Ron Schmelzer | May 01, 2026
Ron, welcome aboard to the show. Thank you so much for joining us today.
Speaker 2:Hey, thank you for having me. Sounds like a great show. I've listened to a lot of episodes. You have a lot of great guests.
Speaker 1:I appreciate that. Did you by chance listen to the guy who implanted a chip under his skin and you can hack your phone by holding it and has like a magnet in his finger?
Speaker 2:Yeah. It's like, it's like, know, the URO ring, right? Yeah. That's really cool.
Speaker 1:Yeah. Crazy, crazy stuff. Well, welcome aboard to the show. We were talking a second ago about Claude Code and token burn. What is the good and the bad in Nagi?
Speaker 1:Tell us what you're doing and kind of what's been your experience here.
Speaker 2:Well, first I want to, you know, thank you so much for having me on the podcast. For those that don't know, I've been writing about and doing AI for decades. I know it sounds crazy, but since 1995, went to MIT, my undergrad advisory was Rodney Brooks, started iRobot and all that good stuff. You know, that's when AI wasn't cool. There was a period of time when AI was like not the hot thing.
Speaker 2:And about a decade ago, started up an AI research advisory firm called Cognilitica, which was acquired by PMI a few years ago. And that's when we started the AI Today podcast, which was one of the top two or three podcasts. And that was part of the acquisition. And, you know, I just can't stop playing. Just like most people, I can't stop playing with AI.
Speaker 2:The biggest thing that's changed of course, was generative AI. Can it came out of the woodwork and now everybody's doing it. And I think, it's addictive in many ways that you can, you know, type in a couple of prompts, start your favorite coding tool. Next thing you you got something, Whether it's a code, whether it's art, whether it's images, whether it's music, I do all of that. But now I think we're getting a little, we're getting addicted, right?
Speaker 2:And the supplier is starting to realize that, there's not enough supply to go around and the prices are going up and what you get for every token is getting down and, I I write about that also for Forbes. So I I'm a Forbes article writer contributor. I've been writing on AI. Do about 10 articles a month and that was actually one of my topics recently. It's like, does it seem like you're getting less per token?
Speaker 2:And the answer is yes, you actually are getting less per token, so something technical, but there is something there to it.
Speaker 1:So, you know, they're trying to get us addicted, right? The old joke about how software uses drug dealer terminology to find users and That's true. That's been around for a while, but let's start at the beginning. We're still seeing engineers and CTOs saying, hey, you know, Clorb code' and such, it just doesn't work. You can't use it.
Speaker 1:It's unreliable. We see these memes on LinkedIn where, you know, this train not going on the track, but going inside and all that crap. What do you say to the people that are not on the bandwagon What is the, what are the actual issues with, you know, quote unquote vibe coding and how do you get around them? What is the way that you can actually make it work?
Speaker 2:You know, there's a really funny, there's a lot of funny memes. There's a little really funny video about how this guy decided to go back to Quora and to Stack Overflow to say, look, I'm not gonna use this AI vibe coding stuff. I'm gonna go to Stack Overflow, and I'm gonna ask my questions there to real people and get real answers. And of course, what happens is it's crickets, and then all of a sudden, it's the AI bots are answering in Stack Overflow. So it's like, you're you're not gonna win on this.
Speaker 2:So I think we've sort of crossed the the chasm as it were, and it's we're not putting the toothpaste back in the tube. I'm like using every metaphor here. Like, you know, it's just at this point, AI is a tool that people are using. It's not a perfect tool. We shouldn't treat it like a perfect tool.
Speaker 2:It's not magic. It's a predictive tool. It's been trained on a lot of stuff and it can help us, but it's not the kind of thing you can just close your eyes and hit a button and things will work. But it it is accelerating. The reason why it is getting so much usage and of course, you know, codex and and and all the other tools and actually now a lot of the Olama tools is because they provide real value and real benefit.
Speaker 2:I think sort of this happens all the time with AI. I've been seeing it since the nine nineteen nineties that people, like, want to put all the sci fi stuff on it, and they see a lot of movies, and they think, oh, AI can, you know, run itself and take over the world. And then they just give all the responsibility to the AI. They give none to themselves. They're like, well, it doesn't work.
