Episode 232: Exploring Artificial Intelligence with Daniel Faggella

We're back. All right, Kelsey, so I. A little technical issue there, so we had to cut that around. I do want to thank our core partners to make the show a reality. Who's the folks?

Nextiva and Risi Bank.

Good. We'll give you more details on them in just a little bit. Daniel Fagella. Let's talk about this guy. Yeah. CEO and founder of Tech Emergence, the only market research and company discovery platform focused exclusively on artificial intelligence and machine learning. He's the previous founder and CEO of an Inc. 500E commerce company.

5,000.

Oh, Inc 5,000. Okay. But he was in the top 500. I heard it's called the science of skill. I'm of the belief. This is Daniel speaking. I'm of the belief that the most important ethical considerations of the coming 30 years will be the creation or expansion of sentience and intelligence in technology. And when we saw that, we're like, daniel, you gotta come back on the show and you gotta explain what the hell does that mean? Daniel, welcome to the show, brother.

Glad to be back, Mike. Cool to be here with you guys.

Oh, it's awesome to have you back. Artificial intelligence is clearly the future, and it's so misunderstood. What do people think is true? Which is far from true.

Yeah, well, I'm definitely of the belief that the long term concerns of AI are a big deal, but in many ways, the short term of AI is kind of plodding along progress. And so I think there's kind of two camps right now in the business world. There's the people who really kind of think it's all impossible and silly, unless you're Amazon or Google. And then there's the people who think everything plus AI is a more valuable version of that thing, when in fact, AI can't. Can't do much of it at all. So I think it's a matter of not having a nuanced understanding of what AI can actually do and either thinking it's the greatest or it's the worst. And both of those are certainly wrong.

Well, let's define what is AI exactly? Besides the words artificial intelligence, what does it really mean?

So, broadly speaking, these days, Mike, when people say AI, the reason you and I are talking about it, for example, is mostly because of the branch of AI called machine learning, which has become prominent in terms of its business applications in, let's say, the last four or five years. Really? And, well, we could even argue that really, it's only been in the last two or three years. So machine learning is the idea of Instead of, let's, let's say old school AI Mike, where, how, how we made machines smart, which is broadly what we mean by artificial intelligence. Having machines do what humans used to do, you know, complex SAS humans used to do. The old way to do this was to hard code a set of rules. Okay, well, if this happens and do this and if this happens and do that, and you know, to some degree we consider that to just be software now. I mean, that's, that's kind of what we think about machine learning is the ability to train a machine. Very, very specific sort of, kind of neural network, so to speak, on many, many instances of something within a business, whether it's instances of fraudulent payments, instances of malignant tumor images, instances of, you know, high customer lifetime value customers, for example, and allow machines to make inferences, judgments and predictions that used to require human beings. So when we say what is machine learning? It's really the ability for machines to learn from past instances as opposed to having to be programmed from scratch. That's the big revolution here. That's a big revelation here. And broadly, that's where most of the exciting business applications lie.

So, Daniel, is a machine basically writing its own if then statements? Is it writing its own rules?

You know, it's actually even harder to explain than that. So in this case, the machine actually isn't writing really any if then statements at all. And in fact, most, let's say neural networks have no ability to explain or understand what they're doing. So if, for example, Mike, I'm a, I'm a machine and you give me a million images of tumors and you label which ones are malignant and which ones are non malignant, you know, benign or whatever, right? So, so you give me, you give me a million images, half benign, half, half malignant. As a machine, I'm not actually looking at colors, I'm not actually looking at shapes. What I'm looking at is a series of ones and zeros because all those pixels are some tiny permutation of color within a tiny space. And that's just being read off as ones and zeros. So when I'm looking at, quote, unquote, commonalities of images as a computer, in the case of machine learning, for the most part the vast majority of applications, I'm not, I'm not really in any way identifying, oh, these have the same shape or this, this sort of darkening is very similar. There's none of that acknowledgment, darkening, lightening, none of those things are kind of clicking in the Quote unquote brain of the computer. I'm just finding patterns in the ones and zeros. And we don't really know if that's darkening. We don't really know if that's the angle of these curves. We don't really know if that's a color combination. And so a machine might be able to make a pretty good prediction, one that's better than humans. But unlike humans, in many cases, machine learning is unable to, to tell you why because these if thens are not really being determined. Rather ones and zeros are being drunken in, in a way that's very different than human thought. And so for that case, there is a challenge with the interpret, interpretability of these systems, which is a big, big problem for machine learning in general. Doesn't stop Amazon and Google and these guys from leveraging it in a very powerful way, but sure does make it hard for, let's say a doctor to use it if you have to explain to a patient why you're giving them a certain treatment.

