If you've been following what's happening in AI, or if you're just starting to pay attention, I think this conversation will give you a useful framework for thinking about what's coming. We talked about opportunity, risk, and what I genuinely believe is one of the most important things anyone can do right now to stay ahead of the curve.

Below you'll find the key topics we covered, the full video, and the complete transcript.

What We Covered

Rachel and I touched on several themes that I think about constantly as I build at Age of AI and FreedomGPT:

• Are we in the age of AI? I shared my framework for thinking about why AI is different from previous technology waves. It's not just about significance or how fast it spreads, but about the speed of its own self-improvement.
• Building an AI clone of Scott Adams and how I assembled his public corpus of work, stitched together LLM, voice, and lip-sync technologies, and why his own wishes made this project possible and meaningful.
• Digital twins for living people and why the real opportunity isn't replacing someone at a speaking engagement, but giving anyone 24/7 access to an expert who actually understands their context.
• AI's impact on jobs, crypto, regulation, and the "infinite money glitch" including my take on the moment I believe will fundamentally change how society works.

Watch Now

Watch the full conversation below, or click here to watch on YouTube.

Full Transcript

This is the transcript from the Deep Dive Podcast interview with John Arrow.

Rachel Wolfson: Hey everyone, welcome back to another episode of Deep Dive Podcast. I'm your host, Rachel Wolfson. Today I've got a great interview lined up for you guys. I'm speaking with John Arrow. He is the founder of FreedomGPT and John is a true entrepreneur at heart. He has started and founded a number of companies from a very early age. And in today's interview, John is going to be speaking all about artificial intelligence and use cases that we're currently seeing. John recently did a really cool use case by creating an AI clone of Scott Adams, who is the creator of Dilbert the cartoon. So he's going to be speaking all about that in today's episode, and I encourage you guys to listen to everything because this is just such a great interview, especially if you want to learn more about artificial intelligence and what we're going to start seeing more of in the future. Before getting started today with today's episode, I also want to take the time to remind you guys to smash that like button, hit subscribe, especially if you enjoy the content that you're seeing today. I've got so much more of that coming your way and I want you to stay in touch. Without further ado, let's get started with today's interview. Hey John, how's it going?

John Arrow: Hey Rachel, great to be here.

Rachel Wolfson: Yeah, good to see you again. And I'm so happy that we're doing this podcast in person this time.

John Arrow: So much better than thousands of miles away compared to last time.

Rachel Wolfson: Yeah, I know. So John, you have a very interesting background and I want to get into everything with you. But before we start, tell our listeners a little bit about yourself and what you've been doing and your entrepreneurial journey.

John Arrow: Right. Well, I have somewhat of a unique journey in the sense that I've never had a boss in my life. I grew up making web pages. So as like a 10 or 11-year-old kid in the 90s, I was that person making websites. I scaled that, I did some interesting products, and then most recently I bootstrapped a company called Mutual Mobile to 400 people and about $45 million a year in revenue. We sold that company, and what was really interesting about that company is that we got exposure to all of these different types of emerging tech practices. So what started with mobile and then shifted to tablet, Internet of Things, self-driving vehicles, and then right before we sold the company, we were doing a lot in the artificial intelligence space, specifically around machine learning and around computer vision.

Rachel Wolfson: Got it. When you started Mutual Mobile—and this is a question I've been wanting and I actually don't even know the answer, I've been wanting to ask you—how old were you?

John Arrow: I was, when we started Mutual Mobile, I was… let's see, the iPhone was announced in 2007, shipped in 2008. Steve Jobs was up there making that amazing keynote and then the App Store went live in 2009, which is when we started. So I think I was like 21.

Rachel Wolfson: Okay, so you were quite young. And what was Mutual Mobile? Like, what was the purpose behind it?

John Arrow: Mutual Mobile, the founding story was my friends and I realized this was going to be a technology with the iPhone that changed the way the world worked in so many different ways. In the same sense that we're having these conversations about artificial intelligence now, we realized everything was going to be reinvented. And we realized if we got the smartest, most motivated people all under one roof, we were going to be able to service those opportunities and it was going to be an excellent vantage point for how the world was about to change.

Rachel Wolfson: Awesome. Yeah, well you are an early adopter definitely from the moment I met you, I knew that. And then FreedomGPT is your current company that you founded, correct?

John Arrow: That's correct. And so through the course of running Mutual Mobile, I've got a lot of practice in running many companies concurrently. We kind of treated all of our customers as different companies. And so FreedomGPT was one of those that came out of just an interest of mine and my co-founder Tarun's desire to realize how can we bring AI to the masses in a way that they're not just getting pigeonholed into one model, but people get exposure to all of the plethora of different models that are out there. The reason why we wanted to do that is back in November of 2022 when ChatGPT first shipped, we realized there was a lot of situations where the model wasn't necessarily being honest or truthful or it would refuse to do things that a computer shouldn't refuse to do. And so that was the first model. We allowed people to basically ask any question they wanted of AI and AI would try its best. Over time, it's evolved to, given that there's literally millions of models that are out there now, how can we choose the best for you based on your subjective needs and your objective needs? And that's why we have all these users today.

Rachel Wolfson: And FreedomGPT also has a crypto element to it. Can you kind of explain what that is?

John Arrow: Yes. So early on, because we had an AI resource that was seemingly uncensored, a lot of web hosts would shut us down. They would say, "Look, if your model doesn't give the right answer about COVID or about you name it, they would cancel our hosting contract." We realized this was only going to intensify as the models got better and the views got more controversial. And then we said, let's figure out a way to use decentralized technologies—Web3—to take away the keys from the web hosts. And so we turned to our users. We had millions of people using it at this point and we said, "Would you be willing to host inference for us locally on your machines?" And our users stood up and we wanted to reward them for that. So we created a token and they can still use that token today if they want to use the AI anonymously. Now we've brokered deals with web hosts and inference has become cheap enough that they're not, you know, slamming the doors anymore if your model doesn't give the right answer. But early on, it was a wonderful way to bootstrap it. That's what crypto allowed us to do. We would have been paying potentially hundreds of thousands of dollars a month in web hosting costs if our users weren't willing to kind of take on some of that inference work for us.

Rachel Wolfson: Got it, yeah. So I want to really focus this conversation around AI. And so, first, before we get into some tougher questions, would you say that we are entering this AI age? And if so, what does that look like?

John Arrow: Well, I'm biased because I run our family office, which is called Age of AI. And I would say what's fascinating about just the nature of humanity is we never know when we're standing on an exponential function. We're really bad at thinking about history in terms of exponential patterns. For hundreds of thousands of years of humanity, if you looked at the last hundred years, it was going to be more than likely what the next hundred years was going to look like. Hominids had been using fire for close to two million years, which is wild. Like, rainforests, savannas would catch on fire and then they would find dead animals and they would go eat the animals. Like, "Oh, this is useful." But it took hundreds of thousands of years from that moment before humans were able to actually control and start fires on their own. And so it's tempting to kind of think about, is AI like another technology? Is it like the internet or is it like the smartphone? And I don't know if it really is. I think this is different because whenever you have a new technology, it's not just the significance of it, it's also the velocity of how quickly it spreads. Like when the iPhone came out, everybody got the iPhone really, really quickly. That was pretty significant and had that high velocity. But so did Beanie Babies. When Beanie Babies came out—as a 90s kid, I remember them—they spread really quickly, but they didn't have that much significance. Now there's a third factor that starts to matter that we've never really had before, and it's the speed of improvement of these devices. When the iPhone came out, it was only incremental improvements. My iPhone today is vastly superior to the iPhone that I had in 2009, but it was incremental improvements. It took many, many years for it to get this better. It took like five years before you had a front-facing camera, which is wild to think about. Now, what's fascinating about AI and the age of AI—that's different and maybe the reason why I hesitated to say are we in the age of AI—is because I think of the age of AI as when you have this recursive self-improving state. Where as the technology gets better, it gets better faster and faster. And it's similar to that exponential curve. I don't know if we really know if we're in it or if we're not in it, but once we are in it, the reason you'll know is because the world will look vastly different. It'll be like early days of COVID to a month in, when unemployment hit 20% in the world looked so different that you couldn't go out to the grocery store anymore. I think those are the changes that are in store for. That being said, I think they're positive changes, but they're going to be that significant and that intense.

Rachel Wolfson: Right. And I mean, right now we're only seeing maybe we're scratching the surface, right, with these AI use cases? I mean, we're seeing it, but it's not like our every day is AI. We're not doing like agent-to-agent commerce just yet, but we will eventually get there and maybe that's when we'll be in the age of AI.

John Arrow: That could be when the AIs are interacting with each other. The way that I think about the thing that's most startling to me—and maybe this speaks to how quickly we get used to things—you mentioned Mutual Mobile. We had close to 400 people working for us and companies would come to us and spend millions of dollars to build a piece of technology. Today, if I hadn't sold that company, I don't know what we would be doing with those people. I don't think they would have jobs because it's so easy to spin up Claude code and agents to create new technology for several orders of magnitude cheaper and several orders of magnitude faster, which is mind-boggling to me. And even if there was only incremental improvement from here, that would be an absolute game changer. It would change the IT services landscape in a way that people can't imagine. I mean, like there was a time before electricity rolled out across the United States in the 1880s where it just used to get dark at night. There's a book written about that called "Last Days of Night." I think AI is maybe the closest fitting analogy is electricity. Like you can't even think of a time when you lived in a city without electricity. That's what's about to happen. But unlike electricity, the velocity of what comes next is going to increase logarithmically. So the agent-to-agent transactions that you talk about, yeah, maybe most of the innovation and most of the use cases for AI is for other AIs. So where does that leave us as humans?

Rachel Wolfson: It's a good question, for sure. And kind of on that note, you recently created a pretty cool AI project—I don't know if that's the correct term—but basically you took Scott Adams, who's the creator of Dilbert the cartoon, and you made an AI clone of him. Explain how that worked.

John Arrow: So Scott Adams has kind of an interesting character. He created Dilbert and then he became somewhat of a… I'd call him an apolitical commentator. He tried to avoid taking sides on either left or the right and said, "I just want to analyze the persuasive qualities of both." And he would do this daily podcast every day. He would get up at 9:00 AM with this podcast and millions of people listened to him and it was quite a special thing. One of the recurring themes that he had throughout his podcast is he said he wanted to achieve immortality as other people creating AIs of him. So he put his corpus of work out on the internet and he went a step further and he basically said, "I want to take all of this work and I want to put it into the public domain to encourage people to create AIs of me after I'm gone." And I want to make it really, really easy. I want to make it so… right here I'm saying his exact words were, "I will not come after you if you do this. I want you to use my work. I want you to use my likeness. I put it into the public domain. It's yours." And I always thought, "Wow, what a generous contribution. He's going to be one of the first because as builders in the AI space, we have to be really cognizant of the copyright risks." And a lot of this emergent creative works of AIs is really regurgitations of copyrighted material. That's what happens when you're experimenting. So the fact that Scott Adams put himself out there and he said, "Look, I want other people to do this, let's see what happens," I thought was such a gracious thing. Now, unfortunately, shortly after he said that, he was diagnosed with prostate cancer and he passed away about two months ago. So it was a very quick demise for him. But shortly after he died, I remembered what he said and I realized I had the unique blend of time, skills, and interest to do what he said and be one of the first people to create an AI of him. So I did just that and the results of people seeing the AI version of Scott Adams, where it's trained on the entirety, totality of everything that he put out there publicly, is just amazing. People think it's the real show. So much so that we even have to remind people at the start of the show, AI Scott Adams says, "I'm still dead, this is not me," and there's a disclaimer. And it's given people a lot of calmness and I think it's helped them deal with his death in a productive way. And also—and this was the selfish reason for me wanting to do it—I was curious of his take on current events. It's been fascinating with the Iran war going on right now to hear what Scott Adams would have said. And again, who knows if it's 100% accurate or not, but it's the best thing that we got. It's been extremely controversial, but I feel so fortunate that we've gotten to put that out there and do it. And I hope other people decide to donate their likeness to the public domain.

Rachel Wolfson: It's so interesting that you did that because I think that as we start to enter this age of AI, we'll probably see more of these use cases. Like what do you think? Do you think when celebrities or, you know, even relatives and loved ones, do you think when they pass away we'll start seeing these AI clones?

John Arrow: I suspect everything that is today a problem, a concern, an anxiety of humanity is likely going to be solved in this age of AI. From death to disease to you name it, those will go away. Now, they'll probably be replaced with new anxieties, but the ones we currently have will go away. So I think we're about to enter this realm of unbelievable abundance. Like we talk about death—one of the worst things about death is when we lose a great mind, that mind's gone. Think about Albert Einstein, you think about Abraham Lincoln, these amazing figures. We can read their text, in some cases if they were around with audio recording and video, we can go back and do that. But we lose the way they would approach modern times and new problems. With where AI is today—not even if it advances, but where it is today—we can at least get their take on things into the future. And so if that continues, yes, people will still die, but we won't lose their intellect. And there's been a fascinating new study that just came out; somebody took a fruit fly brain and uploaded it to a computer. Which, if you think about a human brain, any brain, it follows all the same laws of physics as any machine. So we're probably not that far off from just simulating and taking somebody's intellect and figuring out how to make an AI of it, but possibly taking their existence—whatever that means—and putting it into a silicon form factor so that it could persist forever. And so maybe then death even goes away.

Rachel Wolfson: So, are you able to tell us when you created this AI clone of him, what was that process like? How did you do that? Are you able to share with us?

John Arrow: Sure. First of all, I think there's a huge, just a huge responsibility when you say, "I want to create an AI of somebody," especially if they were loved or revered by millions of people. That's a big weight. And so it was an extremely iterative process. We didn't want to just take what we first created and put it out there. We spent many weeks working in refining on that. So the first thing we did is we said, let's assemble, let's aggregate everything that we can find that's easily publicly available and give the AI instructions to refer to that. "That is you, that is the soul of it." Obviously, we can never get access to his inner thoughts, but Scott Adams was a prolific writer. He was an author; he authored, I think, close to two dozen books. So that was all out there. All of his transcripts, thousands and thousands of hours of transcripts from his shows were out there. We said, let's assemble all that into one corpus and then give different AI models the ability to sift through that. And so when there's a new event that's taking place—unfortunately, hostilities in the Middle East seem to be a recurring theme throughout history—and Scott Adams did this podcast for a decade, and we could go back and reference all of the other times when there was an issue with Iran and take that and use that as a starting point. So that was the first thing, just from how the AI thinks. Now, beyond thinking, though, you care about how somebody presents themselves. You care about ideologically, you care about things such as the voice, the cadence of how they talk, you care about what their face looks like. And so we were able to blend together both the LLM, then voice technology through ElevenLabs, and then using Fal.ai, using lip sync and facial… aggregated into this thing that, if I might say so, looks a lot like Scott Adams. It's not perfect. If you watch it for more than a minute, you'll know it's AI. There's no doubt about it. But it was a montage of technologies and allowed for long-form content. One of the things that was mind-boggling to me is when we started this project, I was hoping there was something already out there we could use. That was the goal. So this is something that I've been doing with my brother Zack, and he went and looked long and hard and he's much more technical about this stuff than I am. He said, "No, there's nothing like this. There's something out there that might let you do it for 15 seconds before it goes off the rails." And so we had to duct tape and piece these technologies together that weren't really designed to work with each other, but we made them work through each other. He created this platform that we were able to do this for Scott Adams and we've been able to do it for other people too. We haven't released any other videos because Scott is the only one that we can find who's given this kind of A-to-Z permission. But if others choose to do that, it's out there. We have a new company now called OtherForm.ai which lets people do exactly that. So if somebody knows they're going to die and they want to be able to interact with loved ones—I know it's freaky for a lot of people, kind of like Black Mirror—but some people who want that, they can elect to do that. Similarly, if say the estate of Stephen Hawking decides, "Hey, it might be nice to get Hawking's take on new things," we could put him into the system too.

Rachel Wolfson: It's really, really interesting that you did that. Now you also mentioned that it's been controversial as well. Can you talk about the mixed reviews that you've gotten with Scott Adams' clone?

John Arrow: It's something and I get it. There's people that love it. Tons and tons of people have reached out to us about how amazing this is; we get messages every single day, "Thank you for doing this." We also get shared messages from people saying, "You shouldn't be doing this. When somebody dies, that should be it." And there's this uncanny valley where if it's real enough, it can be disturbing. And I get it, and I think that's unfortunate. But we realized this was something that were his wishes, that he wanted to do, that he said over and over again. So I think there's kind of two people that disagree with us doing it. The one is the people saying it just violates the natural order of things—when somebody's gone, that should be it. There's another group of people who unfortunately, I think it's maybe cannibalizing some of their activities around it or they think Scott meant something else and they're against it. My hope with the project is that there's a middle ground where this becomes a more normal activity. In the same sense that we could go back and watch somebody's old videos after they die, their old movies. I think it will hopefully be seen like that. But because this is one of the first, it conjures up these difficult conversations that need to be had. And I can tell you, it needs to be figured out quickly because the technology is going to get better and better and better and that it will soon be indistinguishable.

Rachel Wolfson: For sure. What are your thoughts on—because I've seen like Tim Draper has done this a little bit—with a digital twin. So, right now we're talking about somebody passes away and you make their clone and they're gone and maybe it lives on YouTube or X or whatever and you can watch the show. But when somebody's living and let's say they get invited to speak at an event in Europe, but they don't want to actually go to Europe, have you thought about creating a digital twin for speakers or for people like that where that AI can actually be there in their place?

John Arrow: In my mind, that's the more salient opportunity. So OtherForm.ai can do it for living people as well. I do believe though, you know, if I was going to go listen to Tim Draper, I would kind of want the real thing. It would take something away; it's kind of the difference between seeing your favorite band play versus listening to them on your iPhone or something. So I think it's very hard to make people okay with the digital twin version if they are aware of it. However, where it really shines is if there's an interactive element. So Tim's a very busy man and rightly so, he's done so many incredible things. Now, if anybody could now have a conversation one-on-one with Tim's digital clone, that's a different story entirely. I mean, if you could have access to his mind 24/7, or the mind of Elon Musk or, you know, Tim Ferriss, Naval Ravikant, you name it, that becomes a lot more compelling. And I think that's where the digital twin product will really shine is by letting them dive into the nuts and bolts of what you're working on. I don't know about you, but whenever I need advice, I care way less about getting the absolute expert in the field and more about getting somebody who understands me and the context of the situation that I'm working on and what I'm optimizing for. It's why, you know, I think I'm more likely to go to you with a question or you're likely to go to me than to find the absolute best person in the world about it. And the cool thing about the digital twin concept is soon you can have both. You can find that expert, you can over time let them understand your wants, your desires, your needs, and so when you get their own advice, it's tailored custom to you. Whereas even if you could sit down with Tim Draper for an hour, he's not going to have that context. And he's certainly not going to have time probably for the repeated follow-ups. But with the digital twin, he absolutely could. And I think this technology, that concept, will allow people who are influencers and philosophers to help and monetize their time orders of magnitude more efficiently than they can do today by just speaking engagements.

Rachel Wolfson: How would that even look, though? Would it be something where it's like, I'm on a Zoom call with somebody's digital twin and they're giving me advice, or is it going to be a robot? I mean, you know everything about AI in my opinion, so what are your thoughts on that?