Speaker 2:And then they sort of try to take it back, but it's just a tool. And used properly, you can get great results. Used improperly, you know, hey, I've got I've got a miter saw in my, you know, garage here. I could probably use it the right way and I could probably use it the wrong way and end up with less fingers, but I'm not going to do that. So I don't know if that answered your question.
Speaker 1:I think that's a very fair approach. What do you, what have you heard from your guests and from your own experience? What is the difference between using it the right way and the wrong way? What are the things that you would say, no, don't do this. And here's some good practices for people getting starting, getting started with, you know, any kind of vibe coding platform.
Speaker 2:Well, best thing I've heard, I think I'm trying to remember who told, I think it might've been like the CTO of Home Depot of all places actually said this, that AI is the great averager, because it's been trained on all of this data. And so if you ask, if you use the model in a way that it's been trained, it'll give you basically on average, what is the general what what if you could ask the Internet a question and get the an answer back from the Internet, that's basically what you're getting from an LMS. If there are things that you can't do that are at least average, an AI system will at least get you up to average, maybe a little bit better because, you know, it's gonna give you the best of the average. Right? It won't it won't make you a rock star.
Speaker 2:It won't make you a superstar and AI won't perform right now, can generate art, but we can all tell. It's it's like we've all you know, I call it like it's like the subway sandwich effect. It's like you could have any sandwich you want from Subway, but it'll taste like a Subway sandwich. I don't know what it is about their sandwiches, but they taste like Subway sandwiches. And it's the same thing with like AI generated stuff.
Speaker 2:You can smell AI generated, whether it's text or or images. Now, interesting thing about code is that we haven't trained ourselves to recognize AI generated code as much, but it's going to look like that. Now, a great artist will of course outperform even the best model because the models are not trained for outliers. They're trained for generality, such as what they do, generalization. So I would say for people who are trying to say like, look, I'm not a great marketer, I'm not a great salesperson, or I'm not a great coder.
Speaker 2:AI will get you to that set level of, well, at least I can make you an average or slightly better than average salesperson, marketer, coder, whatever. Won't make you exceptional. And if you treat it that way, that's great. The mistakes that people make is when when they look for, exceptionality, in the system, and also when when they take their when they when they take the human out of the loop, that's when they get into trouble. The other thing that I'm starting to see more and more of now, especially that as tokens are getting more expensive, people are realizing that they're asking for repeatability.
Speaker 2:I asked this prompt to do it. It did a great job the first time, the second time, then the third time, it's went way off the rails. Like, what the hell happened? You end up screaming at the AI system. Didn't I tell you 10 times?
Speaker 2:Blah blah blah. And that's because, as you know, AI systems, they're they're not deterministic. They don't do the same thing every single time. They're probabilistic. That's how probabilities work.
Speaker 2:It's like you play the roulette table at some point. It's not gonna land on red or black. It's gonna land on green, and you're like, what the hell just happened? Well, it's a probability. So so if you want, you can need to treat AI systems as probabilistic systems, and you need to find deterministic systems, programs, rules, scripts that will do the same thing time after time.
Speaker 2:And if you're getting smarter now, what you're realizing is use AI to help build the deterministic system and then use the deterministic system over and over again, like have the AI system write a script to do something, and then run that script over and over. And if you want the AI system to operate, just have it change the script rather than having the AI system do the same thing over and over. Long winded, but hopefully that helps.
Speaker 1:I think that's very fair and I think a key element of this, and we'll get a little less geeky in a second, is that you need to build in these feedback loops that verify that what it's supposed to do, it's actually doing. So if you write, if you create your test cases first and then your code needs to be built against test cases and you have to build it again and again until the code actually passes the test cases, that's a feedback loop that will just make it work, right? But I want to step away from this kind of geeky area of coding and go into, and I'm a big fan of Claude and so, but there's other solutions from other companies that do similar stuff, but Claude has a thing called Claude Cowork. Now to me, this is Claude Code, but for the regular Joe, and what I've seen people do with this and myself, it will actually drive your browser and just an experiment that I showed a CEO of this company, I was like, hey, look, this is what you can do. It basically grabbed off a website the attendees for a conference, then went and went to LinkedIn 300, 500 times and found the LinkedIn for each and every one.
