Albert Chun, Founder/CEO of AI Circle, on Training a Frontier Model, and Why "Everyone's B+" Without Experience

Dennis Yao Yu: Welcome to AI for Business Leaders podcast. I am the founder and CEO of the Other Group, a strategic advisory firm that partners with commerce and AI technologies to unlock revenue opportunities and solve go to market challenges. With 20 plus years scaling revenue teams in consumer brands and high growth technologies like Shopify, I've spent my career at the intersections of commerce and technology. This podcast explores the future of AI through conversations with founders, investors and operators who are building what's next. They will share strategic insights as well as tactics that we can all learn from. Hi everyone. Thanks again for tuning in in the AI for Business Leader podcast. Today we're diving into how business leaders can actually leverage AI with someone who's building the premier community for AI builders. Albert Chang is the founder of AI Circle, a vetted community for business leaders and operators building at the frontier of AI. From startups to global enterprises. Founded in 2024, AI Circle has become a trusted network where founders of operators learn, build relationships and navigate the business challenges of AI adoption. Altogether for transparency, I'm also a member of this great community as well. So welcome Albert. So good to have you on the pod.

Albert Chang: Thanks so much, Dennis. That was great. That was a great pitch. I love it. Thanks for having me.

Dennis Yao Yu: I'm always happy to be your cheerleader. So thank you for building a great community. And obviously I gave a little bit of sort of high level background about AI Circle. Right. But I think you have a very interesting background in your journey to AI Circle. So, um, if you don't mind, I know you went from chief of staff and director of AI operation at an insurance company, to working at, you know, invisible Technology, to now founding AI Circle. Can you give us an overview of your career arc and tell us a little bit about what made you make the leap of AI into AI and what did those operator roles teach you about business, the businesses that actually need to succeed with AI?

Albert Chang: Yeah, great question. So, um, a little bit of background. I was in education for almost a decade. I had helped start three schools in the Bay Area and I was a teacher. I taught every subject. I was a principal. I ended up starting a robotics program and um, a nonprofit, uh, think tank, um, and a few other things when I moved to New York City, continue the same. I helped start a couple of schools and within that ended m up starting a, uh, mental health clinic, hospital, a dentist's office for students because it was in one of the poorest congressional districts in the United States and kids didn't have access. So it was like, okay, well, the community comes in to help provide the need. And after a couple of years of doing that mid career decided, hey, you know what, let me apply to two schools. One was Harvard, the other was some like six person exec thing at Columbia. I didn't get into Columbia one, but uh, yeah, get a half right from Harvard and decided hey, let's do this thing.

Dennis Yao Yu: Uh, that's not a bad plan B.

