← The Bitcoin Treasuries Podcast with Tim Kotzman
The Bitcoin Treasuries Podcast with Tim Kotzman
Tech, AI, & Bitcoin Treasuries with Mike Flaum aka Grain of Salt
00:51:09
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- [00:00]
A
Welcome back to the Bitcoin Treasuries podcast. I'm Tim Kotsman. I'm here with Grain Grain. Thanks for joining us today.
- [00:08]
B
Thank you for having me, Tim. Really looking forward to this. We've been talking about. We've known each other for what, five, six years?
- [00:14]
A
Yeah. End of 2020, beginning of 2021. So it's been quite a few years. Do you want to start with a little bit of your background and how you ended up in the Bitcoin Treasuries and maybe also AI space as opposed to more traditional finance or just Bitcoin?
- [00:34]
B
Sure, sure. So, a brief background. So, first of all, my real name is Mike Flm. I've been in Silicon Valley now for 25 years. I'm originally an east coast guy. And what happened was you're always doing investing. You work in your job and you're doing investing and you're thinking, like, how do people invest? Well, and then what I did was I applied my investing, like, as I would launch a product. I figured each company is a product as a product manager. And I just looked at it that way. If I was building a product, which company, what I would buy is the best. And so I went through the typical ideas. Back in the old days, it was Fang, Facebook, Amazon, Netflix and Google. And. And then it expanded to the Mag 7. And so I just looked at it that way. I said, there's gotta be a better way to invest. And I took control of that 30 years ago on my own investing. And that's where I've been. I think that's, you know, that's the overall story about the investing, you know, in the Bitcoin treasury space. I did find my original post on MSTR back in August of 2020. And I was so happy that Michael Saylor came about because it wasn't the usual cast of characters that we had during the previous cycles. And I was like, wow, what he is doing is awesome. He if bitcoin is good for a person, it's good for a company, it's good for a sovereign nation. So I saw what he was doing immediately. I'd never heard of him, never heard of MicroStrategy, never heard of a company buying bitcoin on the balance sheet. There were some ideas that were some companies that had it, but as soon as I heard that and then I posted, I commented on it, I did a post on it and I found the original post. I was like, these. These will become the most valuable companies in the world.
- [02:18]
A
Yeah, it's interesting when you talk to people in the real world and you get a dose of reality of how early we are. I was speaking with someone recently who works or worked in the New York City mayor's office, and I was Talking about strategy, microStrategy, Michael Saylor. She said, never heard of him. And I said, oh, well, I work in an office. Have you ever heard of the name Anthony Pompliano? She said, well, yeah, because he was on the short list for the mayor of New York. But that was, like, the only reason. So it's like, we're early. No one has heard of anyone in this space outside of if someone's really, really inside a very small circle. So was there a pattern that you saw early on that other people weren't seeing or were missing or what was. What was the landscape as you kind of started out? Maybe, sure.
- [03:17]
B
So a couple things. So I wrote this book on AI, which we'll talk about in the last 20 minutes or so. But the pattern has always been the same. Technology changes everything. It always has. And as a kid, I saw this is that I'm dating myself. I did not get cable. I did not get cable to watch cable TV until I was in high school. And what was the number one channel to watch? MTV Music Television. And basically they just stopped doing any music on mtv. So that's back when that came out, and that was so exciting. There's all these technological revolutions that happened. And so I just remember hearing about Apple computers, and you could do desktop publishing, and it had scalable fonts, and if you wanted to make a spreadsheet, which was called VisiCalc or Lotus Notes or Claris Works, it's like the technology was. You didn't have to hire somebody else to do it. You had to learn to do it yourself. And one of the things that happened in the 90s is that this friend of mine said to me, Brian, he lives in New York City, he's a very successful lawyer. And he says. Calls me in 1997. He says, hey, Mike. I'm like, brian, hey, what's going on? He's like, this Internet thing is going to be really big. You should do it.
- [04:34]
A
Do the Internet.