Speaker 1:Then it went to the LinkedIn and basically he described a bunch of 10 criterias to be like, what is his sales criteria? Like who does he actually sell into? And of course, because it was this conference and it was more relevant, but even in the people at the conference wasn't exactly. So then it said, is it a fit or not? And then it had Claude reach out to them automatically all through automating his browser.
Speaker 1:So now it's just working in parallel to him and doing all the stuff. It just, it lit his imagination on fire because like that was the work of a couple of people. Right there. So he has a guy doing some of this, another doing some of this, but now he's like, Hey, all this pre work. Now the guy could just pick up the phone and talk to the person as opposed to, you know, doing all this, this work on the internet.
Speaker 1:On the one hand, I mean, can hear and imagine business owners all over the world going, holy shit. But on the other hand, and you can talk to this too. I imagine massive, massive, massive, massive demand going down. Cause if one person is 10 times productive, does that mean we need 10 times less people in the workforce? Or is there going to be some other magic coming around where we're suddenly going to be 10 times more innovative?
Speaker 1:What does the future look like? How are you expecting this to explode?
Speaker 2:Only the easy questions you're asking.
Speaker 1:You're welcome.
Speaker 2:Let's see it. Well, there's so much wrapped into that. Well, first, first of all, on the whole co work. For a while, was this movement. A lot of people may not be familiar with this, but there was this movement called robotic process automation, which was this idea.
Speaker 2:It had really actually nothing to do with AI. It was the idea of you can automate your machine to do these repetitive tasks. And it was really for people like call center workers and data entry people that they had to open one tab and another tab and copy something from like an email or a spreadsheet and paste it. And basically the people were acting robotically. Like, it's like a person was not not even using their head.
Speaker 2:They're just like cut, paste, cut, and they're making mistakes and it was slow and blah blah blah. And these tools came out like in in the mid part of the last decade in 2010, twenty fifteen, seventeen, where where they would just record your activity. Macros. Yeah. Basically, fancy macros.
Speaker 2:And then they're they said, oh, this is robotic. I think they used the wrong term because then people thought it was AI, but it wasn't AI. And then they added some AI and made it smarter. But basically, was this. It's like, co work before co work and and didn't even use tokens.
Speaker 2:And it was all that all that sort of stuff. But the same argument was like, Hey, we don't need to pay all these people to do data entry. We don't we have all these people off offshore. And it did make a major dent in the what was called the business process outsourcing industry, which is you can imagine India and The Philippines and Vietnam and all these places were low cost of labor. Like, you know, why would you pay somebody in a high place to just type stuff in?
Speaker 2:So it did make actually a pretty, pretty big dent. Now what we're seeing here is the democratization that's now everybody has power of RPA. And and it actually will make a huge impact on the the workforce. So the argument has always been like, well, has been the case with technology all the time in the nineteen fifties and sixties. If you went to an office, you'd see a room full of secretaries with typewriters and filing cabinets, and that was all replaced with Microsoft Office.
Speaker 2:Basically, you didn't need any of that. Please send a memo. All, you know, all of that was replaced. Did we have mass unemployment? We didn't because new needs came about.
Speaker 2:The problem that a lot of people are talking about, economists mostly, is that that AI is so broad reaching. It's not just one industry and one impact and one technology. It's all the industries all at once and all these things that there may not be enough workforce capacity to take all that. And there are, you know, political candidates out there who are arguing for things like now universal basic income because they're like, you know, we need to offset. It's actually interesting.
Speaker 2:Even even Elon is talking about it, Elon Musk. But we won't get into that. The whole point is that that this is this is act macro factor, and I think nobody really knows, to be totally honest, what the long term effects are.