Albert Chang: Ended up biting the entrepreneur bug, not realizing, yeah, it isn't a bad plan B. But uh, you know, I started a couple of startups there in these little incubator programs at the graduate school of Ed and at hbs and you know you win a couple of like pitch deck competitions or whatever. But I learned it wasn't what building a business was and ended up uh, working at a, uh, an edtech company and helping the three to four exits revenue in less than a year as a first commercial hire. And then I thought, you know what, let me try to do this in a non education setting. Went over to an insuretech company. It was 6 mil at the time. I started managing three people and um, I ended up managing about 211 people. So about 40% of the company across like 12 to 15 different uh, departments, 13 of our M&As and ended up doubling the business and having 75% of the revenue orgs reporting to me. Um, and there's actually like a tough thing that happened there is that I, I got laid off despite like, you know, I put like max effort into this thing. I think people know what it's like to build and scale like you, you put like everything into it. But this is, and that's important because one, it was. These are part of the motivations of starting something like AI Circle, right? Uh-huh. Not everyone has a golden parachute. Not everyone has whatever it might be. And your network is certainly I learned the value of the network but anyway was building, I had built up a fractional portfolio at that time and it had been doing quite well. I ended up, this is a kind of funny story, I ended up making LinkedIn friends with the CEO of Invisible. Like one of the friends of the CEO of Invisible. They were hiring for the role and yeah, I interviewed with them 10, 10 rounds of interviews and all the things. And you know, the job was basically hey, we have the second largest client. You'd be, you'd be managing 180 people and you'd be training this frontier model. And um, I had several other opportunities that candidly paid a lot more. I had C level offers, I had like numbers that were triple, if not 4x, what I was getting offered at Invisible, but they were not in AI and I thought, you know what, the way I saw it from there, the ceiling was way too high and I always held titles and numbers a little bit there. They've always been more of a scoreboard, if I'm being honest with you, than actual like the driver, if that makes sense. And so it was like cool that the offers were there, but figured, you know what, let me just do this thing where there's more learning, we more opportunity. Let me solve this. What I appear to be the a problem for this org that is in my opinion in like the, a space in the industry. And yeah, I would say like doing that job felt like the job of a CEO, um, or like a founder. And I say that in hindsight because I worked harder then than I do now. Um, and I, yeah, I obsessed like lost sleep. I'd wake up in the middle of the night, go feed my newborn, read research paper and figure out, out how to pitch this into a campaign that would draw more signal for my client. You know, like I was hell bent on making my client's model number one in the world. I was obsessed with getting my POCs promoted and making them look amazing in front of their, their bosses and all the things. Like I, like, I obsessed over being the best, if I'm being honest. And along the way built like a very remarkable team. I think the thing I only understood in hindsight was that we were doing state of the art everything because like we would like when you read an archive paper that just got published and you translate that into a business proposal or a task or a uh, campaign design that would elicit higher signal for the data. Like you can generally trust that not many other people in the world are doing that at that level of immediacy. And yeah, I think the part that was really amazing about that work was Invisible. Had this culture where they celebrated ownership and they were very curious and they weren't like, hey, I know this and do it my way. They were like, yeah, like figuring it out cool. Like, so do more. They would push, I would, I was pushed like none other and uh, any other org I've ever been a part of to be excellent. And I wasn't excellent and I didn't feel that way. But it, it gave me something ah, very lofty to chase. And along the way like our client ended up becoming the number one open source model in the world. And then, you know, my team ended up being like, I'd have analysts who would advise very senior people on what to do. And they would advise on the most state of the art labs and frontier models you could think of. Like we'd have our, some are more junior people advising and sharing what state of the art looked like to them. Uh, and so that was a very remarkable time. But that's what kicked off, um, my career in AI and ended up being kind of the incubator for what was AI Circle. Because as you do, what I learned is when you're doing these things that are at the cusp of innovation, there are a few people who know what you're talking about and you get to talk about like there's no one else on the planet. I feel like I could have talked to about these things. And here they were. And I ended up bringing them into, yeah, just an informal community. I understood the community business model. Just having been a fractional exec at a couple of professional orgs and I thought, you know what, I actually pitched the idea of AI Circle to many people, say, hey, I need this, please start this or do this. And I got actually varying degrees of yeses and nos and they were like, that's a good idea. Like, but they needed to see more. And I figured, you know what, let me just do it myself. Um, and kind of here we are.

Dennis Yao Yu: That's fantastic. I think this is the part that I can say, this is why I love entrepreneurial stories. Entrepreneurs a lot of times obviously have the drive and motivation, but they also are able to spot opportunities in the marketplace. And oftentimes the story that you're telling me where you're suggesting your clients to build this AI community and they said no, but you saw the opportunity. You're like, well, if you're not going to do it, then I'm going to do it because I know that it's going to work. So it's really fantastic story in that sense. Yeah. Um, and kind of in line to what you're talking about. Right. Like you're Quite active on LinkedIn and you made some friends on LinkedIn as well. And ah, it is a great platform when it comes to professional network and just social interaction. And you're also very intentional about building in public, documenting your journey on LinkedIn and sharing the learnings and your stories pretty openly. I would say, um, there's a few other people that do that, but I would say building in public is still kind of like a newer concept for a lot of founders and a lot of people. I'm curious what made you do it? And you Know, is there a very sort of specific strategy that you're using, uh, or deploying?

Albert Chang: Yeah. Well, I think if you ask any of my, like, real friends. I don't want to say real friends, but people who've known me for a very long time, they would generally say I'm very honest and real, and I won't sugarcoat the truth. Right. Um, and so that's my disposition in general. And I actually. I would actually argue one of the biggest drivers for me to build my own thing is that I want to do and say what I want to do and say, like, autonomy and independence is. Is extremely important to me. And that's. So I thrived that invisible, like, hey, figure, uh, all this out, like, with little guidance in a new industry, and knew everything. Like, I loved it. It was really in my element there and in my element now. And so on LinkedIn. I mean, I started writing on Ling. Well, I. If I really want to talk about writing, I had been writing since. Yes. Like Xanga, uh, Friendster, like, you name it. Like, what are. What are all those, like, old.

