Episode Summary
In this episode of "Emerging Founders" with Manav, we dive into the entrepreneurial journey of David Pang, an innovator building cutting-edge AI solutions for finance teams. David shares the backstory and evolution of his ventures—first with SheetWiz, a productivity-boosting Chrome extension that brought powerful Excel shortcuts and advanced functionalities to Google Sheets, and now with Daimley, a company using AI agents to automate complex B2B billing workflows.
David discusses the real-world challenges he faced managing enterprise billing manually at his previous role, which inspired the creation of Daimley's AI agents. Designed specifically for finance teams, Daimley's agents can review contracts and automatically generate properly configured invoices in popular tools like QuickBooks, Stripe, and NetSuite, drastically saving time and reducing manual errors. David explains the difference between simple automation tools (like Zapier) and true AI agents, which can reason about complex, nuanced processes and make judgment calls rather than just following preset triggers.
The conversation covers David’s YC application experiences, the dynamic landscape of AI agent startups, the importance of co-founder fit, nuanced advice for early-stage SaaS entrepreneurs, and even the lighter side of startup life—how David unwinds with fantasy football. David shares insights into the rapid speed of AI development, the potential of new AI models like Sora, and the future of slim, AI-powered business tools replacing bulky legacy systems.
Transcript
Speaker A [00:00:00]:
In this episode of Emerging Founders with Manav, I interviewed David Pang. He's building the AI agent for B2B billing at Daimley. And he also created another company called SheetWiz, which has over 1.8 million shortcuts and counting. David realized AI agents were becoming increasingly feasible. And David and Peter have launched a new company in the AI agent space. And interestingly enough, I've been interviewing a lot of IC companies, and most of them are building an AI agent for different verticals. David is solving his own problem because when he was creating invoices in his past company, he realized he was wasting a lot of time creating these invoices. And with his company, Dimely, he's automating that process with AI agents. And in this podcast, we talk about everything, how David unwinds, what he's focusing on, how he raised money from investors and how he got accepted into yc.
Manav [00:00:53]:
David, how are you doing today?
David Pang [00:00:54]:
Hey, I'm doing great. Thanks for having me on the show.
Manav [00:00:56]:
So before we discuss timely, I'm really curious about you as a person, so I want to understand, what were you doing before this? Did you Dr. From school to start a business or what was your prior venture? Sheet Wiz. Can you explain what problem you were solving in that company?
David Pang [00:01:10]:
Yeah. So Sheet Wiz is a Chrome extension that brings Excel shortcuts and functionality to Google Sheets that we take shortcuts as simple as, you know, inserting a row or in a column in a Google Sheet, or sizing a column or row, and we bring those shortcuts to Google Sheets or the Excel specific ones. We also support more complex functionality like trace precedence or Trace dependence or auto color or format cycling that exists in Excel plugins that finance and consulting professionals use. And we bring that over to Sheets because we know there's a ton of ex consultants, ex bankers, ex Big four accountants that grew up using or started their careers using Excel, but now they're forced to use Google Sheets at the company that they're at. And so that was my experience. I started my career in tech private equity before making the jump into a startup. And when I made that jump and was forced to do financial modeling in Google Sheets, I lost maybe 80% of my productivity. And so this tool that we created was a way to solve that problem.
Manav [00:02:03]:
Yeah, I feel really blessed to live in this time. And now we can use AI ChatGPT for even, like all Excel formulas and Google Sheets. But back in the day, this is a company before the ChatGPT era, right? Who was the customer for this product and how were they using it to increase their productivity?
David Pang [00:02:20]:
Yeah, well, I would say that even with AI, you're still having people in a spreadsheet and you're still doing financial modeling and you're still kind of running your analyses. The AI might help interpret the information, but in the end, you're still in a spreadsheet. You still need to make it legible and digestible for whomever is looking at it. And so even in the world with GPT, you still are in a spreadsheet. You still need to make sure your formulas are tying correctly, because GPT can build a formula for you, but you could use a trace precedence tool to make sure that the formula is actually accurate and calculating what you want it to calculate.