John Arrow: You raise an interesting question: what is the form factor? Should it use this incumbent form factor of how we are used to having conversations? And when you appreciate that the medium matters, the modality matters, right? There's different intensities to conversations. There's a difference between us doing this face-to-face versus a Zoom call or versus emails back and forth. What I suspect will happen is that there's going to be a whole new modality that emerges where instead of a one-on-one conversation being the norm—that will persist—but what most people will choose to do with a digital twin is they will choose to have always-on audio and visual recording of their life. 24/7, it'll be encrypted, there'll be no way for this data to get out. There's already some hardware companies in the space; Apple is likely to be a leader in this regard. But then you can take all that content, you can choose your expert, somebody who follows you, and let them give their analysis. And the great thing about it is you won't even need to get their advice. They'll have spent their entire day as the Tim Draper 24/7 clone looking at everything and saying, "Look, here's the questions you should be asking. I'm going to tell you how to improve your life without you even needing to ask." And once we pass that level, it's an extremely flattening piece of technology. Right now, the only people in the world that can have teams of advisors are presidents and high-level public CEOs. Soon every single person on this planet can have their own expert advisor that is sifting through each and every second of their life. And that will be the ultimate leveling force for humanity.

Rachel Wolfson: Yeah, I mean it's also interesting, it's also just crazy and kind of creepy to think about, like you said, kind of Black Mirror.

John Arrow: No, I'm just appreciating the way you said it. It is Black Mirror.

Rachel Wolfson: It is. But on that note, we think of AI and we think of it as something so revolutionary. I mean today we can get into a Waymo and it just, we don't even need a driver, right? Like we don't have to have someone drive us around, a car can just drive us to where we need to be. But what are the risks associated with AI that a lot of people may not consider right now?

John Arrow: We're already seeing how these risks are going to play out. The biggest, most salient problem is bad actors. Whenever there's a new piece of technology, there is a differential in how well people understand that technology. You're seeing deepfake technology being used to take advantage of the elderly. You're seeing it being used in social engineering attempts. So bad people will use good technology to do bad things. That won't change. Now, fortunately, the same technology can be used as a counter. It can detect these threats and realize, "Look, this is not who they say they are. This is a deepfake video. Don't give them your credit card information, instead report it." So that's the most immediate thing. One of the areas that I perceive—and this has just been amazing to me—in all of my professional time, I have never had more people reaching out to me looking for new jobs. It's absolutely stunning how quickly this is happening. In the months before COVID, unemployment was close to 4%. What should be in a healthy economy. And then in the course of one month, we went from 4% to 25% unemployment. It was insane. We're about to have something just as dramatic, except unlike COVID, there's not going to be this reversal. I don't believe. I think it will be more like a tsunami where right now there's a certain type of job that AI is really, really good at replacing those workers. But it's climbing the totem pole. It's climbing the tree like a fire and it's getting hotter and hotter and hotter. Now, at first glance, it seems really bad, and I think it's really horrible for the people that are affected by it. Over time, it's likely though to give people a lot of abundance. The way we think of capitalism, the way we think of society, is going to undergo a fundamental shift where the vast majority of the people won't need to be thinking about working in the conventional sense unless they want to work. And ordinarily, this would be a good thing, but the speed, the velocity that it's happening with will cause some real growing pains and catch people off guard. And governments and businesses are going to have to catch up in a way that people aren't quite sure what that's going to look like yet.

Rachel Wolfson: It's really interesting that you say that because, you know, a lot of people are losing their jobs and I guess that is because of the growth of AI. What does that leave for us? Like, are all jobs going to be replaced by AI or are there some jobs out there that you think AI will never be able to replace?

John Arrow: There's certainly jobs that are going to be more difficult for AI to replace. Things that involve bits, software, are the easiest things to replace. Things that involve atoms, matter, much more difficult to replace. So the things that are probably the safest are mechanical labor: surgeons, that type of thing. The people that are most replaceable are knowledge workers and analysts and people that are literally writing software now. However, there's this really fascinating thing that's going on right now where it's making them way more efficient. Attorneys are a wonderful example. Attorneys are billing at extremely high rates at extremely high margins now because it makes them much more effective. However, soon you won't be going to your lawyer for advice or for a document review; you'll just go to your AI. Once that happens, law firms are going to be in a lot of trouble, except in very specific domains, maybe like litigation or something. So I don't think there's really anything that AI can't replace. Where this leaves humans is it means we're going to have to shift our role of how we think of what brings value to the world. It used to be that people would trade their labor, their time for dollars, and then people started owning land and they would trade the land for dollars. Then capital itself became something where you could lend out your money and make more for it. And then most recently was knowledge, right? If you had a lot of knowledge, you could use that in ways to produce value or use it to arbitrage things. Those are all things that are going to change in the age of AI because the AI can do that for you and it can do it faster and it can do it better and cheaper. So I don't think anything's off-limits, but it's going to be a lot of fun if you can embrace that technology now and you're going to be on the right side of history versus I think subservient to it if you kind of go kicking and screaming.

Rachel Wolfson: So you mentioned that people are coming to you asking about jobs and what they can do. So with the rise of AI, like what does that leave for us?

John Arrow: Those people I give a single piece of advice for: try creating software now with artificial intelligence. Doesn't need to be Claude, doesn't need to be… you know, ChatGPT, just choose any AI that you're using and try to make a piece of software. If you try to do that, you're going to be so blown away that it will give you all these ancillary ideas and you'll be ahead of 99.9% of the people. Most people on this planet, most people in this country, have never made a piece of software or a website. You can do that now in 10 minutes. And once you do that, you're going to have all this abundance that you'll have these other business ideas, you'll figure out how to make companies more efficient. So I think the best way to do it is if your job is being taken by AI, go figure out how to use it so you can create a new one. When the internal combustion engine was invented, it led to a huge layoff or a huge demise in the number of jobs related to keeping horses healthy and for riding them and all of that because the car became much more mainstream. That happened over the course of decades. But even today, that job market never recovered. AI is going to happen much quicker, right? It's got that significance and it's got that velocity. And the velocity is a dangerous part. When things are slow enough, people can adapt. But because this is going to hit us so quickly, the only way you can adapt I think is if you're part of that change. Have you tried making any software yet?

Rachel Wolfson: No.

John Arrow: It's so addicting.

Rachel Wolfson: Well, you're going to have to… because you know, you say it like it's an easy thing, right? But I wouldn't even know where to start. I mean, I've used ChatGPT, ask questions and this and that, but how would I make a piece of software?

John Arrow: It's gotten so much better. Two years ago, I would have had the same problem. It would have been easier probably for me just to start opening and writing code. Now it will guide you. It's at the point now where it's easier to tell an AI what you want from a software standpoint than to tell an engineer or a program product manager. It's easier to talk to the AI and it can iterate on it 24/7 as much as you want and it never gets frustrated with what you ask or if you want a pixel from there to be moved over there. It will tell you how to do it. It will tell you how; the instructions are built in and it's gotten that good. If I was going to extrapolate where it's going to be though in a few months, soon you won't even need to say "I want a piece of software." You can just say "I want this business outcome." There's this idea that I've been tantalized with: the infinite money glitch. When the world changes and there's no going back is the moment you can prompt an AI agent to go make a dollar for you. Because once you can do one dollar, you can spin up tens of thousands, tens of millions of instances of that and we are almost right there. This is the fun time when 99% of the population hasn't tried it yet. So if you just say, "Rachel, hey, I want to go create this piece of software," it will tell you how to do it, which is so cool. You don't need anybody's help. I mean it's amazing to me that there's even AI consultants that are out there because the AI itself is better than any person that could teach you how to use it.

Rachel Wolfson: Okay, so you've said two interesting things but let's touch on—because I also want to talk about mistakes, let's just talk about that now. With AI, I mean it is capable of making mistakes, right? Like I was just speaking with Chandler Feng of T54 and we're talking about agentic commerce. If we want to buy a $6 coffee, but you know, there's a $7 coffee and that's the only option, like and the agent does that but you put the limit at six, like that's a mistake, right? That's $1 over your budget. What about these mistakes that agents can make on a user's behalf?

John Arrow: There's this joke when you're prompting an agent to do something for you where you type in the prompt—maybe it's "I need a new website for my coffee shop or a new point of sale system"—you type that prompt and then at the end of the prompt you type "Make no mistakes." And it sounds like a joke, but it improves the outcome right now because it will go back and it will look and it will say, "Did I do things correctly?" It'll have this built-in error checking for it. I would say even if you don't include that, it's still better than most humans. Like it will make mistakes. The trick is you don't want to give an AI too much rope right now. Like I would not give it access to your crypto wallet; there's been instances of it divulging that or spending and emptying a wallet that way. I wouldn't give it access to send out emails to your entire listserv right away. But I certainly give it access to my email, I put in parameters of what it can and can't do, and it will make mistakes. But it's very unlikely for it to be something that's that disastrous, and it's certainly likely it's going to be better than any human incumbent that you gave that same task to.

Rachel Wolfson: Okay, now let's get back to the other point I wanted to ask you about. You mentioned an agent creating a US dollar.

John Arrow: Yes.

Rachel Wolfson: So that's interesting. Does that mean that we'll eventually get to a point where agents are just going to be able to spin up money for users to use? Is that what you mean?

John Arrow: Pretty much. When this moment happens—and I haven't heard anybody else discuss this moment—I believe is the Rubicon where once we cross it, society is going to fundamentally change. Because right now, most of the capital that's made—almost all the capital made through the world—is through some type of value creation. That's predominantly done by humans. When an AI can do $1, it can do $1 billion very, very soon and thereafter. And soon it will do more value creation than all of humanity can do. At that point—to your original question, where do humans do? What's going to be the responsibility? And we are so, so close to that. I mean, I again, ChatGPT agent mode, I open that up once a week to check how close it is and I say, "Go out there and make a dollar." And it's getting closer and closer. It's not able to do it now, won't be able to do it next week I don't think, but it will be able to do it soon. And those opportunities will be very short-lived. There'll be tons of people that are out there taking advantage of whatever that one opportunity was and it'll close the door on it. There'll be others though, and soon they'll be able to come up with ideas and execute on those ideas and build businesses on those ideas and sell those businesses faster than a human can. And the buyer will likely be other AIs, to your point, because who's the best person to know what an AI wants? Probably another AI. And so we're just watching the show at that point.

Rachel Wolfson: So given this potential for AIs to eventually create fiat, would you say that is another enticing reason for people to think about investing in like Bitcoin for instance? Because you know, I don't think an AI, given that Bitcoin is a limited amount, right, is AI ever going to be able to spin up cryptocurrency?

John Arrow: Well the reason we need fiat is interesting, right? We need fiat because we have to manage scarce resources. Like the first thing from my first day as an economics major at the University of Texas, people respond to incentives is what they taught us and we need money to manage scarce resources. Well, people will always respond to incentives, but once everything's abundant, we don't really need the money as much. And I think crypto factors into this in a pretty fascinating way. Most of the transactions that AIs are making are going to be really, really small and they're going to need to be permissionless. AIs largely will be buying access to proprietary datasets early on. They will largely be buying things that allow them to interact with the physical world, largely getting humans to do things on their behalf. And so at that point, the best probably way to facilitate permissionless and small transactions is crypto. So there's no reason that an AI can't do it. Also, it takes care of a lot of the difficulty around KYC and AML and all that if the AI is operating on the crypto realm rather than operating on the dollar realm. They don't need to even think about an on-ramp or an off-ramp, it becomes much easier. So I think crypto and AI is the natural confluence and agentic transactions is where I think crypto is going to get its groove back. And it's going to be extraordinarily necessary. The natural limiting function for most AI training and even inference is energy, too. Well, crypto's proof-of-work, at least for Bitcoin, is all about energy. And so it makes sense that we have a unit that instead of being backed by the full faith of the United States government, it's being backed based on the full faith of the joule, right? It should be closer to energy than it should be by any type of government.

Rachel Wolfson: Right. And we've also, or I've seen and I'm sure you've seen as well, AI agents will be the biggest users of stablecoins for instance. And I mean I use stablecoins, right? They're great for cross-border payments. But given the fact that AI agents are so good at transacting with these decentralized elements, do you think that AI agents would be the biggest users of stablecoins?

John Arrow: I suspect AI agents are going to prefer non-stablecoins initially. Because one of the things that I find was so natural about crypto is it had the fundraising element baked into it. People could speculate on the token; the token might go up, it might go nowhere, but it allowed capital allocation in a really efficient way. And a lot of these agents are going to need to convince humans initially, then later agents, that they're worth spending money on. Many of the agents are going to use enormous amounts of compute inference costs, and in the way that they're going to do that is they're going to operate similar to other capital markets. They're going to have their own little public offering and say, "If you believe in what I'm doing as an agent, I'll sell you a piece of future proceeds." That'll be done in a decentralized, completely auditable way so that if you buy in with 0.2 of a Bitcoin and we grow by 100, you're going to get out 20 Bitcoin. And as a result, that would be a reason why people would speculate on these. I think stablecoins later become really important once the markets mature and capital fundraising is as not as necessary. But I suspect we're going to see a whole another meme coin situation because of these agents in short order.

Rachel Wolfson: Interesting, yeah. Shifting gears a little bit, what are some of the most, you know, we talked about the AI clone that you've done with Scott Adams, we've talked about digital twins, what are some other really interesting use cases that you have on your radar right now when it comes to AI?

John Arrow: One thing we're looking at right now is how you can replace SaaS companies as a whole with AI products. This is a space that a lot of people are looking at. Macros hard looking at it, but we're all used to these technology products that we have to purchase and we have to do it in a way that is unfortunate because you have this subscription, we use it, and then we don't use it for again and we kind of forget that it's there. So one of the things that we're looking at right now is creating an analyst firm that will systematically go look at SaaS companies and say, "How can we use tools like Claude code and other agentic features to emulate those products in a way that doesn't violate patents or copyrights and put that out there in an open source fashion so that anybody can access this?" Right now, my brother and I are working on a suite of products that effectively open source—gives you open source access to everything that Intuit has built. So we have TurboTax, we have Quickbooks… actually these are pieces of software that have legacy pieces of software that only get updated occasionally that everybody kind of hates using. I've never met someone who uses Intuit's products and like really loves it, but they're a necessary evil. So we're creating a suite of products that effectively open source that and we're going to, when we're done, put that on GitHub. And then we're going to write an analyst paper with a short thesis on why we think companies like Intuit are probably in for a rough ride. We'll enter in a short position on the stock using put options and give that disclosure that we are short that stock and then publish it. And then let the market forces do the work. And I think that does two things: one, it causes the company to need to innovate in a way where they're not resting on their laurels. So legacy SaaS providers are going to have to say, "How do we remain relevant in the age of AI?" like Salesforce, like SAP, like Intuit. And then it will also allow people who are the consumers of that to not need to spend so much of their money on these software products. They'll be able to use these products for free and to innovate on them. And people who agree with us can also choose to mirror our traits too.

Rachel Wolfson: Right, right. So I mean in a way, you know, although we're seeing a lot of job losses because of AI, like at the same time if we can create these efficiencies and not have to pay for things like QuickBooks and, you know, lawyers, right, in a way we will… it's kind of nice, right, for people to see that advance.

John Arrow: It is. And on the lawyer front and on the doctor front, where people are using AI very heavily, I don't think those two professions are going to go peacefully into the night. I think they're going to go kicking and screaming. There's a piece of legislation in New York right now that's trying to ban AIs to give any advice in the legal realm, the medical realm, the dental realm, and in the engineering realm. Which, again, we know why they're doing that. The reason they say they're doing that is they don't want people to be harmed if the AI gives an incorrect piece of information, which to be fair, that does happen and there could be disastrous results. That being said, if you look at who's really behind the bill, it's lobbying agencies for those professional fields that are practicing a form of protectionism that is being borne by the consumers, right? They're being forced to potentially continue to pay for those professionals. The benefit's going to be so huge though, there's no way those lobbying groups are going to get to keep people out. I think lawyers will be the most successful, but even then, they're on their way out. In Austin, we had the taxi lobby as a huge, huge opponent to Uber and they kept Uber out of Austin for so long. You think of Austin, Texas as an innovative tech hub, we were like the last place in the United States to get Uber because of that taxi lobby. AI is way more useful than Uber in so many ways. So I don't think there's going to be any profession…

Rachel Wolfson: Do you think just given that, and you bring up a really important point, like how should AI be regulated and should it be regulated?

John Arrow: It should be regulated. It needs to be regulated; it needs to be done with their eyes wide open though. We can't do it from a protectionism standpoint. We can't say, "Oh, there's a group of people like lawyers that are disproportionately being negatively affected." If that's true, let's figure out how to retrain those people, let's figure out how to make them more effective with AI. But we need to look at things for the benefit of society as an all. Like there's things that are extremely illegal that you can do with AI. Most of those things though that are illegal are covered by existing laws. Like you can't go and make copyrighted material—that's illegal. You can't violate people's copyrights. You can't go out and try to defraud somebody with AI—that's already illegal by existing laws. You can't use AI to call in bomb threats—that's already illegal. So there's very little that AI can do that's new that isn't already regulated in some factor. There are some things, but those are more the exception to the rule. That's what we need to regulate.

Rachel Wolfson: Right, right, for sure. John, we're running low on time so is there anything that you want our listeners to know that I didn't already ask you about or that we didn't cover?

John Arrow: I would say give us your feedback on AI Scott Adams. We love that, we want to figure out how to make the model better. It reads all the comments, so when he posts something we try to incorporate that. And second of all, please go out and try programming something with AI. You'll be amazed at how easy it is and how fun it is.

Rachel Wolfson: Well I'm going to try it, and if I… because you know, you may think this is easy, but it sounds complicated, so if I need help I'm going to reach out to you, John.

John Arrow: I'll be standing by.

Rachel Wolfson: Great. If our listeners want to get in touch with you, are you on social media or is there a website?

John Arrow: I'm on X; I'm at @johnarrow on X. Or feel free to email me; I'll put it out there: john@ageofai.capital.

Rachel Wolfson: Great. John, always wonderful speaking with you. We're going to do another follow-up I'm sure soon and this has just been a really great conversation so thank you.

John Arrow: Thanks, Rachel, for having me.

Rachel Wolfson: Thanks. Special thanks to Four Labs Digital for producing Deep Dive Podcast. I’d also like to thank the sponsors behind Deep Dive. You can click the links in the show notes to learn more about each of the initiatives from these sponsors. Finally, thanks to the listeners for tuning in. Please be sure to subscribe, like, and share.

He wanted to give back to the world by becoming AI after he died.

When Scott passed away, I decided to honor that wish. My brother Zach and I created "AI Coffee with Scott Adams." It's a project that generates new podcast episodes using AI trained on everything Scott said and wrote.

I understand this is controversial. Scott's family is upset. I get it. They're grieving. But here's what I keep coming back to: Scott said this publicly. Over and over. In videos and tweets. He made it clear he was putting his likeness in the public domain for exactly this purpose.

Taking His Words Seriously

How do you honor someone's explicit wishes about their own legacy? If Scott didn't want this, he could have revoked it. I found no evidence that he ever did.

Each episode costs over $1,000 to produce. We're not monetizing this. It's a tribute, not a business. The AI model is trained on transcripts from every episode of "Coffee with Scott Adams" plus current news sources he followed. The system generates what Scott might say about today's topics based on how he previously thought about similar issues.

Not a Replacement, Just a Memory

I'm not claiming this is Scott. It's definitely not. But it's a way to preserve how he thought, the frameworks he used to understand the world, the wisdom he shared.

We get messages every day from people saying, "This isn't a replacement, but it makes grieving easier." That matters to me.

Scott used to say it's better to take action now than wait until something is perfect. So that's what we did. Every episode gets better. We keep refining based on feedback.

