[00:00:00] Speaker A: Servers in the cloud are just way faster than our human brains.
[00:00:03] Speaker B: The big dreams cost money.
[00:00:05] Speaker C: You love Stephen Singer. I hate Stephen Singer.
[00:00:08] Speaker D: I'm Richard Gearhart.
[00:00:09] Speaker E: And I'm Elizabeth Gearhart. You just heard some snippets from our show. We had amazing people on. Listen for the rest of it.
[00:00:16] Speaker A: Want to protect your business? The time is near. You've given it heart, now get it in gear. It's passage to profit with Richard and Elizabeth Gearhart.
[00:00:28] Speaker D: I'm Richard Gerhardt, founder of Gearhart Law, a full service intellectual property law firm specializing in patents, trademarks and copyrights.
[00:00:35] Speaker E: And I'm Elizabeth Gearhart. Not an attorney, but I work at Gearhart Law doing the marketing. And I have my own startups.
[00:00:41] Speaker D: Welcome to Passage to profit, everyone. The road to Entrepreneurship, where we talk with startups, small businesses, and discuss the intellectual property that helps them flourish. We have an amazing guest who I'm really looking forward to talking with. Kevin Serace, a Silicon Valley innovator, serial entrepreneur, disruptive keynote speaker, and Broadway exhilar producer.
[00:01:03] Speaker E: Yeah, but how many patents does he have?
[00:01:05] Speaker D: He has a lot. He has 94 patents worldwide, which makes him close to my heart. And he's also made some startups too. So going from zero valuations to a.
[00:01:16] Speaker E: Billion dollars, can't wait to hear his story. And then we have two amazing presenters. These people are really cool. So, Linda Hollander, I met at Small Business Expo in New York. If you have something that you want sponsors for, like a podcast or a show or a nonprofit or something, she's the woman to talk to. And then we have Stephen Singer. Now there is a story behind what he does. I'm going to let him tell it when his time comes.
[00:01:39] Speaker D: I was amazed to see that he was going to be on because I didn't know there was a realist Stephen Singer until.
[00:01:45] Speaker E: Because of the Billboards. I hate Stephen Singer.
[00:01:48] Speaker D: Absolutely.
[00:01:48] Speaker E: We're going to find out why people hate Stephen Singer. I'm not telling what to wait for him.
[00:01:54] Speaker D: But before we get to our distinguished guests, it's time for IP in the news. And this week we're going to be talking about Google and a new policy that's obviously designed to encourage people to use AI. And if you've been listening to the show and following, we talk about artificial intelligence frequently. And most of the disputes around artificial intelligence are being worked out in the court system. And so they're trying to decide, well, if AI makes a creation, can there be an inventor or can there be an author to it? And there's a lot of debate about who's going to own this content going forward. So Google has decided that if you use one of their tools like Vertex or duet, those are two Google AI tools that they will defend you in a lawsuit if somebody sues you for copyright infringement.
[00:02:47] Speaker E: So the creators that feel like the AI has stolen their creative content and used it to make something new have to go up against Google if they want to file a lawsuit.
[00:02:55] Speaker A: Right?
[00:02:56] Speaker D: And this is setting a pretty substantial policy because one small player may be willing to sue another small player. But if Google's going to defend the AI generator, then that makes that kind of lawsuit a lot more expensive and is a real discouragement to pursuing that kind of lawsuit.
[00:03:12] Speaker E: Well, there's a lot of conversation around this topic. Like, we were talking to one of the radio personalities and he was saying, but what if somebody takes my voice and generates my voice using AI to promote a product that I don't believe in? What am I going to do about that?
[00:03:26] Speaker A: Right?
[00:03:26] Speaker D: There's still a lot of issues to be worked out. I just think that this policy by Google is really interesting. And we have with us Kevin Sarace, and Kevin is an AI guru. He talks on this topic frequently. Kevin, what are your thoughts about this new policy of Google?
[00:03:41] Speaker A: I think this is an important move and I think we're going to see it by the big tool makers, if you will. And the reason is they are putting protections into these tools that don't allow you to generate something that you shouldn't and also generate something without attribution, for example. And you know copyright law a lot better than I do. But we all learn from the books that we read. We could read 100 novels and then we go write a novel. Now, we can't write a derivative work, of course, but we can go write a novel, right? And that's not illegal as long as it's ours. But it was informed by everything we read. And I think that you're seeing Google, say, with our image and or text generation, we put the right guardrails around it to not generate a derivative work and not generate something that is exactly the same without attribution, but otherwise you should be protected because you couldn't possibly know everything we trained on, right, I. E. Google. And therefore, you're going to have to trust that they put the right protections in so you can't get sued. And lastly, I think you hit the nail on the head is it's going to make it hard to sue individual users of these tools because Google has a lot of money and a big legal team.
[00:04:48] Speaker D: Well, I think the interesting part, though, is how do you create a tool that really draws the right line between something that's original versus something that relies too much on something that somebody else has already created?
[00:05:03] Speaker A: So how these large language models work, and I think that's one that we can look at, a large language model works by statistically placing one word after another. It has learned a trillion phrases, give or take. And from those trillion phrases, it knows how to build the English language based on the sentence structure of the English language. And then your prompt. So if we said today is Kevin's birthday, it isn't. But if we said that, it would probably respond happy, then what would come after happy? Well, we know birthday and maybe a third word, Kevin. They're the only practical responses. Right. So now if I say, I would like a poem about the weather where it's raining outside in the style of Shakespeare. Well, it kind of knows the style of Shakespeare, right. It's read plenty of Shakespeare. It was attributed as Shakespeare. And so it'll go ahead and write that in that style. Not exactly quoting anything from a book without attribution. So there's been rules put in place to make sure that it doesn't exactly do that. And I think they've tried to put these rules in place so that no one's going to call it a derivative work. Right. So you're not going to grab this novel and just change the names of the people, and it's exactly the same story.
[00:06:12] Speaker D: That's great. And this is the first time I've really ever heard how the AI process works. Steve, what do you think about all this?
[00:06:20] Speaker C: Well, not only am I not an expert on AI, artificial intelligence, I don't even know if I have any real intelligence. So it's a big problem for me. I'm a dopey jeweler. I could tell you that on our previous website, our previous platform, and our new platform, Artificial intelligence, was built, I was, at least at my level, I was slightly disappointed as to what it could do, what it couldn't do, and how much work and how much input it takes. I think it has unbelievable potential, and I think the fact that Google is protecting everyone is obviously, that's a game changer. I did a keynote speech at Google a couple months ago about a different topic, and Google is one of the most amazing companies on Earth. The things that they're doing, I don't think we even have a clue what's going to happen in the next three months, six months, a year. I think if we talk a year from now, we'll be in a whole different world with this. AI is just, I just don't know. I don't have the capacity to understand everything that it's going to do.
[00:07:19] Speaker D: My concern with AI is that it's just going to make me lazy, right. Because where I used to write out an introduction for a show, now just I ask chat GPT and it writes it for me and it does a really good job.
[00:07:32] Speaker A: Right.
[00:07:32] Speaker D: And if I don't quite like that one, I'll ask it again. I'll get a slightly different version. Right. So I wonder overall if our ability to generate content ourselves is going to diminish over time.
[00:07:45] Speaker C: Well, one of the things I think we have to worry about too is just, I think I'm not alone with this. I don't know any phone numbers anymore since I have my iPhone, I used to remember 5100 phone numbers. So I think the fact that anytime something does it for you automates it for you when you say it makes you a little stupider, I think, and a little lazier, I think it's true. And that's a concern.
[00:08:05] Speaker D: I mean, that's a great question. The question is, do we really need to know all those phone numbers if we don't have to?