- [04:35]
B
Do the Internet, whatever. He goes. He goes, you're a smart guy. You'll figure it out. I'm like, okay. So I go get this job at this little ISP in Florida. It was a very small company. It had five people in it. And what happened was I go to their. They were installing Internet in schools, and so they would bring in a frame, relay connection and then they would wire up all the Mac computers that were in it. And this was really, really early. So the computers had to be updated with a network card so they can connect to the network. They had the very early versions of Ethernet. And what happened was while we were in this class, the teacher starts talking where I'm setting everything up. And the teacher says to the kids, this is your class. In the computer class, every kid is going to have everybody has a birthday. You're going to create an invitation, typeset it. By the way, this is like a sixth grade class in 1998. You're gonna create an invitation, typeset it onto an 8 and a half by 11 sheet of paper. You're gonna have an envelope with everybody's address on it, do a mail merge based upon each student's name and their physical address. And then you're gonna put it into the envelopes, print the invitation, print the envelopes, and then we're gonna mail it out to everybody. And this is for sixth grade kids. And I'm listening to this and I'm like, I don't know any adults that could do this. These are sixth graders. So there's always been this difference between the young people understand technology and the value of it. And the teacher put this in a very concrete way. Every kid's got a birthday, every kid's going to have a party, you know, and show your creativity and write your invitation, invite everybody else into it. So. And then you had to figure out how to do it. So what happened? There's always been this technology shift and it's always the young people experience it first and the older people are like, they don't see the value in it. Right? I'm going to hand write something or something anyway. So from there I got a job at Internet company and then it progressed incredibly fast because I developed a skill set on Internet. I went to go work for AT&T and then Global Crossing and then I moved to, moved to California. And so because I had that skill set, because I was in for three years, I functioned at a very high level. And so that's what changed. And so technology changed everything. And I knew that, you know, if you're uncomfortable by technology, the only solution is to learn more about it. Not learning more about it is the worst thing because you have less options in the future. So I think that's the take of what did I see? I see if I don't learn the new technology, I'm not relevant anymore. And by the way, I think that's the same thing that happened with Michael Saylor with strategy. He has said this all publicly, paraphrasing him. And what he had said was he rebuilt the company 10 times, and every time he did it while it was successful, he could not get it to double or triple revenue. It stayed kind of flat. And so he had a choice. And then when he found out about Bitcoin, he's like, oh, because he was very successful investing, he wrote the book on the mobile wave. He, he's like, let's change the whole, let's change the, the, the accounting of the company. So he did that. So that's always been the way it's been for tech people.
- [07:51]
A
Yeah. It reminds me of his, the way he kind of talks through each billion dollar idea that he had. And I don't know, but I would assume that investing in the tech companies, maybe, maybe it made billions of dollars, but probably more likely hundreds of millions or tens of millions, depending on how far back we, we go. So all of this tech experience that, that you're working through in your career, how did that inform how you thought about incentives and balance sheets and failure and these different topics?
- [08:32]
B
So, so when you want to. So maybe I have an unfair advantage because I was a product manager when I started and then I went higher up through the ranks. So what a product manager does is like you have an idea, you're going to build a product. Okay. It's pretty easy. Well, and I, originally I was in, in the hardware side. So there's a physical cost to the products. So what you do is you build an Excel spreadsheet. And what I can tell you is that I've worked at five different companies. Every company does everything differently and they, and they all think they're right. But in the end, let's say you're selling the product for $100, right? These products that we're selling, these ethernet switches, typically range from $10,000 to a million dollars each. But let's say it sells for $100. Keep the math simple. If you sell it at a, that's the MSRP, right, is $100. But these are capital goods that are sold to large deployments for enterprises and for service providers. They, they typically want at least a 50% discount. You already know that. That's the starting point. It could be a 60% discount or 70% discount. So you already know that. So if the product is selling for 50% off, you have to make sure that you have enough profit margin left in there. So the way that you do it is you would just say, look, if the product's $100, you discounted 50% and you want a 50% product profit margin. You, your all your costs have to be less than 25 bucks. Just the way it is, because $25 is half of the $50 that you're getting. So I would then go back to engineering and say, you guys got $25 to build a product. You know, like, you're crazy. I'm like, well, that's the way it works. What do you mean it works? There's a discount off of it. So I natively understood this, this pricing model. I. And that's how it works. So what I would do is I could always create these Excel spreadsheets from scratch. They'd always be blank. And, and I'd go in there and I'd start off with, this is what the list price of the product is going to be. This is the typical discounts, the number of units I'm going to sell. Hopefully I'll make it up on that. And then what I would do is, is determine how much money we're left with and what profit margin we want and do that. So I understood, I always understood this math. And so from that perspective, I just applied it to the, to the businesses that we're looking at. And any product manager should be able to explain what I just explained in 30 seconds. And if they can't, then they don't understand this. But in Silicon Valley, it turns out that that ended up being a really valuable skill. I just thought everybody could do what I just said. And what I turned. What turns out is a lot of people fail what I just said. It's surprising how many people I talked into the bitcoin space. I'll talk to them and they'll build a business. And I'm like, great. You built this business? Like, oh, yeah, we built it on Bitcoin Lightning Network. And we did this, this, and this. I'm like, okay. I go, what's your, what's your profit margin on doing this? Point.0001%. I'm like, you guys are running a charity. I mean, how do you make any money on this? Is that very good for the people that use the product? That it's very low cost? Absolutely. And then the look on their face is always like, oh, we're supposed to make a profit. I'm like, yeah. I mean, I hope that that brings it all home. That how I look at things is that in the end the company has to make a profit.
- [11:55]
A
Yeah, I forget the Exact definition of how many years in a row if something loses money, it's not really a business, it's a hobby, it's a project. It might be a passion project, but it might not be deemed a business through the lens of the IRS or other agencies.