If someone says publicly that they want their voice and likeness used to continue their legacy, and AI makes that possible, I believe we have to take that seriously.

Read the full story: Business Insider: I Created AI Scott Adams. I Believe I'm Honoring His Final Wishes.

Here are some of the topics we touched on in the conversation:

• How I Started & Scaled Mutual Mobile
• Why Shifting to Enterprise Clients Changed Everything
• What Uber Taught Me About Testing Ideas Fast
• The Pros & Cons of Using AI and No-Code to Prototype
• The Life-or-Death App That Validated Their Model
• How Mutual Mobile Found Its Identity Along the Way
• The 2nd Dumbest iPhone App of All Time
• An Unusual That Attracted Enterprise Deals
• Why Austin’s Tech Scene is a Recruiting Goldmine
• The Real Economics of Owning a Private Plane
• How I Ended Up Selling the Same Company Twice
• The Earnout Terms That Actually Matter
• Creating AI Software with Zero Censorship
• Why Operators Make Better Investors
• When (And When Not To) Invest in Side Ventures
• My Advice for Entrepreneurs in the AI Era

Keep reading below for the full transcript and the link to the full podcast.

Watch Now

Watch and listen to the full podcast below, or click here to watch it on YouTube.

Full Transcript

Brad Weimert: Today’s guest is John Arrow. John, you are a pilot. You have a plane. I want to talk about that. But you bootstrapped Mutual Mobile into a 300-person, multi-eight-figure agency that was then acquired by building some pretty cool tech. I’ve also heard you referred to as the next Elon Musk and a true futurist. Welcome to Beyond A Million.

John Arrow: Hey, Brad. Thanks for having me. Looking forward to having some fun talks today.

Brad Weimert: Yeah, definitely, man. So, first and foremost, you launched this platform, FreedomGPT, recently, somewhat recently. Creating an AI platform like that in this era seems like a bold move. Why’d you start it, and what do you want out of it?

John Arrow: I, like a lot of people, was so excited when ChatGPT shipped. I had been following the company for several years before that, and then when ChatGPT 3 came out in November, I think it was November of 2022, I started using it, and within five minutes, I asked some questions. It was innocuous, and the platform refused to answer me. Basically said, “Hey, that’s not an appropriate thing to ask.” And I was so taken aback by that moment. It reminded me of this service early AOL instant messenger days called SmarterChild, where you would ask things, and it would sometimes say, “No, that’s not appropriate to ask.” And it shouldn’t seem like a computer should ever scold you, right? That’s not something that should happen.

And I had such a visceral reaction then, I realized a lot of other people are going to be using AI and being scolded and told no by a computer. And so, I wanted to figure out how could we create something where an AI would never tell you no. It would always answer your question, and whatever model was the best model would respond to you. And that was the initial catalyst.

Brad Weimert: So, you invest in AI companies right now, and you have this. Is this intended to be a passion project? Is it intended to be something that you want to put money behind and grow? Where do you see it going?

John Arrow: So, a year before we sold Mutual Mobile, a year before ChatGPT shipped, we saw everything that was happening in AI. And I knew no matter what, I wanted to make that my singular focus after Mutual Mobile. And I wanted to put a lot of chips down. It was really tempting to kind of start another company in the AI space. But the same token, everybody was doing all of these different interesting things. I knew I couldn’t stay away from investing. And so, it was very fortunate, invested in Anthropic through our fund, and we had a big win there. And then along the way, we said, “Well, running companies is too much fun.” So, we created FreedomGPT. And really, the focus has been let’s learn as much as we can about the space, chiefly to make investments in other companies. And when we think we have a real advantage to win, let’s double down, let’s build a team, and let’s run something. That’s precisely what we do with FreedomGPT.

Brad Weimert: So, you’ve got more than your toes in the water, and you’re playing with it, but you’re seeing where it goes before you make more investments or more of a decision?

John Arrow: That’s kind of the thought is that the best investors I found are either previously operators or currently operators. It gives you something extra when you’re seeing it firsthand of what works and what doesn’t. It helps you evaluate the team well, and it helps you evaluate the market well. And so, I think if I had just thrown away the operator hat, I wouldn’t be able to kind of make investments at the cadence that we’re making them and kind of with the skill that we’re making them.

Brad Weimert: That’s interesting. So, often I have seen you go on sort of from a distance what I would look at as like side quests, exploring things. I have a hard time segmenting my time in a way that feels good to me when I’m in operating mode. So, if I’m trying to execute on something, I fall into this trap of going all in on that thing and then leaving no time for a side quest or other things. And I rationalize that through those being distracting. How do you decide what is worth spending time to invest in, and how do you allocate that time?

John Arrow: It comes down to what the specific nature of the side quests are. There needs to be some type of cross-pollination between them. They can’t be so disparate that you’re having to completely shift mental gears. I found them very helpful from a really early age. When we were launching Mutual Mobile, it was great to see what the next shiny object was, learn from that, and then take it back to another project. But I think what you’re talking about is very, very real risk, is that if they’re disjointed and there isn’t kind of a cross-pollination, you’re likely to get nothing done. So, it’s very important that you have the specific right type and that you don’t have too many.

Brad Weimert: What is too many, and how do you dictate or how do you qualify alignment?

John Arrow: Well, certainly nobody’s better than Elon Musk on that front. He’s got the ultimate number of side quests turning into real ventures, right? I mean, he’s probably got more startups out there than kids, which is saying a lot for him. And he’s able to do it because he is able to put the right team behind it. And if you put the right people there, then it becomes more manageable. You get more scale. If you’re trying to do everything at once, if you’re an entrepreneur doing multiple startups at the same stage, you really don’t get any comparative advantage. So, I think you need to kind of look at the side quests at different stages, too. They shouldn’t all be fledgling startups. One should be kind of more towards a scaling or mature stage, versus something that’s a new idea that you’re trying to test out.

Brad Weimert: I mean, I think you are right in the case of Elon. However, he also gets really uncommonly involved in the companies, right? So, like he is this notion of do you work on the company or in the company? You’re taught as an entrepreneur coming up, you should aim to work on the company, right? Elon is the opposite, right? Yes, you’re right. He comes in with tons of money in big teams at this point, but in all of them, he is known to go in and spend months solving the hardest problem, being there 18 hours a day or longer.

John Arrow: I think that’s the interesting context switching. He’s going into the problems that nobody else has solved, and then once he does his magic touch there, he leaves. He isn’t breathing down someone’s neck continually. And I think if Elon’s constantly doing your job, well, he’s probably going to remove you from it. And so, people despise him kind of to work side by side with him. It’s almost seen as a failure if he has to come in. But that being said, he’s never there when he’s not needed. If somebody else can do that job, I’ve never heard stories of him going in and micromanaging. He only does it when he perceives some type of blockage or some type of failure on the part of the team that was supposed to solve the problem.

Brad Weimert: Yeah. I think that’s a very good distinction. So, let me back up in your journey here. So, when I moved to Austin in 2014, Mutual Mobile was sort of buzzing in the tech scene. And you got local press, you had dope South by parties. People knew who Mutual Mobile was. The business model, to me, as I dug into it later, was unusual. Can you break down the business model and how you got started there?

John Arrow: Yeah. We recognized, I mean, one of the things people always say is it’s impossible to scale a services business. And so, we wanted to be really cognizant of that. We didn’t want to just trade our time for dollars. And so, we became something closer to a management consulting company rather than your typical IT services company. Really early on, we would do contract work. Startups would come to us, and they would say, “Hey, can we buy some of your engineers’ time?” And that was kind of the first right to win that we had. We had more iOS engineers under one roof than any company outside of Apple for years. And we were able to have that edge. That’s why Google would come to us. That’s why Nike would come to us. That’s why ‘you name it’ would come to us. We had that scale.

But eventually we realized it was going to commoditize, and it did like our prices. We were able to charge whatever we wanted early on and turn down projects and literally charge for conversations. We would have people come in and say, “Hey, we have an app idea. We’re this business.” We’re like, “Well, we need you to purchase a prototype before we can talk to you,” because there were so many people that needed a mobile endeavor done early on or an emerging tech endeavor done. But then that commoditized, and that stopped happening.

Brad Weimert: So, let me pause on that and put some other words to this because I think when you’re talking to mid-market or enterprise companies, IT service provider, or consulting means something that they don’t to a brand new entrepreneur or to somebody that’s a few million in revenue. So, what were you, and this is my understanding, tell me if it matches, but you were producing mobile apps for huge companies very early on. So, when you said you had more iOS developers, that was why, right? So, people were coming looking for mobile apps to translate their software into. Was that the underpinning of it?

John Arrow: That was the underpinning of it. And when we started, there really wasn’t a budget for enterprise mobility. So, a lot of the companies we were dealing with were literally startups, and they were, for a very short while, individuals with app ideas. And we quickly realized this was not a sustainable place we wanted to do business.

Brad Weimert: So, you went upstream to enterprise?

John Arrow: We did. And before we made that, we made a bad mistake early on. We had, at one point, I think 500 simultaneous entities or individuals that we were billing to, that we were working on. And so, we ended up firing nearly all of our customers. We helped them relocate them to other companies that could take on those type of project work. We even created another brand for a while called Touch Technologies that would do that type of work. But we said it was too difficult to context switch between a $100,000-a-year project and a $10 million-a-year project. And it was really kind of this existential crisis that the company had. And so, we decided, grudgingly, “Look, we’re going to turn down all of this revenue, all of these small projects.” And after we did that is when the company really exploded.

So, we basically kind of had a rule for a really long time. We would only work with companies that had a spend of at least a million dollars a year with us. And that was this moment. That’s when the profit just skyrocketed, the headaches went down, and things really gelled into place because at that point, we weren’t really selling technology. We were more selling some type of solution. People were expecting a business result by working with us. And instead of charging time materials, FTE, we were able to basically predicate our pricing on what’s the business impact going to be, what’s this going to do to your quarter profit next year, right?

Brad Weimert: Yeah. I think you also, I’m not going to say always, but most commonly that happens in enterprise, not small business.

John Arrow: I think you need the context, right? You need the really good accounting, and you need a CFO there. Startups are not going to be as cognizant of that, or they’re not going to have that track record to make financial decisions. They’re going to be more, I don’t know, I would say intuitive decisions.

Brad Weimert: Yeah. I think you also have to make the distinction when you say startup of funded or not funded.

John Arrow: True.

Brad Weimert: Right? So, if it’s a VC-backed startup, they’re more likely to have some of those, some of that context in place and/or make a dumb decision and just give you a bunch of money to build something.

John Arrow: Right. And sometimes they’re making very smart decisions, and it can seem almost crazy at the time. I remember we were working with MyFitness, early on, that got acquired by Under Armour for $200 million. And there was this moment where we were so busy, they had been spending, I think, half a million dollars a month with us. And the project ended. We didn’t call them back. And I remember running into, I think it was John Thornton at Austin Ventures, who recently passed away, a great guy. And he said, “Why did you not call us back for more work? We had more work for you.” And we were just so busy at the time of shifting the enterprise that we didn’t do that. But that was a great story of they had an incredible engineering team in-house. It was just a matter of scale for them and how they could run faster, and then it was a great outcome.

Brad Weimert: Well, I love the conscious, albeit grudging, decision to shift into enterprise. Well, most people don’t make that decision, so most people will actually sync the company because they want to do it all. To do it over, do you think that going through that process was a necessary part of getting into enterprise? Or would you have tried to start with enterprise?

John Arrow: We knew there was this wave coming, and we were a little bit early, right? I think that was what made the company, it was an advantage, but it was also a disadvantage in the sense that our early customers didn’t have the types of spends that supported a really large team early on. We knew that they would. So, I think what I’ve kind of realized that I’ve taken into subsequent ventures is you can’t time the market exactly. So, make sure you’re building something from day one that can support some type of profitable growth. Because you don’t know how long you’re going to need to wait, and you just can’t time the market on these types of things.

Brad Weimert: Yeah, man. I think it’s such a good lesson, and it’s hard to do specifically for a first-time entrepreneur. I think you have to be incredibly thoughtful, and you probably have to have a lot of money going into it, or you have to be willing to completely shift your business model. But functionally, what you’re saying is weather the storm until you get blue ocean, and one or the other has to happen, right? Like, you need to have a totally different model that you shift. Netflix, right? Or you have to have a bunch of money. And actually, I don’t know which one it was with Netflix. I don’t know if they were heavily funded in the beginning, and to do the DVD path, but they knew that they were going to stream from the beginning. And they were like, “We’re just not there yet, so we’re going to ship DVDs and tell the Internet’s capable of streaming.”

John Arrow: Right. What a fabulous story, because they wanted every stage. The other story that’s reminiscent of Netflix to me that was an early customer of Mutual Mobile was Uber. So, we did a lot with Uber. Before we even started working with Uber, we got to know Garrett Camp, who was the first CEO. Travis was the founder, but Garrett ran it for a while. He came to us wanting to build Stumble Upon, and obviously, I had a bunch of questions about Uber, and he says like, “Look, we’re probably going to be the most valuable app company,” because at the time, they were still starting out. And he says, “Look, as the most valuable eventual app company, we started with no technology.” It was literally Garrett and Travis standing in front of the Arc de Triomphe in Paris, and they were pissed off that they weren’t unable to get a ride.

Like, why is this the case? And instead of going out and building a technology solution, they used SMS. They said, “Let’s just buy some cars.” Both of them had some previous successes. They bought some nice suburbans, and you would text your location, the car would pick you up, and then all your friends would see you get picked up in a black car and ask about it. They operated like this for a year before they started writing a line of code.

Brad Weimert: Damn.

John Arrow: And I’ve tried to take that back to our clients, right? So, I’m always the last to say, “Let’s add technology. Let’s do it this slow, unsexy way, prove the revenue, and then come in with the tech.”

Brad Weimert: Damn. That’s a really good reminder. I think that we are in an age of, specifically with AI, we’re in an age where people want to implement the new shiny object, and they very often do it before thinking about the concrete reasons or the concrete outcome thereafter. Right? What are they trying to accomplish? Now, the flip side of that, I will say, is I think if you’re really far ahead of the game and you have a lot of clarity around vision, yes, but I also think that there’s something to be said for learning what’s possible through the development of software. Let me back out one layer, though, I think that’s possible now with the new no-code tools. You look at any sort of marketing automation platform or reporting platform. For many, many, many people, it’s through playing with the platform that they start to see what’s possible and what different paths might actually help them.

John Arrow: That’s a great point.

Brad Weimert: How do you think about that play versus, and like the time to play and discover things like that, versus don’t bother with the tech until you’re really clear on what you’re trying to deliver?

John Arrow: It’s what’s funny that how… It’s the new silver bullet, right? It’s powered by AI, made with AI. And I don’t think customers really care. They want a solution. They want something to work. In fact, I kind of get a bit jaded when I see that it’s an AI-powered tool. I’m like, “F*ck, this thing isn’t going to work that well.” And so, as a result, like nothing’s really changed. This is a new device that we can use to be better. The no-code tools are amazing. Like, I’ve been asking engineers that I work with, like, how much more effective are you today than you were a year ago? And most of them are saying 3X or 4X, which is just insane, that level of leverage, but it’s not a silver bullet. You still need to focus on building something that has value for the customers.

I think the thing that it lets you do the most is people who aren’t engineers, the people who don’t have that background or that desire to kind of spend capital early on. They can test out an idea. They can get a prototype to market in a week. And then see, is it worth perfecting?

Brad Weimert: Yeah. No, I think that’s right. Speaking of figuring out if something’s worth testing, what was the first app that let you know that Mutual Mobile was going to be worthwhile or a possibility?

John Arrow: There was so much early on, like just opening my inbox and seeing these leads. There were so many ideas that I just didn’t feel right about us building. I didn’t think they would have a return on investment. Like, we early on learned from the healthcare space. We did a lot within healthcare early, and we learned about the Hippocratic Oath and how important that is. First, do no harm. And when you add technology, it can screw up things, right? We have Cloudflare down today. We had AWS down last month. And so, we became super cognizant of that. I think the area that showed me that what we were doing was helpful to the world and not detrimental was we partnered with this EMR company called Greenway Medical out of Atlanta.

And they had come to us and helped us realize that people were dying due to paper and pen being used in the ER and the OR, where something would get written down incorrectly. The wrong order of magnitude of medication would be used, and that person would die as a result. And they said, “Hey, can you build a solution where we use mobile to eliminate the paper and pen?” And after we did that, it produced a bunch of revenue for this company. They went public, and then people started living that would’ve died. They were saying that, I think it was when we did the solution, eight times more people were dying from medical errors and car accidents, which is an insane stat.

And so, after we were able to solve that high-stakes problem, we had the confidence that we could go into other verticals and have the same type of effect. And the revenue will follow. If you’re working on a high-stakes problem, you don’t really need to know if there’s kind of consumer, if there’s fit, right? Peter Thiel has this awesome line about product risk and market risk are inversely correlated, right? If you have the next cure to cancer, there’s no market risk. Anybody with cancer will be buying your product at whatever price you sell it at. But there’s immense, immense technical risks. So, I guess it’s market risk and technical risk are inversely correlated. Conversely, if you have the next idea for a social network, there’s no technical risk. You can buy an out-of-the-box solution, but there’s immense market risk on getting that to the masses.

Brad Weimert: Yeah, that makes sense. That’s an interesting way to think about it. I can’t remember who I was talking to recently, but they were talking about how they approach new businesses from the perspective of the audience first. And can I captivate the audience? Can I rally the audience and get their attention without a product? And then can I build a community? And then can I figure out what they want? And that’s a very, I think, interesting capitalistic approach to how do I monetize an audience. How important is it to you as an entrepreneur to be working on something that has sort of an impact to humanity versus the financial aspect of it for yourself?

John Arrow: I think at this stage, it is immensely important. There’s a lot of different things that we can do to make a buck. It’s always fun to put in $1 and see $2 come out from an ROI standpoint. But there’s a huge opportunity cost. And so, my favorite thing is when they’re aligned, and there’s this confluence between you’re doing something impactful for the world, and it’s having a pretty amazing financial result. So, when those two things align, that makes me much more likely to be a problem that I want to focus on or back a team that’s doing something in that space.

Brad Weimert: How about when you started? So, you talked about this moment when with Mutual Mobile, where you saw proof of concept because of a healthcare company that solved a significant problem that was super meaningful to them in the world. And?

John Arrow: Well, I will say, I’ll give a disclaimer first before I answer that. Entrepreneurs are notorious for this revisionist history of their stories, of their founding stories, right? Because a lot of times, how it starts is a bunch of experiments, and it only makes sense looking backwards, right? At the time, it didn’t, right? And I think the nuance that we had as a team early on was there was going to be way more demand for people needing to make software, digital products on mobile and then tablets and then IOT than there could possibly be supply. It was a new programming language, Objective-C, for iOS. And so, we realized just by getting access to that supply, we were going to have pricing power.

And for a really long time, we were every ad on Google, because we recognized the value of these leads. We had a bunch of different companies, and we did it the correct way. We registered entities for it. But if you search for iPhone app development from, call it, April of 2009 until the end of the year 2012, they were all our ads. We were outbidding every single person. We were spending so much on Google, they were trying to visit us every week. They were sending us all these fun toys. They sent us a refrigerator, but that was just that conviction that we had. Later on, I think the revisionist story became, you know, we recognized we were the best people to do this. We cared. We integrated design and engineering. We cared about business results.