Oh yeah. You know, one thing that's interesting is the acceptance of artificial intelligence. You know, one application of course is in autonomous cars. Cars driving themselves.

Yep.

And I was just in Vegas, I think it was last week, else I was out there for an event and I use Lyft, whatever, you know, Uber competitor, and it's like, oh, would you accept an autonomous car? I'm like, yeah, no problem. I told my, my brother in law was over and was talking with him and my sister and their family and I'm like, oh, it's autonomous car. And they're like I would never do that. And, and that's the response of most people. Like I would never sit in the backseat. That's so dangerous. Yet statistically already these autonomous cars have proven to be safer drivers. And they're just in the neophyte stage of developing this technology. Why, why is it, Daniel, that's that mankind people are so resistant in some aspects to artificial technology?

Yeah, you know, yeah. AI in general. It's a good question. I think there's, in my opinion there's kind of two categories of that fear. So there, there are, there are types of that fear that I think won't go away. And there are types of that fear that I think we can suspect very much will go away. And I'll explain what I mean, let's talk about, you know, airplanes. We don't even really need AI involved in this. We'll just talk about airplanes. You know, if it's 19, I don't know 1919 or something. If you fly in an airplane, yeah, you're, you're some kind of weird wacky daredevil guy because, well, to be honest, back then they just, they frankly weren't that safe. I mean, today an airplane is, you know, the safest way to travel, but they weren't all that safe. But also there's just the fact that you're actually up in the air and that's really weird as a human being. Like, like you're, you're doing things that naturally incite fear. Today though, I think many of us are in airplanes so frequently and everybody we ever met for the most part has been on airplanes and they're still alive. And so there's reason to suspect that, you know, that that kind of fades away. You know, for, for our grandparents, the idea of 10 year olds having iPhones is, is the most tragic sin in the world. But for a 10 year old, it's just the most normal thing in the world. So there's kind of two aspects of this. To one degree. When these technologies become more and more commonly used, I think much of it, and in fact I might argue the vast majority of AI applications will become as normal as tech is today, which we should all understand is not normal. If you just got transported from 1920, it's mind blowing and confusing if you got Transported from, from 1920, but to us it's very normal. So for the most part that's going to happen. However, Mike, there will be instances where there's still a thing that naturally we fear and flying up in the air in a metal tube is a thing that people can still fear even if, you know, statistically it's safe. Also, handing over control for something that admittedly does involve danger to a machine may not fade entirely. I think overall self driving cars will be much safer, assuming that the technology continues to improve and adoption kind of kicks in, which I think we can suspect in the decade and a half ahead. But, but there will always be some fear. So I think most of it'll evaporate. But anything where there's visceral danger or visceral kind of, it could be danger with our finances, danger with our physical bodies, whatever. There probably always will be a bit of a tinge to man, I'm letting a machine do this. But for the most part, Mike, I think adoption's just gonna, just swoop on by and we're just gonna accept it the same way we do the Internet, the same way we do iPhones, the same way we do, you know, automobiles as they are today. People used to be nervous that we weren't riding horses anymore.

I was going to ask, in the near future, what industry do you think will be most impacted by AI in terms of taking over human jobs?