Speaker 1:You know, you talked about one element of the problem where it's happening everywhere all at once, right? But the other element of this is we look at the microwave, you know, from it being the size of a room and being military usage to every house in America and in the world took twenty years. AI has done that in basically two years. If you look, if you compare it to the internet, right? The early days, the DARPA project turned into internet that took ten years.
Speaker 1:This is happening on a timescale we've never seen before. I think that's one of the scary things. So, know, the real answer is we don't know. How let me ask you this. You have kids, let me assume.
Speaker 2:One is getting into SAT time. Actually, it's coming up right now.
Speaker 1:What do you reckon? So SATs, that means his university potentially is around the corner.
Speaker 2:Which is an interesting place to be. It's like, what are they going to go to college for?
Speaker 1:Right. So I was talking to this lady. She works at a university, wanted me to come be kind of on their board of advisors, never mind. She's like, Oh yeah, I'm sending my son to learn computer science. My response was, No, don't do that.
Speaker 1:That's the last thing he wants to study. But your son, is that correct? Yeah. Is soon going to university. What are you advising him?
Speaker 1:What should he study?
Speaker 2:Well, he's he's still kind of in the sophomore, junior. Yeah. So he's got a couple years to go. But interestingly enough, he's very interested in computers and computer science and computer engineering. However, I think he has a very well, in part because I'm telling him about all the stuff that's happening.
Speaker 2:He's very healthy perspective, which is that there has been, like there was, like, this big movement that everybody should be a coder. And, like, you know, maybe ten years ago or whatever, it's like, everybody needs to be a coder. And, of course, now that's, like, not great advice. But the thing is, it's like, what are we trying like, what is the next what are we trying to do with technology in general? I think he's thinking much more holistically.
Speaker 2:It's like, you know, the the the basics of computing to, you know, quantum to all these other movements. I think honestly, though, my biggest concern is I don't really know what the workforce itself is gonna be like, and so I I've been trying to provide advice like, go to treat college as like a in its in an like an ends in and of itself, like, go to learn something. Don't just go as a means to an end, like, I'm going so I can get a job where because I'm like, I couldn't even tell you. It's like, you know, ten years ago, people were like, go get a coding degree so you could work at Google, Microsoft, whatever, but they're all laying everybody off. So now that's terrible advice.
Speaker 1:That's right. I mean, the horrifying thing is that we, we truly don't know. I mean, the only advice I can think to give my son is either be a physician or a plumber because in the cascading effect of AI, those are probably the last two things to go.
Speaker 2:Yeah. In videos like, tell everybody to get into HVAC and be electricians because that's all they need.
Speaker 1:I mean, at certain level, you know, we say there's some truth to every joke and I think that's definitely true over here. So I think there's something to be said, wouldn't you agree, about not knowing where the future is going? And I think we've kind of agreed to that premise. What do you do? How do you carry yourself in the interim?
Speaker 1:Is it about working hard? Is it about studying? Is it about relationships? Is it about networks? How do we basically tell our children to prepare for a future, which is completely unclear?
Speaker 2:Yeah. What do we tell ourselves too? Right? It's like, telling the kids, it's like, I was like, well, let's tell ourselves this. Well, you said a couple of really good things in there.
Speaker 2:Building relationships and building networks will never do you wrong. It's like, you know, we are still a world of people and people still talk and work with people. Much as machines are doing things, it's still a world full of people, and it's the people who decide what things are important and what things are not important, what we should focus on, what we should not focus on. So yeah, I mean, absolutely. So like, know, when I'm telling telling my son, he's like, do like, do more activities, meet more people, spend your summers doing things like internships that you're interested in.
Speaker 2:You know, it doesn't matter what the the career, just like go and meet, discover new things. I wish we you know, when we were in school, it was actually a huge opportunity. It was it was like this this chance that we had to spend our time learning things without any real pressure, to be totally honest. It was it was a great amount of freedom in that. And then, of course, we, you know, get get older, we graduate, we get a job, we get married, we have kids and all sorts of stuff.