[00:08:11] Speaker A: Right? Well, let me give you an example that everyone takes for granted. Now, if that's okay, which is the following. Back in 1985, Excel Spreadsheet showed up. It's probably the last time anyone did long division by hand, except to teach our students, maybe teach our kids. And we don't seem to rue the day that we couldn't do long division anymore. I mean, we could probably do it, but we don't do it every day. And so Excel became a tool that scared financial people. But pretty quickly, accountants found more work to do, and they became the robot overlords of Excel, but they didn't add up numbers and columns and rows anymore. And so no one looks back and says, oh, I wish I could just add columns and rows. They go, we now have tools that do that for me. Right?
[00:08:56] Speaker C: I don't know.
[00:08:57] Speaker D: I've had a few moments when I've missed my log division, but I don't miss walking to Philadelphia. I prefer to drive.
[00:09:03] Speaker A: Right?
And now we've got these large language models, and that's one part of AI, one small part. But people are talking about chat, GPT and OpenAI and things like that, and these large language models are finally a language tool. We've had math tools for 40 years, including the calculator before that, and now we have a language tool, and that's going to make us much smarter about prompting and editing, and probably not as strong at writing from a blank slate. That's true. And maybe that's okay.
[00:09:31] Speaker D: Most of my chat GPT information that I get is written in good Grammar. I mean, it's better than what you see on Instagram or Facebook.
[00:09:42] Speaker A: Yeah, the prose is quite good. Way better than ours can be.
[00:09:45] Speaker D: Linda, what are your thoughts here?
[00:09:47] Speaker B: I actually help people write sponsor proposals, and I went to chat GPT and said, write me a sponsor proposal. And it was very underwhelming because it hit some key things that needed to be said. But it didn't really get into the psychology of convincing somebody to make the decision to give you money and underwrite your business or your event or your podcast or whatever. So unfortunately, it's not there yet with what I do. But I think there are a lot of really good uses for AI. And as we've been talking about, it puts us in a whole different model, because instead of being in creative mode, we become in editor mode. So if I'm going to write an article, let's say, and I do chat GPT, then I just edit it. And I think something is lost a little bit there because I am a high creative and I love creating things, but I think it's good for a lot of things. And the Google, wow, I didn't even know that. So we'll see what happens with Google stepping up like that to protect people.
[00:10:53] Speaker D: Well, it's certainly going to drive AI even further into our lives, I think, if people can use it with little fear of legal repercussions. Kenya?
[00:11:01] Speaker F: Well, I would say the conspiracy theorist in me is a little skeptical. Still only know. I liked the point that Kevin made about Excel and all these other advances. But I think my only issue with AI and its use is the use of people's likeness.
[00:11:15] Speaker E: Right?
[00:11:16] Speaker F: So when it comes to using people's voices and things to create something in a space where typically it would be a human, I'm also concerned about the necessity for humans to do daily things and job creation and job loss. So there is a lot of things that I feel like, great, this is awesome, great technology. But on the other hand, I'm skeptical because I don't want it to replace what potentially could be opportunities for humans to function and have livelihood.
[00:11:42] Speaker D: Well, those are great questions, and I think with Kevin here, we'll have a chance to get into more detail on those a little bit later in the show. But I guess for now, I would just know AI is here to stay. Whether we like it or not, it's forcing itself into our lives. And if you're not using it and you're in the professional sphere, you're probably going to lose out. And that's just unfortunately, the bottom line there.
[00:12:07] Speaker E: You know who is going to win, though? All the lawyers.
[00:12:10] Speaker D: They find a way, don't they?
[00:12:12] Speaker E: Yes.
[00:12:14] Speaker D: Well, speaking of intellectual property, if you have an idea or an invention that you want to protect, contact us at Gearhart Law. We work with entrepreneurs worldwide to help them through the entire process of patenting, trademarking and copywriting. And if you'd like to learn more, you can visit
[email protected] or learn more about Trademarks.com for free consultation or downloadable content. So that said, I think it's time now to pick up again with Kevin Sarace. He's a Silicon Valley innovator, a serial entrepreneur, CEO, TV personality, and edutainer, which.
[00:12:49] Speaker A: Is a word that we use a.
[00:12:50] Speaker D: Lot around here, right? Edutainment. Kevin has been featured by Businessweek, Time, Fortune, Forbes, CNN, ABC, MSNBC, Fox News, and has keynoted hundreds of events, from Inc. 5000 to TEd to the US Congress. And I'm sure that was quite an event. He was also Inc. Magazine's Entrepreneur of the Year, a CNBC top innovator of the Decade, World Economic Forum Tech Pioneer, and chair of Silicon Valley Forum. He has a technical background with 94 worldwide patents and has built multiple startups from ground zero to 1 billion valuation. So that's really an amazing resume, Kevin, and we're really pleased to have you here.
[00:13:31] Speaker A: So happy to be here.
[00:13:32] Speaker C: Thank you for having me.
[00:13:33] Speaker D: In preparation for the show, I went to chat GPT to generate questions for this interview. Excellent. The first question is, does AI understand Dad jokes?
[00:13:46] Speaker A: With the correct prompting, you can ask it, how would it interpret this joke as a good joke, a bad joke, a fair joke, something that people would laugh at. And it's going to give an opinion on that. Now, even when I say the word opinion, of course I'm anthropomorphizing the darn thing. It doesn't actually have an opinion. Again, it's a math model. These large language models, they are math models, and they are guessing at the probability of one word coming after the next, based on your prompt and based on what it's learned. And it's learned everything we've ever written, virtually, right? So yes, it will opine on that, but the best use of a large language model like chat GPT is to give you ideas that you didn't otherwise have. This would be the case, in a legal case, even. Give me some ideas that I might not have thought of, and it'll give you twelve of them. You wouldn't use them verbatim. They may be wrong, they may not be correct, they may not apply, but, wow, I've got ideas that I didn't have. It's like this assistant sitting next to me.
[00:14:41] Speaker D: Oh, absolutely. When I asked that question, it came back with 32 potential questions to ask you. And if I had sat down and thought about it, I maybe would have come up with ten. So there's a lot more content there now than I would have been able to generate on my own.
[00:14:57] Speaker A: Exactly right.
[00:14:58] Speaker E: Well, I did go on your app, Vance website, and that is the use of AI that I don't think a lot of people have talked about. Everybody knows. Well, not everybody. Most people know chat GBT. But what you're doing with AI as kind of quality assurance.
[00:15:12] Speaker A: Yeah, software quality assurance. Finding bugs in software. That's right.
[00:15:16] Speaker E: So when your software identifies the bugs, then what happens? Do they fix somewhere?