Yeah, I mean, tough, tough call there. This is a different question than what industry will be applying AI the most? £ for pound, that's, that's a question I also like. And these answers are kind of different if we talk about like automation potential. You know, if I were a betting man, and I'm not really a betting man, but if I were a betting man, we've done a lot of interviews on this space and domains where. So what I'll articulate is two things I want, because I don't want to come across like I'm a prognosticator, like, oh, yes, why these jobs will fade away. Instead, what I think would be helpful would be to say, here are the qualities of jobs that won't be automated soon. And then by me doing that, I'll also kind of tee up maybe a few categories that feel like the lowest hanging fruit, if that's an acceptable way to answer your question. Okay, so tons and tons of interviews. So I did a TEDx at the university of Rhode island less than a year ago here. And just, just Dan Fagella and Google will pull it up, but it's about, it's really crappy. They, they left the title Just Artificial Intelligence. I gave them a title. I was like, it's about the three factors for job security in an age of artificial intelligence. And they were like, yep, we're just going to call the title of your video Artificial Intelligence. Super unfortunate. Nice job, guys. God bless them. But anyway, so not bitter.

Not bitter.

Yeah, not bitter at all. Yeah, I don't go to sleep with just, just seething anger in my heart at all. Not about. But anyway, yeah, it's my alma mater. You know, I got love for him, but I was just a little bit sad about that because no one knows what the hell the thing's about when they click on it. Regardless, it's one of four TED Talks up there that I've got on the site, but I went back and looked at. I mean, we do multiple interviews every single week with experts in all kinds of industries. And there's always questions around what facets are getting automated and optimized and looking across patterns from the last two and a half years of interviews. And we've been doing them for five, but doing a deep dive in the last while. Really one of the biggest Kind of commonalities for what will make a job secure are the following. One of them is sort of ability to do connection and cooperation. So, for example, if you manage people and if kind of high touch, personal tact is extremely important in your job, and you're very good at it, machines are highly unlikely to take your job. What does that mean? What does that mean? Well, managing a sales team where you have different personalities, different motivators for different people, different kinds of, you know, touchy points and prickly points for people, different kinds of goals you're setting for different people and ways to interact with them, that would be a position that involves a lot of tact. If you are a sales guy and you're. You're closing big B2B accounts, you got to talk to executives, you got to smooth talk someone at the front counter. These kind of things are not going to be that kind of trust building. That kind of high touch is not going to be machinable anytime soon. But we don't even have to stay white collar. We also talk about connection and collaboration, or connection cooperation. You know, a preschool teacher does a lot of that, does a lot of both of those. Now, we're not talking about running a huge, you know, manufacturing operation as a manager. That's very different than running a classroom. But still, you got to be able to cooperate, you got to be able to relate, and you also have to be able to care and have kind of that resonance and empathy. So as a nurse, as a teacher, almost nobody is bullish on or. Yeah, almost no one is bullish on those jobs being evaporated anytime soon. So high cooperation, high connection, if they're critical for your job, and if you're damn good at them, then it's likely you're going to keep your role. There's some jobs where you don't need to be good at connection. Right. If you're a checkout clerk, yeah, it's nice if you can talk people up, but to be honest, if that's not critical as a KPI for your role, if it's not, honestly, if somebody who can't smooth talk can also check out items at the front desk, then it's not necessarily something that's going to keep your security. So it's critical for the role, and you're damn good at it. Collaboration and connection, then you're safe. Is that concept. Click. I got one more for you.

Oh, it landed. Yeah.

Okay. Sweet. So machines are not going to be owning the trust and the tact that a person can in roles where that's critical. A second thing is what I refer to as context. This is a broader concept, but I'm going to explain what it is. And this is, in my opinion, this is a, this is a hard one and piercing insight from many, many, many, many, many dozens of interviews with the likes of people at, you know, exec level, people at, you know, Facebook and DeepMind, and you name it, kind of talking about what's automatable, what's not. Here's what I mean. High context and low context roles. High context roles require knowledge about aspects of the business and aspects of the world that are pretty desperate, pretty spread out. Low context roles imply almost no knowledge of anything outside of the inputs you get, the work you do right in front of your face, and the output that you push out the other side. So let's talk about two low context roles, one in white collar, one in blue collar. A low context role in white collar might be like an auditor. So what you do is you get financial statements, only certain varieties of financial statements. You comb through them and do a certain kind of reporting and assessment and double checking and verification in a specific order and way. And then you push it along to, you know, whoever, you know, whoever that, that, that you know, paper goes to in the auditor flow. Next in line, more or less. I'm not saying auditor is a, not a respectable job. I'm not saying it's not occasionally a high paying job. I'm just saying if all you knew was you sat down, you had no idea what the heck was going on and you got that paper in front of you, you need your input, you need to know the work you do and then you push it out.

