WEBVTT 00:00.031 --> 00:11.587 [SPEAKER_00]: To me AI feels different, it feels like it's moving so fast and so far in each leap that if you're trying to wait till the like the finished version, you're not going to be able to catch up at that point. 00:11.607 --> 00:14.771 [SPEAKER_01]: I think as these models are doubling in power every six months, right? 00:14.791 --> 00:16.834 [SPEAKER_01]: And they're getting 90% cheaper every year. 00:17.054 --> 00:22.041 [SPEAKER_01]: Basically, if you can have compound that out over five years, that's a hundred million improvement in price power. 00:22.061 --> 00:26.427 [SPEAKER_01]: If you're telling me AI to go in the wrong direction, the AI is going to take you there really, really fast. 00:26.467 --> 00:28.570 [SPEAKER_01]: That's like a self-driving car with the wrong address. 00:28.550 --> 00:30.453 [SPEAKER_00]: It's not about being right, it's about getting it right. 00:30.473 --> 00:32.596 [SPEAKER_00]: And if you can't get past that, we can't move forward. 00:35.221 --> 00:46.178 [SPEAKER_00]: I am so far down this AI agent rabbit hole that it's in scene. 00:46.198 --> 00:47.520 [SPEAKER_00]: I, um, 00:48.749 --> 00:53.117 [SPEAKER_00]: I'm not technically, I'm not like a native tech guy. 00:53.137 --> 00:57.564 [SPEAKER_00]: I get people, people misconstrued, and I think this is interesting. 00:57.584 --> 01:03.354 [SPEAKER_00]: I'm interested in your take on this, like there's nothing technical about me by nature. 01:03.835 --> 01:05.157 [SPEAKER_00]: I'm not great at fixing things. 01:05.398 --> 01:06.800 [SPEAKER_00]: I can't code at all. 01:06.840 --> 01:10.707 [SPEAKER_00]: I took like one week of C++ in college and was like screw this. 01:11.969 --> 01:28.898 [SPEAKER_00]: But a lot of what I do is technical and so much as like I love getting into AI, I love learning about these things because I see them as to me it is undeniable what is coming and to be unprepared as a leader regardless of your 01:29.148 --> 01:34.277 [SPEAKER_00]: technical proclivities feels you're denying yourself a skill set that's going to be very relevant. 01:35.018 --> 01:41.049 [SPEAKER_00]: And I get a ton of questions from the audience about how much of this should I be putting into my business. 01:41.910 --> 01:44.254 [SPEAKER_00]: Should I be building myself an open-class? 01:44.334 --> 01:45.476 [SPEAKER_00]: Should I have co-work? 01:45.837 --> 01:50.124 [SPEAKER_00]: Like how much time should I be delegating to learning about this stuff? 01:50.144 --> 01:54.632 [SPEAKER_00]: Because I think there's this group of people 01:55.861 --> 02:01.669 [SPEAKER_00]: We'll say skip off new technology and wait till it's mature and still be okay and what they're doing. 02:02.129 --> 02:11.842 [SPEAKER_00]: And I'm worried is the wrong word, but I'm concerned for them in so much as to me AI feels different. 02:12.303 --> 02:24.619 [SPEAKER_00]: It feels like it's moving so fast and so far in each leap that if you're trying to wait till the like the finished version is available to you, you're not going to be able to catch up at that point. 02:24.700 --> 02:36.157 [SPEAKER_01]: Yeah, most of you, I think senior leaders, and by the way, when I was at Google, I met over a thousand CEOs and advised them right on their digital transformation, marketing, and AI strategy. 02:37.098 --> 02:39.502 [SPEAKER_01]: And I was one of the typical questions. 02:39.582 --> 02:41.985 [SPEAKER_01]: This is like, you know, things are moving very quickly. 02:42.025 --> 02:44.289 [SPEAKER_01]: Do a lot of it fast, follow anyone want to be a leader. 02:45.270 --> 02:47.333 [SPEAKER_01]: My answer is you always want to be a leader. 02:47.313 --> 02:50.897 [SPEAKER_01]: you don't have to take on the most complex projects at first, right? 02:51.438 --> 02:52.339 [SPEAKER_01]: But you can't wait. 02:52.799 --> 03:01.889 [SPEAKER_01]: And so, if you're not building your AI muscle right now as a cousin company and as an individual, I think as these models are doubling in power every six months, right? 