Speaker 2:And now we feel like we don't have that luxury to spend that time. But I'm thinking maybe that's what AI can give us. This is the luxury of being able to focus on the more important things that are out there. I don't really know. I can't tell you what the world will be at the end of it, but I think that's a great idea to be spending our time.
Speaker 1:I love your philosophy. You're saying like, we don't know anyway, so just do something, enjoy it and do it just really well. Just lean in and be awesome in your field and you'll figure it out one way or another. There is something really relaxing in that. I appreciate it.
Speaker 1:And, you know, just to circle back to your comments around interconnections and human connections, if you kind of look at that paradigm shift where AI is doing all the kind of busy work on, you know, finding these and connecting you, at the end of the day, that's really putting you in a position is you're spending more time connecting with people on meetings as opposed to, you know, looking for their contacts or whatever. So at the end of the day, it feels like those human skills are going to be more and more important in this new world. So I think that's a very fair comment.
Speaker 2:Absolutely.
Speaker 1:Let me ask you this. You have a wonderful podcast, so I want you to tell us a little about it. What is it called? How do people find it? And I'm gonna, since I have a podcast too, you're on it right now, I don't mind when people ask me what my favorite episode was.
Speaker 1:So I'm gonna ask you that same question. What was one of your favorite episodes?
Speaker 2:Well, of your podcast?
Speaker 1:Of your podcast. Not that. Not that. Not that.
Speaker 2:Oh my god. I haven't listened to all of them. So, oh my god, so many are great. So let me talk about the exponential scale. So this is actually my second podcast series that I've done.
Speaker 2:The first one, as I mentioned, was this AI today podcast started in 2017 when it was still data scientists, to be totally honest, who were like doing the AI machine learning engineers, started really wonky, all of a sudden it became, oh, AI is everywhere. That was acquired with the company acquisition and there's it's actually still running. AI today is like, I think on 600 episodes at this point. When we sold it, it was like 500 episodes. Was a lot of I feel your I feel your energy.
Speaker 2:By the way, side note, podcaster to podcaster because I love podcasts. I love podcasters, which is why I couldn't say no to Ari. Of course, I had to say yes to this podcast that when we started, it was just audio. And the funny thing is, like, now podcast is audio and video. So when I started the second podcast, which is called exponential scale, that's what I'm working on right now, I have, like, now I gotta do audio video editing, which is a lot more time intensive.
Speaker 2:Now I got to use more AI for that. That's a whole other story. But the Exponential Scale Podcast is focused on the idea that people can now scale themselves and their organizations to a much greater degree without having to rely on raising a lot of money and hiring a lot of people. So it's basically this response to sort of what entrepreneurship was, has traditionally been, especially in the last twenty, thirty years, unicorn dreams, raising a lot of money, hiring big teams. I think that's starting to fray at the edges now.
Speaker 2:And so what I do is I look for this is actually a challenge. I'm looking for those small teams that have scaled really big. It's easy to find big teams who have scaled big. It's easy to find small teams who are not scaling, but the small teams have scaled a lot. I all of them are like my children.
Speaker 2:I love every single one of them. The one that's sort of like has has been the most interesting to me so far, we only have 30 episodes in the latest podcast, 500 in the previous one, but 30 something in this one is the is the company called Datto CMS. They're an Italian company who has this what's called a headless CMS, which is you can basically use a client, you know, CLI or API, and you can create websites and all sorts of stuff. Interestingly enough, not AI, it's just scripts and stuff, and it's just 13 people. They're doing $6,500,000 in revenue, which is fantastic, over $500,000 per engineer, per person, and they have the most chill perspective on work because there's no pressure.
Speaker 2:So they're like, one hundred thirty, thirty five hours a week is what you should be working. If you have to work more than that, then they're like, something is probably not right. We don't want to work more than thirty to thirty five hours a week. Why should we ask anybody else to work thirty to more than thirty, thirty five hours? Because they're so small, they have a personal relationship with all their customers, which is great.
Speaker 2:And so, know, you guys email them, they're there, they don't have like layers of management, you know, so and then they talk. So one of the interesting things we talked about is like, well, how do you give your employees incentives? Because that's usually one of the responses is like, well, if you're not venture backed, you're not gonna become a unicorn. How can you retain people? They're like, we have this profit sharing plan, which was developed by a company called Balsamiq, and they and they put that out there.