Manav [00:02:51]:
Yeah, one thing I notice about GPT is like, it hallucinates a lot with numbers and I've used it for like simple calculations and it would be incorrect and I would do it manually. I realized like, oh my God, this numbers are totally incorrect. How do you see that problem? Have you experienced like, something similar with like basic calculations?
David Pang [00:03:08]:
Yeah, so calculations, at least for dimely. We don't necessarily really rely on ChatGPT to do that. We do that ourselves, but we empower the agent to identify that there is a need for a calculation. But then when that calculation is actually needed to be done, that's done by us.
Manav [00:03:21]:
Got it. So let's segue into the founding story of daimley. What was the problem you saw in the market and what are you solving? I know this is about AI agents for B2B sales, but can you explain in more simple terms what Dynley does?
David Pang [00:03:35]:
We have to target the finance team. So Daimley builds AI agents for B2B SaaS, finance teams. And so there's a ton of kind of complicated manual finance workflows that exists today in any finance team. And we build agents that are configurable by the finance team in order to solve their specific problems. And so why did we build something like this? Well, this was a problem that I faced personally at the startup that I was at called One Signal. I was there for over two years. During that time, both my co founder, Peter and I were building and running billing at One Signal for their over a thousand enterprise customers. And so actually, maybe, maybe I shouldn't say a thousand because maybe that's a. Maybe that's. We should probably cut that a little bit. But we experienced a problem. Yeah, we experienced the problem firsthand when we were at One Signal where we built and round Billing for their enterprise customers. When I met running Billing, it was basically me in a spreadsheet doing a bunch of calculations quite manually and having to do that work. I frankly thought I shouldn't need to be doing this. This is not work that a human should be doing. An AI could do this just as well. And so when we became aware of these AI agents that you could, this new technology was coming up back in early 2024, late 2023. We really saw it as an opportunity to kind of use this new tool that we have to solve a problem that plagues almost every single company in the world.
Manav [00:04:48]:
Yeah, one problem I experienced personally was using QuickBooks and creating invoices in QuickBooks was such a pain, like creating the same thing again and again every day. So with Daimly, are you going to be integrated into tools like QuickBooks or what kind of tools would you be integrated into? And how exactly are you helping people save time?
David Pang [00:05:08]:
Yeah, so we launched our first AI agent back in August and that AI agent focuses on connecting where your contracts are stored and where you're sending out your invoices. So exactly. Kind of the flow that you mentioned with QuickBooks sending out invoices. But your contracts are probably not being generated in QuickBooks, it's probably generated in Salesforce, HubSpot, maybe it's getting signed in DocuSign, PandaDoc, Ironclad, various systems like that. And our agent takes the PDF copy or maybe there's metadata available and we read it and understand it and then we decide how we're going to create an invoice for you in your end system, whether that be Stripe or Netsuite or QuickBooks, you know, Chargebee, Maxio, whomever. And so all you need to do, instead of manually clicking around in a ui, you can just review the changes that we're going to make, hit approve and we'll make those changes for you in your, in kind of your billing system or system of choice. We've observed that especially for B2B billing, that is actually a very complicated and time consuming process that takes 20, 30 minutes for a single contract. Because these are heavily negotiated, very nuanced contracts that somebody with advanced knowledge of the system and tools needs to go in and read. And that becomes a very overwhelming task, especially at the end of the quarter or at the end of the month when you have, you know, a bunch of, of deals closing sales, trying to take their quota and suddenly you're the accountant and you get all of these pings and you're processing all of These and it's getting late at night and there's a bunch of other things that you have to do in order to close the books. So that's kind of our first problem that we're solving is building an agent that helps with invoice configuration for contracts.
Manav [00:06:32]:
Do you mind explaining what AI agents mean?
David Pang [00:06:35]:
Yeah. So the way that I think about an AI agent is something that can take information from somewhere and understand it, create a plan of action and then can execute on the, on that plan. And so whether that's, you know, pull contract, read the contract, extract the amounts and the products and then identify the products and the amounts in another system and then make an API call to create invoices. Like that would be an example of an agent.