[00:15:21] Speaker A: Let me baseline this for a second, and then I'll answer the question, if you don't mind. Is people over the last year think AI is chat GPT, and chat GPT is AI. That is one instantiation of work that has been done since the 1940s and 1950s in artificial intelligence. There are literally hundreds of algorithms, all of which can be applied, what we call applied to a variety of fields. Right? And in fact, AI in most large businesses has been highly available for a decade or more to analyze big data. So we've been doing this for a long time. Facial recognition on Facebook was AI. Is AI, right? All of a sudden, chat GPT has become the soup du jour, the AI of the day. But it's just one version of a type of AI. It's just a very huge neural net built on a trillion phrases. So to answer that question, what we do at AppVance, and I'm involved in a number of companies, but appfance is fascinating because millions of people worldwide try and test software, and most software is behind the firewall, like your ERP system, meaning it's for your internal use. A large bank may have 10,000 to 15,000 applications. Almost all of them run the bank, and maybe eight of them go to the outside world. It's really fascinating. So, of course, you want to test your ecommerce sites and things, but you got to test the stuff that runs your company. And this is a really hard problem to solve. We've been working at it for twelve years. Introduced the first product about five years ago that uses AI, and the idea is to generate automation scripts, call it automation or test automation automatically with virtually no human involvement. So you train the AI, what's important in your application, what are the outcomes, what are you looking for? And just let it go. Generate thousands and thousands of flows trying to look for problems. And to date, AI finds way more problems than people writing test scripts themselves or people testing. Now, what's interesting about that is it can write these tests about 100,000 times faster than humans could. That's a big number, and it sounds like a marketing number, but it's a measurement actually. And the bottom line is servers in the cloud are just way faster than our human brains, right? So we're going to get to a point where I think in the next five to ten years, all software bugs are really found by AI. And people can analyze which ones are the most important to fix, but they're all going to be found by AI. It would be ridiculous to think that we're still sitting there writing test scripts in some kind of code like selenium, and hoping that it finds bugs for us. Right? It's going to be ridiculous. Already there is with Copilot, GitHub, Copilot, and also Codex from OpenAI, there are tools now that are making programmers about 50% to 60% more productive than they were just three months ago. It's amazing. And that already completes some of your code. Now, even that completion of code, that automatic generated code, isn't perfect, but we're getting to the point where we will be able to find the bugs. Then the next step is find the piece of software that is causing those bugs, automatically generate new code that replaces the code that caused the bugs and close the gap. This is fascinating. Now a lot of you will be thinking, what do we do with the people? Well, we start focusing more on what it is we want our software to do and less about making it do it. Right. What do we want it to do? And so some people are saying, what happens to all the programmers? We've got these millions of people who write code. There will still be code to write, but you will be now ten or 20 times more productive than you are today, being able to generate far more features far faster. And we all want features, we want them faster, we want our software to do more, and we want it to be bug free. Yeah.
[00:19:05] Speaker D: Every time I start to feel uncomfortable about AI, because a lot of what you're saying, honestly, does make me a little uncomfortable. I also hear the positive side. And then I look at a database for a business I'm familiar with that has all sorts of problems and inconsistent data. And I'm thinking, well, wow, wouldn't it be great if you go through there and clean all that up? Because it would be just about impossible for a team of humans to do that. You look at the benefits and they're just irresistible. But then there's a price for that. And the price is we don't know what the world's going to be like if we make all of those changes. We can guess, but we don't really know.
[00:19:43] Speaker A: We can look at history. And when the wheel came out, if you were a person who carried things on their back and then there was a wheel, you go, my life is over. What will the world possibly be like if everyone has two wheels and then four, it's over, right? And if you were truly, if you were an accountant in 1985 and Spreadsheet came out, you said, I'm going to resist this horrible thing. It's going to take my job. There are more accountants employed today than there were when the spreadsheet came out. And they've all become spreadsheet experts. Right? So all of these tools that we have put out over the years have made humans more productive. Thus the net result of all of this is that GDP goes up. And yes, there's a long tail there to make that happen. But the more productive companies are and people are and countries are, the higher the GDP and ultimately sort of a better living comes out of that. Right? So if you as a lawyer could handle twice as many clients as you have, if you could, we're not there with AI, but if you could, that's pretty good for your law firm. It's probably good for the client. It's only good for everybody. Everybody gets faster service. You've got more clients. Life is good.
[00:20:53] Speaker D: Well, I'm glad to hear somebody saying that more lawyering is actually a positive thing. But anyway, we have to take a break. We'll be right back. Fascinating discussion here with Serace passage to profit with Richard Elizabeth Gearhart. We'll be right back. I'm Richard Gerhardt, founder of Gearhart Law. We specialize in patents, trademarks and copyrights. You can find out
[email protected] we love working with entrepreneurs and helping their businesses grow. And here is our client Ricky, to tell it like it is.
[00:21:22] Speaker F: Hi, I'm Ricky Frango, founder and CEO of Crime Six. We manufacture high performing clean and sustainable fuels like charcoal and logs. We've been working with Gearhart Law since the beginning, really, and they've helped us figure out the trademarks, the patents, everything that has to do with product development and how to protect our inventions. And we're extremely grateful for the wonderful team that has been supporting our Business since day one.
[00:21:47] Speaker D: Thank you, Ricky. To learn more about trademarks, go to learnmoreabouttrademarks.com and download our Free Entrepreneur's Guide to Trademarks. Or book a free consultation with me to discuss your patent and trademark needs. That's learn more about Trademarks.com for your free booklet about trademarks and a free consultation.
[00:22:03] Speaker A: Now back to passage to profit once again, Richard and Elizabeth Gearhart.
[00:22:08] Speaker E: And our special guest today, Kevin Sarace. This guy will blow your mind with what he knows and what he's done. And listening to him is such a pleasure. Richard, I have haunted the conversation, so now I'm going to throw it to Kenya. Kenya, do you have a question for Kevin?
[00:22:21] Speaker F: Oh, it was a great conversation. And I actually came across an article in Forbes magazine about just some of the downfalls and the pitfalls of AI. And one of the things that they bring up in the article is bias and discrimination. So it says that AI systems can inadvertently perpetuate or amplify societal biases due to bias training data or algorithmic design. And then there was also an issue with ethical dilemmas.
[00:22:47] Speaker E: Right.
[00:22:48] Speaker F: So instilling moral and ethical values in AI systems, especially in decision making contexts with significant consequences, presents a considerable challenge. So I just kind of wanted to see what your take was on.
[00:23:01] Speaker A: All great. It's a really great question. So let me separate AI systems that you're building within your business, say HR or whatever, from the large language models. Okay. And we'll just talk about them really quickly, separately. If I'm building AI, and I'll give you a great example, I'm building AI. A lot of people have to go through all of your HR data for all of your employees and make a judgment call on who makes it to the top in the company versus who doesn't. This is a fascinating thing, right? We all want to study that. Who makes it to president? Who makes it to VP? Because you might bring in 3000 people a year. Only one every five years makes it to vice president. Why is that? What makes them special now? That data is highly biased. You didn't mean to make it biased, but it is. And I'll give you an example, let's say at the VP level, you had someone that graduated, say, from my alma mater, Rochester Institute of Technology, and they interview all candidates, or all candidates in this division. Well, if a candidate comes in and happens to say, oh, by the way, I went to your alma mater, the chance of them getting hired is much higher than someone who didn't go to your alma mater. Just because you already bond on something you bond on. It's Rochester. It's Rochester Institute of Technology. Did you have this professor, et cetera, et cetera, already? And again, not getting into race or creed or anything else, we have a bias. And so that bias overemphasizes people that went to rit not because they're better students. I think they are, but I'm biased. But so did MVP. And by the way, we all do this without trying to introduce bias. We introduce bias, and so those biases are stuck in the data, and now you have to figure out how they got stuck in there. Why is that? And the chance of figuring out that that one VP 20 years ago interviewed and mostly hired people from RIT, it'd be hard to figure all that out. Right. So that's one area, and you can think of 15 others. So that's a challenge. Now, with large language models, they've gone out and learned from everything we've ever written that's on the web. And that has a bell curve of representation. Well, that bell curve itself is biased. So you could get certain people of certain countries. The United States would be an example that put far more, or have put far more onto certainly the English web than any other country for lots of reasons. It'd be population and access to the Internet earlier and GDP and things like that. So we are overrepresented. Overrepresented. And so if you naturally ask even an image generator, generate an image of a beautiful human or beautiful woman or beautiful man or whatever it is, it's going to generate right at the middle of that curve, which is, unfortunately, likely white, likely thin, likely looks like a model because it's right in the middle of the curve. It doesn't know any better. Now, if you prompt it differently and say, what I want is something over here, of course it has the capability to generate that. But if you don't pre prompt it and you're not careful, you're getting something down the middle, because that's what a model does. It doesn't know any better unless you ask it. So these biases are built in, and it's hard to get rid of them. Now, here's the bigger problem, if you want to talk about the problem, the more we use these models, for example, generating content for our blogs and blog posts and our advertising, and the more that people don't realize that they can prompt these things to go to the edges and do some really wonderful things, you could prompt it to generate an image of, rather than just a man, you can say an older man with gray hair who's a little overweight, blah, blah, blah, right? You could do that, but people don't. So they generate. What happens is they'll start generating the same thing that'll make the middle of the bell curve, when the model goes out to learn from the web bigger and bigger and bigger, because it's now learning from its own generated content, not knowing it generated it or another model generated it. So it could end up overemphasizing the middle of that bell Curve and continuing to de emphasize the breadth of the human experience. All of our models that we've got access to today, including Bard and Chat, GPT and llama and others, have a huge rules engine at the output, and I mean millions of rules. So when you say, do you love me? It now has a rule that says, even though I recognize how to construct sentences that would reply to that because I read all these novels, I'm not going to do that. I'm going to say, I'm a model that is incapable of love because people took it before those rules were in place to say, oh, this thing is sentient. It knows how to love. It doesn't know how to love. It just puts sentences together. It's all it is, right? It's not sentient at all. I can guarantee it. It's just math. So lots of rules like how to build a nuclear bomb. I'm sorry, I'm not able to discuss that, right. Or things that we don't think are appropriate for our society. So we put millions of rules on the output of these things. So you don't always see the original output. You see that filtered by a set of rules.