- [12:14]
B
When you, and that's, that's an interesting part. You could build a product or have a business that the customers love and that gives them massive value because it's a very usable product. But if you're not making a profit, that's problematic.
- [12:29]
A
I mean, you got to eat.
- [12:32]
B
Yeah, it's a complete thought. I mean, I mean that's where it ends. I, if you listen on a whole bunch of Bitcoin spaces and Bitcoin treasury spaces, the word profit never comes up.
- [12:46]
A
Yeah. Bitcoin treasury companies, did you view them as levered bitcoin proxies in the beginning and if so, when did you start to think of them as a new asset class?
- [13:02]
B
Okay. I mean that's a very insightful question. Really great question. So I was heavy into grayscale bitcoin trust because I did not. I'm your. Because I'm an. I'm, I'm 58 and a half. So when I got into Bitcoin May of 2017, and I've mentioned this before publicly, what happened was the VAT, 99% of my net worth, besides my house was in my IRA account or 401k. So if I was to take the money out of my IRA account, I'd pay this massive amount of taxes. So I bought grayscale bitcoin trust and there was no tax consequences because it was in the wrapper that it was an equity and it gave me access to bitcoin. Now when I bought grayscale it was already trading at a 50% premium to net asset value. And I knew that on day one. And so many people said that's a terrible idea because it's a closed end fund. So when the bitcoin treasury companies basically came out the and a half years later, I had already incorporated, I already understood between trading at a premium, trading at a discount, which is equivalent to M nav I already already had that skill set, so I had this advantage. So when strategy came out, I was like, wow, this is going to be a big deal. But did I know the exact behavior I was going to trade? No. So did I buy a few shares? Sure. Did I go all in? No, because I, I just didn't know the track record yet. Did I think Michael Saylor. I remember calling Plank Constant. Right. And you know Plank and he has a real name. Right. But I called Plank and another guy and I said, you guys see this Michael Saylor guy? Like, oh yeah. And we're like, what do you think? We're like, sounds great. We don't know how it's going to play out. So as soon as we heard that, we knew it was big. We just didn't know where it was going to go. I mean, I think that's the most honest answer.
- [14:57]
A
Was there a shift in your thinking to when there was more than one or two companies pursuing this bitcoin on the public company balance sheet? Was there like a moment of, oh, this is a whole new thing, not just the next grayscale sort of iteration or how did you kind of experience that?
- [15:20]
B
Yeah, so, so that's a great question. So, so what happened? So they started In August of 2020, 2021, the market, it hit its all time high on bitcoin. And so for me, I cashed out a large amount of money. And, and, and so what happened was, and then we went into a crypto winter. FTX imploded, Terra Luna. There was all kinds of nastiness that happened. And so at that point I was like, well, this is going to take a while. But we wanted to spot Bitcoin ETFs to get approved. Well, what happened was a couple years go by and I still had grayscale. And then I realized I don't know if the spot Bitcoin ETFs gonna be is going to get approved, but I was like, I want something else. And so I did more research. And by, by this time Michael Saylor had already done two and a half years worth of videos. And so I really understood then what he was trying to do. And it survived. It survived the crypto winter. So from that perspective, I was like, well, this is pretty durable. So then after watching hundreds and thousands, I wouldn't say thousands, hundreds of hours of sailor videos, doing my own research, then I felt more comfortable. So it wasn't about other companies jumping into it and that really didn't happen until later. And I was like, oh, they survived the downturn, so therefore this could work. And then I shifted and then I bought in. And I think most of it, I bought in. I'm trying to make sure I get my dates correct. Was January of 23. Yeah, that's about right. January. Yeah, January of 23.
- [17:01]
A
Got it. So you have written a bitcoin treasury book. Who was it really written for and why did you write it in the first place.
- [17:13]
B
Okay, so who did I write it for? I wrote it for the CEOs and CIOs of Bitcoin treasury companies. That's what I wrote it for. I did not write it for retail. I wrote it for retail in the sense that they can now understand what happened in the second wave. So the first wave of Bitcoin treasury companies was Strategy, originally called MicroStrategy. That's the first wave. The second wave, which happened in 2024 and 2025 and maybe some start a little bit before that. But basically the second wave and then where we are today, this is January of 2026. I saw what happened and I said, you know, I've never written a book before and I just didn't want to do a tweet or a post on it. I wanted to have something that was rigorous. So I looked at it and I said, we need to have a book that is a post mortem or an analysis on what took place for the CEO and cio. So I figured a book would be something good to do. So I never wrote a book before. So I wrote a book and I rewrote the whole thing three times. I figured out how to publish it myself on Amazon through Kindle, which I did. I'll be releasing it also in a PDF format. And I learned that whole process and then I had the clarity of thought. So I think that was your first question is who would I publish it for? And the second one was, why did I publish it? Yeah. And the second reason was I wanted it. If somebody says in the future, you know, what went wrong in 2024 and 2025. Oh, this guy. Grain of salt, Mike. My name is on the book. In addition with my Twitter handle, he wrote a book on it. $9.99. And if you upload, you're allowed. There's a license allows you to upload it to AI. And you upload to AI and you say, what? What went wrong? Read this book and it will tell you the reason. And that's why I wanted to write it, because it survived scrutiny with AI also. So that's why I did it. What I can tell you for me is that writing a book and completing and hitting the publish button, it's kind of like being at school and you write your paper and then you work on it and then you got to hand it in. You're like, I hope I did everything right. And you hand it to the teacher. That's what it was like. And so I think that if you don't. It's much different than an X post or a substack art. I'm not saying those things aren't important. I'm not saying they don't have great information. But there's a big burden that when you publish a book, the impression is much different. So that that's what happened.