Speaker 2:And we also have this other plan. This this interesting way of doing monthly planning. And they're like, there actually is this growing body of work that's out there. That's this other it's like a different way of thinking. So this is I might have mentioned prior to recording that I'm I am working on another book, like, well, let me collect all these things together.
Speaker 2:It's like, not raising money, at least this way. It's raising money, maybe a different way. It's growing through profits, of course, and bootstrapping, but it's growing through that sustainably. And it's a different way of hiring, a different way of giving incentives. So stay tuned for that later in the year.
Speaker 1:There's something, I mean, something's going to fundamentally break. I mean, if we kind of look at the economics, then, you know, one person can do the work of 10 and I'm seeing that firsthand. I am one of those small companies that's scaling. And you know, on the other hand, we're seeing the sales and marketing on the sales and marketing side. We just talked about the fact that with Claude coworker and whatever come next, you can do the work of 10 people.
Speaker 1:So this seems to be the future. Let me ask you this. For the aspiring entrepreneur who knows nothing about these things and is like, really? I can be, you know, I can build a million dollar business with just a handful of people or less. How do you get started at that?
Speaker 1:What's the best way to kind of think about this, started beyond listening to your podcast? Where, where should people go? How should they think about it? And is there maybe more importantly, do I need to be like a hardcore engineer to be able to do this? Or like, is there a certain skill set that I need?
Speaker 1:Or is this truly the democratization dream we've been waiting?
Speaker 2:That's a great TF. Thank you for asking.
Speaker 1:Hey, there's a story arc here. There's a story arc.
Speaker 2:So, there aren't many communities of people who are in this sort of mode of thinking. So one of the things I built about six months ago was a community called Scalabrate, which is a membership community of people who are building businesses like this. The interesting thing is, is that most of the new builders are non technicals because as you know, what it takes to build a business is not really technical know how, it's know how of the problem you're trying to solve, the domain. So some may be like, well, know this problem really well. I just I might not be a coder.
Speaker 2:But the funny thing is is like I see a lot of coders who build companies, but they don't know anything about sales. They don't know anything about marketing or finance. So the thing is is that you have your area of expertise. That's what's usually called t shaped skills. You have an area that you're really deep on.
Speaker 2:But if you're if you don't have any breadth, you're super narrow. You you don't know whatever. So you have to build t a little bit of knowledge in a bunch of places and a really deep knowledge. But the interesting thing is if you collect people together, then all of a sudden you have some good breadth. Now the old way to do it is you got to hire a salesperson, hire a marketing person, hire a finance person, but you don't have to do that anymore.
Speaker 2:As I mentioned, AI can at least give you the T, the basic level of competence. And then I can actually go out and find other people in my network. Say, hey, you're a marketing person. Let me just contract you like you because you have a t shaped skill. You're great at marketing.
Speaker 2:You're just not good at other stuff. And so the the vision, the grand vision I have, this is a bit of a dream, is that I do think that if we can all get together and we can all build our little multimillion dollar business first of there's nothing wrong with a multimillion dollar business. We we build our multimillion and we we join together and we each support each other. And like there's a hub. It's like an alliance.
Speaker 2:I think we can do much more together as a group than any of us could try to be like, well, I want to be the one unicorn and take over the whole industry. It's like, first of all, it's not good for me, but it's also not good for you either. Know, being a unicorn ain't so hot. So, you know, that's my vision. And and I it's also very international because these sorts of companies, as I mentioned, this company is Italy, there's companies in Africa and in Europe, just everywhere, Asia, America, it's like we can build this, you know, we could do more together as a whole than we can individually.
Speaker 2:So that's the that's the big dream. And maybe economically, it'll have much bigger impact. I think, as you know, the economy has really gutted the the middle class. And I think if we can give people more empowerment to build not just financially viable things, but sustainable things, maybe we could shift the weight has gone like this, right? Billionaires and trillionaires linear.