[00:27:47] Speaker D: Who controls chat, GBT or the AI engines or the large language engines are the people who set the rules.
[00:27:54] Speaker A: Yeah, that's right.
[00:27:55] Speaker D: And who is doing that now?
[00:27:57] Speaker A: Actually, OpenAI and Google and others have hired people overseas in all kinds of crazy countries, from Turkey to, like, Vietnam, and they've given them a set of areas that they never want the AI to be able to respond in. And we hire people over there because they're a dollar an hour instead of $25 an hour, basically. Now it's a hard job because you are looking at the requests that people are making, and every day you go, we don't want that response to ever be there again. Let's put a guardrail in. Call them guardrails. Let's put a guardrail in to not allow you to get that response. So people try to break these rules all the time and try to get the thing to jailbreak, basically, and then their job is to put it back in jail. So for OpenAI, it was over a thousand people for a year wrote rules. Over a thousand people for a year. And they were each writing potentially a hundred rules a day. Think about that.
[00:28:50] Speaker E: So anybody reviewing them, ultimately somebody is controlling this.
[00:28:55] Speaker A: That is true. That's true. You're right. You can choose to use those models. There are models that are out in the open source world today that have no rules. You could write your own rules. They're just free and they will tell you that they love you. We dealt with this. I built the first AI virtual assistant models back in the 90s that ultimately got licensed and became things like Siri and General Motors, OnStar and Alexa and all of those things. So the core technology was developed in the late ninety s at a company called General Magic. And her name was Mary. Actually, her literal name, the person who recorded the voice, her name is Mary Mack. Literally, Mary Mack. And so Miss Mary Mack would record the voices, and she recorded thousands and thousands and thousands of words and phrases and all the things to have her literally talk to you on your phone. It wasn't one day before people said, oh, Mary, I'm so in love with you. Will you marry me? And we had to say, I don't know if these guys are stupid or whatever, but how are we going to respond to that? So we decided to not block that. Instead, we'd respond the way the human would, which is, oh, I'm already taken, or, oh, I'm not available. And she had multiple various random responses that would come back. And we didn't make any bones about it. We just said, you want to play that game? We'll play along. We can absolutely create a large language model that has no guardrails around that and will interact with you in every way of a relationship. Right? Forget robot right now, just on the screen, every way of a relationship. And the reason it can do that is this. Remember, these models were trained on fact and fiction. If they've read enough fiction novels, they clearly can describe and can reply to loving kinds of things, right? So if they're programmed right, and they've got a large enough data set, again, from novels, they could be very convincing as a partner. And especially in a country where there might not be enough women, there's a lot of men, there's not enough women for a whole bunch of reasons. You could imagine those guys. This is the only true, I hate to say true partner, because that's not fair, but you know what I'm saying. It's really the only true kind of interaction they're likely to have. If the guy wants one with a.
[00:31:03] Speaker E: Woman, that's really sick.
[00:31:07] Speaker A: It may be, but let me tell you about the good use of that. How about older people that we're all getting older in this country, and people are getting to the age where we'rE getting to a time where in the next 20 years, we'll have more people over 80 than we have under 20.
Of course, the problem with that is there's no one to take care of them. So they need companionship, but you just can't. Even as their child, you can't be there. Twenty four seven. So a digital companion, of which there are already several available, is amazing for these older people. They feel that they have a relationship with this digital companion. They're not stupid. They know it's digital, but at least it's a relationship. And I'll tell you what, that digital companion will listen to your story, the same one over and over again.
[00:31:54] Speaker D: We have time for one more question before we wrap up, but where do you see the future of AI going?
[00:31:59] Speaker A: Regular AI outside of LLMs is going to continue to get better at sorting through our data, fixing our data, and giving us real insights into that data, including pattern recognition. Pattern computer is another company I work with up in the Pacific Northwest, and what they're doing to look at drugs that can treat certain kind of cancers and finding the patterns to match what can happen is just unbelievable. It's unbelievable. So these are huge breakthroughs. The humans would have never said that molecule really wouldn't have tried that. And that's going to change medicine forever. And this is very exciting. The second thing is when you look at large language models, five years from now, maybe two years from now, it's just going to be a tool that we all use. Of course, we use excel for math. We use an LLM for language. It's what we do. If you write blog posts all day, of course you're going to use an LLM to give you the first start of writing that blog post. You'll probably edit it, you'll probably change it but you might have gone from taking two days to write a blog post and edit it and think about it and sleep on it to like 20 minutes. So we may have made you 1020, 40, 50 times more productive. And in the end, I call this, actually, I'm borrowing this from Reed Hoffman, so I will borrow it from him. Is amplified intelligence. AI amplified intelligence. What we're doing is amplifying your intelligence, because in the end, you're in charge. You own it. You decide what the prompts are and you decide what you use from its outcome. But we're amplifying your intelligence. We can make you 510, 20 times the number of brains that you had. So instead of one brain power, you could have 20 or 30 or 50 brain power.
[00:33:34] Speaker D: That sounds good to me.
[00:33:35] Speaker E: Pretty scary.
[00:33:36] Speaker D: Devin Serrace, Silicon Valley Innovator, Entrepreneur, thanks so much for joining us. We hope you'll stick around. Passage profit will be back right after this.
[00:33:46] Speaker G: Hi, I'm Lisa Askley, inventress, founder, CEO, and president of Inventing A to Z. I've been inventing products for over 38 years, hundreds of products later, and dozens of patents. I help people develop products and put them on the market from concept to fruition. I bring them to some of the top shopping networks in the world, QVC, HSN, EvineLive, and retail stores. Have you ever said to yourself, someone should invent that thing? Well, I say, why not make it you? If you want to know how to develop a product from concept to fruition the right way, contact me. Lisa Askeles, the inventress go to inventingatoz.com. Inventing a to Z.com. Email me, Lisa, at inventing a to Z.com. Treat yourself to a day chock full of networking, education, music, shopping, and fun. Go to my website, Inventingatoz.com.