All right, sorry, you want to say something else?

Well, I was going to go into kind of what a higher context version of that would be to contrast it, but if you got a question, hit me.

So, yeah, I do have a question for you because I want to translate this into marketing, so.

Oh, great.

Yeah, yeah, yeah. So you know, we kind of talked about the, where artificial intelligence is going, the potential resistance that people have to it, and the exuberance for it too. But I think everyone on this, listening to this podcast can maybe apply it to marketing. And that's your specialty. So how does it apply? Just give us all the details there.

Sure, sure. So would it be okay? So I'm happy to go into applications in marketing. Do you want me to cover this kind of automatable, not automatable like in terms of a job role thing in marketing or just move into the applications.

Application, I think.

Okay, sure, sure, sure.

Time here. I want to get that. I want to get some juice.

No problem at all. So in terms of, in terms of the application space within, within marketing, there's, there's going to be a whole bunch of different domains. But most important thing to point out here, Mike, is that the businesses that will use this first, okay, not necessarily forever, but first are the businesses with the most digital footprint for their marketing and sales efforts. What do I mean? I mean this. If you are mailing out paper and if you are going to people's, you know, offices and, and you know, bringing them, you know, a wine bottle and having a conversation, those are not wrong ways to do marketing, but they are very hard to track digitally and to train a marketing machine on.

Gotcha.

So. So the businesses that are most likely to leverage marketing first are going to be the online and brick and mortar kind of retail folks and the online media folks, where almost all the touch points of how to get someone's interest, how to retain their interest, how to bring them back to the page, how to get them to add something to cart, how to get them something to buy and track them over time and look at customer lifetime value. All of that is in a digital system and relatively easy to see.

Makes sense.

Those are the people that are going to see this stuff first. Yeah, yeah. We can talk about where within marketing if you'd like.

Yeah, so I want. Yeah, so. So it's interesting. Last week we were talking with, or a couple weeks ago now, we were talking with a group that explained how the funnel works, how to collect all this digital data. We ourselves here at our office, a lot of our listeners, we've collected email addresses, contact information, but that's not AI, that's just data. How does AI apply, how do we leverage it and can we start leveraging today?

Two really important things. So I'm going to get into where AI is going to intersect with the marketing role, what aspects it's going to bring value. And that's actually based off of 50 different executives, interviews with people who are building companies around AI and marketing. So it's kind of the practitioners in the field, it's more than my opinion. It's sort of the broad categories from the experts who are kind of, you know, sitting on the cutting edge here. But in terms of can you apply it? This is. And I'll make sure to reference that research so people can check it out. But in terms of if you can apply it, the answer is going to be yes. If And I can tell you that, yes, if.

Is this an if then statement?

Yes, I am an algorithm. So the answer is yes, if vendor solutions can make themselves accessible to small businesses like you and I. So right now, Mike, okay, the state of the art right now in terms of AI and marketing solutions is that many kind of online media and E commerce oriented businesses can apply artificial intelligence. Can use, let's say, a vendor company for. For artificial intelligence. But for the most part, Mike, it's very high touch, and it has to be with larger businesses. Let me explain why let's say Target or Wayfair or Harley Davidson or something is much more likely to use machine learning than a small business. It's mostly because the volume of data required in order to be able to train an algorithm to judge who's a high lifetime value customer, what offer can we test and rotate in front of those people? The volume we need to train a system to get those incremental improvements to see an ROI is much higher than most people would wish. It is. And so for that reason, most of the vendors in this space, Mike, are very high touch, very expensive, and very much outside the reach of most small businesses. Although gradually that is changing. I can either talk about what they're doing with the big boys, or I can talk about how it's going to change and become accessible to me and you. Where do you want to go?

I want to go with the me and you. Because our listeners are small business. We want to compete with the big guys. I'm interested and intrigued how they're doing, but I'm really want to know how's this going to impact us when we can jump on it, stuff like that.