Speaker 2:You can kind of squeeze it a little bit.
Speaker 1:I'll push back just a little bit of that and I'll say that if you look at, you know, five hundred years of history, every single class is better off than they were if you look a hundred years back and a hundred years back, so you know, I don't entirely agree with this idea of the bigger gap between the rich and the poor because every single class is better off. That's easy to prove and show
Speaker 2:up Yeah, on general level has gone up. The problem is that the weight has been pushed quite a bit. Like there's been a lot of new wealth creation in the last, say, twenty years. It just hasn't, you know, it's been overly emphasized into
Speaker 1:And here's my, it's true what you're saying, but here's my point. Every class has been doing better. And if we look at those points in which each class has done better, jump to that next level, It has never been, unfortunately, some kind of advocacy or nonprofit doing it. It's always been the introduction of some technology. So when we go back and we look at the printing press or the mobile type, we look at the internet and AI in my opinion is that next innovation that is going to redistribute wealth in this incredible amazing way.
Speaker 1:So I'm more on the optimism side when we come and talk about the wealth gap. I think it's getting better and I think this is the next big opportunity for it to get better.
Speaker 2:I actually completely agree completely agree with you. What I what I'm saying is that, there there there can also be a movement component to it, that it shouldn't just like happen on its own, that maybe there's ways of, I I'm totally with you. AI is a democratizer, if you will.
Speaker 1:Perfect. So let me tell you this story. I was just, this was just yesterday. I do some pro bono work. I was talking with a firefighter and I've been working with him for a couple months.
Speaker 1:I started off by just showing him and teaching him over a couple sessions, Lovable. And this is a few months later now yesterday, he basically built a product to help firefighters like manage their PTHD, their stress, their insurance, getting help, etcetera. And you know, now I basically gave him a one hour session on, Hey, this is how you build a business plan. And I sent him to the Techstars Toolkit. I don't know if you've ever come across that.
Speaker 1:You know, think about this firefighter, plumber, now with AI is building a business. Absolutely. Is incredible. I mean, this is the dream that you're talking about and this is the potential future. My question, and here's my fear.
Speaker 1:My fear is if we don't get on board with this sooner than later, we're going to have a horrific downside with massive unemployment. So my question to you is this, how do we propagate this education of AI building businesses, creating this positive future that you're talking about sooner than later?
Speaker 2:Yeah, I think a lot of it is ironically doing more of what you did, which is that people need to it's like talking only gets you so far. It's it's learning. It's kind of the doing and the sharing and the knowledge. I love this idea of hack athons and show and tells. You know, before I did Cognolica, I ran this big demo event series called Tech Breakfast.
Speaker 2:I'm actually wearing it right there. Tech Breakfast, which is just a morning demo event. Exactly what it sounds like. I did it all over the country and it was just show and tell. And there's like this power to show and tell.
Speaker 2:Even if I even if not I'm not showing you something that's specifically for you, like, of a sudden, like, the light bulb goes like, wait a second. I know you're showing that for the dentist business, but, hey, I can actually do that for me. And a lot of it comes down to like, it's just those light bulb moments. Now, does that mean we need new forms of workforce education that should come from some, you know, organizational, could be governmental, could be nongovernmental, could be universe? I think so.
Speaker 2:You know, when I think about the universities, they're actually struggling quite a bit right now. We're going back to the conversation. Struggling for many reasons. Some of it's self inflicted because what we're happening here in The US, whole other story, what we're doing with international students. But also a lot of them are struggling because of the cost of of education has got so high and the job force is not there.
Speaker 2:Actually, lot of schools are shutting down, has been has been the story. Well, what if what if universities could tap their resources for those who are already in the workforce? And and and we could somehow there's been a lot of talk of reskilling and upskilling and all sorts of things, but it's to your point, which is that a lot of it comes just to lack of opportunity, lack of knowledge, lack of experience, lack of guidance. And if they can be self empowered to create things that go and bring it back to the scale of bright vision here, and we can all band together, then yes, we can not just the whole, you know, the the tide raises all the boats, which is to your point, like tide raises all the boats, but it's that people can feel that they have control agency over their future, and they could build something that will last and that they won't be like, you know, at the whims of some employer, or, you know, that's really the big issue is that you can't really count unemployment anymore. But there's to a self employment.