- [19:40]
A
That makes sense because you can maybe edit or re release or update version two of a book, but you can't really delete a book in the way you can delete an X post. So no, that makes a lot of sense. Was there a main misconception that you found in how the market values or valued continues to value or not value Bitcoin treasury companies?
- [20:08]
B
Yeah, so, so what happened is there's this concept in the book in AI, but it's the same thing in life. The perception of reality may be the perception of reality is reality how you perceive it. And so if there's a group or consensus thinking that we all think that it's this, then it is then, then it is that. Now that may be wrong, reality may be different, but there's that. Those people have that perception. So the perception that happened was when I read this, I was like, and I figured out what happened and I said, I have to analyze this. And so I did analyze it and I analyzed it from first principles and I reconstructed what happened. So what does that mean? Well, strategy has the most amount of bitcoin, right? And they're far and away, at least anywhere on the order of 10 to 20 times or 30 times the smaller one. So strategy has 670,000 bitcoins plus now and the next one is right at the 50,000 mark and then it drops off pretty quickly, it goes to 30,000 and then in below 10,000. So I said, well, I got to analyze this. And so what happened in this space? So a bitcoin treasury company is a capital markets vehicle. It's not a spot bitcoin ETF and it's not a closed end fund. Grayscale bitcoin trust was a closed end fund. But strategy is an operating company. And I was like, oh, I have to think about this. I think the biggest miss is this with operating companies. And I got to hand it to what Michael Saylor did was, and I've talked to him about this, about what he did was I said, fasb, Fair Value Accounting. I said, how did you convince them to change? And his response to me, and he said this, this is not a private conversation. This, this was at, in, in Orlando. In Orlando. And I said to him, how do you convince them? He's like, well, we basically looked up what it takes FASB to, to, to submit a request to fasb. We figured that out. He said that. He goes, I used A.I. i, we wanted Fair value accounting Mark to market for the bitcoin. And he goes, we filled out the, we wrote out the forms, we filled everything out. The lawyers looked at it and we submitted it. And I'm like, you just submitted it? He's like, yeah. And we followed the process. And I go, well, what happened? He goes, they wrote back and said, we're going to review it for a certain amount of time. And he waited and then it came back and it got approved. And I was like. And he just said it matter of factly. And I'm like, wow. So you wanted to change the market, you read the requirements, you submitted it and, and it changed. And I was like, and by the way Tim, you could chime in anytime. And I was like, thank you. And so what? So why is this important? What this means is that all bitcoin treasury companies are balance sheet based companies. But when you think about a Silicon Valley and a startup, they're all income statement based companies, or all almost, except for balance sheet based companies. Everything else is an income statement based company. The balance sheet is kind of cool to look at. So what does this mean if you have two different startups, right? They could be valued differently because you're thinking the addressable market, Amazon selling books and switching into selling every widget in the world and then they go to Amazon prime and then they sell, you know, you get movies and media and everything, that's Amazon. So what's their growth trajectory in their total addressable market? And then you can look at Nvidia, what's their growth rate? And, and they're selling GPUs that can be used for acceleration, for gaming and for video and for all kinds of, even for bitcoin mining and AI processing. But Amazon and Nvidia, until I know Amazon has aws, they're in two different groups, or you could say even Apple. And so you have these different valuations because they're in different groups, right? That's income statement. Everybody listening to me right now goes, yeah, that makes sense. Brain, what are you talking about? Well, if you're a bitcoin treasury company and they buy bitcoin, well, bitcoin goes up and down the same for each company. It's the same bitcoin, it's the same value. So there's no relative difference. The way that they make unrealized gains in the bitcoin, because the bitcoin is the same. What's different is the capital efficiency. That's what the book is about. So to make sense, Tim, you know, so I write this book. It doesn't sell a lot of copies and people listening is like, what the hell are you talking about? I go, how do I distill the book down? So I do this video, 8 minutes long. Watch it at 2x speed. That's not fast enough. I distill the book down to a post. And that was last Friday. The book was published December 22nd. Last Friday I do a post. People want to be able to understand information instantly. That's what I'm telling you. The perception is this. The reality is this. So I do a post and I show in each year I flip this metric. The old metric is bitcoin per share. That metric's been available for years. I flipped the metric the reverse way. Shares per bitcoin and strategy in 2020 had 1700 shares per Bitcoin. 