Dennis Yao Yu: Whatever. The things are.

Albert Chang: Like, I worked. I wrote on every platform. Yeah, every platform. Yeah. Um, and so live, journal, whatever. Like, I always wrote.

Dennis Yao Yu: Yeah, right.

Albert Chang: I always had a. I've always gotten, you know, I've always gotten rewarded or affirmed in my writing at school and whatever by my peers and all these things. Right. And. And so that's always there when I learned about. And I actually. I will say I actually learned the power of storytelling when I was a teacher. So I was. When I was teaching in one of the worst neighborhoods in California and one of the worst neighborhoods in New York City and arguably in the country. Like, I would share my story with people online. Here are a couple of ways that I was affirmed in my writing and storytelling. In one, it was a parent teacher conference night. I shared a story about how one of my students broke his glasses. And in my class, we had this microeconomic system where you kind of paid rent for your desk, and it was very expensive to sit in the front row because I said, hey, like, this is prime real estate. This is where you get the A's. You know, you all want to always sit in the front. Anyway, he broke his glasses and he's like, Mr. Chun, I can't. Yeah, listen, real world, we all know it's like. And no one prepares you for it. Right? And that was always the way I do life. But broke his glasses, couldn't afford new ones. And I actually broke mine, too. Like, I did a Header and I, I was going around with like one leg and I. But I gave it to the student because we ended up having like similar eyesight or whatever, vision. And when I shared that story, it's like a two second story out of that. It so happened. The CEO of a very large eyeglass company was in the room in my classroom. Their student was in my class, uh,

Dennis Yao Yu: what do you know?

Albert Chang: And they said, I want to donate a quarter million dollars worth of glasses to your old city. And I thought, wow, that is the power of words and storytelling.

Dennis Yao Yu: Uh-huh.

Albert Chang: Another, there were these small things, right? Sure. About, hey, you know, I donated my iPad or whatever to this class and someone broke in and stole everything. I got people donating their iPads to my students, like getting pizza parties for my students and doing these things. I'm like, listen, like, these kids are like, my kid went from knowing kindergarten math to getting caught up to eighth grade math because I work with them at 7am every morning until 5pm at night. Right. Like, people are like part of the journey. Let me like celebrate your kids and all the stuff you're doing. And so that's when I really learned. I was like, okay, one, it felt good to share what I was doing. I think others felt good to be part of the journey and they wanted to be involved somehow and transit that over to Corporate World. Here's the thing that's interesting. Corporate World says, don't storytell. Keep this in house, keep it our tone. You represent us, do all this stuff. But I always had that voice. And so while I read the tea leaves saying, don't write, don't do this, I decided to say, I'm gonna do it anyway. So I started writing in 2020, around there, 2021. And uh, listen, it was thousands of cringy posts, you know, trying to find your voice, what resonates with corporate people and what you're doing and all the things. And so for me it's like one, it's just a natural expression. It's a way for people to jump in. And like, sometimes I'm delighted and surprised by responses or ways that people want to engage. And then I think there's this other component which is like the thing that kept me from writing was fear. Right. What would my boss think? What would my peers think? What would they think if I Posted at 9:01 When I'm supposed to be working? Right. Like, and I realized how silly it is to live by that kind of fear even in such a small thing. And I thought I'll embrace it and overcome it. And then there's this other feeling of another sense of fear of is this cringy? Is this. What have you? Is this. There's a little bit of shameless self promotion here and all these things. And again, not answering to that fear and just, you know what? Just going to share it because I can, because I want to. Uh, and then, and then I think the other part, you know, you talked about some level of strategy. I guess the thing that I have always felt like, whether it was when I was a teacher or friend or whatever, it's this feeling of I'm going to do this. Like when I was in Oakland, it's like, I'm going to do this for the block. When I was with my friends, I'm going to do this for my friends. If I'm going to do this for the school, like, I get this partnership, if I get this after school programming, I'm doing this for my students, right? Huh. Over, under, through, I'm going to find a way to help us win and do something. The greatest thing I could do is to bring something remarkable to this collection of people who entrust me with something. And when I have AI Circle, people entrust me with introductions and resources and these things. And if AI Circle isn't winning, they are not collectively winning, you know. And so for me, I will do whatever the heck I need to do to put us on, to help our members win in whatever way they are trying to win. And so if that means me posting all the time, finding these partnerships or these events or whatever, like I'm going to do that because like my mission is like I'm like hell bent on helping this select group of people win at whatever, whatever their endeavor is.