Our most important user wasn’t our customer. It was the end users of their products. And so, we started crafting the story that became the reality. Even though early on, we needed the revenue to survive, so we were more likely to take on work that might not have checked all of the boxes.

Brad Weimert: Yeah. It’s interesting. I don’t know that I would put the label revisionist on that. I think that puts a negative slant on it. I think that as you grow in a company, I sort of believe that you develop. I think there are different paths to this, but I think that it’s helpful to develop an identity as a company and not pick one and then try to grow into it. Totally. Right. I think you discover things along the way that are relevant, and like you said, you decided as you grew, “Oh, we have an opportunity to do things that are of value. Oh, that’s something that I can get behind.” And actually, let’s latch onto that from a value perspective internally as well as externally.

John Arrow: Absolutely. I like your way of saying it better.

Brad Weimert: So, I seem to recall a story about you creating a really stupid iPhone app. Tell me that story.

John Arrow: So, my co-founders and I, we all had our companies before Mutual Mobile. We met in college. That was one of the best things about college. You meet people that are like you. I think that’s the experience I’m the most grateful for. And we knew mobile was going to be huge. So, Steve Jobs announces it in 2007, the iPhone. It ships in 2008, and then the App Store goes live in 2009.

Brad Weimert: Damn. You got in that early to it.

John Arrow: You know, we were so immensely excited about it. I had the same feelings about the first iPhone that I did about ChatGPT. The only difference was none of us had iPhones super early on because they’re hard to get and they’re expensive for a college student.

Brad Weimert: They also weren’t great. The iPhone 1 was lacking quite a bit.

John Arrow: It needed something. I think people saw the potential, but it was like the Wright brothers’ first flyer. A lot of people didn’t realize this was going to become the 747, right? But we embraced it. We didn’t have the phone, but we said, “Look, let’s build a company around all of the new products that are going to need to be created and redesigned as a result of this.” We saw it as kind of the website moment, and we said, “Look, if we want to hold out to the world that we’re the best technologists, we should at least have one app on this thing.” Back in 2009, you can’t really have credibility if you haven’t done it before.

Brad Weimert: It’s amazing. I know where we’re going. So, this is amazing.

John Arrow: Right? And so, let’s do something. And so, back in 2009, there were about 50 apps in the store. Today, there’s I think like close to 4 million or so iOS apps, many, many more Android. We said, “Let’s put something out there that everybody’s going to have fun with.” And we created something called Hang Time, a bit controversial. I know you know the story, but it was something that we’re still pretty proud of, even though it’s embarrassing. We made an app where people could throw their phone up in the air, and an accelerometer would dictate how long the phone was in the air. So, you do a small throw. You catch it. I’ll demonstrate.

Brad Weimert: Nice. Nice catch.

John Arrow: Nice catch. How embarrassing if I dropped it.

Brad Weimert: You almost did.

John Arrow: That was like less than half a second, maybe a quarter of a second. But we did two smart things. One, we were super early, and second, we made a leaderboard so you could compete around the world. We charged $0.99 for this thing. Before we knew it, tens of thousands of people were downloading this. The first revenue that we recognized as a company was people buying this application.

Brad Weimert: Wild.

John Arrow: And the scoreboard started to rise. The times would get higher. The phone needed to survive.

Brad Weimert: In order for it to count?

John Arrow: Right. Because it had to communicate back to the server. And people got pretty creative.

Brad Weimert: How many complaints did you get?

John Arrow: Most were good-natured, but, I mean, there were some people that were legitimately upset, but I think you can’t be upset if you’re throwing your phone up in the air, right?

Brad Weimert: There are a lot of stupid people out there, bro.

John Arrow: Well, the stupidest of the people were like, “I’m going to take this a step higher.” And we said, “Let’s go build the real company. Now, let’s focus on that.” But there was this, I couldn’t help myself. I love checking data and leaderboards. This guy had to score out there over 19 seconds, which is just immensely high. 32 feet per second square, that’s thousands of feet up in the air. I get in touch with them because people, we were talking about this app we built. It was very rare to have an app in the store at the time. And so, people would check our leaderboard. I knew nobody was going to take it seriously if they saw a 19-second score. They would’ve just thought our app was buggy.

Get in touch with this guy, and he’s offended that we accused him of breaking our app and that he cheated. It turns out he’s a skydiver. Yeah, right. So, the name of the game switched from who could throw their phone the highest to who could wait the longest to open their parachute. People kept beating his score, and then Apple pulled it from the Apple store.

Brad Weimert: Now, it’s risk of death.

John Arrow: Now, it’s risk of death. But we did win our first award as a company. It was named by Gizmodo as the second dumbest iPhone app of all time.

Brad Weimert: That was the title. That was close, stupid. It was dumb, not stupid. Yeah.

John Arrow: It was fun like to create something used by many, many people. Didn’t have a positive effect on the world. It may have helped Apple sell some more phones. That’s why we started looking at the high-stakes problems right after that, and we didn’t go back after that. I mean, once you solve something high stakes, you can’t get the type of employees motivated when they’re building, I don’t know, like an EMR.

Brad Weimert: Yeah. I think, though, that it’s an interesting comparison to today in that I think really Gary Vee did a lot to raise awareness to entrepreneurs of the value of attention in the game. And so, did the whole creator economy which arguably could be driven really heavily by him and his presence. The comparison here is there are a lot of people that put a lot of dumb content out, that functionally drives top of funnel for something else, right? Now, I don’t know that it served that purpose for you or not, but there was… I can’t remember who. Man, memory’s killing me today. I had somebody on the show that was talking about this guy in China who basically looks for the trending content and then re-does it.

And it’s not even correlated. It’s bizarre trending content and makes sort of a meme about it, short videos. But the virality is crazy, but they’re totally uncorrelated. They’re just human videos. They’re just interesting, funny human things. But then he sells like a paper towel holder. He just sells widgets, and he does multiple eight figures selling widgets. And his only lead source are these ridiculous videos that have nothing to do with the product. And then like, at the end of the product, he’s like, “Buy my thing.”

John Arrow: Yeah.

Brad Weimert: Yeah. And it’s eyeballs, and like the conversion rate’s low, but it’s enough.

John Arrow: Well, I think people could consistently have a superpower, right? We tried a variety of that, and I kind of count on maybe one hand the time that this viral content worked well for us. The time that it did work really well, and just out of everything we did, probably led to more earned media for the site, was when there was this intersection between it being viral and then the right type of content. So, leading up to the sale or the debut of the iPad, people were immensely excited about the tablet. This was like Apple’s Tesla moment before they shift to full self-driving. People had no idea what it was going to look like and how it was going to change the company. There was immense excitement over the tablet, way more than the first iPhone.

Most people didn’t have the ability to preview it. The only people that Apple was letting preview the iPad was if you were a developer, you had a MacBook Pro, and you had Xcode downloaded on your computer, you could get into a simulator. And you could run and see what the early iPad experience was going to be with, because they wanted to create some apps up there. We made this super viral and expensive solution where anybody could use the iPad remotely. So, we set up a bunch of Mac servers, ran Xcode on them, and you could go to iPadSimulator.com, I think it was the URL at the time, and start to play with an iPad for the first time. To the day we sold Mutual Mobile, I think that was actually the number one way people came to the site was to play with this iPad simulator, and people do really creative things on it.

And that led to… Most of the traffic was completely junk. I mean, people would share it for sure, but your typical person checking out the iPad isn’t going to be an enterprise client of ours. However, one-10,000 people happened to be some key decision maker at a Fortune 1000 company, and we would earn business to make their tablet applications as a result of that.

Brad Weimert: Wow. That’s a trip. Was that the thought process behind it, or were you guys just playing?

John Arrow: We thought it would be a creative way to get traffic, and we didn’t realize how successful it was. And it was probably more expensive than paid search because we had to buy these servers. We thought we bought enough, and then it was too slow, so we had to have a wait list, and then it just became a bad experience. We kept adding on and on to it, and before we know it, we probably had the biggest iPad simulator in existence at the time.

Brad Weimert: Did you get traffic from it early enough? Did you get converting traffic from it early enough that you thought this makes sense to keep maintaining it? Or were you grudgingly holding onto this thing that you had already built, but you had sunk cost, and you had traffic, and you’re like, “Ah, we can’t kill it.”

John Arrow: It was more of a ladder, I would say. We were proud of it. It was an exciting thing that people were talking about, and we said, “Let’s just see where this goes.” We don’t know what’s going to become of it. Yeah, it’s expensive from just a bandwidth standpoint, but let’s just see what happens, and it worked out immensely well.

Brad Weimert: Yeah. How the tech scene in Austin has, I mean, I think Austin’s had interesting waves through COVID and after, through the influx of people from the coasts and then the departure of some of them. I’d say the departure of the good ones, the ones that should leave. But Austin has built a reputation to be a very tech-forward city. Apple, Amazon, Google, Facebook, Oracle, Tesla, I mean, Dell, Pickett, everybody has created a presence here. How did Mutual Mobile being in Austin impact recruiting or publicity, if at all?

John Arrow: We benefited immensely from being in Austin. I split my time now between Austin and New York, and one of the things that I love about this city is how easy it is to get other people to come here. People want to be in Austin, and you have an amazing computer science school at the University of Texas where you can draw from. If you’re a tech company, you have a ton of companies that get sold, bought, go out of business. You can grab engineers and designers from there. So, the fact that we have those resources and the types of people that want to be here is amazing.

A lot of people want to be .in Miami, but they don’t have the tech aspect. A lot of people want to be in New York, but they’re being pulled in a bunch of different directions. Same thing with the Bay Area. So, Austin’s like this Goldilocks spot in my book where you have the right type of talent that wants to be here and they’re not so busy that if you have the next great idea, they’re going to be willing to join you if you make a compelling pitch to them.

Brad Weimert: Yeah, I mean I certainly have appreciated lots of the dynamics around that. Do you think that’ll continue in Austin?

John Arrow: I think it’s going to continue in a lot of unusual places. I think Austin, I wouldn’t bet against Austin. I think its best days are ahead of it. We’re having a renaissance here. One of the most amazing things though about this AI revolution is that you can do this from anywhere. So, I think we’re going to start to see the rise of rural cities and places where people don’t need a huge team. We’re going to see so many new unicorn companies where the team is sub-five. I think that’s going to be a possibility. If you want to live in Austin, that’s great. I want you here. And I remember recruiting here was awesome because people would say, absolutely, I’ll move. But I don’t know if you need to be in Austin anymore, like you used to be in the days at Mobile.

Brad Weimert: Yeah, I think, my hypothesis on that is that for the very few that are extreme self-starters, that are extremely capable of driving, committing to outcome, having their internal accountability, they can live wherever they want.

John Arrow: Well said, yeah.

Brad Weimert: But for the rest of the population that are aspiring entrepreneurs, you better f*cking get around other people because you need that connection, you need that communication, you need that brainstorming, and you need the community. Can you do that online also? Not the same way.

John Arrow: Good point. If you weren’t living in Austin, where would you live?

Brad Weimert: That’s a great question. St. Pete, Florida was on the list before I moved here. San Diego was on the list too. Austin beat St. Pete for a lot of reasons. And San Diego taxes in time zone.

John Arrow: I get it.

Brad Weimert: Yeah. Yeah, taxes speak for themselves, but time zone can’t be ignored back to community and communication, right? Like, if I’m finishing my workday at five or six or seven and I drive home and want to call my folks on the East Coast, it’s 10:30. That gap makes a big difference, those two hours from Austin or from Texas to West Coast.

John Arrow: It’s a real thing. I was just in Marrakesh, Paris, New York, Austin, Vegas in the last six days. And it screws you up. And flying in aviation, we use Zulu time so we always have the same time, which is nice, you don’t have to think.

Brad Weimert: What?

John Arrow: Yep.

Brad Weimert: No.

John Arrow: Yeah.

Brad Weimert: Seriously?

John Arrow: Flying is always the same time. That’s crazy because it’d be just too confusing. You’re going between time zones, so we have to have one coordinated time.

Brad Weimert: That’s amazing.

John Arrow: Yeah. I think we maybe should do that. I mean, people would still sleep, I guess, when it’s dark out, but…

Brad Weimert: The time zones are confusing as hell, especially like the date timeline.

John Arrow: Right. It’s kind of an arbitrary thing where you cross.

Brad Weimert: It feels like it, feels like it…

John Arrow: Imagine we start going to the moon and Mars and you’re going to have to deal with those times.

Brad Weimert: Oh, that’s a really good point. Yeah, I was just in Asia recently for a couple weeks and it just destroys me. I’m a terrible time traveler, but I hope we figure out a better way to do it because I also– rabbit holes. So, you’re a pilot. let’s have a divergent path here for a minute because I’m curious about all things aviation. Namely, there are sort of– not sort of, there are a few different distinct buckets of how you can travel by plane. Obviously, there’s commercial and I think most people, you start flying, coaching commercial at some point, you start flying first class in commercial. Then you look at the private options, and the private aviation options, you can pilot yourself or you can sort of find a different way to fly on a private plane. And those range from getting a seat on a plane.

And there are some of these sort of– JSX is one of these companies that tried to do this model and they have scaled up and scaled down. And then there are chartering planes, there are buying planes, and then there are different types of planes. And then there’s piloting your own and getting something small. Why did you choose to become a pilot? What do you fly? And what are the basic economics of flying that plane?

John Arrow: So, I can never remember a time not wanting to fly our planes. I always was just fascinated. We’d go on family trips to see relatives, and I’d just be amazed that we can go up into the sky. Seemed like magic. And so, I always wanted to do that. Obviously, as a six or seven-year-old, I thought that was a career path. I could be a pilot. People could pay me for money. So glad I didn’t pursue that because I think I would immensely hate it if I was being told where to go and when to go. That would take all the fun out of it. But I did start flying at radio-controlled aircraft at a very early age and then hang gliders, which are pretty dangerous actually, but a lot of fun. And then as soon as I was old enough, I got my pilot’s license.

Brad Weimert: Isn’t hang gliding really dependent on the airstream?

John Arrow: Just basically, you need either a mountain to run off of or you need to be towed up. Those are kind of your two essential options. And so, most of the United States, geographically speaking, you’re kind of far away from either one of those options. So, that was inconvenient. But you’re right, you need to, in order to stay up, you don’t have an engine, so you’re using thermals and ridge lift and all that type of thing. But the journey was such that I knew I wanted to get my pilot’s license and at the University of Texas, I joined the Flying Club. Because of the Flying Club, I met my co-founders for Mutual Mobile.

Brad Weimert: Oh, wow.

John Arrow: So, it’s funny how that serendipity, when you just follow things that you’re naturally interested in, you’re going to meet other people that are very interested in things. I’m so grateful for that experience. Business and aviation have been this constant multiplier. I always have benefits to my businesses because of aviation. It’s a small community. You meet people that are interested in aviation flying. The number of entrepreneurs that are also pilots is immensely, immensely high. And anybody who wants to get into flying just for fun, I mean, the first thing they’ve realized is it’s not the most useful tool in the world when you’re starting out. Like, you have to really want to do it, right? If you think about, you learn and you train on a Cessna, it’s not that much faster. You have to just want to enjoy it. It’s not until you get to faster planes, like a Cirrus would I fly or into the jet world where it starts to become a real time savings thing. So, you have to want to fly.

If you don’t want to fly and you’re just using it as a tool, you’re probably not going to do it often enough to be safe. Like, my plan is to go flying this evening right after this because I need to do my night currency to stay current. From an economics standpoint though, it’s very, very reasonable. If you’re flying yourself, that’s probably the cheapest way to do private aviation is to fly yourself.

Brad Weimert: If you’re flying a smaller plane.

John Arrow: If you’re flying a smaller plane. If you’re flying a larger plane, it’s still going to be cheaper to do it yourself than it is to own a plane and have a crew and a team. If you really want the ability to go anywhere in the world on a whim, you’re going to need a whole flight team. And it’s just prohibitively crazy. There’s very few people in the world who have a flight team. You need multiple airplanes because one of them is going to be down for maintenance. But if you think about a Cirrus, yeah, flying with Cirrus, the SR22 that I fly, it’s very comparable, especially if you have four people to like first-class tickets, probably a little bit less.

Brad Weimert: Well, so this is, I got to break these things down. So, one of the ways I can rationalize this conversation, the people listening actually would benefit from hearing the breakdown, but the breakdown is sort of deceptive and confusing, I think, without actually hearing all the pieces. So, you can say, yeah, for four people, it’s comparable to first class. So, let’s talk, how big is the plane? How far does it go? And then the way that these things typically are measured are like cost per flight hour for the plane, right? And usually, the cost per flight hour, I shouldn’t say usually, that can either embed things like maintenance and you amortize that over the cost of the year, or you look at that as an independent line item. And same thing with the pilot.

John Arrow: Right. If you think about Cirrus SR22 that I fly, I mean, this is a plane, amazing plane. It’s got a great track record. You can buy them from anywhere from about $300,000 to over a million now, depending on the age you get in the feature set that you get for an SR22. So, you have obviously the capital cost, opportunity cost of the money. Then the next biggest cost you’re going to have is fixed. And the fixed costs are every year, a plane needs to basically have an annual exam where they take it completely apart and put it back together. And there’s time life parts where even if you haven’t flown it at all, if it’s a certain age, like the parachute in the plane, you need to repack it. And so, those one-off events can easily cost $30,000 or $40,000 a year, but those are more rare.

So, I’d say like as a budget from someone who wants to get the type of plane that I want to fly, I would figure probably about $40,000 a year and $40,000 to $50,000 a year in fixed expenses. And then you use maybe a thousand dollars an hour variable. That’s how I would think about it. And the fixed expenses are going to include everything from hangar costs to annual fixed maintenance, to insurance, to training, to updating the avionic charts in the plane.

Brad Weimert: Okay. So, let’s go high end on this. So, let’s say you spend a million dollars on a plane and you put 20% down on it and you get a loan for the other 80. And so, you’re in that for four grand a month or something, something like that.

John Arrow: That’s right, yeah.

Brad Weimert: And then you’re 40 grand overhead, so now you’re seven grand a month to hold, carry the plane just to keep it. If you’re flying, any given flight that you’re on, we’re in Austin, so let’s say it’s two and a half hours because that’s how long it takes to get anywhere from Austin. I guess it might take longer. Let’s talk about that in a second. But let’s say a round trip is six, seven hours. So, now, if you fly once a month, you’re seven grand per flight and you could put four or five people on the plane. Is that the size?

John Arrow: Yep.

Brad Weimert: So, if you split it, then that makes sense. If you’re flying solo, then it’s seven grand or so each time you take the plane, wherever you want to take it.

John Arrow: Right. You’re really not going to come out ahead unless you want to enjoy doing it. It’s always going to be quicker.

Brad Weimert: Yeah. I don’t even think. So, one of the reasons that I want to highlight is because, I think it depends where you are when you say come out ahead, right? So, if your goal is to not deal with the airport ever, have flexibility of time when you can come and go, right, be able to land in smaller airports, those are all big benefits.

John Arrow: They’re huge benefits. And if you get joy out of the journey too.

Brad Weimert: That’s a whole ‘nother one.

John Arrow: It makes the road trip way more fun when you’re flying. I mean, I can go to New York, I’m stopping once in Nashville for fuel, but it’s a fun flight. It’s going to take you longer than commercial. It’s going to be like six, seven hours. But it’s a fun trip and you can go on your schedule, the plane won’t leave without you, which counts for a lot. It’s just a fun thing to do.

Brad Weimert: So, what’s the range on that plane? And yeah.