1767 or 1747. And now it's 500. They cut the pizza into less slices. I write the post and that day it goes 10,000 views. You've seen this before, Tim. And chime in. Like I said, chime in. You do a post, you're like, I hope it resonates. 5,000, 10,000 views. Goes up to 20,000, 30,000. 11 o' clock at night, brush my teeth. 80,000 views. I get into bed. 11:15, 150,000 views. 12 o'. Clock, 300,000 views. 12:15, 600,000 views. 12:30, 900,000 views. Those were the batch updates that were coming in. So I go to sleep and I said, said to ChatGPT, what do you think is if it goes to a million, that'll be great. A million and a half. In the morning, I go to sleep. I wake up the next morning, it's at 2 million views. 2 million. And so what happened was I'm like, oh my God. So I start responding back to all the comments that are in it, all the retweets. Everybody read it and I was like, well, well, this must have hit a nerve, right? Because now people can understand. You went from 1700 shares to 500 shares per bitcoin. And some people are like, wait a second, the number went down. I go, it's correct. If I was cutting a pizza into a slice, I had a slice of pizza and I can only do one slice, right? What would I do? I'd cut it in half, and I would take one half and that'd be 50% of the pizza. If I cut a pizza five times, depending on there's 10 slices, each slice is 10%. Of course, I only want to say so the lower the number is better, the less slices are better. That's what the metric is. And so many a bunch of people said to me, it's like, oh, we always knew this. I get that you knew this, but apparently 5 million people did not get what I just said because it got 5 million impressions. Why? Because I showed each year. It was. It was a very simple each row 20, 20, 21, 22, 23, 24, 25. And the number went from 1700 all the way down to 500. It got three times more efficient. Now, I wrote a book that's 60 pages of hardcore detail, but that tweet did more to change the business than writing the book. But I don't think I could have wrote, written the tweet or the post, whatever you want to call it, unless I wrote the book. It gave me the clarity of thought. So that's the takeaway.
- [28:26]
A
What other metrics do you think are important to cover? Maybe bitcoin per share, mnav, BTC yield. How do you think about these things and where they maybe should sit in the universe?
- [28:44]
B
Yeah. So MNAV is a pretty. Is a terrible metric. Multiple to net asset value. It's a terrible metric. And I'll explain why. You could have two different companies. Let's just assume that their M Navs are the same, right? And let's say they're both trading at an m NAV of 1.25, a 25% premium. People will be like, okay, those companies are the same. Okay, well, what happens if One company has 600,000 bitcoins and the other one has 6,000 people? Be well, those are the same. That's what MNAV would say. If you have. If you look at M Nav, it does not take into account the market. The amount of Bitcoin that it holds just does it. I'll give a better example. Nvidia is worth over $4 trillion. Let's say there's another company that's worth a trillion, right? And the stock price goes up 2% for both of them. You're like, okay, they're exact. And assume that the prices of the stock are the same and they Both go up 2% in a day. What you'll see on mainstream media is both those companies are the same. The stock price went up 2%. For both of them. And everybody's like, yeah, it's the same price. They Both went up 2%. I'm like, no, they're not the same. And you're like, why? Because that's like an M Nav calculation. Let me explain why. If Nvidia is worth $4 trillion and it goes up 2% and there's another company that's worth 1 trillion, it goes up 2%. In order for the 1 trillion dollar company to equal that rise, it needs to go up 8% because it's only 1/4 the size. Why would I take more risk if I could buy Nvidia and get a 2% return? It's the biggest company by market cap in the world. Why would I buy the cheaper one to get the same 2% return? That's what M Nav doesn't tell you. So in order for the smaller company to, to have to, to justify its risk, it has less, it has less cash flow or less Bitcoin it for apples to apples, it needs to go up by 8%. Four times one is four. Nvidia's four trillion, this is one trillion. And they're both going up 2%. No, this one needs to go up 8% because it's smaller. And that's what MNAV didn't do. It didn't tell you the size of the company. The other metric that I think that led people astray is that basically for Last year, strategy, 23% BTC yield. So the best company, the biggest company, the best it could do is a little bit over 20%. The other companies are like, well, we're going to ramp up The BTC yield 100%, 200, 500%. And everybody's like, oh, they're smaller companies, of course they could ramp up faster. And this caught me off guard because I didn't think this through. The catch is they're all balance sheet based companies and bitcoin is the same for all of them. That smaller company, their revenue growth or profitability is not growing any faster than Strategies because it's bitcoin now. That's the difference. That's what I learned. So hopefully this makes sense and you can ask me any questions about it to make sense. So the two things that led us astray was MNAV and BTC yield.