Manav [00:06:59]:
This reminds me a lot of the company Zapier, you know how they've integrated and like it automatically takes action. For example, every time an upload a newsletter on Beehive, it automatically gets published on my personal website. So are you using those tools or you're going to be building your own in house tool to integrate with other tools?
David Pang [00:07:17]:
So we're building our own in house house integrations and data model and structure in order to remain as flexible as possible that with the agents that we're building. So Zapier, I think more focused on kind of more simpler tasks that you know, maybe something closes and then you write it somewhere in like a Google sheet or you save something down or more tasks that are a little bit mundane or repetitive, but not something that requires an agent necessarily. An agent's benefit is that it can consume information and understand it and make judgments on what would be the best next steps. And that really becomes valuable when you're in more complicated and nuanced environments like processing a contract, for example, or even reconciling payment information or information between two systems. That requires some sort of judgment that, you know, with Zapier you probably, I mean maybe you can build out the logic, but it's a lot more difficult to necessarily do.
Manav [00:08:04]:
Whose idea was this like. And was this like something that you just had an aha moment or this was like a problem you kept seeing and you were like, I need to solve this?
David Pang [00:08:14]:
Yeah, well, I would say there's, there's the problem and then there's the recognition of the tool to solve the problem. And I think especially when it comes to, to AI, it's a newer technology, so you also have to be kind of aware of the trends that are swirling around that tool. And so being in Silicon Valley really helps with that you could be talking to a friend at a house party and then meet somebody at a cafe and they're all talking about AI agents and AI. And we actually met somebody from Gemini who really explained some information to us that kind of catalyzed that aha moment. And we were like, hey, like this tool is actually something that could be valuable and useful for us. And so I would say that that's one component of realizing kind of daimly. But the problem was, as I mentioned, personal for when I was running billing, it was just very manual workarounds to a billing solution that was frankly one that maybe wasn't quite suitable to the company in its current form. It was chosen years ago when the company was a different company. You know, it hadn't kind of grown to the extent it didn't have as big a direct sales function as it has today. And as a result there was a lot of manual workarounds that you had to do to make it work. And as a result, I was the one suffering from that. And so that was the realization that hey, it's probably likely there are other people in finance that are also working with the system that probably, probably is not quite matching exactly the use case that you need. But there's not enough buy in. If a company to replace a billing system, for example, that takes like six months. Right. And a bunch of parties need to be on board that you're changing your billing solution. So there's probably a lot of finance teams with a lot of solutions that don't quite match their use case and they have had to create manual workarounds to accommodate that. And that's the work that our agents are aiming to tackle because you know, it's a lot easier to adopt daimly agent than rip out your entire billing solution.
Manav [00:09:52]:
Interesting, interesting. Do you mind talking about like the pricing structure and the business model of Dangly is that how are you going to be charging for the service?
David Pang [00:10:00]:
Well, it depends on the use case, but I would say that and today it is maybe more of a direct sold motion. And so we can determine that based on the clients that we work with. And so we always like to think about the amount we charge as a ROI for the customer. We don't want to be overcharging you for something that you know, the value that you're extracting is, is not even equivalent to what we're charging. Right. We always aim to be between kind of like that 20 to 30% of the value that we're generating for you. And so we always tend to do, like an RI calculation. Right. How much time are we saving you? How much greater visibility to the tools are we creating for you? Are you collecting faster because you're able to send invoices faster? Are we, you know, enabling sales to sell more complex billing structures? Because now our agents can handle the complexity versus a finance person spending a lot of time to handle that. And so I know these things are not necessarily calculable, but we always like to think about it from an ROI perspective, if that makes sense. And I know maybe it's not as straightforward, but the reality is, you know, when it comes to these pricing discussions, we really want to make sure that the ROI is there. And so just saying, like, we charge X amount of dollars per contract that we process doesn't necessarily do justice to pricing that we.