Yeah. Can you, can you actually still hear me here, Mike?

Yeah. Can you still hear us?

Yeah, it got a little bit like underwater.

Yeah, that's. That's our system is, is. Well, what our system does is it analyzes your voice as you're talking. We're collecting this data. It's artificial intelligence. We're going to replace Kelsey with.

That wasn't interesting.

Damn it. You're gonna. Okay, you're gonna replace. Sweet.

Thank you, Daniel. You now work for us. We own you.

Damn it. I've been absorbed into the mic board and all I did was show up to the darn podcast. For crying.

I'm sorry. We can only pay you $5 an hour. Thank you.

So, so what have I wrote myself into? Okay, so, so how does it become accessible to small business? This is critical. Ready? Okay, so here we go. So the way that These technologies will become accessible to small businesses is mostly through these vendors getting a lot of traction with kind of the mid tier and the larger tier. I'll explain what I mean. Okay, so there's vendors like, let's say there's a company called Persado that helps do kind of copywriting, edits for companies to create more high converting, let's say, email subject lines or landing pages or things like this. Okay, When a company like Persado, which right now involves a lot of data, a lot of experts, a lot of integration time, a lot of money, frankly, Mike, a lot of money to work with Prasado, you're not going to work with them as a small business. Generally, when they work with, let's say, 400 different fashion companies. Okay, when they work with 400 fashion companies selling to women, what's going to happen, Mike, is they're going to understand the possibility space of subject lines, landing pages, page design, et cetera, within women's fashion. And hypothetically, a small women's fashion business. Now, this isn't any business, it's a women's fashion business. This is still pretty narrow, but a smaller business. Instead of needing to say, okay, Persado, I have reams and reams of data for you to train on. Instead, they can say, hey, Prasado, I know you have already trained on reams of data to sell dresses, necklaces and shoes to women between 20 and 40. That's what I do, too. I want you to just hit me with the already amalgamated kind of general best practices from what the algorithms have picked up with your other clients that are similar to me. Now, that is how the machine learning idea, the machines taking in instances, making judgments, the only way that's going to become accessible to us little guys with little bits of data is when it gets trained on the people with big data who are very similar to us. And then we suck up those best practices and leverage the same damn algorithms. That's going to take time, but that's going to be the trajectory of these technologies.

What about Watson? Like, there's always commercials. You can get access to Watson for free. So I tinkered around, like, upload your data and then I'll tell you the questions you can ask me. Yeah, what about that technology? Is that just right now kind of a toy or is it valuable?

Yeah, so, okay, good question. Everybody in the startup space is going to hate on Watson because IBM is a big guy. And when you're a startup, you always have to present yourself as better than the big guy. But also Because I think in a bit of an unfortunate way, they've really put everything under this umbrella of Watson. They've got all kinds of different analytics and NLP and other things that basically everything that they're calling AI gets thrown under this umbrella. And you see kind of their visualization of what is Watson and it's like, you know, a flowchart with a bajillion different things. And it's kind of, some people feel like it's them rehashing and kind of connecting, you know, the back ends of like a bajillion of their other services and then calling it all AI. And so what is, you know, when you say, what do you think about Watson? Unfortunately, kind of because of the way that they framed things, I don't even know where to start with that because I don't know what part of it you mean. I presume you're talking about like the demo able. You don't have to be an AI programmer to try stuff that they do. Yeah. So, yep, yep. So what does that mean right now? Well, what, what's going on there is they are aiming to figure out how to get us kind of little folks to tinker around with and garner value from their solutions. Here's some things that Watson could be usable for. Now, to say that anybody with a fifth grade education could jump in and use it would be a lie. But to say that, that, you know, let's say somewhat technical person could get some reasonable value out of it, I think would be truthful. There's some things around like basic sentiment analysis that I believe kind of Watson can do. So you can, you can take, you know, reams of, let's say, blog posts or emails and you can get a sense of what are the angry ones, what are the happy ones, what are the, what are the, and then, and then what are the angry ones that involve this company's name and what are the angry ones that involve this other company's name or this person's name, for example, and we can maybe sort through some of our data in a little bit of an easier way. But, but nlp, Mike, in that broad sense, let's say sentiment or identifying individual words, you'll notice that that's not necessarily something bespoke to every company. If you train a system on the English language, you can upload something and do that because it's such a broad insight. So I think what Watson's doing for small business is a fun way to show people that you can tinker with, with AI and you can do kind of some fundamental stuff to turn that into consistent and robust business value is really not the same thing. And right now involves a lot more hands on work with people who are more formally trained in data and involves frankly a lot more data and insight than I think most small businesses can muster. At least today.