Dennis Yao Yu: Yeah, no, I love that. And I think just even as a member, we can feel that passion, we can feel that intention, like just from all the interactions as being you also on WhatsApp as well, just, you know, interacting and chatting with everybody, you know, almost. It feels like it's 24 7. So I mean, really good for you. Yeah, no, that's really awesome background. Um, and obviously we talked a little bit about this. I think you have a very interesting background. Like I said, I mean you were a teacher, you also had, you know, you worked a startup, but you also manage hundreds of people. People management is no joke as we know. Um, AI is a product, but people management is another thing. And I think that's one thing that a lot of corporate leaders or operators are kind of facing right now, is that you know, uh, they've gone through sort of AI will change, change everything, hype cycle. And then now they're either getting pressure from their C levels or perhaps their C levels and they're gaining pressure from their board asking, well, where's the roi? Like how do you calculate the roi? Right. And everybody's different, but based on what you're seeing, you know, with our community and also in the market and where things are right now, you know, from a use case standpoint, like, are there ones that are measuring, like, you know, a better at measuring business value? Um, and which are some of the frontier models that you feel like are still more of a lab science project at this point?

Albert Chang: Yeah, um, um, yeah, there's, there's a lot there and a lot of good things there and there's some that I feel more equipped to answer than others. But I'll start with sharing that one. I'm not a great, I'm not the kind of leader or manager I want to be. Like, I think I've always, uh, sought out mentorship and guidance and found ways and trying to find ways to doing that better. Sure. Like, you know, managing several hundred people and having. I'd have varying degrees of managers who. It was their first time where their first like their goal for the quarter was to speak on a client call, to literally say one word. But the thing I learned about leadership is one, there's times to meet people where they're at. Other times there are times to push them and kick them off the ledge and say, figure it out. And so like here's an example, like one where, you know, there as I was working with her, you know, trying to figure out how to just like contribute to a call to by the end of the quarter, close A, uh, eight figure deal like that was the same person. I have always been pleasantly surprised when you give someone more than they think they can do. You give them some room to fail, you give them an abundance of guidance and you just trust that, hey, we're going to figure it out one way or another. But like the part that I think is. So there's that part and then it's also, it's like what you reward. Right? Uh, it's what you affirm to be true. Right. So here's something I am ruthless about. You can ask people I worked with, like, I am ruthless when it comes to serving and protecting the ends of the business. Like it's business is very simple by the way. You know, my parents had built business like they had always run businesses and Doing all these things, like it's like there's something, there's a lot of muscle memory here. What do I know? You make money, you save money, you keep money and somewhere in between you hire some people to make you save more, make more, keep more. There's not, there's not that much to it, if that makes sense. It's actually why it probably didn't do that well in the public sector because their one metric is spend as much as you can so we get the same or more right. And that paradigm never made sense to me personally. And so that's the part. And I think sharing that fluency with the team is very important. Like I will, when I coach and mentor people, I. It is like by immersion, right? Like they need to know how I think and operate. Because a team is, the quality of a team is measured by the quality of their decisions when you are not in the room, right? Uh, and so how do I get you to think the way I, how do you, how do I get you to make the highest quality decision that I understand is for you to understand all of my decision tree in order to produce something. And it is always tethered to, let's say it's a top line revenue number, let's say it's an NPS number, maybe it's a bottom line margin number. Whatever it is, everything is anchored and tethered to this business metric, right? And so even when, and so this is the thing, if you want to care about ROI calculation, which I don't feel prepared to share about based on your MTP pricing and inference costs and blah blah, blah, not me. But what I do know is like when you look at the numbers and you obsess over these numbers, you have to ask really cold hard questions about like what value do you bring? What are you holding? Like, here's an example. I knew I was doing well at this company because we attributed revenue per person per team, right? And when I saw it, each person was bringing on half a million dollars per bearish case to this, in this particular, in this particular work. And I thought, okay, you are getting paid less than a fifth of that for some of these people. And so I knew generally eyeballing that this ROI was justified, this headcount was justified as an example. And when I saw that globally, I saw that others were half a, uh, third, a fourth. And I'd hate to say it but like some of the, some are not always objective. It is a relative thing. And so if you are relatively making more at a certain Multiple of what you're bringing in. This is where. How I start to calculate things. Um, and then it, like, comes down to like. So there's that piece. So that's on the management piece. This is around like, you know, just like, again, like, ruthless observation of the finances and the numbers. And then I'd say, if we're talking about model outputs, I would say that is something many, many people struggle to do, if not all of them. Um, and what do I mean? Here's why it gets tricky. Like, let's say from a model perspective, and we're just talking about data annotation, and maybe there's some people out there who figured it out, and it's amazing and I'd love to learn from them. But from what I. My personal, very limited scope of experience, if model outputs are stochastic, if things are pretty relative, you're. You have this finger in the wind thing, right? You're like, I think it's, I don't know, loss function and, oh, I think it's whatever your metric is. Maybe it's an MMLU score or a blue score or whatever. Whatever it might be.