Manav [00:11:04]:
Yeah. The biggest thing I've realized is that essentially, if you can replace like, a whole employee, which is anywhere from like $70,000 a year minimum, or even more if you're in New York, California, how do you justify, like, replacing a fun person with just like, a subscription? Right. Even if you charge like 500amonth, still, like a great deal for the company. That's why I feel like these AI companies, they're just making more and more startups, like, more leaner. Like, I have noticed, even in my companies, we used to have like 20 or 18 employees, and now we're like two or three. We've become so lean because of that. And also it's giving, again, advantage to international employees, like people from Philippines who are not that great before, but now because of these AI tools, they had the same equal output as a US employee. So that's, I think, one of the dilemma. We'll see how that unfolds. But going back to Gemini, do you guys reveal what AI model you use?
David Pang [00:11:57]:
So I would say our base model is OpenAI is ChatGPT, but in terms of the additional work that we do on top of it, that's more of our trade secret.
Manav [00:12:04]:
Yeah, that's your proprietary model. Yeah. And the biggest thing for me is, like, I was listening to the podcast of the replit founder, and he was talking about they actually got rejected by YC four times before they got in.
David Pang [00:12:15]:
We were also rejected by yc.
Manav [00:12:19]:
And then when they finally got in, it was like the most insane, like, three months where they kind of like really went all in and like, put insane amount of hours during those three months. Well, how was your journey like, getting into yc and how much work did you put in? Let's start from the beginning. Well, how do. How come you got rejected? Do they give you a reason for rejecting?
David Pang [00:12:38]:
We were rejected for summer 23. We. And we pitched them Sheet Wiz. At the time, we weren't rejected from Daimley, but for Sheet Wiz, effectively the feedback was, this is a chrome extension. How is this going to grow to be the next, you know, Airbnb, Stripe, Instacart, whatever company? At the time, you know, we had just launched, we hadn't even monetized the product yet. And so that was a little bit harder of a sell, necessarily. And so we were rejected in summer 23. We then applied again in summer 24 with Daimley after realizing the problem and the technology had kind of reached a point where we felt like, hey, this is. This is something that we can go after. And then with Sheet Wiz, at that time, we had also grown it quite significantly. We had started, you know, charging for the tool. You know, it's only 4.99amonth, but at least that's what we were charging. And we could go back to YC and say, hey, as a founder, you know, me and my co founder, we've worked together, we're also coworkers at another startup. We're also remains. We know each other very well, we know our working styles. We've built a business already, we've monetized it, and now we want to go after a larger opportunity. And this is the opportunity that we're going after, and we want to use AI to do it. And I think that was a much more compelling value proposition or much more compelling pitch to YC than necessarily Sheet Wiz, which I think at that time had maybe like 10 users.
Manav [00:13:46]:
Yeah, it's crazy because I've interviewed a bunch of YC companies from this year, and a majority of them is like, building an AI agent. So that's definitely. I feel like the theme of this year is the AI agents, and we're going to see them getting better and better. Better as we come out with new models. But have you also experimented using, like, the free models like Llama 4 or Gemini, or are you looking forward to use them in the future?
David Pang [00:14:09]:
That would be a question for my cto, Peter. I trust his judgment and expertise, and it's gotten us to a great place so far. And so I would defer that question to Peter.
Manav [00:14:18]:
Got it. And what are you guys currently focused on with Dangly? Like, what's the next step? What's the next big thing you guys are cooking?
David Pang [00:14:24]:
Well, I mean, I Think usability is, is the name of a game for any AI agent. Like the value prop of it is that you can write to it in English and it can do things for you without much knowledge of how to code or anything like that. And so for us, it's usability. Let's make this as easy to use as possible. Whether that's kind of our, with our first agent or the agents that we're building and releasing shortly, I think that's going to be our main focus for, for the tool and for, for daimling.
Manav [00:14:49]:
And you're going to be essentially targeting like the financial firms, banks. Who are you going to be targeting when you launch?
David Pang [00:14:55]:
Yeah. So the people that we've been speaking to so far and we continue to target are VPs of finance controllers, typically B2B SaaS, startups with some sort of complicated billing component to it. Right. Whether it's usage or maybe you have a ton of SKUs, or maybe it's just some nuances to your billing that makes it just a little bit complicated enough where you have to do it manually and you can't rely on automation tools as easily.