[00:34:41] Speaker A: Passage to profit continues with Richard and Elizabeth Gearhart.
[00:34:46] Speaker D: Sign now for our Power Move segment.
[00:34:48] Speaker F: Kenya Gibson, Excited About Power Move today I'm going to be talking about Larry Morrow, who is a serial entrepreneur. He was recently on my Power Move podcast telling his story about how he started off by founding Larry Morrow Events as a promoter, an entertainment business platform that's featured celebs like Floyd Mayweather Drake, Rick Ross, Meek Mill, and then he moved into launching Larry Morrow properties. And he's the owner of the New Orleans based restaurant Morrow's and the legendary Treehouse. So if you're ever in New Orleans, you want to check those places out.
[00:35:21] Speaker B: He also talked about his self help.
[00:35:22] Speaker F: Book, all bets on me, the risk and rewards of becoming an entrepreneur. So you can check out his full story on my Power Move podcast.
[00:35:30] Speaker D: That sounds great. And where can people find your Power Move podcast?
[00:35:34] Speaker F: Wherever you listen to your podcast, you can watch it on Spotify. So I always tell people to go to Spotify because you actually get a visual.
[00:35:41] Speaker D: That's great, Elizabeth.
[00:35:42] Speaker E: Yes. So I still have Blue Streak directory. It's a video directory of UB businesses, but I'm taking a little break from it right now. I think with the struggles I've been having, I kind of have to wait for the tech to catch up a little bit. And I'm also doing passage to profit, which I love. I learn so much every single time we do this. We have the most amazing coupon. I have a podcast with Danielle Wooley called the Jersey Podcast, where we talk about Cats. And I have started a new podcast. Well, I'm helping a ghost with the podcast, actually, it's called Ghost Stories. The Flip side, it's by a ghost named Fiona Favil Savill, and she can communicate with her niece who can get her on video with audio. And when she passed over, she found a bunch of ghosts that were really mad because they have ghost stories told about them and they don't get to tell their side. So she's telling the side of the ghosts. It's for children, so feel at Babble Stabble. Ghost Story is the flip side, where she tells the ghost story and then lets the ghost give a rebuttal.
[00:36:37] Speaker D: Well, that sounds great. I've heard some of these and they're really hilarious. So where can we find Fiona right now?
[00:36:44] Speaker E: It's on Libson and Apple. I just launched it today, actually, and it's on YouTube. Ghost stories, flip side, and Instagram is at Fiona's Applesep. So we are very excited to have Linda Hollander here today. I met her at Small Business Expo in New York. She's a sponsor, concierge, and I'm going to let her tell everybody what she does. Welcome, Linda.
[00:37:04] Speaker B: Great to be here. Okay, so I want to talk to all the entrepreneurs out there. A lot of people have all these great ideas for a podcast. They want to do a show, they want to do a nonprofit charity, they want to create a business, an event. And you think of this great idea, but then your second thought is, uhoh, how am I going to get it funded? Because we're all taught to have big dreams, but the big dreams cost money, and that's where sponsors come in. So I help people get corporate sponsors to underwrite their show, their event, their nonprofit, whatever project they're doing, and I've been doing it for over 20 years. And it's very cool because sponsors give you money you don't have to pay back. And the reason they give you that is because, like you, they connect a company with their core consumers. And that's why sponsors can fund you.
[00:37:59] Speaker D: What are your secrets for entrepreneurs who are looking for sponsorships?
[00:38:04] Speaker B: A couple of things you want to do as an entrepreneur. First of all, you want to do what we call the sponsor Wish list, the list of companies that you would like to work with as a sponsor to have them fund you. Now, the way you do your wish list, it's all about your demographics. We always say your demographics are your destiny, because for you, with passage to profit, your demographics, your audience is entrepreneurs. What do entrepreneurs buy? They buy office supplies, they buy shipping, they have a bank account, et cetera. And then they also buy consumer goods in their personal lives. So you make your wish list. That's the first step. I'm going to give you three steps. The second step is you need to create a sponsor proposal, and it has to be an industry standard sponsor proposal. And then the third step is the funding step. And I can't tell you exactly how much you'll make with sponsors. Most of our clients get between 10,000, even up to 100,000 from each sponsor. And there is no limit to the number of sponsors you can have. So it can be quite lucrative. You could do your five, six, seven figure deals. You just combine a few different comPanies. So that's the basic process to getting corporate sponsors.
[00:39:23] Speaker E: What are some of your success stories, Linda?
[00:39:26] Speaker B: Well, I'll tell you how I got started, and then maybe I'll give you a couple of examples. I got started because, and you'll love this, I wanted to do a women's small business expo because I was in the poverty trap. I was in an abusive relationship, and I got out of that situation because I started my own business and I started it with my best friend, and we knew nothing about business. I was an art major, she was a cinema major. But we grew it to a multimillion dollar enterprise, and I wanted to show other women how to do what I had done. So, like I said, I had this great idea to do a women's small business expo, but then I said, uh oh, how am I going to fund it? So my very first sponsors before I started my very first event were bank of America, Walmart and IBM. So before I even started, I made a profit because I had sponsors underwriting my events. And then after that, I got Microsoft and Staples and. And, you know, all these really great sPonsors. I think this is important for people to know. I had no experience with events and I had no Following. Nobody knew who I was except for my brother in law and my cAt.
A lot of people think, oh, well, when I get this many people on my fan base, then I'll go after sPonsors. No, you sell them on the concept.
[00:40:49] Speaker E: Do you have to have all your brand to get everything all set up first?
[00:40:53] Speaker B: You should, because sponsors don't want to start the train. They want to jump onto a moving train. So they want you to get your website up, your branding. They want you to do the basic things. You need a business account because they want to write a check to a business account. So you need to get your basic infrastructure and your brand going, and then you could go after sponsors.
[00:41:14] Speaker F: Kenya, sponsorships can be a little tricky in terms of measuring RoI. So what do you say is a good return on investment for someone who's doing a sPonsorship? And what are some of the KPIs that you put into place for your clients?
[00:41:27] Speaker B: Well, there are intangibles and tangibles in sponsorship, so let's talk about those tangibles first that you asked about, the metrics. Shares, land visits, et cetera. Those are the tangibles. Those are the actual metrics. Attendees at an event, et cetera. But then there are intangibles, like building goodwill, cause marketing. And this is how sponsorship changed after the pandemic, because cause marketing became so important. People buy from companies who give back to the community. And that's really, you can't really measure things like that. But it's very, very big in the world of sponsorship. So sponsors know that there are certain things, yes, you can measure, and you could give them a report on and show them metrics, but then there are certain things that you can't like, those things of brand building and cause marketing.
[00:42:21] Speaker E: Ben, do you have a question or comment for Linda?
[00:42:24] Speaker A: Yeah, I think this is really fascinating because it is different than, say, venture capital or seed rounds or that kind of funding. And I think we're all used to seeing sponsorships with, say, nonprofits, right? Well, we need a sponsorship for this program, but you're saying you're doing this as a for profit. But the return for these sponsors is views, clicks, et cetera, et cetera, which is almost on the edge of a social media star because that's how they fund themselves. It's. Here's the hairdryer I'm using today. You would really love this hairdryer right. So take us through that. How did you make that work?
[00:42:57] Speaker B: I made it work because I started one before social media, and I sold them on the concept. So I went to sponsors. Like my first sponsor was bank of America. And I said, hey, how would you like me to connect you to the biggest spending block on the planet, which is women? And I did so much research on the women's business market, found out that women are starting businesses at twice the rate of men. But more importantly, I found out that women make or influence over 85% of the purchasing decisions in America. So that's what you do when you want to get sponsors. You research your demographic, what they buy, where they go on the Internet, what their lifestyle is. And that's how you sell sponsors on the concept of who you can connect them with, because that's what they're looking for. They're looking for sales, they're looking for brand loyalty. They're looking for lead generation.