Dennis Yao Yu: What.

Albert Chang: How you do on a sheet, bench or whatever. Is that really a metric for quality or output? I actually don't think so. I think that's. That's someone trying to say, hey, someone bought this thing. Someone bought more of the thing or they bought a more expensive version of the thing. Huh? Right. That's actual roi. Right? And I don't know if there's a causation between blue score and $50 a month versus $75 a month. That's the part I don't know. I think people will say, well, no, it's always this. And this score is always this cousin's number. I don't know, maybe. And then I think the other part is, well, what are you investing in it? Right? So it's like you're trying to. You're assuming that the quality of data I input will move this number, that may move this other number. And because it's not an exact science, you don't really know. And again, if the theory is, hey, well, it reduced the loss function this much, go to market, did it. Did it impact that number? Are you sure you can attribute it to that? You're sure marketing just didn't get sophisticated. What? You know, as a commercial guy, right. A million reasons why people didn't cancel. Right. They may have just forgotten about it or it's too hard to find the cancel button on your website. I don't know, you know what I mean? And so that's where I feel like everything is a little bit of a finger in the wind, like in the way that you do it. It's like there's a, uh, big need still for intuition and just like kind of going with gut feeling right now. Unfortunately, from my, again, very limited point of view, someone knows better, please let me know. But that's, that's like my potentially hot take.

Dennis Yao Yu: Yeah, no, I think this is great because this is, this is exactly the type of answer I feel is very valuable for folks that are not, you know, in AI, uh, sort of day to day. Right. Because from the conversation I've had with some folks who are CMOs, VP of E Commerce, VP of Marketing, a lot of times, yes, they're exploring AI at different tools and they trying to keep up on, you know, all the latest from media and also LinkedIn. But as we all know, there's a lot of noise out there and they don't know what's true and what's not. And if they're just keeping up the media, it almost feels like a lot of them may feel like they're basically the world's passing me by. I'm missing out. Everybody's adopting all these types of tools and there's a way to measure and they're getting pressure from the top as well. So there's that sort of dynamic. And to hear from you saying, look, this is still very early innings of what we're building. The commercial org traditionally, whether it's AI native or software or any other industry, attribution has always been a big issue. There's always loopholes, there's always variables. It's never perfect. And for something so early, you gotta go through this art and science. And right now there's probably a little bit more art than science and that's okay. But the art part of it really comes from, if you've been in industry for a long time, you have lived experiences, you have the domain expertise, you have the taste, you're probably going to be better than somebody who doesn't. So yeah. So it's good to hear that.

Albert Chang: I'm going to say one thing there.

Dennis Yao Yu: Yeah.