Manav [00:15:18]:
Got it. And what's been like, the hardest thing about building this AI agent? Because this space is moving so insanely fast, it's like hard to catch up. It's overwhelming, honestly. I have a personal AI tutor teaching me every day and I'm overwhelmed the. The way things are moving.
David Pang [00:15:33]:
You have a perfect AI tutor. What does that mean? Or does that look like.
Manav [00:15:36]:
Sean Puri is another host of my first billion. He said he found someone to teach him about AI once a week or twice a week. And then I was like, oh, I should do that too. So I have a AI tutor. Basically I'm not learning the technical stuff, but I'm keeping track of all the new AI tools and I'm actually implementing everything in my workflow. For example, like before it used to take us like a week to edit videos. Now we cut it down to like. And we use AI tools to literally remove everything in the video. Like, we use it for animation, we use it for removing all the ums and we use it to edit the transcript. We use it for enhancing the audio, we use it to enhancing the lighting, like, everything. So it's great. That's just one thing. And then of course you can use it on like New York businesses as well. And I think this AI thing is just going to make more teams leaner, which is great because every business, the biggest expense is the overhead and like in paying the Employees and stuff like that. And then now you don't even need an office. Like, you can just be like, I work remotely. You don't need to pay for your office. And then you don't need extra employee. Now one employee can do a task, three people's job with these AI tools. And that's what I'm looking at. And I feel like with Timely, it's going to be the same thing. Like before you needed like three people to do these invoices. It's going to come down like, then only one person will be doing that 100%.
David Pang [00:16:53]:
I would also say that specifically curious how Sora will change how you guys do your video editing and creation and animations, because I know that's just right.
Manav [00:17:01]:
It's so funny you said that because I used to hate those talk videos. Like, they were always like so cringy, you know, like, not Sora, but like the other websites where you could like pay and get those stock videos. They were always so bad. And with Sora, it's amazing now I can make anything. Like with Sora, I'm actually making a video on that today. And Sora is like incredible. And I can't believe it's already out there for me. What I'm curious about is how many new upgrades OpenAI is going to do or is it going to reach like the iPhone thing where after like iPhone 11, the innovation kind of like stopped. So I'm curious about, like, how are these models going to. Are they going to keep growing order of magnitude or they're going to like, kind of like slow down?
David Pang [00:17:40]:
I feel like there is a limit to the number of GPUs that you can kind of train a model on. But maybe I'm speaking out of my realm of expertise because, you know, I'm not building these models. I don't know. Yeah, it's a good question. I feel like the companies that are building within AI agents need the models to get better. But if there is kind of a cloud release there, agents that can click around for you, if there is a world where you get to that AGI and they can kind of do everything for you better, OpenAI has killed everybody. And so you kind of have to bet on that it's going to get better and we need it to get better. But the world where, like the Wall E world where everybody's work is completely automated for you, I think if it continues at this pace, it'll get there. And maybe that's not something that necessarily is what people want or is beneficial. For people.
Manav [00:18:21]:
Yeah, there was this really good talk by Dharmesh. He's the founder of HubSpot and he gave a talk on AI agents. You should actually watch it. I'll put it in the show description for our audience as well. He explains how they're using AI agents to basically automate every task, even acquiring customers. The amount of custom settings you can put, I just feel like it will be much easier to acquire customers in the future with the help of these AI agents. Again, there's going to be the next two, three years are going to be like super interesting and you can put an AI agent in literally everything.
David Pang [00:18:51]:
I mean you saw how Karna for example, they, I think they ended their partnership with Salesforce and potentially even Workday because they were just going to build their own agents to do a lot of the work that Salesforce and Workday were built to do and create their own kind of CRM in a much skinnier down version of Salesforce. I definitely think that the complicated UI rigid backend solutions of the past are going to be replaced by AI agents that abstract all of the complexity. And all you need is something a lot slimmer to just be the place where you're storing all of your information. And so you know, that's kind of what Daimly is focused on is you know, we believe that the next billing tool, the next accounting tool, the next erp, the next tool for the finance tech stack is not going to be another SaaS tool like NetSuite or SAP. It'll be an army of agents, all of them hopefully powered by daimley.