- [32:11]
A
Tell us about the AI book that you wrote, the two versions and how all of that came about.
- [32:18]
B
Okay, so the two versions, there was actually three versions in reality. Look, the book says. So this, this is what happened. I wanted to write a book on on AI. So I use AI in order to compress time frames. I could. I could manually calculate anything in Excel or Google Sheets myself, but with AI, I already know what the output's going to say. I don't know the exact number, but I could tell to do it. So I decide, okay, I'm going to write a book so people understand what AI is. So I write this book, and halfway through the book and I did something different. I want to say this. Book publishing is obsolete. Let me explain what I mean about that. Book publishers hate AI because if your book gets ingested into AI, you don't have control over it. So I grant a license for my books that you're allowed to upload to AI but it's not allowed to train the LLM. And what that means is that you get a copy of my book and the. The AI book will be published. It is completed. I'm doing the Shopify account right now. You'll be able to download it and you're allowed to upload the whole book to AI. Ask any questions you want about about it. I grant that license. So I write the first manuscript. I get halfway through it, I'm like, oh, I should test this. I load the book to AI and I put a special section in there, how to an ask questions. And it fails miserably. AI comes back in a nice way and says, you wrote a bunch of definitions and terms and definitions and it's a piece of crap. It didn't say it was a piece of crap. It basically said, it's useless. That wasn't particularly happy about that, but I was like, oh, so what do I do? I rewrite the whole book again from scratch. I loaded in and I basically said it was great. I'm like, oh, wow, this is great. So I give it to a couple people, a few people, they read it and it was unanimous. They come back and they're like, look, the first chapter was so hard to read, we gave up in reading the book. And if you sell it, people will ask for a refund. I was like, well, tell me how you really feel. That's what they said. And they're like, it was so dense. It was. And so then I. I get the feedback and. And I'll stay in order. So a couple people said to me, they said, you should write a book where if anybody reads it, no matter whether they're 8 or 80 years old, they will understand AI. And I'm like, oh, so what do I do? I rewrite the whole book again from scratch. From zero from a blank sheet of paper. I, I give that book out to a bunch of people. I say, read the first chapter, call me back if you like it. People read the first chapter. They call me, they read the whole book. They're like, wow, this book is great. They just said, you wholly hit it out of the park. And I was like, really? And I said, if I give this book to an 8 year old or an 80 year old, they can then explain what AI is. And they said, absolutely. So now I load them both back into. I check everything with ChatGPT. Perplexity Claude. I don't really use GROK as much anymore. So now I have this problem. So I asked, I asked him like, why did the book, why did AI like the second book? And it thinks the third book is pretty good, but why didn't. So I asked. It was Claude was gave the best response. And Claude came back and said, what reading level are you reading at? Claude said, oh, I read at a PhD level. And so I wrote that book. Book number two at a master's or PhD level. I don't have a master's degree. I don't have a PhD. But one of the problems throughout my life is that people said, mike, can you dumb this down? Silicon Valley? I present these executive briefing centers and they're always like, tell us about a very complex technology, but make it simple. I'm really, really good at doing that. But I did not want to write a book like that. I wanted to write something on my level. I wrote that book, but the people that read it said, dude, we don't want to read your book after they read the first chapter. So then I was like, what am I going to do? And so Claude came back and said that. Claude said, well, your third book is great because anybody will read and understand it. And I'm like, yeah, but you just said the second book was really good. So what did I do? The third book is the first part of it. You get both books for the same price. It's $10 if you read the first part and you're like, wow, this really makes a lot of sense. I wish there was a detailed analysis of why this works. That's the second book. And so that's what I did. And this is the craziest part. So I bundled them together in one file. I upload it back to. Back to Claude and back to Perplexity. Claude comes back and says to me, this is the craziest part. Claude comes back and says to me is says Flaum, it Refers to me by my last name. He's conflicted as an author because the third book is so good it should stand on its own. But he's indecisive, and therefore he includes this other book for free because he doesn't know what he wants to do. Now, I made this as a conscious decision, and so this is the takeaway from the conversation. When I went through this process, I didn't know what was going to happen. I just thought I'd write a book on AI and hopefully it was pretty good. But I ended up writing a total of three books. I actually rewrote book number two again, and that's in there. And so that's what you have. Anyway, the point about this is if you rely on AI just to give you output, you're not. It doesn't help. Oh, one last thing. I wrote three books, right? And this is the same thing with the other book on Bitcoin treasuries. Each book, each chapter was rewritten three times for each version. Every time I wrote a chapter, I was like, oh, the previous chapter doesn't matter. Chapter after doesn't match, so I have to rewrite everything. So what AI did allowed me to compress that timeframe down. And so that's the takeaway. Hopefully it's not over pitching the book, but I think that everybody has to realize that this is the takeaway. If somebody says to me, I don't like your book for these reasons, I'm like, okay, you're entitled to your opinion. And that's it. You'll get silence from me like, oh, well, you should have done this, this, and this. My response to that is, that's great. You're entitled to your opinions. What I would love for you to do is go write your book on AI and I would be happy to read it and then we can have that conversation, right? As soon as somebody tells me your book should have said this, I'll be like, okay, I'll listen to them. It may be good feedback. Maybe I'll do that in the next book. But anyway, the takeaway for this and same thing with the Bitcoin treasury book, is the second water effects. Once you write that, people will come out of the woodwork and be like, wow, now we know how you think. And it's much different than a medium article or a substack or anything or just posting up a PDF. So anyway, hopefully what I put together here is hopefully people will find it interesting.