Albert Chang: Is the thing, I think the AI ecosystem overvalues and undervalues. I think they tend to overvalue the young, scrappy, X trained AI ML engineer from whatever institution. Right. And to say, hey, like you, whatever. And that is important and it is good and you do need it, but you don't have what you said, which is the uh, Years of experience to know just the truths about business, the truths about customers, and these things that are just like you've inherited, like you just accrued over your intuition and gut and all of these different signals that are hard to codify and express. And just like when you don't have the reps, you just, it's very difficult, you know. And I think like the, the part that I wish, it's, it's like interesting. It's like we've over indexed on the educated and trained and the technical without the experience and then we've overlooked the experience just because they lack some technicals. And then I think the part that most people don't realize is the technicals aren't that difficult. And it's like the best way to actually overcome some of these things. Like, you know, it's, it's interesting. My journey in learning AI was like I would literally read glossaries, right? Just read glossaries, read use cases. I would go through every Stanford MIT course on computer vision and all of this stuff, history of AI, all these things. And I'd, my retention would be what you'd expect. Like as a teacher you'd think I know how to learn better. But like that was like not helpful. I took every, I took all these LinkedIn courses, all this stuff. The reason why I like the best way that I learned to learn was like Whoa. In third, in my 30 minute conversation with this guy who is as impressive as heck, he just dumbed down months of months of my own personal research until like a few lines that just clicked right. Or like another way, you know what I'd read about reinforcement learning through human feedback or supervised fine tuning all this stuff. I just go and annotate a few rows of data and I'm like, whoa, this is, this is what was hiding behind that like that intimidating polysyllabic word. You know, it's just like preference ranking and it's a likert scale and it's a, ah, like chain of thought reasoning is just me sharing what I thought and why I did it, right? These things were the, the level of gatekeeping, timing I think is like a pretty gross atrocity during this AI stage. When in reality people, I think what, it's interesting, right? You uh, have, you get a lot of people who are very cerebral and research oriented and all these things and they're used to these very esoteric talks and all this stuff. And like something that Suzanne, um, our chief field always says, like a confused mind says no, it's like guys, if you actually simplified it or if you actually educated your client more, you would win more, you know. And so yeah, there, there's, sorry, a bit of rant, but I wish there was, yeah, a more honoring of the experience, more accessibility in terms of education and like, I think everyone would win adoption. The implementation acceleration would. I mean that, that's, that's, that's also part of the reason why the AI circle build. And here's the thing to consider why. And I, I haven't talked to these guys, but I just observe and I imagine, I wondered, why did Andrew Ng, of all the things he has done, could do, is doing. Why did he build deep learning AI? It's fascinating. Like he already teaches at Stanford. He already built Coursera. Like, uh-huh. Why did, why did he think that this was a place to use a significant portion of his time? And then another thing I wondered, why did Andrej Karpathy with his stop sign ran and all the things he did at AI, Tesla and MIT. Why did he start a, uh, education YouTube channel? Why, why are, why are the smartest people on the planet who could do all of these remarkable things? Why are they trying to educate? And it's. I think there's likely a philanthropic reason. I think there's a high level missional reason. And maybe it's hate these words, both professors. So hey, like professors and academics continue on their education. Maybe it just made sense. And that's true. And maybe that's the same for me. But, but it does make me wonder like, perhaps like one of the biggest bottlenecks in AI acceleration development is. Is education. You know, maybe it's not all these GPUs and data and all these things. Maybe it's that, uh, like, no, like try to convince or teach all the Fortune 500 executives who are making these multibillion dollar decisions what this means in layman's terms and their impact to the business.