Manav [00:19:40]:
Okay, that's a great, great prediction by David Bang. Guys, we're gonna see an army of AI agents at work. It's like they say, you gotta make your money go to work for you when you're sleeping. And we're gonna make our AI agents work for us when we're sleeping.
David Pang [00:19:56]:
Just point your army at different things and then you get the work done. That's it.
Manav [00:20:00]:
I can't wait for that day. Just you know, like not even using a computer or anything, just on my phone, just giving orders all day and just does it for me and makes me money. Cool. So I wanna ask you what are some lessons or things you learned along entrepreneurship? What would you give one advice to people like starting companies like why do most companies die? Why is the startup failure rate 95 to 98% statistically it at the earliest of stages?
David Pang [00:20:24]:
It's, it's co founder fit and how you work with the person that who you're calling your co founder. And if you're a solo founder, it's maybe a little bit different. But typically if you're two or more, it's co founder breakup is like maybe the leading cause of startup failure. And so as had highlighted earlier, that was why we, we think that, you know, maybe we got rejected in summer 23, but for summer 24, after we had demonstrated that, hey, me and Peter, we've worked together now for this other tool. We haven't split up, you know, it's been successful. I think that's like a really good hallmarker of business that has legs. And as long as you and your co founder can work together long enough and get enough at bats, I think you'll build something that people want eventually. And I think that's the bet that a lot of VCs take when they're looking at startups is who's the founding team? Can they stick together and do they have the knowledge to solve the problem that they're aiming to go after? So I would say that's kind of on the. Why is the startup failure rate so high? I think the fit is most important.
Manav [00:21:15]:
What advice would you give to other entrepreneurs starting a company? It could be a contrarian opinion, you know, like things that people quite get wrong about companies.
David Pang [00:21:22]:
I would say there's a lot of advice out there and sometimes it's not advice that you should necessarily be taking. And so for example, I think you hear a lot about ACVs for SaaS and where they should be. And there's like a dead zone because.
Manav [00:21:34]:
Can you explain what ACV is?
David Pang [00:21:36]:
Average contract value. So like the size of, of the contract.
Manav [00:21:38]:
Got it.
David Pang [00:21:38]:
Okay. So the contract size effectively. And you want those contracts to be, you know, if you're above 2k, for example, and you can't expense it with a credit card, then you have, you know, you're gonna have to go through more. But if you're below 20k, right. It's harder to hire salespeople to, you know, if they're taking a commission, they're not really incentivized to sell because the amounts are a little bit lower. You're kind of in that dead zone where it's too big to be expense on a credit card and too small to be. To incentivize anybody to sell it. And I think you hear that feedback over and over again. You need to be above like 20k, be above 25k, push those value up. And the reality is when you just start out building something that you can Just sell immediately. Like 20k off the bat is a company would build that for themselves. If you're just starting out and you know, been working on it for a couple of weeks or a month or whatever. And so that feedback that you hear might discourage a founder from necessarily being like, oh, this idea is not a good idea because I can't get 20 to 25k out of this. But that's larger company, I think problem. And that's maybe a hotter take. But start with the customers that are willing to pay for your. That's like the first step, right? And if they're willing to pay for you, there's more value that you can add on top. But if you're just being like, okay, this idea isn't going to work because people aren't willing to pay like 20, 25K or it's, you know, you're saying it's not going to work because. Or you're rejecting customers. That's, that might be even worse. You're rejecting customers because they're not, you know, you're below a certain ACV when you're just getting started. That's not something that you should necessarily be doing because customers is better than no customers. And you can leverage those learnings by working with them to build something that is maybe more deserving of that contract value. And so that's maybe one example of that maybe you shouldn't necessarily take for face value, especially if you're an early stage startup.
Manav [00:23:14]:
That's great advice in my opinion. I think a lot of people, especially in the beginning, they just want to see some traction or you want to see some win right away. And I feel like getting that dopamine head from getting a customer, it might be the juice to keep the company going. And I feel like that's. That's such a great advice. I love that last question for you. What is the book or books you've given most of the as a gift? It can also be just a book that has completely changed the way you think and you live your life.