[00:43:50] Speaker D: Can just about any business look for sponsors?
[00:43:53] Speaker B: Sponsorship doesn't work for everybody. It doesn't work for every business. It probably wouldn't work for the neighborhood gas station or the neighborhood dry cleaner. You have to have some kind of influence. You have to have some kind of what we call extended reach. If you have that, you can get sponsors. Some of the challenges are, first of all, doing that proposal, because the proposal is really important because that's all sponsors see, and that's how they make their funding decisions. So I read a lot of proposals, and some of them are created by OpenAI and Chat GPT. And some of them are created by canva, and sponsors are calling me saying, what's happening, Linda? I could tell this is a canva proposal. I could tell it's created by chat GPT so you want to have the right proposal. That's a big challenge for people. And then another big challenge is really asking for the right amount of money because asking for too little, and I think Kevin can relate to this because he deals with big companies big money. Asking for too little can hurt you in the world of sponsorship because you're telling sponsors you have nothing of value and it's not worth their time.
[00:45:02] Speaker A: That's very true. I used to say it's a lot easier sometimes to get $100,000 than it is 1000. 1000 is not even worth my time. What, are you going to introduce me to three people? There is a mindset there, but I think you have to work your way into it and have the guts to go up and say, how about $100,000? And here's what you're going to get for that 100,000. And I don't think you could spend your quote unquote ad sponsored dollars any better than that.
[00:45:25] Speaker E: So, Linda, how do you get to those people that Kevin's talking about, the decision makers who actually hold the pocketbook strings?
[00:45:32] Speaker B: You're completely right, Elizabeth, because your relationship, it's not with a faceless corporation. It's with a person. It's with a human being. It's with the decision maker. And that's a big challenge in sponsorship, finding that decision maker, because sponsor companies have thousands of employees. There's usually one person that can Greenlight a sponsorship deal. A lot of people go on LinkedIn and they say, oh, I'll find a sponsor on LinkedIn. That becomes an exercise in frustration because a lot of sponsors don't even put themselves on LinkedIn, or they block content because they don't want to be slimed. So it's best to get a list of the decision makers, because I'm telling you, that is going to cut down on your time and your frustration and really lead you to the right people who can really greenlight that deal. Now, my company has the list, and at the end, maybe you could tell people how to contact me, but we do have a list of the decision makers.
[00:46:29] Speaker E: Well, how do people find you?
[00:46:30] Speaker B: Go to sponsorconciers.com, or the better place to go is successwithsponsors.com. So just go to Successwithsponsors.com and you could contact me through that website.
[00:46:43] Speaker D: So what does sort of a typical process look like then, when you're looking for sponsors? So you have your sponsorship proposal, and you have your list of preferred potential sponsors. How then do you follow up and connect your proposal to your preferred sponsors?
[00:47:01] Speaker B: You send your proposal. Most sponsors want you to introduce yourself by email because they don't want to be surprised by a phone call because they're usually busy. So you get the person and then you have your first conversation. This is really important. On your first conversation, you do not mention sponsor fees because they want to see if you're a fit for their goals and their visions and what they want to accomplish. So you'll have a couple of conversations, you'll come up with a sponsor fee. Then you go to contract, and you have an agreement that you sign with them because you're probably going to be working with companies that are bigger than you, so you want to protect your rights in the deal. Now, after I get the agreement signed, I start to implement. So for instance, if I tell a sponsor I'm going to put their logo on my website, I do that. I don't wait for the money to come in. Usually the money will come in in the form of a physical check. And sometimes it comes in quickly. And as you know, in business, sometimes it takes a while because they're setting you up as a new vendor. Now, after you've got that sponsor deal in place, the decision is just starting because you have to keep the relationship going. You want to send a sponsor a report, at least quarterly. You want to talk to them when there's something new, and then there's something called renewals. The renewal is, we suggest a one year contract with your sponsor, even if you're a podcaster. And then at the end of that year, they can fund you for the next year and the next year. So if you keep in touch with that sponsor and show them what you're doing and talk to them about the program and be open, because if they don't like something, I had a bank sponsor and they said, well, we don't like this thing you're doing. And I was devastated, but I said, you know what? If we change that, can we have another conversation? They said, yes, and they sponsored me for five years because we were really open and we didn't let what we call residue build up because sometimes they don't like something and then they just won't renew with you. You want to get that out in the open. So it's kind of a rinse and repeat process. But here's the cool thing about sponsors. You don't have to report back to the sponsors how you spent their money. You just have to fulfill your contract. So we'll take that example again of putting the logo on the website. If you said you're going to do that, you got to do that because they're going to check up on that. But you can spend the money in any way you want. You could pay yourself. You could hire a team, you could put the money in your business, et cetera, because you don't have to submit a budget to a sponsor.
[00:49:34] Speaker F: I just was curious from an asset perspective about some of the programs that you're building and what someone can expect.
[00:49:41] Speaker B: We're using digital and traditional because I ask a lot of people, I say, how are you going to promote what you do? And they say, oh, social media and the digital platforms. Sponsors are traditional. Radio, television, print, it's not dead. And sponsors love the traditional platforms as well as the digital platforms. As far as the media mix. Now people can also get media sponsors, and I've gotten media sponsors to help me get the word out. And that is really going to amplify your assets and your effectiveness with a sponsor. Another asset, a meet and greet like I did for Verizon. We did a Verizon relaxation room at a conference where people came in and they got chair massages, and there was a monitor that had the Verizon logo and outside a traditional table with information about Verizon. But I thought that was a very creative way to promote Verizon with a relaxation room.
[00:50:38] Speaker E: Well, this is X.
[00:50:39] Speaker D: As long as everybody turned off their.
[00:50:41] Speaker E: Cell phones, we have to wrap up. Once again, how do people get a hold of you?
[00:50:45] Speaker B: Successwithsponsors.com. And go to successwithsponsors.com. And I even have a free gift for everybody who is watching and listening to us today, because I'll give you the number one secret to getting your sponsors if you go to Successwithsponsors.com. That's nice.
[00:51:02] Speaker D: Sounds great.
[00:51:03] Speaker E: Yes. And now, last but not least, Steven Singer with Ihatestevensinger.com. What is this all about? You're a jeweler. Why would people hate you? Tell the story.
[00:51:13] Speaker C: Well, you're about to find out right now. There's kind of an alter ego. We have Steven Singer Jewelers, which was founded 19, 80, 43 years ago. And we have I hate Steven Singer, which is the website. And we use that because it's a little stickier. It's Easy. People can't spell jewelers. And we've had quite a big success with I hate Stephen Singer. The birth of it was 25 years ago. It actually started probably 21 or 22 years ago when it actually launched. And we've been running it ever since.
[00:51:43] Speaker D: And it's been great.
[00:51:43] Speaker E: So how did you get that name?
[00:51:45] Speaker C: Like I said, about a quarter of a century ago, I was waiting on a customer in our store, and it was a young couple. And this guy just spent $10,000 on an engagement ring. And there was another customer sitting right next to a gentleman sitting by himself. The young couple, she said, I love Steven Singer. I love the store. I love my ring. I love the whole experience. I mean, this girl's been waiting her whole life for an engagement ring, and she is over the moon. Meanwhile, this guy spent $10,000. I didn't do anything. All I did was make the ring. And the guy that's sitting right next to it looks with a deadpan look and turns around and looks at her.
[00:52:18] Speaker D: He goes, you love Stephen Singer.