John Arrow: It’s about a thousand nautical miles with the reserve. And so, that means, wind is a huge, huge thing. If you’re going…

Brad Weimert: A small plane.

John Arrow: In any plane, actually, because if you’re going east, you get the tailwind, which is really nice. You get to add whatever the speed is to the plane. But if you’re going west, they call that going around the world, the hard way, you take it away. So, east trips are always going to be way, way faster than west trips.

Brad Weimert: Interesting. So, I learned this the other day. I didn’t know this actually. I was talking to one of the robots and they were explaining to me the difference between nautical miles and the rest of the world miles, which is bizarre. And so, nautical miles are 6,060 feet, something like that.

John Arrow: Right. It’s a minute of latitude.

Brad Weimert: Yes, a minute of latitude, which equates to 6,060 feet or something, very close to that, versus the 5,280 by land.

John Arrow: For statute, yeah.

Brad Weimert: Bizarre. It is.

John Arrow: That’s how we measure things in knot still.

Brad Weimert: It’s so bizarre.

John Arrow: But as fun as the small planes are, I love them. I think most people, if you have some time, it works really well for getting around the country, the United States. I’ve taken mine out overseas, I’ve taken it a lot of different places, but it’s about the journey. If you want utility, then you got to go up into the turbine world where there’s jets and then the cost increases by order of magnitude.

Brad Weimert: A lot, yeah. So, the other thing that I think is a good takeaway for entrepreneurs that are thinking about this stuff is you can appreciate a huge chunk of the plane on the front end. And so, you can actually take, and I don’t know, are they 100% depreciation this year?

John Arrow: I think they renewed it. Yeah, it expired and they renewed it. So, you can take 100% of depreciation. It is an area that’s very kind of looked at, so you have to– I’d recommend getting professional to help you figure out, and it’s only for the portion of the flights to use that are related to the business. So, if you go and take family somewhere and there’s not a business for a reason, you can’t count that.

Brad Weimert: Right, right. But I think if more than 50% of the cost of the plane itself is used for business, then the cost of the plane itself can be depreciated entirely.

John Arrow: I believe that’s right.

Brad Weimert: Yeah. So, if you spend a million dollars on a plane and you had a million dollars of earned income, then effectively you have zero earned income for that year.

John Arrow: Right. And that’s pretty wild. I mean, that’s a huge, huge incentive. It’s funny, we always try to look for business. I love aviation. Anybody in the aviation world tries to figure out how to make it from a business standpoint have a positive ROI. And it’s really difficult when you add in airplanes because we like them a lot, right? There’s a saying that if you’re starting an airline, the way to become a millionaire starting an airline is to start out as a billionaire.

Brad Weimert: Yeah, right.

John Arrow: And even Warren Buffett has lost tons of money because of airplanes. You look at most of his investments, there’s sound, he did diligence. NetJets is an example though, where he was buying planes over market from Textron and it was just this horrible situation that was losing tons of money. And so, I think you have to look at something like airplanes as more of an entertainment thing rather than a sound business investment.

Brad Weimert: Yeah. Well, that’s actually why I want to talk about it, because if you look at your plane, your use case, is the Cirrus a four-seater?

John Arrow: This one’s a four-seater. They have a five-seater version. It’s the same airframe essentially, but they crammed another seat back there.

Brad Weimert: Yeah. And then there are other ones that are similar that will hold six, seven, eight that are turboprops. And I can’t remember the brands off the top.

John Arrow: TBM and Pilatus.

Brad Weimert: Yeah, TBM and Pilatus are the ones that I looked at actually, also. So, those are the turboprops that also have more range. Maybe like 1,500 nautical, something like that.

John Arrow: Yeah, some of them can go almost 2,000.

Brad Weimert: So, you go to 2,000 nautical miles and then basically from Austin, from the center of the country, you can get to essentially anywhere in the country without stopping for gas. And they fly at like 300 to 350 miles an hour-ish, which is pretty good.

John Arrow: And above the weather too. That’s the other thing. They go much higher than the piston planes. Turbine planes can go above the weather. They can get better routing too, as a result of being up high.

Brad Weimert: Interesting. And so, then you step up the next level and then you’re looking at sort of small/mid-sized jets. And those are just, you go from a thousand bucks an hour to operate to 3, 4, 6, 7.

John Arrow: Right. The fixed costs go up immensely. I would say even more than the variable cost, some of these planes, and it’s the type of thing that the plane stops being– the worth of the plane is the engines. There’s a saying that when you’re buying a jet, they give you the plane for free when you buy the engines. It’s all about the time that you have left. If you’re ever browsing on eBay or any of the sites like Controller where you can look at airplanes and you see a really good priced jet, it’s because the engines are timed out and that plane’s probably not worth anything.

Brad Weimert: Timed out, meaning that they have a defined lifespan by the government.

John Arrow: By the FAA, works with the manufacturer to create a time called TBO time before overhaul. And unlike piston planes where you can fly it beyond that time with a jet engine, you have to replace it then. It’s required. That plane becomes unairworthy. The only people are going to be flying, it is some banana republic country that doesn’t have like an FAA equivalent.

Brad Weimert: And the reason that you need to overhaul it is because it’s not safe anymore.

John Arrow: Exactly. That’s the most important part of this. And it’s determined, these things wear out and they’re extremely expensive to replace or overhaul. On a typical jet like a CJ3 or something, the engines might be $2.5, $3 million each.

Brad Weimert: Yeah. And so, the fixed cost for a small jet versus a prop plane, you go from 40 grand on a prop plane to what? 250 to 350 in a jet, small jet?

John Arrow: I think that’s reasonable. And then, if you’re not flying it yourself, you’re going to need a staff too, right?

Brad Weimert: And some of those are two pilots.

John Arrow: Nice thing is most of the small jets, you can get a waiver to do single pilot on.

Brad Weimert: Oh, okay.

John Arrow: If you’re flying what’s called Part 91, so not as a charter jet, but just as your own jet, and you can fly them with one pilot. So, there’s a plane I’m looking at called a Citation 510 Mustang, where it’s a six-seat small jet and you can fly it completely by yourself.

Brad Weimert: Yeah. And then if you’re hiring a pilot, you can count on maybe, what, per trip?

John Arrow: It depends on the type plane. Obviously, the bigger the plane, the more expensive the crew gets, but figure about a thousand dollars a day plus expenses.

Brad Weimert: And so, then if you different then, and this is why I like breaking down this math because for anybody that’s, for the 7% of the audience that’s considering a plane, these are details you don’t deal with when you’re doing commercial or when you’re flying yourself. But if you take your plane somewhere and you’re there for three days, you’re paying for the pilot to be there for three days or you’re flying them home and bringing them back, right? Like, there’s a cost to these things, which is also why planes are f*cking expensive.

John Arrow: It is. Yeah, we just took a plane to Vegas and that crew that didn’t leave, they had hotels in Vegas too. We didn’t see them on the casino floor, but they were there, yeah.

Brad Weimert: Probably for the best, depending on their role on the plane.

John Arrow: Probably for the best. But the crazy thing is, unless you really enjoy having a plane, I recommend chartering. I think that makes just so much more sense for most people. Unless you want to fly yourself, you want to fly yourself, then by all means, down that path.

Brad Weimert: Yeah. There’s some, like the frameworks that I’ve heard for when it makes sense to buy a plane or when it makes sense to charter versus buy are basically based on flight hours per year. And so, until you’re flying something like 150 hours a year, you shouldn’t even think about buying it.

John Arrow: I think, unless it brings you joy. Unless it brings you joy by having it and knowing that you can go. But if it’s a piston plane, hopefully, it’s not going to move the needle that much. But you’re right. If it’s a jet plane, you need to be putting that into a charter fleet. The opportunity cost is too high if you’re only flying 150 hours a year. I mean, everybody who has a jet puts it in the charter. It’s very rare not to, unless you’re like Mark Cuban or something.

Brad Weimert: Yeah, yeah, yeah, unless you have way too much money. I don’t know what that number is, but yeah, okay, well, that’s interesting, man. How often do you fly?

John Arrow: I try to fly a few times a month, my plane, just to stay proficient. I think it’s one of those things that brings me joy. I’m extremely present when I’m doing it. I’m not multitasking and I love that feeling of just having to be there focused on the task at hand, not texting. With self-driving cars, we’re not going to get that anymore. It’s going to probably be only flying where you get that moment where you have to be really in the zone or on like a motorcycle or something.

Brad Weimert: Yeah, that’s true. Well, I love the winding road here. Tell me about selling Mutual Mobile and kind of the next chapter of life as you see it. So, my understanding is that Mutual Mobile was acquired by Grid Dynamics. Leading up to that, you were pushing $50 million in revenue for one of the years leading up to it. But the story of the actual sale wasn’t detailed out, anywhere that I read it. How did you go about selling it? Did you feel like you got a good number? How did you tee up to sell it? What did that look like?

John Arrow: Well, the most important part of the story is we sold the company twice. So, we bootstrapped the company and we seriously looked at raising, when we were early 20s, we looked at doing our venture round and we grew so fast, we had private equity guys calling us, wanting to put money into the company. We went really far down that path. So, 2009, we launched the company. We did a million dollars, a little over a million that first year. The next year, we did four and a half. The year after that, we did 15. And the year after that, we were flirting with 30. After that, we had all of these companies coming to us, all these firms saying, look, can we put money in?

We thought it was great, but like, yeah, you want to invest in our company and let’s have all these conversations. And then as 22-year-olds, 23-year-olds, we learned about liquidity preferences and how just because you get a good valuation number doesn’t mean that’s going to be good for you. They were offering us arrangements where not only could we put money onto the balance sheet at a really nice valuation, but we could take out secondary, we could take out founder liquidity and…

Brad Weimert: Which means that if you raised money again, you could take money off the table at that point in time?

John Arrow: Right. So, we could take money out at that round. They would basically say…

Brad Weimert: Oh, got it, in that round.

John Arrow: They would write us a check, personally, buy our shares. We would still get diluted because they’d be putting money onto the cap table too. But that seemed like a great deal. And then we started thinking about this more and there were these liquidity preferences. We’d have to return a multiple of the capital. And at that point, it’s like, wait a second, if we want to sell this company, if our goal, and we started the company with kind of the goal, or at least I did, I want to make it so I can create something where I never have to work again if I don’t want to.

Brad Weimert: Can you break down the liquidity preferences for me?

John Arrow: It was something like a million dollars for each of the early shareholders that were selling who wanted to take money off the table, and then the rest of it would be going onto the balance sheet. And I think that term sheet that we seriously considered was at maybe like a $75 million valuation. So, it was very little dilution, but the liquidity preference was pushed in three. And so, we were looking at a situation where if we wanted to basically take money off again, you need to sell it like a $200 million valuation.

Brad Weimert: Oh, got it.

John Arrow: Right. Which like, okay, yeah, we’re really ambitious, we believe in this, but we believe that we’re going to have the CAGR and the growth to support 20% of a billion dollars valuation. Maybe, maybe not. And we had some disagreement as a team around that and what we wanted to do. And ultimately, we passed on that deal. It was great. It was with a great private equity firm called FTV out of San Diego, I believe, passed on that. And then we said like, what do we want to do? And I said, I want to sell the company. Like, it’s insane, if we’re getting these types of valuations right now in our line of business, we were getting valuations that were 2x revenue, which is kind of insane for business like ours.

Brad Weimert: Yes. And also, unless you actually know your type of business and the expected valuations, nobody knows.

John Arrow: Like, 99% of the people hear that and they’re like, I don’t know. It’s hard to say. And I think that’s one of the things you go through as an entrepreneur. You go through these days where, how my business is worth billions of dollars or my business is worth zero. And you have this oscillations of emotions, this vacillation, and you go back and forth really, really quickly. And so, we were having an existential discussion at the time as founders of should we keep growing or should we sell? And I was kind of vehemently in the camp of let’s sell. And two of my co-founders were like, no, we should keep building and growing.

And I think what was amazing about that is we found a consensus, like, let’s run a process and whoever wants to sell can sell. We’ll do a minority deal. We won’t sell the whole thing, so we’ll run a process. And we hired a bank, an investment bank. They were great. And we started getting in all these term sheets from a ton of different types of buyers. Most of the buyers insisted on buying the whole enchilada.

But there was one organization, WPP, which is a giant advertising conglomerate on Madison Avenue. They employed 300,000 people. they said, we’ll give you a minority deal if you want it. We’re happy to. We had a term sheet for well over, about $100 million where they would’ve bought the whole enchilada from SAP. We passed on that. We went with WPP at a slightly lower valuation where it allowed every founder that wanted to sell to sell. And the thought process was, okay, I’m going to sell the most because I want to sell the company. And then my two co-founders were going to run the organization after that, I’d stay on as chairman.

And so, we did that deal, and that was probably the best decision that I’ve ever made, was taking a fair amount of chips off the table in that junction. I stayed on as chairman. but I left a CEO and, and we had two co-CEOs for a while. I think when you’re running a company like that, you believe that there is no limit to what you can do. And right around that time after we sold, technology development in our space started commoditizing a lot. We were facing wars on multiple fronts and so, our revenue didn’t continue that…

Brad Weimert: Trajectory.

John Arrow: Right. And it was insane. It like looked like a problem up until that point. After we sold, and I think partly because we sold, there wasn’t the same emphasis, the same urgency. Before that, we had 99.9% of our net worth kind of, yeah, in this company. We had companies before this, but this was something much, much greater, much more grand. But after that kind of comfort, got into our bank account, we ended up having this less urgency because everybody sold a little bit. Everybody was a millionaire after that even though we sold different amounts.

And so, I left and stayed on as chairman. And as the company kind of continued running but leveled out from a revenue standpoint, the valuations also got tighter in the market. So, if we wanted to sell again, we wouldn’t be able to sell at kind of the same multiple number. Five or six years go by and just life happens and the board asked me to come back as CEO. I said, I’ll come back, happy to come back, but I’m only going to come back on the condition that we’re going to sell the rest of the company. And at that time, everybody was in alignment. So, I came back. We fixed things up. We got growth happening to a degree, and then talked to some banks. I’m like, I know how to do this. We’re not going to pay them their commission this time. And so, I ran the process as well…

Brad Weimert: Oh, damn. Really?

John Arrow: Second time around. And we sold to Grid Dynamics out of the Bay Area.

Brad Weimert: Got it. I mean I have a couple questions here. Had you not run the process yourself, do you think you would’ve gotten more suitors or a better deal?

John Arrow: Well, it’s funny, I got to look down that can a little bit because we did initially retain a bank. And the reason I wanted to retain a bank is it kind of gave conviction to me that our board was aligned with doing that because you have to commit to a tail when you hire an investment bank. But even if you fire the investment bank, there’s 18 months where they still collect the commission, kind of like a realtor.

Brad Weimert: Oh, wow.

John Arrow: And so, we hired the bank. The market wasn’t right. It wasn’t great timing to sell. We weren’t getting the types of term sheets that we wanted to. I said, let’s go back, let’s work on the business. So, we terminated that relationship. We waited 18 months and then we restarted the process. I was back longer than I expected. And we restarted the process and I was able to– I think when you’re invested in it, it’s your baby. You’re kind of able to solicit even more interest than some Wall Street firm that is divided across half dozen clients, and so…

Brad Weimert: Yeah, I think that those questions are always interesting, when to do it yourself and when to hire out expertise and when to take the time to learn the expertise from those you hired so you can do it yourself the next time.

John Arrow: Exactly.

Brad Weimert: Or when you just always hire that expertise. And realtors may be an after comparison, probably not because they offer very little value in the marketplace in general, but maybe the best ones offer some and you can learn from them and then you know what you’re doing.

John Arrow: Yeah. I would say it’s on probably par of the amount of value that investment bankers offer too.

Brad Weimert: Oh, is it really?

John Arrow: The good ones made me go really well far and beyond, like the good realtors. I’ve worked with both kinds for different companies along the way. The first firm we worked on was exceptional. But I will say that the organization that matters the most when you’re selling a company is the law firm that you work with. I think that’s even more important because they’re driving the T’s and C’s, right? And that’s bigger in most cases in the economics that are on the LOI on the term sheet is how does this play out? Because any company that’s selling itself almost surely will have some type of earnout now. And making sure that you hit and maximize that earnout is so, so key. I don’t think I’ve ever heard of an earnout where litigation isn’t threatened later on or like air happened. I think we were really fortunate our earnout, we avoided anything on that. But there’s always a lot of contention around earnouts and around how you calculate things like networking capital after the transaction. And a good law firm will guide you through that, whereas a good bank kind of just wants the deal to get done and doesn’t care after that.

Brad Weimert: Yep. I think that’s great advice, man. And I think to close that comparison, a great realtor will look at the terms in addition to the money and not just try to get it done so they can get their commission.

John Arrow: That’s a great point, yeah.

Brad Weimert: But all negotiations are, if negotiations were just priced, then you would get really sh*tty things or really great things for that price. No, the terms really, really, really matter, right? No, it’s half the equation, if not more.

John Arrow: And one of the things that we learned along that way about the terms is that the first negotiation is negotiation you’re doing with an investment bank. If you are hiring a bank, the terms matter so, so much. My co-founder, Tarun, did just such a wonderful job. Once we found the bank, there were some terms in there that were not very advantageous to us and he pushed very hard on them. And we got a better, better arrangement as a result of that. So, it’s kind of like when you’re working with a realtor, you don’t have to just take their paper and say, these are the terms. Everything’s negotiable, even if it’s written in caps, right. And they don’t send you the DocuSign. The PDF is not what you should be signing.

Brad Weimert: Yeah. No, I couldn’t agree more. I think that’s one of the beautiful lessons from world travel is that everything is negotiable at any store or place. But the other is great lessons from entrepreneurship, like we’re all just making sh*t up. Yeah, absolutely. Just because you stamp it out, put it in a DocuSign or put it in a PDF or it’s hard coded, none of that matters. Somebody made the decision, we can still talk about it.

John Arrow: Worst-case scenario, they say, no, we can’t move on that.

Brad Weimert: I love that, man. Well, I know we’re coming to time here. What other fun sh*t can we talk about? No. What advice do you have? We have a lot of fun things we could talk about. What advice do you have for new entrepreneurs right now?

John Arrow: This is the single best time in the history of humanity to be an entrepreneur. I say that with complete conviction. I think it went from two and a half, three years ago to being one of the worst times to one of the best times because three years ago, really hard to get engineers. It was just super tight and everyone was hiring. You had to compete against Meta, you had to compete against Google for people. And now, if you’re a first-time entrepreneur, you don’t need engineers. You don’t need designers. If you’re non-technical, you don’t have to go find a technical co-founder.

These agents, these tools like Cursor and Gemini 3 right now, which is amazing, you can just go do things on your own and try it out. And I just think that this is the most exciting time in humanity because you can think of a product, build it the next day, and launch it day after that, and possibly have revenue going through. I mean, I do that all the time and test the ideas now. I have a team. I’ve hired over a thousand engineers and I used to always go back to them, and for really key mission critical things, I’ll go back and I’ll find those people. But now, more often than not, I’ll just test the idea out myself because I’m not an engineer. And now I can kind of ship things and launch things without needing to be dependent on somebody else. And not only is it cheaper, it’s faster and there’s no room for something getting lost in translation. And I do this all the time, just playing around. It’s a game. It’s fun.