David Pang [00:23:40]:
Well, I would say that in terms of books that I give as gifts to people, I don't actually tend to give too many books as gifts. I think you can download a book. You can. There are other ways to get physical copies. I tend to give maybe more, you know, personalized gifts or things that we can do together or you know, we can leverage or is more meaningful or personalized or customized. But in terms of books that I've read and have drawn inspiration from the elite Elon Musk Biography by Walter Isaacson is such an inspirational book for any startup founder that's looking to. I mean, obviously, Elon is just like, way, way, way. You know, the Face company is different from a SaaS company, but just like, the mindset that he has is very inspirational for anybody looking to start something, whether it's a startup or anything else, really. Also, it's just really cool to see the progression from effectively, like, you know, PayPal days now, Tesla space, like, how that all happened, because you look at these people out there and you're like, how do they even get to where they are today? How's that even possible? And that might discourage you from, you know, thinking, hey, that that person is just way better than me. I can't. I couldn't even possibly think about doing that. But reading their journey, you start thinking, hey, this is something that I could do. And that's really the first step is believing in yourself. So I would just say that that book was. Is a really good read.
Manav [00:24:47]:
I actually read that book. The biggest lesson for me from. From that book was the amount of risk taking Elon can do and the amount of asymmetric bets he's taken. And also, he's been a visionary. Like, I remember he got ousted from PayPal because he wanted to build it, like, the X.com, like, the UL for everything. So he had that kind of vision, like, even back then when no one had it. And again, like, it's a grit, and keep doing it. Like, he had a $22 million exit when he was 27, which is incredible. I mean, he. And then he completely reinvested most of that money back into the next company, and then reinvested all that money back into Tesla and SpaceX, so. And now his net worth is 400 billion. It's just a crazy story.
David Pang [00:25:29]:
Yeah, you just. You just give yourself more and more at bats. I think this is maybe where business diverges from, I guess, the baseball analogy that I was starting with, where, you know, give yourself more at bats and you eventually hit a home run. But a home run in baseball is only worth a run. But in business, if you hit a home run, that's. That can be so much more outsized than a single or a double or triple. Right. It's exponential at that point.
Manav [00:25:49]:
I'm so happy to have you on the podcast. I want to ask you, like, one more thing. What do you do for fun? Like, what do you do outside when you're not grabbing, grinding, and working on Diamond? How do you reset and, like, unwind.
David Pang [00:26:01]:
Yeah, well, I'll say, actually, tonight I'll be doing that. I'll be watching football. Love football. I play fantasy football. We're in the playoffs now, and it's round one, and my team is facing a opponent that is projected to win. But I believe in my team. I believe in the underdog. And so I'll be watching football Thursday and then Sunday and then Monday to really cheer my team to victory. That said, I think fantasy football is not something that people don't care about my team necessarily as much as I care about it. And so if we can build an AI agent that celebrates me and, you know, bring value to, you know, how much time I've invested in my fantasy football team, that could be the next big company.
Manav [00:26:36]:
We could build an AI agent. It's like, if your team wins, you automatically get a pizza delivered from DoorDash.
David Pang [00:26:43]:
An AI agent that cares about things that only you care about so that you feel a little bit more validated when your team wins or something like that.
Manav [00:26:50]:
I was listening to this podcast, and they said, like, one thing men really face in life is, like, we don't really get compliments. We could build an AI agent that just gives you compliments every now and.
David Pang [00:27:01]:
Then, has a great job starting that player on your team, or great decision on resting that player or picking up that player, because I, you know, nobody's telling me that. I don't think anybody is really kind of giving that feedback to anybody who plays fantasy.
Manav [00:27:15]:
Awesome. David Peng, everybody. We had such a great talk about AI agents. We will do another recap video in a year or two and kind of recap this podcast and see where our journeys have led us to. Thank you so much, David, for coming on the show. I really appreciate you.
David Pang [00:27:29]:
Yeah, thanks for having me.
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