[00:52:19] Speaker C: I hate Stephen Singer. You want to know why? And the girl thinks, oh, this guy must be a complaint, must be a nut. I don't want to get involved. And she goes, no, that's all right.
[00:52:27] Speaker A: She goes, let me tell you why.
[00:52:28] Speaker C: 20 years ago, got my wife a ring from Steven Singer. And I had two grown children, one in college, one just started college, and we had everything paid for, everything mapped out. We're ready for the next stage of our life. I get this ring from Steven Singer, my wife thanks me that night, and now I have another kid, and it's his fault. Wow.
[00:52:47] Speaker D: Think about a bad attitude. That's horrible. He said it in jest.
[00:52:52] Speaker C: And it's funny because the kid, the original I hate Steven Singer, baby only found out when she was like 17 or 18 that she was the I hate Steven Singer kid. And the story is absolutely true. And what happened was we were talking about it a few days later. I said, that was really funny, so we should make that into a commercial. And I said, you know what? We're going to do it. So we made it to a commercial. We made radio spots, we made bIllboard, we did everything we were going to do and nobody would run it. We went to the radio station, which we'd already been on, by the way, for 20 years, and they said, this is the dumbest idea we've ever heard. We're not going to run it, you're not going to do it. You're going to go out of business, and we're just not going to participate in. So it took me almost two years to get them to do it. We had to sign a legal document, which you'll appreciate, that was like two or three inches thick, that I was going to hold them harmless, that I'm doing this on my own accord, that they've informed me that it's a bad idea, that their professional opinion was the worst, dumbest thing ever. Don't ever do it. And we did that with the billboard company, we had to do with the radio stations, and we did it with everybody. So what we did, in the middle of the night, we got these fake stickers that look like graffiti. And we graffitied our whole building and says, I hate Stephen Singer all over the building. We changed our voicemail to say, I hate Stephen Singer. We changed our website to look like somebody had hijacked it. And it said, I hate Stephen Singer all over it. Like with graffiti. It looked like it was all like somebody destroyed it. And we put only one billboard on Interstate 95 in Philadelphia. So it looked like there was a really angry customer, angry person that put it up.
[00:54:23] Speaker A: It worked.
[00:54:24] Speaker D: I've seen the billboards many times, and I always thought it was like a jolted fiance or something.
[00:54:29] Speaker C: Well, that's what everybody thought in the. Originally it was either that I was cheating on my wife, I was a drug dealer, I had an angry girlfriend, I broke up with somebody.
Everybody had all these different theories. As a matter of fact, the day we launched it, we had, I don't know, maybe four or 500 calls that people say, listen, I don't know what you guys did, but I love you. I still go there. I don't know what happened, but I'm still going to come there. So everybody hated the idea. Everybody in my industry groups hated it. EveRybody in my store, my family, I was on an island nobody wanted to do. I said, you don't see what I see. Everybody goes left, we're going to go right. Everybody sells love, we're going to sell hate. I said, we're going to stand out. And nobody would do it. A year later, we won a billboard of the year. We won two addies for advertising. And CBS asked me to speak at their National Sales convention in New York to say what a brilliant idea this was. So the year before, I was an idiot. The year later, I was a genius. We've rode that ever since. And it's been great. It's been a wonderful, wonderful thing. And people, every time I go somewhere, I've had the governor of Pennsylvania, I've had the mayor of Philadelphia at different events, I've had different celebrities and different people, they'll see us at an event and they'll just blurt out, I hate Stephen Singer. Just because they think it's funny and it becomes very sticky.
[00:55:42] Speaker D: You have to live with that.
[00:55:43] Speaker C: It's fine with me. Listen, I take it as a compliment everywhere we go and everywhere we do it, so it makes us stand out, and it's different. What it has evolved into is that all my competition hates me because we have a thing called a love guarantee, which we guarantee the diamond, the ring, and the relationship. So even if you just break up, we'll take it back. There's nobody else in the United States that does or nobody that I'm aware of that does it. Let me put it that way. So we go opposite with everything that we do, and it's worked very well for us.
[00:56:11] Speaker F: Kenya, you're like the shock jock of advertising, right?
[00:56:15] Speaker C: A lot of people say that because we are the oldest continuous advertiser on Howard Stern.
[00:56:21] Speaker B: Yeah.
[00:56:21] Speaker C: We've been with him since 1986 or seven. Whenever he came to Philadelphia, when he first came to Philadelphia, he couldn't buy a sponsor. He couldn't get like McDonald's or Coke or Pepsi or nobody that would have any kind of. He was like a pariah. So not only were we one of the first sponsors to go on there, I said to him, listen, I don't care what you say about us, Just mention our name once in a know. You can say whatever you want. These are the ads that stick out and the funny things. Sometimes he sings our ad. We do everything in marketing and advertising. One of the things is nobody is tired, in my opinion. Wendy's where's the beef or Nike, just do it. Or Coke is the real thing. The advertisers get tired of it, or the advertising agency gets tired of it and they want to generate new copy and new business. But customers and the clients and the people out in the public aren't tired of it, so they change it just for the sake of change.
[00:57:11] Speaker A: Sake.
[00:57:12] Speaker C: Matter of fact, people like the consistency. People didn't like new Coke. They liked old Coke, the original Coke. So we keep the I hate Stephen Singer people love it. I mean, we have Christmas ornaments to say, we have shirts, we have all kinds of paraphernalia and things that we give out to people. And I have people that come from all over the country. Somebody come in from Washington, from Baldwin, people from LA that come in just to come in, get a picture, or they want an I hate Stephen Singer. They don't even want to buy anything. They just want to get something that says I hate Stephen Singer on it. So everything that we do stays under the I hate Stephen singer Umbrella and then is underneath that. So we keep that focus on there. And then anything else we do is underneath that. It's like, why do other jewelers hate me? Well, maybe this is the reason. That type of thing.
[00:57:52] Speaker E: There was something in the show notes that I thought was hilarious that you said about lab created diamonds. What was that?
[00:57:57] Speaker C: Nothing says I love you less than a lab grown diamond. It's a fake. Lab grown diamonds are like Frankenstein's. They're made in a machine in a laboratory. They are not the same as natural real earthborne diamonds. They are very similar, and they're the closest thing to natural earthborne diamonds, but they're not the same. And anything that you can mass produce as much as you want of picture it as Oceanfront real estate, God's not making anYmore. That's why Oceanfront real estate is so expensive diamonds, real natural diamonds, are a sign of affection, a show of commitment, part of the marriage contract, and they're enduring, and they have intrinsic value. Decades, decades, hundreds of years. And you can give all kinds of examples to that. Lab grown diamonds are like the plague of our industry. They've gone down 95% in value in the last five years.
[00:58:45] Speaker D: Are jewelers required to disclose whether it's natural or 100%?
[00:58:50] Speaker C: 1St off FTC the Federal Trade Commission of the United States says the only thing you can call a diamond is a natural, earthborne diamond that came from the ground, from a river, better from a mine. Anything else has to have the simulate name. The prefix in front of it has to say simulant, man made, lab grown, or whatever it must disclose right up front. And it has to be in front of the word diamond. The only thing you're allowed to legally call a diamond is a diamond that came from the Earth.
[00:59:16] Speaker E: Do you also design jewelry for people?
[00:59:18] Speaker C: In our facility, we have our own shop, our own designer. We do our own CAD, computer aided design. We do our own wax making for everything. And we make all kinds of custom jewelry. And we've done a lot of really cool things for a lot of celebrities and sports teams and rock stars and all kinds of things that I'm very proud of. And different things, different logos and different pieces of jewelry for them. And it's been great. And whether you're just coming in for a $2,000 engagement ring and you have a custom idea you want to do, we will create it for you. It doesn't cost any more to make a custom ring. And again, as far as I know, we're the only ones in the country that will exchange, return. You say, you know what? Now I see it not hitting the mark. I want to do something else. Take it back. We'll change.