To me that's the future, but maybe not now. I'm thinking like sometimes I get an email from someone and it could say in the notes like, you suck. Right? So that sounds like it's very negative. You suck. I hate your business. But also be, you suck. That gift blew me away. I would have never done the same for you. Which is actually a compliment, right? Yeah, it sounds like the software is not sophisticated yet enough to distinguish that.

Maybe you, you, you're, you're right to suspect that if you had a lot of instances of exactly that, it may not do well. If you had a lot of strange, anomalous, colloquial phrases that were a combination of good and bad, then yeah, if you have, if you have a hundred thousand emails like that, yeah, you're going to get some wonky stuff. But if you take all your emails, my guess is there's only so many instances of that. So yeah, at a high hyper granular level, you may not be perfect, but I think you'd find that in the broad shake when it comes to things as simple as sentiment. A system like Watson or you can use the APIs from Google or Amazon or something would very much cut the mustard for that kind of a basic task. But here's the thing. How do you turn that sentiment information to business value? Realistically, like, okay, how do you integrate that so that all your most angry customer service messages get forwarded to your best customer service people? Well, now you got to figure out how to hook Watson up into your system. You got to see if it's financially worth doing that. Can you, can you pump that data out of there to the sentiment system and then have it fed back into your Zendesk and sorted in a different way for a different person? All of these things now become complicated. So yeah, can you play today, Mike? Sure you can. Can you get business value as like a solo non technical person? The answer would be nay.

Does not compute. What does nay mean? Do you mean no? Watson says he means no. So, okay, that just threw me off the whole nay thing. So what can we start doing today? If we want to start leveraging AI, are we just in a learning phase or is there actual practical applications that small businesses can do?

So, so in the small business world. I've got a, I've got an article that basically sums up like where like the state of AI in small business and frankly why most small businesses are not and should not necessarily be focusing a lot of time on AI. Here's, here's what people can do today though Mike, that would be productive. So the answer isn't well here's a sweet tool and if you send out emails you can make twice as much money with it. I'm not there, I'm not there with you right now. But where we are is the following to become familiar with the language and the value of data. Sort of roughly familiar with the concepts of what is machine learning? Why is data valuable? Those kinds of things could be useful all kinds of resources on that, you know, my site and otherwise. To, to and most importantly would be the following. If, if you're a business in let's say you're you're in E commerce fashion, let's say you're in, you know, you're a car dealership. Let's say you are geez, let's say you have a chain of shoe stores, you sell yourself footwear. The, the the ball game would be to find roughly analogous businesses that are more mid size or large. So, so who, who's a roughly analogous business to you know, if I do fashion online, who's a roughly analogous business who's who's selling fashion stuff online and then asking the following question. What is it that they seem to be using for AI related kind of tools? What is it that they seem to be using for advanced recommendations or search that seems to be garnering a positive roi? And the good news is this Mike, until the big or the mid tier people in analogous businesses to you are garnering robust, consistent value from an AI application, it doesn't actually need to be on your radar because the likelihood of you being able to get ahead of them is not necessary. So this might sound like a disappointing statement Mike, but I'd actually like to frame it as a positive because now you'll be the person who's not distracted by oh, is this a shiny object that could optimize my emails? Is this a shiny object that could do this for me? No. The people who are let's say 10x50x above my revenue, where are they using recommendation that seems to be garnering high ROI or anything in terms of AI when we find those cool now I will let my now I'll allow myself to Google it, allow myself to look for solutions. But until that happens it's damn well irrelevant for most small businesses. And that's kind of freeing, I think, because there's more hype around this than there ought to be at the small business level.