Dennis Yao Yu: Um, yeah, I agree. Um, I completely agree. Especially where we are right now. And I think this is why probably, you know, I appreciate what you're doing as well. Is that. So I spent a couple of years at USC teaching as well. So having that sort of mutual educator at heart, understanding, like how to bridge, you know, where things are separate. And I think there is a big gap. So just even as a very anecdotal story, really quick and you know, like, like we are right. I'm based in Silicon Valley and I'm surrounded by builders all the time. The AI. And AI is a very powerful. You have a lot of people. Everybody's risk on this is the greatest thing ever. When I go to la, New York, it may be a little bit different right. When we host dinners, but majority of these people are in tech and they're, they're stay on top of technology. But when I go to conferences in Phoenix or Houston, the conversation is completely different. These are also business leaders of their own. Right. The first thing they brought up is an uh, oh my God, how much money can I use? Can I make using AI? The question's always been what's going to happen to my job? Second question a, uh, lot of times is how do I let my kids use this? Like what's, what's the best practices and how should we implement this in school? So the sort of the dichotomy of opportunities type of topic versus fear driven fear based topics in classroom settings and also call it middle America for the lack of better term is drastic. Right. And I think that's where you know, somebody like you who has more of a long term vision, uh, where that's where the opportunities are. Sure ROI is important. Everybody needs to measure. That's short term thinking but we all need to measure because it's business impact. But for the long term there, there's a lot more steps to go through before we even get to the ROI conversation. Sounds like that's what you're saying.

Albert Chang: 100%. 100%, right?

Dennis Yao Yu: Absolutely. Yeah. So I know we're almost at time. It feels like we, we can talk about this forever. Especially your, your topic of talking about learning how. I think that's a whole different podcast that that should be have, especially just having, you know, the world that we're in right now. Last few questions though. AI circle is bootstrapped if I understand this correctly. Right. Uh, it's, it's funded by membership dues. I think you had mentioned that, you know, whether you don't want to raise money or you won't raise money. Curious. As we talk to like you know, entrepreneurs and people in this field, AI is having its day. Everybody's trying to raise as much as money as possible from product standpoint. What made you choose that path and you know, what does that choice allow you to do differently than like a VC backed competitor?

Albert Chang: Yeah, um, one I would say when I first wrote that I, and this is something I still hold true to, which is I want independence and autonomy. Right. I'm not here to hire a boss, hire a uh, whoever to tell me what to do. I'm not here to say, hey, I have this portco that you don't believe in. But we gave you money and you're obligated to say they're great. Like, I don't want to do that. Um, and I get, I get a lot of that. Yeah, yeah, I get a lot of that without, without investors. And so, yeah, I'm like, I'm, I'm not gonna, I, uh, don't want to do that. And I think, I think because the biggest thing right now, I think one of the most valuable currencies right now is trust. Right. Like, can I trust what you say or what you endorse? Like, can you be bought? You know, and I don't want to play that game. There is a world where. Could we raise a friends and family round?

Dennis Yao Yu: Sure.