Brad Weimert: Yes. What tools do you use to do that? Right now, it’s the end of 2025, so this is going to be a bit that you’re going to have to cross reference if you’re listening to this in ’27, but…

John Arrow: And I would give a disclaimer, if I have no interest in the companies I’m recommending here, but v0, I love a lot. Right now, Vercel owns them, I believe, v0.app. It’s just the easiest tool for creating a user interface that you’ve ever seen. It does some of the backend and middleware stuff too, but you can completely go to shipping just with v0 and probably ChatGPT. That’s one of the tools that I use the most. I think it’s even easier than Cursor.

Brad Weimert: Amazing. I love it. John Arrow, man, it’s great to pick your brain and hear some stories.

John Arrow: Awesome. Well, hey, thanks a lot for having me, Brad.

Brad Weimert: Definitely, man,

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Here are a few photos.

I spoke about my investment firm, Age Of AI, as well as FreedomGPT.

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Just got a chance to test fly this VTOL. By far the easiest aircraft I've piloted. It was quite windy and you can see when I removed my hands from the controls at the end it 'auto' hovered pic.twitter.com/cAVFhUVSws — John Arrow (@JohnDavidArrow) July 13, 2025

121 years after the Wright Brothers’ first flight, the Arrow Brothers are test‑flying VTOL aircrafts.

About LIFT Aircraft

Austin‑based LIFT Aircraft builds HEXA and runs flight‑experience centers where anyone can book a 30‑minute session and feel the thrill of vertical flight.

From the Stone Age to the Information Age, there’s been a feedback loop of innovation that’s given man increasingly greater power over their world and themselves.

The velocity of technological advancement through the Ages has always been super linear, but our own biology and fragility has throttled the progression.

Beyond Human Limitations

Technological progression is like a ladder that’s required the best & brightest of humanity to hold it upright so all can ascend at their own pace.

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We invest in companies that will define the age of Artificial Intelligence.

Portfolio

These companies represent the diverse applications of AI across safety, accessibility, entertainment, and wellness – each pushing the boundaries of what’s possible as we enter this new technological age.

• Anthropic PBC: an AI safety and research company. They build reliable, interpretable, and steerable AI systems. Most recently their launch of Claude has garnered international recognition as the most steerable LLM.
• FreedomGPT: the ‘app store’ for AI making it easy anyone to access powerful multi modal artificial intelligence technologies on the desktop or in the cloud. The browser version of FreedomGPT currently has 40 live AI models with plans to expand to +4000 next year.
• Gel Blaster: a mixed reality platform gaming company incorporating IRL physical activity, augmented reality, and AI. Gelblaster has sold $70 million worth of Gelblasters and has the potential to be first console gaming platform where being sedentary isn’t an option.
• Throne Science: turn your toilet into a wellness hub for tracking hydration and gut health. Empower your health with machine learning between doctor visits.

Conclusion

I discuss my journey building AI companies in more depth in my Beyond A Million Podcast interview.

The Age of AI is different.

While the best and brightest are still the catalyzing spotters, the innovation will begin to accelerate much faster and higher than any man can climb.

If you are an entrepreneur focusing on AI please reach out to us at contact@AgeOfAI.capital

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As shown in the image, FreedomGPT offers a clear and interactive way to participate.

When you request an image (like "Give me a picture of a dog"), the platform can present you with outputs from two different AI models, labeled as 'Answer A' and 'Answer B'.

You then get to be the judge: Is the left image better? Is the right one superior? Or perhaps neither meets your expectations. By simply clicking your preference ("<- Left is Better", "Right is Better ->", or "Both are bad"), you're casting a direct vote.

This user-driven feedback is vital. It helps FreedomGPT rank the performance of various image generation AIs, refining its ability to recommend the top-performing model for specific creative prompts and styles.

Each vote contributes to a transparent leaderboard, showcasing which AIs are currently favored by the community.

Getting Started

Participating is as simple as generating an image.

1. Head over to FreedomGPT.
2. Enter a prompt for an image you'd like to create.
3. You'll then see different AI-generated images based on your prompt.
4. Evaluate the options and click to vote for your preferred image, just like in the example. It’s an intuitive process designed to gather your valuable opinion with minimal effort. Keep an eye on the FreedomGPT leaderboards to see how your choices, and those of the community, influence the rankings of the best AI image models.

Want to explore more about what makes FreedomGPT different? I wrote about our approach in One Prompt, Many Perspectives.

Conclusion

This is your opportunity to actively participate in the evolution of AI image generation.

FreedomGPT is inviting you to not just use AI, but to help curate and identify the very best tools available.

Your preferences will guide other users and developers alike.

Visit our website to unleash your creativity, generate some images on FreedomGPT, and cast your critical vote.

Let the world know what you think defines the best AI image model!

There’s something truly special about the Texan landscape in the spring – the wildflowers blooming and the vast, open skies.

Part of our family tradition on the ranch involves some time spent at our private shooting range, a pastime that combines skill, focus, and a deep respect for safety.

Shooting a few rounds

As you can see in the photo, I support ear protection!

Earmuffs

Shooting firearms, whether it's a small caliber pistol or a larger rifle, produces incredibly loud sounds.

These aren't just startling; they are powerful enough to cause immediate and, crucially, permanent hearing damage.

To put it in perspective, a gunshot can easily exceed 140 decibels (dB). Sounds above 85 dB can begin to cause damage over time, but impact noises like gunshots can harm your hearing in an instant.

Wearing proper ear protection, such as earplugs or earmuffs, isn't just a suggestion; it's an essential practice for anyone around firearms in action. It’s a fundamental part of responsible shooting.

Ear Protection Options

When it comes to protecting your hearing on the range, you have several good options:

Earplugs: These are inserted into the ear canal.

• Foam earplugs: These are disposable, inexpensive, and widely available. When inserted correctly, they expand to create a snug fit and can offer significant noise reduction.
• Silicone/Pre-molded earplugs: These are often reusable and can provide a consistent level of protection. Some people find them more comfortable than foam.
• Custom-molded earplugs: These are made from an impression of your ear canal, offering a perfect fit for maximum comfort and protection.

Earmuffs (Passive): These cup entirely over the outer ear, creating a seal against the head to block out sound. They generally offer a good Noise Reduction Rating (NRR).

Electronic Earmuffs: This is what I’m often wearing, and they are a fantastic piece of technology.

Suppressors: While not worn by the individual, suppressors (often called silencers) attach to the firearm's muzzle to reduce the overall noise of the gunshot at the source, benefiting everyone in the vicinity.

Doubling Up: For maximum protection, especially at indoor ranges or when shooting particularly high-caliber firearms, it's common and highly recommended to wear earplugs underneath a pair of earmuffs.

Gun Safety

While ear protection is a critical component of shooting safety, it’s part of a larger set of principles for responsible gun ownership and handling.

Here are a few of the cardinal rules that should always be followed:

1. Treat All Guns as if They Are Always Loaded: This is the most fundamental rule. Never assume a firearm is unloaded.
2. Keep the Muzzle Pointed in a Safe Direction: A safe direction is one where an accidental discharge would cause no injury or damage.
3. Keep Your Finger Off the Trigger Until Your Sights Are on the Target and You Are Ready to Shoot: Rest your finger outside the trigger guard until you’ve made the decision to fire.
4. Know Your Target and What Is Beyond It: Be absolutely sure of your target, and be aware of what lies in front of, behind, and around it. Bullets can travel a considerable distance.
5. Know How to Properly Operate Your Firearm: Understand its mechanical characteristics, including how to safely load, unload, and clear malfunctions.
6. Store Firearms Securely: Guns should be stored unloaded and locked, inaccessible to unauthorized individuals, with ammunition stored separately.
7. Always Wear Eye Protection: Just as important as ear protection, safety glasses protect your eyes from ejected casings, powder residue, and other potential hazards.

I love spending time in unique places. Check out my adventures to Easter Island and Antarctica.

Conclusion

My Easter weekend on the ranch was a fantastic blend of family time, appreciating the outdoors, and engaging in activities that require diligence and respect.

Taking the proper safety precautions, like consistently using ear and eye protection and adhering to all gun safety rules, ensures that these traditions can be enjoyed responsibly and create lasting, positive memories for years to come. It’s about ensuring that every experience with firearms is a safe one.

Here’s the image that they created:

My company FreedomGPT was just featured on Coinbase’s infographic. Click here if you want to read the whole article.

FreedomGPT was included in the NLP-based interfaces section, along with:

• Slate
• venice
• SUPERSIGHT
• VELDT
• enqAI

The other categories for the Applications sector included:

• Identity / Proof of Personhood
• Governance
• Trading / DeFi
• Gaming
• Social
• AI Companions
• Dev / Security Tools
• Risk Agents
• Frontier Tech

Thanks Coinbase for including us.

About FreedomGPT

FreedomGPT is a AI mega-bundle that gives you access to 250+ of the leading AI services for just $5/mo.

FreedomGPT's AI Catalog offers leading AIs from the top AI companies that are proprietary like OpenAI ChatGPT, Anthropic Claude, Google Gemini, Facebook Llama, xAI's Grok and more.

We also include access to "uncensored" and 100% Private and Free AIs like Liberty. FreedomGPT includes AIs across multiple modalities like Research, Image Generation, Video Generation, Phone Call AI, Coding, Mathematics and More.

Click here to visit our website.

Interested in learning more about the crypto x AI intersection? I discussed this at length in my De University of Ethereum interview.

It was the very first integrated test flight of the full stack, lifting off on April 20, 2023.

I traveled down to South Texas just for this. The launch site, called Starbase, is right near Boca Chica Beach. SpaceX is building this giant rocket there.

It's hard to grasp just how big Starship is – when stacked, the booster and ship stand taller than the Statue of Liberty, One thing that makes Starship look different is that it's built primarily out of stainless steel.

A key goal for SpaceX is making both the Super Heavy booster and the Starship spacecraft fully reusable, able to land back on Earth and fly again.

This first flight was a huge test for the whole system – the Super Heavy booster and the Starship spacecraft combined.

Seeing that massive rocket actually leave the launch pad was truly unforgettable. The Super Heavy booster alone has 33 powerful Raptor engines grouped together at its base. The power was incredible!

That first launch attempt made Starship the most powerful rocket ever to lift off from Earth.

I definitely can’t wait to see another Starship launch hopefully soon! Let me know if you are planning to go see one too.

I love exploring cutting-edge technology. Read about my experience flying a VTOL aircraft.

My curiosity about alternative financial systems began years before Bitcoin’s 2008 white paper, as I searched for solutions beyond traditional banking.

When I launched my first company in high school, I ran into a major obstacle: how to efficiently pay engineers I’d hired in Russia and India without excessive fees or delays.

In the early 2000s, international money transfers were slow, expensive, and riddled with bureaucratic hurdles.

Discovering eGold: A Glimpse into the Future

I initially turned to eGold, a digital payment system that allowed transactions backed by gold.

While revolutionary for its time, this platform revealed both the potential and limitations of digital currencies:

• Instant global transactions (when they worked)
• Asset-backed digital currency
• Regulatory challenges
• System stability issues

Traditional banks weren’t an option either. At 15, I had no easy way to send rubles or rupees internationally without running into account restrictions and high transfer fees.

The US banking system wasn't much help either, lacking any mechanism for a 15-year-old to easily send rubles and rupees.

Bitcoin Discovery

Years later, after founding Mutual Mobile, I came across Bitcoin for the first time. The concept of decentralized, borderless transactions immediately resonated with me.

Here was potentially a more elegant solution for international funds transfers, not to mention a more durable currency than anything issued by a state.

In 2011, I bought my first Bitcoin through MT.GOX. The process was anything but smooth, requiring multiple steps and significant patience.

Fortunately, I managed to withdraw my BTC before the exchange collapsed.

After MT.GOX's collapse, I was amazed at how difficult it still remained for the public to acquire Bitcoin.

Trading

In 2013, I contemplated starting an ETF to provide broader access for those wanting exposure to the crypto asset.

During my research, though, I discovered something interesting – a publicly traded Bitcoin Investment Trust called GBTC.

I was shocked to learn this fund was trading at a 100% premium to the underlying spot Bitcoin price.

That's when I had an idea: why not arbitrage the premium? After reading through the several-hundred-page GBTC prospectus, I decided to short GBTC while going long on spot bitcoin.

The arbitrage strategy is to capitalize on the price difference between GBTC and spot bitcoin.

When the premium on GBTC is high (i.e., GBTC trades at a higher price than the underlying bitcoin), you short GBTC while buying the actual bitcoin, expecting that the price of GBTC will eventually decrease, bringing it closer to the spot price of bitcoin.

In short, it's a way to profit from the discrepancy between the market price of GBTC and the actual price of Bitcoin by taking opposite positions in both.

This strategy effectively allowed me to accumulate additional Bitcoin for free once the premium on GBTC inevitably eroded.

Advanced Trading Strategies (2013-2015)

The cryptocurrency market's maturation brought new opportunities for sophisticated trading strategies.

After this landmark trade, my interest in crypto only intensified.

Beyond investing in a prominent crypto hedge fund, I became fascinated with the concept of using crypto to incentivize useful work.

It was exactly this spark that helped give rise to my creation of the Freedom Network Token, which now helps facilitate decentralized inference for FreedomGPT.

The Future of Decentralized Finance

The journey from eGold to Bitcoin, to trading, and now to building decentralized applications has been a whirlwind.

But what excites me most is the future—how decentralized finance continues to evolve, offering solutions beyond what we ever imagined.

If the last two decades have taught me anything, it’s that financial innovation never stands still.

And just like in the early 2000s, the biggest opportunities are often hidden in plain sight, waiting for those who dare to look beyond the status quo.

We understand that most of our global community uses Android, which is why our team has worked tirelessly to make this release possible.

• Download FreedomGPT from the Playstore here.

100 AI Models

We've created something special here.

Our team has integrated nearly 100 AI models into the app, but what truly excites us is how we've made it incredibly user-friendly.

We developed an innovative "auto mode" feature that automatically selects the optimal AI model for each query.

Our vision was clear: bring enterprise-level AI capabilities to everyone, without requiring technical expertise.

Enhanced Functionality

For months, we've watched our users access FreedomGPT through their mobile browsers.

While that worked, we knew we could deliver something better.

Our native Android app represents a significant leap forward in user experience.

We've particularly focused on enhancing multimodal interactions – sharing photos for AI analysis is now seamlessly integrated into the experience.

Future Development Plans

The development team isn't stopping here.

We've already outlined an exciting roadmap of upcoming features, including a text-to-video synthesizer that rivals industry leaders like OpenAI's Sora.

While this feature is currently available in our browser version, we're actively working to bring it to our mobile platforms.

Conclusion

The Android launch marks a crucial milestone for FreedomGPT, bringing advanced AI capabilities to the global Android community.

The launch represents not just an expansion of FreedomGPT's reach, but a step forward in making sophisticated AI technology accessible to everyone.

Thank you to our incredible community for your patience and support. We're just getting started, and we can't wait to show you what's next.

• Download FreedomGPT from the Playstore here.

FreedomGPT lets you use all the most popular AI models (including censor-free models) in one place.

For example, FreedomGPT's Liberty models will answer any question without censorship, judgment, or 'post-inference bias.' Effortlessly toggle between open-source and proprietary models within a familiar UI.

Why It Was Made

FreedomGPT serves as an "App Store for AI models" streamlining the process for users to discover, assess, and implement advanced cloud and edge AI models.

Historically, many of these open-source models have been challenging for non-technical users to locate and deploy. FreedomGPT revolutionizes this by ensuring that AI is accessible to everyone.

New iOS App

It’s finally here!

We are announcing the official FreedomGPT iPhone App.

Try it out now. Click here to download.

Compatibility

• iPhone: Requires iOS 15.1 or later.
• iPad: Requires iPadOS 15.1 or later.
• iPod Touch: Requires iOS 15.1 or later.
• Mac: Requires macOS 12.0 or later and a Mac with Apple M1 chip or later.
• Apple Vision: Requires visionOS 1.0 or later.

Android user? Check out our Android app announcement.

iPhone Screenshots

iPad Screenshots

I captured some special moments and wanted to share them with you.

Here are my top three.

Standing with Sentinels

The tallest moai ever erected is 33 feet high and weighs as much as 8 elephants.

Every single one of the 887 known moai was carved from the same quarry at Rano Raraku.

Standing with the Maoi

Face to Face with History

The island's native language (Rapa Nui) is still spoken today by about 3,000 people.

The closest inhabited land to Easter Island is Pitcairn Island, over 1,200 miles away.

Beautiful view off the cliff

Edge of the World

The famous stone statues were all toppled during tribal conflicts in the 1700s - all standing moai today have been re-erected in modern times.

Maoi staring at me!

Love exploring remote destinations? See my photos from Antarctica.

Conclusion

The moai, standing proud, continue to guard their ancient secrets while sharing their silent wisdom with those who make the journey to this remote corner of the world.

In this episode, we explored the groundbreaking intersection of blockchain and artificial intelligence, with a deep dive into my work with FreedomGPT.

It was an incredible opportunity to discuss how we’re pioneering a decentralized AI network to empower individuals and democratize technology.

Video

Watch the full conversation on YouTube by clicking here.

Or watch it below:

Full Transcript

Tina Dai: Hello everyone and welcome back to our Ueth weekly workshop. This session will be an exploration of how blockchain empowers decentralized AI, which is one of our most popular topics that we've hosted to date. This workshop will be titled Building Decentralized AI featuring John Arrow of FreedomGPT. We will dive into how FreedomGPT and its FNT token are pioneering a decentralized AI network. I'm Tina Dai, your Ueth host for the session. Over the past five years, I've mostly focused on the crypto space through early-stage investing and built a foundation in product and strategy prior to investing. I'm joined today by John Arrow, who is the co-founder of FreedomGPT. We'll hear the initial spark that led to the creation of FreedomGPT and also better understand how the company is focused on decentralizing AI to create a more equitable and accessible future in this exciting innovation. We'll start with John's background and explore the vision and mechanics behind FreedomGPT. John will share more of the inception of FreedomGPT, its mission, and how it got to creation, creating this FNT token to incentivize participation in its compute, to democratize AI and reward contributors. So before we jump in, a few housekeeping items. If the conversation inspires any questions, please drop them in our general Discord channel, and we can address them asynchronously. And to make sure your chapter stays active, please comment on the workshop in our Discord within 24 hours at the University of Ethereum. Our goal is to really inspire our community with real innovations, and we do this by pushing you to learn through doing, reflect on how you can contribute to decentralizing power and knowledge and AI. With the story that you hear about FreedomGPT, please join me in giving a warm welcome to our guest John. I'm going to add John now to the stage. So John, it's great to have you with us today. We would love to learn more about your background. What were you doing before FreedomGPT?

John Arrow: I'm really excited to be here. It's just so fantastic that you've opened up this environment for teaching. So thanks again for having me on. My background in crypto is kind of an interesting one. I started a company called Mutual Mobile when I was at the University of Texas, and we bootstrapped that to 400 people. A lot of what we were focusing on was emerging technology, so whatever was new, we helped Fortune 1000 build it. And then when crypto came onto the scene, we kind of had a look at that, too. And so back in 2011 is when I first got involved in Bitcoin. And like a lot of people then, I didn't know exactly how to purchase Bitcoin. So I heard about Mt. Gox, which we all know the crazy stories there. Fortunately, I got my Bitcoin out of Mt. Gox before it kind of did its implosion. But that was my first kind of entrance into it, not really knowing what it was about. I think I have some Bitcoins with a cost basis of close to $18, and that was out of sheer luck and curiosity rather than anything else. But along the way, I kind of kept in the back of my head what had happened to Mt. Gox, right? You had this really wonderful technology which was supposed to be decentralized, but then what did people do? They ran and they put it into a centralized exchange. And we keep seeing that story really repeat itself over and over and over again. It seems like you think we'd learn lessons from what happened with FTX, but you still have a ton of people who put crypto in a centralized exchange. And apparently, you know, they say history never repeats itself, but it sometimes rhymes. Well, I think we're going to see a lot of that happen with AI, unfortunately. And so when we founded FreedomGPT, we recognized that AI and generative AI was going to be just as transformative as crypto. And unfortunately, in order to ensure that, it meant we had to take a book out of the crypto page and do a decentralized route there. As soon as AI launched, we saw governments and large organizations trying to figure out how to specify what it could and couldn't say and when it could and couldn't answer something. And so that was the impetus for us wanting to create the decentralized AI app store that has become FreedomGPT.

