university. And so Joshua's starting point with Wolf, named after Virginia Woolf. Was just let's use technology, including AI, to automate a lot of the work to make it easy for a university to become an accredited institution. So there are a lot of educational programs out there like coding bootcamps or like architectural programs or even startup programs like the one that you and I do that very much could and maybe should be accredited. For example, you and I both have done this where we teach on campus and all the students that are in class, of course, receive credits for that class. And then we literally, like, let's say take one block off campus, teach the exact same content, and students who take that class get no credit whatsoever. That doesn't seem to make sense at all. So at first he was just building this tool to make it easy for educators and institutions to make it easy to create content that is accredited, accreditable. And then eventually he started building this to make it available to the student and corporate side as well, where for students, What if you had a lot more flexibility and you could take a couple classes at Oxford, a couple classes at Cambridge, a couple classes at Harvard, a couple classes at MIT, a few more from Berkeley and Stanford, and you DIY that entire thing into a degree? That makes a lot of sense because you've just taken all these accredited courses. Universities didn't historically offer you the flexibility to do that, but now we have technology to track your learning across all these different campuses and actually stitch it together into a degree. Similarly for enterprises when they need to do employee training. If I need to do AI training, why shouldn't you get credits towards a degree? You can absolutely earn a master's in AI, or you can learn, you can start to earn towards an MBA in business administration as long as you're taking all the right courses along the way. I think we're seeing this shift where in the past we transitioned from our role Of a student to a role of an employee, and like it was a one-way street. That's not the case anymore because things are changing so quickly where oftentimes both of those roles coexist in our lives where we're employees, but we're constantly learning. In fact, we're expecting to be learning on the job. That's part of the fun of being on the job. So if you're consistently learning, why shouldn't you receive credits towards that? So that's Wolf, what Wolf does. It started off just in terms of the administrator side, and now it offers That flexibility to students who might want to take courses across multiple universities and still graduate with an accredited degree offers that flexibility to companies that provide training to their employees to say, look, you can actually train your employees and give them an MBA degree in the process as well. And one of the top things that they had just published was a piece where they were writing about how more employees were spending money on AI training more so than ever before. But the completion rate for these self-paced courses are so low. They're sitting at like nearly 10%, only about 12% or so. And so they were doing more investigation to figure out what will actually incentivize employees to finish these training degrees. And it turns out that accreditation is one of the top incentives because I want to know that I'm working towards something. Like, I'm not just doing this random course that I'm required by my employer, but that, oh, like, this will add up towards, like, a graduate degree. Like, I would love to have that, right? So, um, that's the tool that I just shared with everyone, whether you're an employer or in your employee, it's a great platform to check out because, uh, they now have partnered with Accenture to offer the first time ever where you can do an MBA degree online and you pay on a monthly basis. So you work towards the degree at your own pace. You don't have to do this exorbitant tuition at the beginning and then get into a lot of student debt. You can actually work in your full-time job, do a part-time degree like MBA with specialization in AI, and then work towards that. So you can do both at the same time.

Brilliant. Brilliant. Mic drop. Brilliant work. I'm glad you cleared up it was Virginia Woolf, because I thought it was the Wolf from Pulp Fiction, the guy that cleans up all the dead bodies. I thought it was the fixer. It was the whole idea of being a fixer. I watched too many movies, obviously, Shuo. It's been an absolute pleasure, and I know you're under time pressure. You just had a board meeting. I got you in between the board meeting and next appointment. Very, very grateful to have you on the show. Where can people find that newsletter? I will link to it as well, but maybe you'll tell us.

Yes. Well, thank you so much for having me on the show. The monthly note is at shuo.iovc.io. Uh, you can remember my name as shushing someone and saying, whoa, she's awesome. Shuo. So it is shuo.iovc.io.

Fantastic. It was an absolute pleasure having you on the show. Shuo Chen, thank you for joining us.

Thank you so much, Aidan.

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