Albert Chang: Like, as a person who likes to help people win and like, I think that level of skin in the game and what have you would galvanize others or like give me like healthy accountability. Absolutely. You know, but like, like, here's a, here's another way to look at it. And those are some of the reasons. Another way to consider it is like having had board exposure and you know, worked with remarkable VCs, all impressive people love and respect and admire what they do. They, they have a job. It's to get a return. It's to 10x, 100x whatever. And one, the question is, is a community a venture backed backable business? The answer is yes, of course. You have company communities like Chief or Soho House or whatever. You know, like Soho House is a big. Is a unicorn. They raised 100 mil. And we're not talking about wework and all these other potentially like things you could potentially call communities or whatever. But what's the story you always hear from them is, oh, House used to have two references. No one could get in. And it meant something that you went to their club with the indoor pool and all this stuff to. They're letting anyone in. They became the Costco. Their membership is equivalent to Costco's. Right. You pay and you get in. Great. I don't want to do that. And then there's another part because in other words, I actually listen. As a bootstrap business, there's moments where you look at a number. I'm a commercially oriented person. Like, do you, do you like, how do you make this decision? Like, I had to say no to over a thousand people who've applied to say, hey, I want to pay membership to join. That is a hard thing to do as a bootstrap business. Who. This is our stream of revenue. But the long tail way of looking at it is. Well, there's like a couple of ways I consider it is you can never speed up quality in that way. Like you can't, like, uh, it doesn't, it doesn't really make sense, right? Like, I'm not, I don't want to be the person, have some vain metrics, hey, we have thousand members and blah, blah, blah. I could care less. And what I mean, like, and this is my disposition even, even growing up I had like, this is actually, this is kind of a wild story, but I'm going to share it since, you know, it's your podcast and let's have a, uh, an interesting story here. When I learned about quality over quantity, I remember I was a sophomore in high school and I had, there were a couple of football players who had made fun of me and I told them, all three of them that I would fight them right then and there in the morning, it was like 8 o' clock in the morning and they said, no, no, blah, blah, blah. And they went out and they called a bunch of people to say, hey, like, we want to jump this guy. I said, okay, well, I got friends too. And I called on a bunch of my friends and there was supposed to be this big after school thing. There were some weird things there. Anyway, I called off a bunch of my friends. I was like, hey, if this is just going to be one on whoever, let's just do it. Like, I like this kind of thing. They ended up bringing like 40, 50 people to the school. And by that time, by the way, I was like 14 years old and I ended up, um, I ended up having like 10 or 15 people who are like, hey, we're here for you, whatever. And um, when push came to shove, majority of them ran and did nothing, right? Like I fought everyone I could remember getting jumped and looking at the biggest pair of shoes to take down and just unload all I could and, you know, browning out or like slowly blacking out as I'm getting jumped. I remember there were maybe three people who jumped in, right? And I thought quantity never mattered, you know, it never is about that the measure of your network is not by a number. The measure of your network. Jason Gilman from Primary Venture would say this in a much more corporate acceptable way. It's like the measure of your network is how they speak about you when you're not in the room, right? And the playground is. The measure of your friendship is who's down with you to have your back, right? The same way here for the community, it's like, is it the number? Is it the, uh, whatever? Or is it, hey, these are the people who like you. You wouldn't believe how many founders I talk to. We talk some at 3 in the morning, some when I'm on a trip, some when I'm whatever, are like, dude, I'm about to quit. Oh my gosh, I haven't slept in this long. I haven't seen my family in this long. I'm blah, blah, blah. It's not about go to market, it's not about fundraising. It's about like, real stuff, you know. And, um, that's kind of the community I aspire to have is like, who do you want in your. Who do you want in your corner? And who's actually going to show up when you need them? So that's, Those are like a couple of the reasons why, like, not so much VC backed. And then there's other part of, like, I share this with only a few people when I talk about AI circle, you know, there's this like, oh, who's your ICP and what's your value prop? And all this stuff. Like, there's actually a real. There's actually a really core problem. Like, it's actually a core human problem of community and connection and relationship. You know what I mean? It's not like, yes, there's a help someone get a job or an intro or, or this or that, but it's like, it, there's a, like, there's a much deeper thing. Like, Dennis, as you know, life gets very difficult, friendships get smaller, you know, and you need someone like, who's there. Um, and so that, that's like, when I aspire to solve a much deeper problem that's not scalable with a big tam, you know, like that, that's. Those are the kind of decision trees that I like to. Or values I like to anchor myself on. And yeah, just a little bit of how I kind of see, see the world.

Dennis Yao Yu: So even through the story you talked about being somebody who's Asian American and who grew up in a predominantly Asian neighborhood, Acadia in la, and also who used to watch a lot of like, Asian films and Cantonese films. It just reminded me as, you know, like, there's a lot of gangster films, there's this group of people sort of with the machete sort of going at each other. I totally understand what you're saying too. Not sure if you've heard of a book. Uh, well, no, it was, it wasn't a book. It was an article written by Kevin Kelly from IDO that called, uh, a thousand a. No. Okay. Very similar to the concept you're talking about, which is a thousand core fans is better than like, 10,000 or million fans of just mediocre and lukewarm type of fans. And I think that's even more relevant today of AI than anything else. Whether you're talking about content, you're talking about friendship, you're talking about relationships, because that's just gonna. It's easy to exponentially grow by volume, but it's probably even harder now to grow in depth of that relationship. So, with that said, thank you so much for taking the time to kind of to chat. This has been incredibly, incredibly valuable. And, you know, for folks who want to learn more about AI Circle and yourself, like, where should they go?

Albert Chang: Yeah, they can go to our website or find me on LinkedIn.

Dennis Yao Yu: All right, sounds good. Well, thanks for taking the time to chat, Albert, and we'll, uh, catch you on the next one.

Albert Chang: Yeah, thanks for having me. Bye.

Dennis Yao Yu: Thanks to everyone listening if you found this valuable, subscribe to the podcast AI for business leaders on Spotify, Apple Podcast, YouTube, and leave a comment. Please also share with others navigating the future of AI. Thanks for listening and we'll catch you on the next one.