Speaker 1:Yeah, I think that's brilliant. And you touched upon a really important issue here. If we kind of look at government as an operating system and it's got these different modules, I think each and every one of them almost needs to be reinvented to a certain degree, right? Department of Education, how are we doing education, not just at the university level, but what should we be teaching our youngsters? That needs to be rethought and the speed at which AI is kind of changing everything means we really need to get on our game or we're going to find ourselves losing to China and So there's something that needs to change there for sure.
Speaker 1:Let me ask you this. If you had, if you could be God or king for the day, would be the one or two laws that you would change in order to create a positive effect?
Speaker 2:Quite a supposition there. If I could be well, if I could have influence on the laws, let's put it this way. I think the biggest thing that I would really think carefully about is really thinking about how we incentivize people to it's at the whole relationship between the employer and the employee. And I think we need to real really think about, it's not really a law thing, but it's really we need to think about how resources are applied because right now everything is so focused on the employer. Like, we give tax incentives to companies to open up offices in places in hopes that it will do something for the economy, but could we just not use that money more directly?
Speaker 2:It's like, well, wait a second, instead of giving it to the employer and the theory that they might hire people, and then at some point that may end up economically, I could go more direct to people who are going to get more direct benefit. I think it's scary for a lot of people because we haven't we don't have a lot of experience with that sort of, you know, direct, you know, economic environment here. But I think, I think there's a lot to be said about, about investing in people and investing in communities and, not being so focused on the employers. I
Speaker 1:Well, in fairness, in the, you know, the fifties, the sixties, the seventies, the mom and pops were the thing. They were the majority almost of The U S economy. So, I mean, I would love to see your vision, you know, bring back the mom and pops.
Speaker 2:Absolutely.
Speaker 1:And you know, these single, you know, small family businesses or other, I think that is potentially, you know, one way that we kind of save the economy from this massive crash, maybe even before it actually happened. So I think that's wonderful and rethinking the laws and the incentives to support the resurgence of these mom and pops as opposed to these multinational trillion dollar businesses. I think that's a wonderful outcome. Believe it or not, we're out of time. We have only one question that we always ask every single guest and that is this.
Speaker 1:If you had to think about the hardest time or biggest challenges you've had in your life, whether it's two years ago or twenty years ago, what advice would you give yourself?
Speaker 2:So, of all, fantastic. And yeah, we could have, we could be talking for another hour. You're only asking, as I said, you're only asking the easy questions. No. But, the advice I would give myself is this idea that the best way to succeed is just to keep going.
Speaker 2:You know, just don't stop. Don't give up. You know, it's it's, of course, the you hear the thing about grit and you hear the thing about having patience and having all that sort of stuff. It's easy to get sort of lost in the moment, sort of think in the short term, but, like, nothing of consequence, like, you know, happens easily. Everything, you know, that matters takes time.
Speaker 2:And I think, you know, the advice I give to myself is like, you know, just if you believe in something, then stick with it. Keep believing in it. Don't lose the spark and just focus on on and every things will it's just strange. Things just do come together at some point. If you're doing things the right way and you're not chasing the short term vision, will get to the end destination.
Speaker 2:So I have a long term vision now, so I'm definitely going to stick with it. It's not going to take one year or even two years, it may take ten years, but I do think this is the moment, and I'm really all in on it.
Speaker 1:I appreciate it. You know, there's something about this concept of it, you know, it took me ten years of failure to become an overnight success. I think a lot of people forget that that's really what it looks like and you don't know you're a success until suddenly other people see it. Ron, thank you so much for joining the show. This has been an absolute delight.
Speaker 1:I appreciate you.
Speaker 2:Thank you, Ari, for having and I'm gonna look forward to possibly having you on the show. Love to hear you by Happy your sailing
Speaker 1:to do it. Thank you, Ron. Thank you.