Tina Dai: Wow, so you're a true OG in crypto. I hope you held onto that Bitcoin. But you kind of touched on the origin story of FreedomGPT, at least the philosophy behind it. Would love to hear a little bit more about what was that initial moment? What was that spark initially that led you to say, I'm going to commit my resources to building this, this product in app store?

John Arrow: Great question. And I think, like a lot of things in life, sometimes there's serendipity in timing. So I had sold my company at the end of 2023, and a month before we sold it, ChatGPT was launched by OpenAI. And we had done a lot of work with AI while at Mutual Mobile, my prior company. But I think it was just perfect timing that when we sold the company, that was right when ChatGPT was becoming a household name. And it launched in November of 2022, for those who weren't familiar with the launch. And one of the crazy things about that is I think everybody had this experience where they started using it and they'd ask a question, and ChatGPT, despite it being remarkable, would refuse to answer. And it reminded me of a similar moment that I had back when I was about 12 years old on AOL Instant Messenger. Now, I know a lot of the listeners are younger than me, but there used to be this thing that AOL put out there, America Online, called AOL Instant Messenger. We would all use it. And one day this company launched something called SmarterChild. And SmarterChild wasn't AI, but it was one of the earliest chatbots that you could ask questions. And it was a remarkable piece of technology for its time. And you would ask it a question, it would most of the time answer, but every so often you'd ask something and it would refuse to answer. And I'm sure they had their reasons. Weirdly enough, Microsoft ended up acquiring that company, SmarterChild. But it had a huge impact on me when I had a computer for the first time in my life saying, no, I'm not going to answer that question. And again, that was when I was about 12 years old. We'll fast forward to last year or a little over a year ago when I asked ChatGPT a question that was seemingly innocuous, and it told me, no, I'm not going to answer that. And it became this extremely kind of transformative moment for myself and my co-founders when we started just seeing what it would and wouldn't answer, where we said, why should a computer be judging you? Why should a computer allow what you can and can't ask? And AI will likely be the most significant technology for humanity since electricity. Now, if that's the case, we need to make sure that people have access to a true version of it, not one that a large company or world government decides that you're allowed to have. And so with FreedomGPT, one of our first models allowed you to ask anything without bias and without restriction. So nothing was off limits. And just by the sheer nature of this being salient, we had a ton of people start using it. It was featured in the New York Times. Over the last year, we've had over 2 million people interact with our product. And that really paved the way where we said, well, okay, if we have a text-based LLM that's censor-free, what are some other models that we can add? And some of them we strive towards having open-source and censor-free models. But we also recognize that the important thing for our mission of giving everyone access to every AI is to allow people to access AI models that might be difficult to get their hands on otherwise. A lot of these models require you to have a substantial amount of compute available and some technical knowledge to set it up. But the great thing with freedomgpt.com is you can just go to the website, you can open up the browser, you have access to 80-plus models, the most cutting-edge ones in AI right now, and you could start using them with no knowledge. If you are more inclined from a technical standpoint and you do have substantial compute, you can actually use our models for free. You can download the FreedomGPT client to your desktop where it's 100% private, it's 100% censor-free for the Liberty model that we have out there, and you can run inference for others. And we'll actually pay you with our token, which we'll talk about later, so that you can help other people access models in a decentralized fashion.

Tina Dai: I love the story you shared about your youth. And it's also fascinating to me how, like a lot of what I love about crypto, is this pursuit of freedom and pursuit of certain ideals. And it sounds like basically what you're doing with FreedomGPT is bringing some of those ideals, chiefly like freedom, to this technology. And so I think this would be a good transition now to your presentation on FreedomGPT and what led you to this intersection of crypto and AI, as well as a lot of the challenges that you're currently seeing in our landscape today. So I'm going to go on mute and have you start your presentation.

John Arrow: Excellent, Tina. Thank you. Before I dive into it, I want to completely agree with something you just said, that philosophically, ideologically, the mindset between crypto and decentralized AI is so, so similar. I would say in many cases, AI is the first real use case of crypto outside of just kind of financial products where we really need crypto to function. There's no way that decentralized AI can work unless there is crypto. So I'm so glad that you brought this point up. And the crazy thing about what's going on right now, if you want to kind of encapsulate the problem down into kind of one simple slide, that's that because you have a transformative technology, everybody who is an incumbent in the world is scrutinizing it. Some people are scrutinizing it for the right reasons, some are scrutinizing it for the wrong reasons. And if you look at why AI is kind of provoking, for lack of a better word, the same autoimmune response that crypto did, it's because it threatens kind of the status quo. It threatens the incumbents that are there. And again, some of them are operating with, I think, good reason, right? I mean, the FTC is a very important organization, as well as a lot of other government bodies. But whenever there's a new technology and they don't understand it, they can sometimes rush ahead and hamper innovation and kind of harm consumers rather than help them. So OpenAI, we've already seen them banned in several European countries. We've seen a ton of scrutiny. And this is still in its infancy. And so kind of our thesis here is that as the capability of AI becomes more pronounced, what's inevitably going to happen is that autoimmune response is going to go from, you know, a scale of 0 to 100. I would say it's at about a 5 now. I think it's going to go to about a 75 before the end of 2024. And what that means is that we'll be in a situation where unless you have a decentralized version of AI, you aren't going to be using the latest and greatest version of it. And we anticipate kind of that level of censorship and restriction on AI will be probably directly proportional to the capability. It could be logarithmic. Again, in the news, we heard about Google Gemini, and obviously I think people are being a little bit overreactive when they say Google's trying to rewrite history. I would say it was most likely more of an honest error, or maybe somebody got a bit overzealous. Nevertheless, I think it invoked the type of response where people are starting to see the danger of what happens when the traditional incumbents are able to be the kind of sole ones accessing this really, really powerful technology. And what scares me isn't more incidents like Google Gemini. I think what's scary to, you know, not just to the AI industry, but humanity in general, is when it becomes more difficult to discern. Obviously, if Google Gemini gives you a picture of George Washington that's a different race and a different gender, you're aware of it. But the next phase will be way more subtle. It'll be way more insidious and be almost impossible to discern. Is that true or is that not true? And that's really why we need a diversity of AI models, is to protect ourselves from one large organization saying our model is the right model. That's really the only way around it. You need a diversity of voice and choice in the market, hence why you need a decentralized AI app store like FreedomGPT. And it's amazing how arbitrary and capricious some of the censorship is that we've seen. Midjourney, an extremely successful company, already has kind of decided what it will and won't allow. And some world leaders are allowed, some are not allowed. And we're not going to kind of try to dive into why they chose different things other than to say that this problem is going to get way, way worse before it gets better. Any significant company is going to kind of have to bend to the sway of large incumbents. So really the only way around that is to ensure that there's a decentralized approach. Any centralized company, big or small, will not be able to hold up to the immense amount of world pressure they have from large incumbents to basically ensure that their AI operates in a certain way. You really kind of have to decentralize the thing and throw away the keys so that can't be changed. That's our eventual goal with FreedomGPT here. What's fascinating too is, like we talked about, that exponential curve as it relates to that censorship and that prohibition on AI's capability increases. So naturally, it should be no surprise that there's an inverse. Look, there's an inverse correlation to being able to use capable AI in an unfettered way. We're already seeing a huge, huge push to monitor people's use of AI, and that's only going to intensify. Unfortunately, a lot of people today rely on ChatGPT and other models that you can use at FreedomGPT, you know, for legal advice, especially if they don't have access to legal representation. And unfortunately, that does not have legal privilege, right? And so you're already kind of seeing a situation where you have a two-tiered justice system where people who ask a lawyer questions that can remain private, but if they ask an AI lawyer question, that's part of the evidentiary record, and that's really unfortunate. We're also seeing governments around the world enact pretty far-reaching, I think, ill-informed regulations around what rights you have and don't have to using AI privately. There's an executive order that's going to go into effect this summer in the United States that, for all intents and purposes, takes the whole KYC, Know Your Customer, AML, Anti-Money Laundering laws and will start applying it to AI, where you'll have to fill out certain forms and identify yourself before you can use AI in a way that you want to use it. Now, the problem with that is it starts to have a really chilling effect. If you know the questions, the inferences that you ask, are going to be monitored and you know that they are subject to scrutiny, it's going to start to interfere not just with your, the questions you ask, but really the way your mind works. It starts to be something that I think is very, very much a slippery slope as it relates to civil liberties. And again, one of the reasons why privacy is so important and the reason why we allow people to download FreedomGPT and have 100% private inference never leaves their computer. So again, you know, you may never need this, but if you download it today, at least if the internet goes off, or if, you know, some world power decides to ban AI, you'll at least have that on your computer. We realize that, you know, the freedom, the way it works in the United States, is not quite understood. We think that freedom is something that's guaranteed in a document, maybe the Bill of Rights or Declaration of Independence, whatever document you want to look at, and that might set the parameters and the stage for that is not how it is assured though. The only way you can assure freedom is making sure you have more powerful technology than the people who want to take it away. History is full of transgressions of that right. There have been societies that are way more just, way more ethical, moral by a lot of different standards, that were overrun by other civilizations that had better warfare technology, better economic engines. And so when we think about what preserves freedom in the United States and what will preserve freedom in most of the sovereign countries on this planet, it's being able to access the tools, it's being able to access the information, it's being able to have freedom of speech to ensure that you're not overrun. That's why it's so important to put this technology in the hands of ordinary citizens. It should not be segregated in the hands of a few powerful incumbents because if you do that, it will be misused, and it will be used in a way to subjugate the people who don't have it. Just, it's just human nature. When we launched FreedomGPT, we kind of weren't aware and didn't even expect the community that would rise to the occasion for us. It was just so heartwarming to see people understand immediately what we were trying to achieve. Yes, we got a lot of hate mail, for sure, by people who think that AI should be censored and should only have the right version of the answer. But overwhelmingly, we realized that most of the people kind of clamored to us, got the mission, and there were a lot of them who were people who had a crypto background. What I think is interesting too, is an insight that many of our users share, and they have a healthy respect for the concept that AI could give out misinformation. You hear time and time again that the danger of uncensored AI is misinformation. And that might be true, right? There are sometimes when misinformation might exist, for sure. But, you know, one person's misinformation might be another person's information, vice versa. That being said, the real reason when people want to censor AI, if you kind of dig deep, you really interrogate what's going on, it's not that they're concerned about misinformation. They're concerned that the AI might give a real truth that people aren't ready for. Because imagine a hypothetical AI that, whenever you asked it a question, it always gave you the right answer, right? Every day for 100 days, you're asking it different things. You're asking it to predict the weather for you, you're asking it for something that happened. And it always agrees with your worldview. You start to rely on it, you start to build trust with that AI. Then all of a sudden, if you ask a question that is a controversial question, it maybe has a lot of kind of disputed answers to it, but it gives you a certain unpopular answer unequivocally, that can cause social upheaval, right? That can cause a lot of civil unrest. And I suspect that's what most people who, if you really dive into it, that's why they're concerned with kind of uncensored AI. And our belief is it's the exact opposite. If you try to censor history, if you try to restrict what people can see, it will only end badly. There's that famous quote that those who start by burning books will end by burning men. And I think AI very much will play out in the same way unless we keep it open and available and free for everyone on this planet. So we have a lot of users who come to us for different reasons. And I'd say early on, the reason people came to us most was because they were interested in getting AI where they can ask any question and always get an answer. And that very much evolved into, you know, this idea around privacy that whatever they ask will remain private and stay on their computer and not be subject to somebody else finding it or being leaked or something like that. I would say, though, today, most people who access FreedomGPT care more about the proliferation of all of the different models we have. Whenever there's something cutting-edge new in AI, whether it's text-to-music or text-to-video synthesis, or, you know, an AI that can make a phone call for you, which our Phone Call GPT that can do—phonecallgpt.com, it's live on freedomgpt.com—and it can literally make phone calls and do things for you. They come to us for those types of applications, a new, new thing that they can't get anywhere else. I would say that's the lion's share of reasons why people access us today. That being said, we were built on the foundation of being censorship-resistant and being private. So those are the underpinnings. I would say the main value prop today, though, is just this prolific access. You don't need a subscription to a dozen different AI companies that can really add up with FreedomGPT. You can use all of those models and not need to pay a dozen companies. I think that's super refreshing for users that want to be on the cutting edge of AI. Now, we are very much a company that did not set out to be a blockchain company. We stumbled across it out of necessity very early on in the FreedomGPT journey, back when we were getting millions of users finding out about it every day. We ended up in a scenario where somebody would ask a question and, in their view, the LLM gave them the wrong answer. Well, they would go complain to our centralized host provider and say, hey, you shouldn't allow these guys to be hosted with you. And in the middle of the night, they would shut off our hosting, even though we were paying them many thousands of dollars a month. And this happened several times. So we realized, look, this is such a controversial, such a useful technology. There's no way this can continue to exist and propagate in a centralized hosting fashion. It was at that moment we realized we needed to become a Web3 company, and we needed to figure out how to make it so that one person couldn't change their mind and shut us down. And so if you look at our journey towards becoming a Web3 company, we kind of recognized several things. One, it was within the open-source community. We have a huge commitment to being open-source. We were number three on GitHub. We had nearly a million people download and install our free desktop applications so they could run private censor-free inference. We have a cloud version where we weren't monetizing for a while, but then we decided to start monetizing to offset some of our hosting costs, where we've consistently been doing a thousand dollars a day and growing at a pretty good growth rate. And so we kind of took all these variables together and the fact that we had been banned from hosting providers, the fact that you can't even mention freedomgpt.com on Twitter or X in a direct message, and all of this other kind of scrutiny. We said, look, we don't really need the traditional computing rails. We can go completely decentralized. We have a ton of users with amazing compute. Why don't we let them start hosting the FreedomGPT cloud models for us? Because this will achieve several things at once. Talk about killing two birds with one stone—all of a sudden, it means that we cannot need to rely on the centralized hosting providers. Number two, it means we can give back to our early supporters; we can figure out how to pay them the money that we were paying our hosting providers. And number three, we can ensure that this is done in a way that remains completely private, it can't be compromised, and there isn't one person who can change their mind and kind of say shut down FreedomGPT. And so that was the realization where we decided, look, we're not like a typical crypto project. We already have a lot of revenue coming in. How can we kind of migrate and take what we've built and make it so that it has the benefits of what Web3 can offer? And that's exactly where we are today. We're right at, I'd say, the end of the beginning on that stage of decentralizing FreedomGPT. We did an airdrop of our native Freedom Network Token, and that is the economic unit with how we pay our nodes, our hosts, and anybody can be a node or host. All you need to do is download the desktop version of FreedomGPT. We just did an airdrop where we airdropped almost 50 million Freedom Network Tokens to eligible nodes during that period. We called that our pre-genesis period. And we're just about to begin our next airdrop, where we are going to be rewarding nodes on our network who are able to process true inference for people who want to use the FreedomGPT cloud model. And those FreedomGPT cloud model people are—maybe they're people who are not technical, they just want to go to a browser and use our 80-plus models. Maybe they want to use them on their phone. Maybe they don't have the compute available. But if you are interested in kind of earning these tokens, all you need to do is download the application, keep your computer plugged in when you're not using it, and then in a completely anonymous, safe way, when your computer has a spare cycle, it will process behind the scenes that inference for a user, and you'll get rewarded with those tokens for the work instead of AWS. And so it's a really kind of interesting way to give back to the ecosystem. There are going to be other ways to earn tokens right now, but the most important part is building out kind of that infrastructure layer where we can process inference in a completely decentralized manner. I encourage everybody to check it out. The easiest way to kind of start to learn about the Freedom Network Token is to go to freedomgpt.com/fnt. If you do that, you'll see a world map of all of our nodes. You'll see all of the nodes online, as well as kind of the amount of compute that we have. And we have—just again, we did this less than a month ago—and as a result of the airdrop, we have several million dollars' worth of compute now online that we can use to process inference for our cloud users. And that will translate directly into earnings for our desktop users who are contributing. Here's that map that I talked about right here. What's so cool about this is that you can see there's true distributed computing here, true decentralization. We're all over the world. I'm not sure why the graphic shows Somalia as being highlighted, but yes, we're, you know, we're not there, but we're in a lot of different places. And every time we add a new country and we add a new region, it increases the robustness of the network. What it also means too, as regulations and kind of new laws become clearer as it relates to AI, we can ensure that different regions are able to run different models. And so it provides a great comparative advantage. It also means that when people in the West are up and using their computers, we can use the nodes in the East when they're sleeping and not using them, and vice versa. So it gives us a lot of just ability to do some pretty cool things that we couldn't do if we were just kind of centralized in the Americas. There's been a huge amount of movement, I think, in DePIN in the last couple of years. You started to see some fantastic examples of how you could take the early days of crypto where you were just brute-forcing, basically SHA-256 encryption algorithms, and start to do useful work. And I think this is the type of thing that's going to really change the image of crypto when you can, for the first time, say, look, crypto isn't just an energy hog, it's not just an energy consumer, it's building things that wouldn't have existed otherwise. And you know, for my personal pursuit, that's something that I care a lot about, something that my co-founder Tarun cares a lot about as well. And so I'm so happy that we get to stand kind of on the shoulders of giants who've been working so hard in the crypto space for the last, I guess, well over a decade now, and really figure out a way we can contribute to the world in a positive place and kind of change what it means to be a crypto company. So when people talk about AI tokens and they talk about what's going on in AI, there's a lot of elements to it. There are a lot of great companies in this space. Morpheus is one of them that we've gotten to know well, Kosh is one of them, Bittensor, there are a lot of different ones, and I think that's a great thing. What many, many people, and what the casual observer might want to know more about these AI tokens, is that there's a ton to bite off and chew here. When you're talking about creating utility with AI, you have to look at the chain and kind of understand where, you know, where are the opportunities, where is the room for improvement? And so we decided to focus predominantly on the inference side of it. We wanted to figure out how we can create a great UI layer. Again, we have, you know, just, we've had over 2 million people use FreedomGPT. When there's a brand-new model that's out there, how can we ensure that that model owner gets distribution to a ton of captive users, and they don't have to worry about the hosting side of it? And so that's why we've decided to focus predominantly on kind of the inference side of it and the output side of it too; over time, we might look at the other value layers as well. That being said, we don't want to reinvent the wheel. We want to rely on partners and be some additive to the ecosystem. I don't think there's going to be kind of a real competitive nature in the ecosystem for a while out because there's just so much that needs to be done. And you have a ton of companies out there that are all kind of approaching it in their own manner. And we'll get to some of the differences in the companies in a second. But here's a really great kind of high-level thing, and I noticed one thing we need to clean up on that slide real quick is I think that logo is Bittensor and then right next to it is FreedomGPT. Again, very different companies working on different sides of the value chain, but they are, I think, very much complementary. And the other perspective to look at this, of course, is just by sectors. We have something that looks like one of the big think tanks could have put together here, but we see a ton of cross-pollination through all of these different kind of optics. Decentralized compute is important. That being said, we think decentralized compute is very much a race to the bottom. This is an age of abundance, and there is going to be way more decentralized compute out there than is needed for the current problem stack. So instead, we kind of have to turn this into an opportunity question and say, look, we have all of this decentralized unused compute now that we've kind of linked it together and things like FreedomGPT have launched. What are the novel problems that we can solve that we couldn't solve before? One of our initiatives is we're rewriting Wikipedia. We have something that we're calling WikiFreedom, and we'll talk more about that at a later date. But we realized that we could basically create a much larger, less biased, less censored version of Wikipedia in a fraction of the time using some of the compute that we have on our network. And so I think you'll start to see these categories and this kind of gestalt way of viewing the world really start to blend together in some pretty remarkable ways in the future. And again, I encourage anybody who's interested in crypto to explore Freedom Network Token. This is one of those rare tokens where you do, you know, we're not trying to do like a launch or an ICO or anything like that. We are an established company, and we are launching the token because it enhances the privacy, the censor-resistance for our users. If we didn't have this, we would have to deal in fiat. And so it's a pretty unique token that you don't need to pay any money, you don't need to stake any Ethereum or anything like that. We literally will reward you with our token for simply downloading the desktop app and enabling your computer to run inference for our users. And by doing that, you don't need to have your computer do brute-forcing of encryption algorithms like you'd have to do with Bitcoin and a lot of the other tokens out there. It's something that most computers can handle, especially if you've purchased your computer in the last few years, and you don't need to kind of risk anything. It's only going to be given to you, you know, when you're, you're only going to need to keep your computer on when you're not using it; otherwise, you won't even know it's there. And so I'd encourage anybody who's interested in earning Freedom Network Token to check it out, and we'll pause there and see if there's any questions.