- [39:40]
A
I think it's super instructive that you can upload It, Ask it questions, use it, especially with the AI topics, use it with artificial intelligence, and not just read it, highlight it, put it down, and maybe never use it. So I think that's super, super cool.
- [40:06]
B
I have to do a call out here to Tad Smith. So you've interviewed him multiple times, right? And we met him at the same time a year and a half ago down in Santa Monica because Tad went to Princeton and Harvard. I wrote a book. I wrote a playbook about Bitcoin treasury companies as a PDF, and I published most of it in September. It got some of the. Some of the posts about it did 50 to 80,000 views and got anywhere from 500 different sections on it, 500 to 1000 likes. So it did. Okay, so after I wrote it, I had another person said to me, you know what? You should upload that PDF and have it graded. Oh, actually, no. I. I came to the idea. I said, I will upload it and say, I want you to grade this paper as if a. As if it's a master's thesis in an Ivy League school on the East Coast. So I do that, and the paper gets anywhere from an A to an A minus. And it ranked it from Princeton and Harvard and so forth in East Coast. Then this other person says to me, well, that's the East Coast. Have UC Berkeley graded? And I'm like, oh, UC Berkeley on the west coast and Stanford and yes, so. And then the person says, hey, you know what? In UC Berkeley, there's the business school and then there's the School of Economics. They're two different things. So I upload the same manuscript, right, that I'd already published and I'd already got graded in A and A minus by the East Coast. And I say, I need you to grade this as if you're the UC Berkeley School of Business and School of Economics as a Master's thesis, it gets a B plus in the Master's school and it gets a B minus in the economic school. The grading. This is the difference with AI, you could say to AI, I said this. Talk to me back in a pirate tone, right? Or in gibberish or whatever it is. You can make any fun of stuff. But what I did was this. I had IT review my work, and I was like, oh, my God, the same document. And then I said, why would the Berkeley School of Economics grade it as a B minus? It's like, oh, I did not explain how I invented the formulas, applied them, and then did a research and compared it, you know, intrinsically over, like, weeks. And years and did a whole research. I didn't do that. I based it upon, you know, commonly available metrics and my. There was a lot of math in tables, but it didn't think that it cut the mustard at UC Berkeley School of Economics. But yet the Ivy League schools, it was anywhere from an A to an A minus. I mean, so for me, that's what's called a mirror. You're like, do you, do you want to hear that response? Most people be like, dude, I got out of college 20 years ago. I don't want somebody to grade my work.
- [43:10]
A
Yeah, we have a few minutes left. How do you think about AI as far as how it's going to affect people making decisions, investing over the Next, let's say 10 years, that is, that.
- [43:34]
B
That'S what's in the book. And I'll explain. You know, I'll give the idea, AI is a mirror. If you ask it a simple question, I'll give you a simple answer. If you ask it a tough question with lots of prompts in it about businesses, it'll give you a detailed answer. And what I can tell you is Bitcoin. Treasury companies are complex. They're balance sheet, they're not income statement. There's fasb, Fair Value Accounting, there's M. Nav, there's Bitcoin yield, right? There's shares per Bitcoin, the amount of outstanding shares, the market cap, relative differences. This is complex. So if you think there's a simple question you can ask AI if you ask AI a simple question, you'll probably get a simple answer, right? And I think what's going to happen with AI is that it's a mirror. It gives back what you ask. And so some people be like, oh, well, I'm just going to take that, take that answer. And for me, look, my X handle is grain of salt. And I get the question. I get the question pretty regularly. Why'd you pick grain of salt? Take everything with a degree of skepticism. When AI gives you an answer, you're like, okay, say what can go wrong? Look, if you watch YouTube, whatever sport or anything you like to watch, you say, look, motorcycle racing. Type in motorcycle racing, fail. And you'll see all these motorcycle crashes, diving fail. It's the same thing with AI Hey, I'm thinking about investing in XYZ company. Pull up all the latest SEC documents, their yearly reports, analyze it for this and then say, well, what can go wrong? Everybody's like, well, what can go right? I'm like, no, say what can go wrong? Why Will this company always go up? And that's the toughest question in the world. But people don't ask that question, so they don't get that answer. That's the way AI Works. AI Is a mirror. Simple answer, simple question, simple answer, complex question, complex answer. And you may not like it, but that's what it does.