That's awesome. We got to start wrapping things up. I have one final question. What day and what year do we hit singularity?

Damn. Great question. Well, that's. That's what I'm most concerned with, and that's not the topic of this podcast.

I'm really curious what you think.

Yeah, you know. God. All right, well, if you ask me, we actually did a bunch of polls on this, Mike, so I'm not giving you my opinion, but sometimes, you know, let's say 2060 ish might be my ballpark. Although. Although, to be honest. To be honest, that. That might be a little bit more pessimistic than a lot of people who think it'll be a little bit sooner. But if I look at an amalgam of, let's say, the 40 PhDs that we asked that question to, the kind of hump in terms of response, the biggest kind of bump in terms of time frames is going to be in that. In that kind of range. So fingers crossed that that goes well for us, Mike, but we got a little bit of time before it does.

Yeah. Yeah. It's fascinating. And then some people argue we already achieved it, we just don't know it. Right?

Yeah. Yeah. Yes. That's an argument, too. I think it's. It's kind of a poetic argument. I'm more concerned with, like, when do human bodies become, you know, getting eviscerated and uploaded like that. That's. That's sort of when, you know, you can say, we're in it now, but it's like, buddy, unless you can jack your brain into a machine, like, kind of. Shut up. We're not.

Well, see, that's how I. I already made you. I already. You don't know this, but we already transferred you. You're working for us and you didn't even know. So I'm just saying we've already achieved singularity.

At least with you.

Daniel, you're just a lot. A lot of fun, and the insights are fascinating to me. It's such a cerebral conversation, and I think a lot of entrepreneurs don't have the time to hear and digest stuff like this. So just thank you so much for these insights. They're very impactful.

Of course. Yeah. I hope it saves some people some time. Mike, thanks for having me here.

Thanks, brother.

Thanks, Daniel.

All right, cool. So here's what we're gonna do, we're gonna. Oh, Daniel, are you still there?

Yes, yes.

Where can people learn about you before I let you go?

Oh, sure, sure, man. Yeah, it's just. So the new website is actually Emerge. So E m e r j.com is the website. It's all artificial intelligence market research. I'll also make sure Mike, I send along the. The deeper marketing thing. So if people want to know what are all the application areas in marketing? I'll zing that one along. But it's just Emerge dot com.

That'd be awesome. Okay, now. Now we'll let you go.

Perfect. All right, cool, guys. Thanks.

See ya. So, Kelsey, we're recap what we learned. We're going to do. We have a listener mail again. Wow. I want you to ask it though, like you're my artificial intelligence.

Okay.

Okay. And then we. I think that'll wrap our show. But first, let's thank our corporate partners. That's funny. You did that today. So you did that. That head swing just now. So, Jeremy, I'm like, I had a question for Amy. I yelled it out my office and Amy didn't hear me. And Amy's like, what happened? And Kelsey's like, she just did the head swing over there. Like, Mike wants you. Who's our corporate partners?

They are Nextiva and Recipe.

Yeah. So who do you want to talk about?

The other nextiva. Oh, my God, I love Nextiva.

You picked a winner. You picked a winner.

So nextiva is a voiceover IP phone system with the best customer service, and it's just a seamless process altogether.

Yeah, that's a good point. Their customer service is world class, super friendly, super helpful, and it's a very, you know, sophisticated technology, apparently. But for us, it's plug it in. If you have a question, call them, and they set it up for you. And they don't do the, oh, push this button. Make sure you do this. Is that work? They just like, all right, I've connected now.

Yeah.

All right, I'll take care of it from here. What do you want to do? Oh, you want the phone to, like, spring up in the air and land in your hand so you can answer it? No problem. We'll program that in right now. It pops out of the phone thing and hops strand. So I'll pick Receipt Bank. Well, the other winner. It's getting cold in here, isn't it?

Really cold.

At least the camera's working. So Receipt Bank. Receipt bank is a efficiency tool that allows you to take care of one of the most time consuming, frustrating components of running a business. And tracking receipts. You have to track them for your tax purposes and reporting purposes and just for audits and so forth. You just scan the receipts. In goes in. That's the sound effect it makes, which is kind of weird when you're at a lunch meeting and it does that, but in and it's into your accounting system.