[01:00:00] Speaker D: Why would anybody hate that?
[01:00:02] Speaker E: Where are you located?
[01:00:03] Speaker C: Our showroom is at the other corner of Eigth and Walnut in Center City, Philadelphia, about a block away from Independence hall and the Liberty Bell. We have fulfillments all over the country where we ship, because we ship 24/7 all over the country. The thing that sets us apart, or one of the things is we're real jewelers in terms of we touch and feel everything ourselves. We don't have a drop center or call center in India. Everybody that you talk to, it works in our building or has worked in our building.
[01:00:28] Speaker E: I Kate Stevensinger.com passage to profit the Road to entrepreneurship with Richard Elizabeth Gearhart, Kenya Gibson Our special guest Today, Kevin Sarace and we will be right back.
[01:00:40] Speaker D: I'm Richard Gerhardt, founder of Gearhart Law. We specialize in patents, trademarks and copyrights. You can find out
[email protected] we love working with entrepreneurs and here's our client, Peter, who tells it like it is.
[01:00:52] Speaker C: I'm Peter Olson, founder of ON and UP. We recently were elected as one of the best invention of Time magazine for 2022.
[01:00:59] Speaker D: Through this journey, we've been relying on.
[01:01:01] Speaker C: Gerard Law to guide us in the.
[01:01:03] Speaker D: Right steps to build a right portfolio of patents, trademarks to support our launch.
[01:01:08] Speaker C: Of our new product.
[01:01:09] Speaker D: It has been a great experience working.
[01:01:11] Speaker C: With Gerard Law as they have a.
[01:01:13] Speaker D: Deep knowledge into the market both in North America and overseas.
[01:01:17] Speaker C: So we make the right choices at the right time.
[01:01:20] Speaker D: Thank you, Peter. To learn more about patents, go to learnmoreaboutpatents.com and download our FREE Entrepreneur's Guide depends or book a free consultation with me. That's learnmoreaboutpatents.com. It's passage to profit now it's time for Noah's retrospective.
[01:01:37] Speaker E: Noah Fleischman is our producer here at Passage to profit, and he just has a way of putting his best memories in perspective.
[01:01:44] Speaker H: When I was a kid, I used to love the holidays, but my favorite times of the year were actually two nights. Twice a year. It was that night we would take the clock off the wall and readjust it by hand. Daylight savings time and back. Wow. I thought that was fascinating. It was like being at the hall of Science. My first question was, we all have to do this. What if somebody forgets? Next question. How's it working out for you? I don't think daylight savings Time is a bad thing, really. We just need more options. Let's say I want to take 15 minutes, hold onto it this year, and then save the other 45 and accrue it into the next year. If I do that the following year and the year after that, and maybe even add some time onto that, well, by six, seven years later, I've added practically a year onto my life. Think of what that'll do for healthcare premiums. Unbelievable. You know, when technology, science and all the other advancements come together to make this thing happen properly, we're going to have a world that is absolutely just like this one, only more aggravating and complicated.
[01:02:43] Speaker A: Now more with Richard and Elizabeth. Passage to profit.
[01:02:47] Speaker D: What an amazing show. I learned a lot about a lot of different things. I learned about Stephen Singer and I learned about artificial intelligence, and I learned about sponsorships, all very important and useful things.
[01:03:01] Speaker E: Absolutely. So now it's time for the question. I am going to start with Kevin Sarace and his website is kevinsarese.com. That's Kevin Surace.com. Kevin, what do you wish AI could do for you?
[01:03:16] Speaker A: I wish AI would come in, cook and clean. It's very simple.
I hear no disagreement from the panel, right?
[01:03:24] Speaker D: No, we anticipated this answer.
[01:03:26] Speaker C: So I'm going to hate AI is just unbelievable. The things that they're doing. I don't think we even have a clue what's going to happen in the next three months, six months, a year. I think if we talk a year from now, we'll be in a whole.
[01:03:39] Speaker A: Different world with this.
[01:03:40] Speaker E: Linda Hollander, sponsor, Concierge, what do you wish AI could do for you?
[01:03:47] Speaker B: Well, as you know, I'm a big cat lover, animal lover. I wish they would come and clean out the litter box.
That's one of the things I don't love about having a cat. So that would be great.
[01:04:00] Speaker E: Yes, I agree. So, Stephen Singer with Ihatestvensinger.com Diamond man, what do you wish AI could do for you?
[01:04:08] Speaker C: Well, I have two. One, I heard Kevin articulate something wonderful, what the AI does for older people in terms of listening to their stories over and over again. So if I could just get that to follow my wife around and stop telling me the same stories over again and do that for me with my wife, that would be excellent because she yells at me if I listen or I don't listen. So that would be terrific.
[01:04:29] Speaker A: I know who's going to hate Stephen Singer tonight.
[01:04:32] Speaker C: There you go. I'm going to blame it on Kevin. I'm going to say this genius that I was on this podcast with that came up with it wasn't me, but I'm really, really looking forward to what it does in medical advancements, that it can read every medical paper all over the world and know everything at all times. That no matter how great your doctor or your team or your hospital is, that they can have this wealth of knowledge and exponentially change medicine and medical diagnosis and things like that. That's why I think is one of the giant payoffs for AI.
[01:04:59] Speaker E: I agree. Kenya Gibson, our media maven.
[01:05:02] Speaker F: Well, Steven stole my joke, so I was going to say I hope it helps reprogram my husband.
[01:05:07] Speaker C: Well, why don't we introduce your husband to my wife and they can talk to each other?
[01:05:10] Speaker E: I guess.
[01:05:11] Speaker F: For know, I'm in a similar vein in terms of just its ability to maybe create a better, more, well, society.
Mean, obviously there's a lot of advances in the wellness space that I would like to see around fitness and nutrition and people being able to just live better. So if it can create some advancements in that space, I would be happy to see that.
[01:05:32] Speaker E: Richard Gearhart Gearhart Law Patent Trademark copyrighted.
[01:05:36] Speaker D: Know, there are so many opportunities for AI and so many things that I'd like to see automated, but the thing that I dislike the most is brushing my teeth. And so if I could find, I guess that's more of a robot than AI, but if there were an automatic toothbrusher out there, I would be really happy for that.
[01:05:55] Speaker E: Well, that's a good so for me.
[01:05:57] Speaker D: And what about you?
[01:05:58] Speaker E: I have to go with Kevin. Cookie.
[01:06:02] Speaker D: Well, at least you didn't say a new husband.
[01:06:05] Speaker C: I feel like I lucked out there.
[01:06:06] Speaker E: Nobody can replace you, Richard.
[01:06:08] Speaker D: WEll, that's probably true. So anyway, that's it for us on this episode. Before we go, I'd like to thank the passage to Profit team, Noah Fleischmann, our producer Alicia Morrissey, our program director. Our podcast can be found tomorrow anywhere you find your podcast. Just look for the passage to profit show and you can find us on Instagram and threads at Passage to profit show and Twitter. Or if you're even more up to date, X at Passage to profit and on our YouTube channel. Please also join us on our new Facebook group search for Passage to profit show. Listener Community A new Community space for our listeners and guests where you can post questions that you would like answered on the show and interact with the Passage to profit team. And remember, while the information on this program is believed to be correct, never take a legal step without checking with your legal professional first. Gearhart Law is here for your patent, trademark and copyright needs. You can find us at ##Heartlaw dot and contact us for a free consultation. Take care, everybody. Thanks for listening, and we'll be back next week.