- [45:44]
A
Yeah. It reminds me of. Over the. Over weeks or months, I would prompt it with certain pretty simple questions. And I could feel, even emotionally, it was just very positive, like, this is what you should do, and it's going to be great. And I just thought, okay, this is right. It's like. And then should I jump off the bridge? Yes, and it's going to be great. I mean, not that I would tell you to do that, but I was like, all right, we need to. Not. We need to use it as a tool, but also, you know, argue with the author, argue with the AI and be thoughtful with it. Any thoughts there?
- [46:31]
B
Yeah, there. There is. And that's part of the book, or I should say, I've had this conversation with AI and look, I wrote a book on Bitcoin treasuries. I'll be very honest. It sold very, very few copies. Okay, we're talking less than 50 copies. But what it did for me, and if you. And I said to AI Said, how many copies of this do you think I'm going to sell? And they go, well, you have a follow 23,000 followers. They're like maybe 100. I was like, well, I think that number's way too high. So I set a target of 50. But then AI said to me, but this is a valuable exercise because it teaches you. It forces you to think creatively. You have an idea, then you write it, and then you make sure that it passes scrutiny, and then you finalize it, and then you publish it. And that clarity of thought is what allowed me to write a tweet. Now, I'm not saying that anybody that writes a book and write a tweet that can get a million views. I'm not saying that. What I'm saying is it gives you a clarity of thought that scrutiny. And so that's what I did. So was it a valuable use of time? Absolutely. So I think, you know, with. With AI on this, if you say to AI, hey, I'm thinking of this business idea, I'm going to have an ice cream cart and sell it, blah, blah, blah, it'll come back and tell you how to build the best ice cream cart and sell ice cream. But if you tell AI, I'm going to do the ice cream cart right in Vermont in the winter. Right. Versus in Miami, Florida in the summertime. But if you leave that fact out, it's going to tell you you're going to have the best ice cream card ever. Everybody's like, well, it said this would be the best ice cream card ever. What would you do? Well, I was pushing around in Burlington, Vermont and I couldn't sell any ice cream. Did you tell AI that was in Burlington, Vermont in the win? No, I left that out. That's the important part about the mirror. It tells you back, you want to hear. It'll tell you how to build the best ice cream cart. But that is a useful context to give it. And when people hear me prompt AI, they're like, why did you tell it that? I go, because that's useful context. And then it will. If you were to then say chat, GPT or whatever, AI, you say, hey, I'm going to do an ice cream cart. I'm going to do it, build it, and it's going to be in January in Burlington, Vermont. Do you think that's a good idea? It'll be, hey, Mike, I don't think that's a very good idea. I think that you should wait till summertime.
- [49:04]
A
Yeah, yeah.
- [49:05]
B
But if you don't do that, it won't tell you that. So I think that that's the biggest take. I think we went full circle here, Tim.
- [49:09]
A
Yeah, absolutely. Any closing thoughts? Outlook for 2026. Let's frame it as like a 60 second outlook for 2026. 60.
- [49:22]
B
60 seconds. Look, I don't know what's going to happen in 2026. I'm always positive. But if you're always negative and you wouldn't do anything. So I said this quote the other day. I'll quote from Finding Nemo. Keep on swimming. Right. That's the only choice you have. So what do I think is going to happen? I think structurally the US government is going to continue overspending. They have a $2 trillion deficit that won't get fixed. That's the M2 money supply. Therefore they're going to print $2 trillion a year. It's about six and a half percent every year. And then CPI inflation is about three and a half percent. That doesn't get fixed. Why doesn't get fixed? We've got 38 and a half trillion dollars in public debt and private debt in the us or government debt, I should say. And so what's going to happen with that? Is it. It's not going to get fixed, it's only going to get bigger. So what does that mean? They have to print more money and that's what happens with bitcoin. Bitcoin's not going up in value. It just takes more dollars to buy the same Bitcoin. That's what it is. It's monetary debasement plus inflation. So what do I think? I think bitcoin is going to go higher. I think it's going to take an all time high. Do I know if it's going to get much above 150,000? Don't know. Do I hope it does? Sure do. I think that strategy, you know, we got the confirmation that they're still in the MSCI index. I think that's a great thing. And we saw the price reaction. That's the one minute. Be positive, keep on swimming, keep on learning. And when you use AI, just realize that it's a mirror. It's telling you, you ask it a question, you may not be ready for its answer.
- [51:01]
A
Mike, thanks for stopping by and joining us on the Bitcoin Treasuries podcast. Appreciate your time.
- [51:07]
B
Thank you, Tim. Have a great night.