It's like deja vu.

What do you mean? What happened to you?

We had this exact conversation on the air before.

Oh, we did?

Yeah.

Yeah. See? Well, it's more than deja vu. It's the facts. If it happens twice, it has to be factual. So it's pretty cool. It scans it in all your receipts, easy access to them. Great reporting tool. Great efficiency tool. All right, cows, let me hit the button. So what'd you learn today? Remember, you're up first.

Okay, so this was very cerebral, but my favorite thing is that you don't have to follow the shiny objects. As small business owners, you don't have to go crazy over figuring out what AI can do for you, because it can't really do for you right now.

So you say. You said it's very cerebral, but I say it's very cerebral and high touch businesses are the safest from AI. There's also an opportunity. It's just not here yet. Right. So I think the idea for us small business owners is sit and wait. But when it comes, be the first on it. Just like any other technology, be one of the first on it. Did you hear that? Did that come across on our podcast? I wonder. That means we just had a new member join us live on air. We have a big gong in the office. It's like these ones, huge. It's the biggest one I could find. It's probably 3ft by 4 or 4ft wide. Yeah, I would say 4ft, 4ft wide and tall. It's this round thing, and the whole building shakes. We actually got complaints because we had too many members coming over because we're gonging all day. Steve, the landlord, came up. He's an office below us, and was like, could you stop that? And we're like, we can't stop it. Members are. Members are joining us. What can we do?

You can't stop us.

All right, so that's what I learned, is we have time. We have time. So that's. That's a good thing. But pay attention. It's gonna. It's gonna happen. It's gonna come. Okay, do we have listener mail?

We do have listener mail. Okay. So this one comes from Jacob Spencer.

Hey, Jacob.

He writes my business buys a lot of assets as at once. Oh, wait, sorry.

No, no, no, let's do it again. Kelsey messed up everyone. Oh, did Kelsey mess? Oh yeah, she did.

A lot of assets as one off costs as we grow trailers. How can I account for this sort of spend? In your instant assessment tool as it is capital, not opex.

Yeah. So if you have capital expenditures. So when he says trailers, I presume these are truck trailers. Right. If you have a large capital expense where you have to put a larger money down in a one time instance, set up another account called Capex. So opex is for your regular recurring, ongoing kind of costs and Capex is for these one offs. Set up a Capex account and start paying toward it. So let's just say just for round number six, a trailer cost $100,000. I assume they don't cost that much, but let's just say. But say you only need a trailer once every 10 years and I don't know if that's true either. Then you guys save $1,000 a month. That'd be 12,000 a year times 10 years, $120,000 saved up. So you keep on putting away $1,000 a month into this capex account and then in 10 years you can buy it. So that's how you do it. Okay, great job. I get excited about answering people's questions. All right, Kels, I think we answered a question. I think we gotta start wrapping the show.

Yeah. But if you guys have any questions, just email ask mikemikalowitz.com and leave us a comment. Leave us a comment or rating review on itunes. Stitcher, tunein radio, profit versus podcast.com or any podcaster anywhere. Or YouTube. You can watch us on YouTube.

Yeah, definitely do that. And just type in Mike McCallow. It's YouTube. You'll find us there. Give us a review, please, on our podcast and send us a screenshot of it. And you get a free book. Yes, send it to Kelsey. Mike Michalowicz.com also visit my website, which is Mike McCallowicz.com. if you can't spell it, by the way, just go to mikemotorbike.com all the resources you need to eradicate entrepreneurial poverty. That's our mission. It's there for you. Tons of it's for free. You can get book chapters for free. You can get the Wall Street Journal articles I used to write, all for free. It's in our archive. Blood blog cast. It's like there you go blog posts and podcasts and also search is there for free right now. So, yeah, that's pretty cool. I think we're good.

I think we're good, too.

All right. Rock on. Eradicate your poverty first and do it for everyone else. You know what I'm saying? You know what I'm saying? You know what I'm saying? Here it comes. To do it, do it. Nice. Do it, do it, do it.