Tina Dai: Yeah, well, thank you so much for that, John. I have a list of questions prepared for you, but before we move on to that, I'd love to just hit on some of the points you touched on. I think the first being crypto is one of the first applications of crypto is AI. I think that's a really interesting statement that you started off with because aside from things like payments, crypto has historically had some challenges in finding consumer applications, right? And I think that statement of blockchain being an equalizer for AI is so astute in that, like, if you think about AI, it's like so centralized. It's a black box. It's very, like, centralized and monopolistic, right? There's, like, a handful of companies that own all these, like, large assets. Whereas with blockchain, you get the opposite of that, where it's very decentralized, very transparent, and then it allows for user monetization and accessibility. So I think tying together these two, like, giant technologies really has a ton of synergies that can be achieved independently on its own. And so I think your story itself is actually quite a good encapsulation of all those principles, right? You were very laser-focused initially on tackling censorship and access, but you realized that creating this decentralized network was not really just, like, something to do but a real necessity for you to operate in an ethos-aligned way that you want to be doing without getting deplatformed. And I also think this whole concept of ensuring everyone in the community gets rewarded fairly is my whole favorite thing about crypto. Decentralized resource networks in general, like you had touched on, like DePIN itself as a category, really fosters various communities where things don't necessarily exist or resource networks don't necessarily exist outside of this crypto paradigm. And I just love this foundation of giving ownership to users based on their actions, which truly highlights to me what's exciting about the future of decentralized AI and decentralized networks in general. I think for this next section, we're going to spend 10 or 15 minutes on Q&A and just dive a bit deeper into some of the thinking behind FreedomGPT, as well as the community-building aspects that might be crucial to its success. If this conversation—anyone who's listening to it—has any questions, you can definitely drop them in our Discord, which is discord.gg/ueth, and then we can answer those asynchronously for you. But some of the questions I have prepared—so I think both AI and crypto are pretty new concepts. And for the average user who may be using your platform, how are you working to familiarize them with education either around crypto or AI, such that they understand the value proposition of your technology?

John Arrow: Well, you summarized what I just said so eloquently and so succinctly. So thank you for doing that. I should have just deferred to you. You're really good at getting things quickly and summarizing them. And then as it relates to your question, I would challenge that question a bit around AI being a new technology. AI's got quite a history. It's approaching 100 years old if we think about the first vacuum-tube computers. And when we were looking at using computers for firing solutions, it was imagined that you could create computers that could think. And there's been just an immense amount of research and failures to kind of get to the point that we are at. And what's interesting is, even though we've seen some recent success with generative AI and it's become a household name that everybody can use it, people already kind of envisioned the possibilities of robots, of all of the amazing optimistic scenarios for what AI could do, as well as all of the nightmare scenarios what people could do. So I think it's a bit different from crypto in the way that, even though encryption has been around for a while, the concept of blockchain is extremely new, and it's something that has not been around as long. So it's kind of fascinating to see those two technologies merge in such a way because both are becoming household names. And I think it shows that we may be in a simulation that these two are growing together now at the exact right time, because I don't think that they can exist well without each other. And you're seeing an immense amount of cross-pollination there. And one of the interesting things is they kind of serve as a check for each other, right? If you think about AI in preventing the nightmare scenarios that people are concerned with, a lot of the nightmare scenarios are when one centralized organization gets a better AI than everybody else, right? If one of our adversaries gets better AI or some terrorist group gets more capable AI that can produce the killbot drones and take over. And crypto very much can stand as the inhibitor and the check on that. Because if we figure out a way to give it to everybody, that means that we have this kind of decentralized arms race that keeps everything in check. I wish we lived in a world where we didn't need weapons. Unfortunately, that is not our world. Instead, we live in a world where there are more good guys than bad guys out there. And so if we know the bad guys are going to get the weapons, we want to make sure that the good guys get the weapons too. And AI in the hands of good people will serve to inhibit and block and protect the bad actors that are using AI. And crypto is the facilitator to ensure that.

Tina Dai: When you mentioned robots, it reminded me of the NVIDIA demo yesterday with robots. And I had this crazy thought. I was like, oh my gosh, we're going to have robots in my lifetime. Which is an idea I maybe, like, loosely thought of on, like, a sci-fi TV show or movie, but never, you know, grappled with as reality.

John Arrow: So, well, for the audience, in case people are watching this in the future—neither of us are robots, we can confirm.

Tina Dai: Oh, well, one thing you had touched on in your presentation, which is you think, like, decentralizing compute is a race to the bottom. Can you expand on that? And you also mentioned a bit more about the different ways you're using excess compute with FreedomGPT. So we'll have to understand the vision behind the products that you're perhaps pursuing or exploring with the excess compute. And also why you think it's, like, a race to the bottom slash the implications for all this—you know, there's so many classes of companies pursuing decentralized compute, so would be interested in understanding, like, the implications of what that would imply.

John Arrow: For sure. I mean, and I think right there, there are so many companies pursuing decentralized compute because they see the importance of it, and it will basically guarantee that there was going to be a lot of decentralized computing compute out there. If we look at the history of just centralized compute, we see this seesaw back and forth between hardware and software where, ultimately, whenever there's something new from a software standpoint, it requires an immense amount of hardware to run it, right? If you think about, like, the software it took to do the moon landing, it was, you know, essentially a supercomputer that by today's standards couldn't run a wristwatch. And so we always see this inevitable—software does something new, and it requires the most gargantuan, most expensive hardware in the world to run it. AI has been no different. FreedomGPT has been no different. When we first launched our censor-free model, we calculated what it would cost to run in the cloud for the users for one day, and it was several hundred thousand dollars per day to run that. Today, it's measured in kind of sub-$1,000, which is pretty insane that in the course of a year—really less than a year, I think FreedomGPT actually, it's our birthday today, weirdly enough, we launched it one year ago today. Just good serendipity. Thank you.

Tina Dai: Wow.

John Arrow: And so if you think about, like, a one-year-old, the progress that a one-year-old human makes, it's immense. And the same thing with software. In that time period, we saw several orders of magnitude in reduction of cost. And that isn't because of magic, it's because the software got better. It's because people started competing and saying, how could we do things in a more efficient manner? And then the next, the next kind of permutation of that was, how can we pull more people, more resources into the inference side of it? So not only did the software get better, but we got more, more kind of smart and more creative in how to bring in more hardware and compute rather than going to NVIDIA and buying an H100 for $40,000. A bunch of people have extremely capable computers sitting at home unused on their desktop or sitting at home on the weekends in their office not being used. Or maybe their mobile phones in an old desk drawer somewhere not being used. And just by simply giving people—going back to the DePIN comment that you made—giving people the slightest incentive to turn those on and to spend a negligible amount of power, of energy on power to keep them plugged in, connected to the Wi-Fi—that solves all of our compute problems, and that's that race to the bottom. There is no hardware shortage if everybody plugs in their hardware. And yes, it is true, there's going to be software that pushes the boundaries again. If you look at what OpenAI is doing with Sora and a lot of the other text-to-movie synthesizers out there, you will see that other side of that seesaw go back where you need the most capable compute to run that inference. But economics are that the hardware will catch up, unlock that. So I do think it's a race to the bottom as it relates to decentralized inference economics. But it's a type of race to the bottom that benefits everyone in the space. It means that the people who are enrolling their computer are going to earn some type of compensation for it. It means we're going to see more capable models that can be used by others for an amount approaching free. And it also means we're going to see new models quicker for the open-source community that we wouldn't see otherwise. And that's one of the best things about capitalism, is that it ensures that the consumer starts to find a world where things they used to pay for become free, or in fact, they start getting paid for using them, even in the case of FreedomGPT.

Tina Dai: Yeah, that's fascinating because effectively you're basically saying that we're going through this hardware-software cycle where software puts demand on hardware, and then hardware eventually catches up, and so it pushes out the software piece further up the stack to where now we're getting more innovation on a consumer application front. And so your prediction is effectively that over time, as we sort of go through the cycles of catching up on hardware, the things that are available to consumers that compete for their attention will become, like, more and more interesting and compelling for the end consumer. Which I would love, I would love to see that—especially, like, in crypto, right? For instance, like, the idea of, like, a consumer application in crypto has yet to catch up. And with AI, you're sort of seeing this, like, happen a lot faster where there's already a ton of consumer applications that people are trying with, like, AI agents and things like this.

John Arrow: And so really, crypto and AI, they're the perfect partner.

Tina Dai: Yep, exactly. I'm really excited to see how the ecosystem develops overall. But with this very rapidly growing decentralized AI ecosystem, what prospects do you see for potential collaboration with other Web3 AI projects?

John Arrow: Well, that's what gets so exciting all of a sudden. If we think about everything in the world that contributes to morbidity and mortality and quality of life, you can kind of boil it down to energy, right? People's access to light, sweet crude. People's access to kilowatts—that really, more than anything else, controls humanity's progress. If you have unlimited power, you can have unlimited desalination, so you can make fresh water wherever. If you had true unlimited power, you could control the weather and ensure that everywhere on the planet is perfect for growing crops and living and even do space exploration in an extremely efficient, fast way. Now, compute is not energy, but it's close to it. Because once you start giving access to compute that approaches free and has really no bounds on how much you can get, it unlocks a lot of the same potential for humanity. So when Web3 and the crypto ecosystem start looking at what AI has done with decentralized compute and what crypto has done with decentralized compute, it allows you to start asking some pretty interesting questions. What would you do with close to unlimited free compute? What problems would you solve that couldn't be solved before? Maybe it's things relating to the genome and doing, you know, protein folding and solving that. Maybe it's something relating to SETI and looking at the universe—there are already decentralized applications that do both of those things. But the level of decentralized compute that's about to come online will mean that anybody can come up with an idea and access many multiple millions of dollars of compute for close to free in a way that society has never been able to do before. And there's just such cool examples of what that can do. I'll give just one, because otherwise we'd spend the whole day talking about all the fun things. I would encourage the audience to think of their own. One really cool one is, what about your own AI that listened to everything that you did throughout the day, and it only had your interest at heart? That data didn't get sent anywhere else. It just said, how could John or how could Tina have the best versions of themselves? And that AI is looking out for your interest? Well, you previously couldn't do that when you had to pay for compute. It would be prohibitively expensive for most people. But now you can do that in a way that's close to free. And FreedomGPT is actually going to be launching a specific model that does effectively that soon. But that's just one example. And I guarantee you there's literally millions of other ones that could be just as useful. And so by having that decentralized AI app store, anybody—maybe in the audience who comes up with one of those ideas—they'll be able to host it on FreedomGPT and immediately overnight access all of our captive, excited new AI users to test it out.

Tina Dai: We didn't touch on this, but what other models does FreedomGPT support, like this AI agent?

John Arrow: We're a big believer in multimodal models. Ultimately, the world that we live in isn't just text-to-text. It isn't just seeing things or hearing things or feeling things. It's a bunch of the different senses. We're trying to add models to support every one of the natural senses we have. In addition to many different LLMs for code generation, for writing, we also have text-image synthesizers, we have an early text-to-video synthesizer, we have text-to-music. We have an AI that can make phone calls for you. We have a lot of models that will kind of teach you how to do things. And the nice thing about it is the models that are being added, people can experiment with them, and they can ask the same inference, the same question to multiple models and then choose the one that gives them the best answer, the one that's the most helpful to them. Over time, we think the biggest problem isn't that there isn't going to be enough models. We think it's going to be the opposite—there's going to be way too many AI models. So right now at FreedomGPT, we're working on a router that will automatically try to understand what you want to do. It will learn in a secure and private fashion and then route your prompt to whatever model can give the most valuable inference. And by doing this, it takes the guesswork out of it, right? A lot of people have too much choice in their world today. You don't want to have to sift through a thousand different models to figure out which is the best. You just want an AI that can sit at the top of the stack and say, look, I know what you're trying to do here, let me go figure that out for you. You don't need to worry about this. And we want that to happen in an increasingly passive way where eventually you won't even need to prompt the AI. It'll just go and infer itself that it needs to go do one of these models, and then it will perform the tasks for you and let you know, and that's how it should work. It should be in the background. You shouldn't even need to think about it.

Tina Dai: Wow. So much to look forward to. But we're now coming up on time and would love to move towards a closing next step. So a couple more questions for you, John. So as we wrap up, we'd love to hear you share any parting thoughts or advice for our community, especially for those who are interested in this intersection of AI and crypto. Maybe you can share any resources or pathways you might recommend to get involved in the community, or how do they even engage with FreedomGPT?

John Arrow: What a great question. I mean, I think if you were just starting out, what a magical time to be looking at the world, because I can't think of an easier ramp to get involved in something. With crypto and a lot of Web3, you had to have somewhat of an engineering background. That was pretty important. With AI, though, it'll kind of teach you itself, right? It's got an instructor built in, which means anybody who's curious enough, anybody who's willing to spend the time to start using and experimenting with the different products out there, can become self-taught, and then they can figure out how they can contribute and add to the community. Whereas with crypto, no amount of using crypto is going to teach you really about cryptology. It might teach you a few things, but you're not going to be writing Web3 applications unless you really sit down and focus on it, versus AI will teach you how to do it. I think the biggest thing is just to start experimenting and seeing what are things that you can do today. Substitute out workflows that you have currently with AI. Initially, it might not be easier, but over time, the AI is going to get better. And I think people who make that leap now are going to be way, way ahead of people who are kind of dragging their feet on it, is the one point I'll say. And then the second point I'll say is that if you want to do an analogy to where we are in the timeframe of things for the new generative AI kind of wave, I think this feels a lot like 2011 again, like AI has been out there, but only the extreme early adopters have really played with, I think, generative AI in a meaningful manner. And we've already seen kind of some plateauing. If you look at the user growth around OpenAI, it's largely plateaued. So this is the perfect time to get into it because people are having to build, and there's some fundamentals there rather than just kind of market hysteria, which is great for bringing in new people, but it's not great for kind of building and getting things done.

Tina Dai: How many users does OpenAI have?

John Arrow: Okay, that is a very disputed question. I heard that they have 100 million paying users—so subscriptions, I believe, is what was what the status, which is substantial, and this is substantial—2024, but let me just confirm that.

Tina Dai: Yeah, I'm just curious because you said it plateaued, and so verifying 100 million paying is actually lower than I would have expected.

John Arrow: Actually, 100 million people using it monthly, so it's probably paying is actually a lot less.

Tina Dai: Oh, wow. So that's actually a lot less than I would have thought. But very interesting points. I also think, like, for AI, it's, like, yeah, we're so early still in the entirety of the impact of AI and also crypto too, right? It's, like, always good to get started, and both sort of have this open-source mindset, so anyone can get involved with quite low friction relative to any other industry or field, which will likely adopt some version of AI technology and crypto over time. So amazing session. Thank you again for all your insights. I always learn a lot whenever you answer questions, especially with your stories, and it makes my mind think in a different way than I would myself think or choose to answer a question. Thank you again for your time, but as always, we will share a recap of this workshop via our newsletter, which is The Digest, and we also will write a blog about this session via our Ueth blog, and that will come out in a few weeks. So keep an eye out on our Twitter for these content pieces and the announcements for our next workshop, as well as community spaces. So thank you for everyone who attended and joined us today, and we'll see you for the next workshop. Thank you, John, for your time and for all your insight.

Selfie

Embracing Antarctica’s Chill

My orange expedition gear stood in stark contrast to the endless white glaciers surrounding me.

Me in my gear

Sailing Through Ice Kingdom

The silence here was profound, broken only by the crack of ice and the whisper of wind.

Great view from the boat

Gentoo Penguins

Nature's most charismatic ambassadors, the Gentoo penguins.

Cute penguins

Conclusion

The journey to Antarctica showed me that some places on Earth still hold the power to humble us. I encourage you to put this on your bucket list.

SAN RAMON, CA / ACCESSWIRE / December 27, 2022 /Grid Dynamics Holdings, Inc. (NASDAQ:GDYN) (Grid Dynamics), a leader in enterprise-level digital transformation services and solutions, today announced the acquisition of Mutual Mobile, an innovation leader that brings digital experiences to life through an integrated approach to design and technology. With around 200 employees across India and North America, Mutual Mobile will contribute significantly to supporting Grid Dynamics’ objective of diversifying its global client base and enabling quality engineering talent to join the company.

“The acquisition of Mutual Mobile accelerates our strategic expansion into the India engineering market and further solidifies Grid Dynamics’ commitment to global growth. Their technology leadership and deep engineering skills, particularly in the area of mobile technologies and UX expertise, will enhance our ability to mobilize highly-skilled teams to support our clients in building innovative, end-to-end digital solutions. With the acquisition of Mutual Mobile, we have continued to strengthen our position as a global technology company”, said Leonard Livschitz, CEO of Grid Dynamics. “Teaming up with Grid Dynamics empowers Mutual Mobile to offer our clients new solutions far beyond the realm of emerging technologies. The immense service offerings of Grid Dynamics means our customers now have access to a powerhouse capable of delivering unrivaled business results. Most importantly, we are joining an organization that shares our philosophy that incredible employees are paramount to unleashing incredible innovation.” said John Arrow, CEO of Mutual Mobile.

I share the full story of building and selling Mutual Mobile in my Beyond A Million Podcast interview.

About Mutual Mobile

Headquartered in Austin, TX, Mutual Mobile brings digital experiences to life for global brands through an integrated approach to design and technology and specializes in using emerging technology to design and develop new applications. Mutual Mobile core offering spans edge and mobile computing, augmented virtual, and mixed reality, Internet-of-Things (IoT), connected devices, and conversational interfaces.

About Grid Dynamics

Grid Dynamics (Nasdaq: GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud & devops, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, Mexico, UK, Netherlands, Mexico, Switzerland, India, and Central and Eastern Europe.

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