03:01.909 --> 03:03.891 [SPEAKER_01]: And they're getting 90 percent cheaper every year. 03:04.692 --> 03:09.878 [SPEAKER_01]: Basically, if you can compound that out over five years, that's a hundred million improvement that price power. 03:10.432 --> 03:10.712 [SPEAKER_01]: Right. 03:10.752 --> 03:19.906 [SPEAKER_01]: And so if you're not building the muscles right now, I don't think, you know, you're going to thrive in the next five years, unless, you know, the government regulation saves you. 03:20.347 --> 03:21.428 [SPEAKER_01]: And so I'm, I'm with you. 03:21.448 --> 03:25.074 [SPEAKER_01]: I think you've got to go hard on developing the skills yourself. 03:25.094 --> 03:29.300 [SPEAKER_01]: You have to go hard on helping your team develop that skill. 03:29.740 --> 03:33.947 [SPEAKER_01]: The problem is that, you know, a lot of employees are scared as they should be. 03:34.187 --> 03:34.387 [SPEAKER_01]: Right. 03:34.407 --> 03:35.629 [SPEAKER_01]: It's very normal. 03:35.609 --> 03:42.896 [SPEAKER_01]: And so as an executive, what you have to do is not just teach people AI, is you have to show them what their future looks like for them. 03:43.316 --> 03:49.863 [SPEAKER_01]: Like, hey, if you're doing this job right now, and AI is going to take over 50% of this job, and you'll be more productive, and it's going to be great. 03:50.343 --> 03:53.606 [SPEAKER_01]: But let's be honest, eventually, that job won't be the fulfilling. 03:53.626 --> 03:54.748 [SPEAKER_01]: So here's your next job. 03:55.548 --> 04:05.498 [SPEAKER_01]: And we're going to try to get you to stay ahead of AI, and if you become really, really good at this, 04:05.478 --> 04:11.588 [SPEAKER_01]: hopeful message, I think is important, also talking about AI as a growth engine, not AI's a productivity engine, right? 04:12.029 --> 04:25.410 [SPEAKER_01]: Like the speech of like we're going to get 30% productivity out of the AI's not that, you know, warming for employees, but instead if you say we want to double the business in the next five years and it's only 30% white people. 04:25.390 --> 04:28.874 [SPEAKER_01]: And that's a much more thoughtful, optimistic message show. 04:29.434 --> 04:34.900 [SPEAKER_01]: A lot of this is the leader is knowing the stuff right personally and having played with that. 04:34.940 --> 04:37.362 [SPEAKER_01]: More than a play with that, having really achieved something with that. 04:37.963 --> 04:40.346 [SPEAKER_01]: And then secondly, is really getting people on board. 04:40.426 --> 04:42.107 [SPEAKER_01]: AI is not really a technology problem. 04:42.528 --> 04:44.250 [SPEAKER_01]: To me, it's like two things that are really better. 04:44.550 --> 04:45.591 [SPEAKER_01]: One is change management. 04:45.971 --> 04:46.152 [SPEAKER_01]: Right? 04:46.912 --> 04:50.436 [SPEAKER_01]: And the other one is, what are you asking the AI to do? 04:50.720 --> 05:00.418 [SPEAKER_01]: And so one of the conclusions I reached after my five years at Google's Chief Evangelist was that 90% of companies are optimizing the wrong stuff. 05:01.721 --> 05:01.961 [SPEAKER_01]: Right? 05:02.021 --> 05:08.012 [SPEAKER_01]: And so if you're telling the AI to go in the wrong direction, the AI is going to take you there really, really fast. 05:08.072 --> 05:10.517 [SPEAKER_01]: It's like a self-driving car with the wrong address. 05:10.666 --> 05:11.027 [SPEAKER_01]: Right. 05:11.127 --> 05:24.902 [SPEAKER_01]: So in my experience, what's happening a lot is people are deploying AI systems and employees reject them sometimes and or AI systems is just focusing on the wrong thing. 05:25.824 --> 05:28.050 [SPEAKER_00]: This idea of focusing on the wrong thing. 05:30.562 --> 05:33.108 [SPEAKER_00]: I don't think one, this is not a conversation that's being had. 05:33.849 --> 05:36.876 [SPEAKER_00]: I do not hear very many people talking about this at all. 05:37.617 --> 05:46.757 [SPEAKER_00]: It's mostly, and to be honest with you, I think for a long time, I was guilty of this as much as anybody because this has only been something that's been at our fingertips. 05:46.797 --> 05:47.659 [SPEAKER_00]: What for? 05:47.639 --> 06:05.100 [SPEAKER_00]: two years, two and a half years ish and it's gone so far from what GP2 or GP3 was to 5.4 and then obviously there's a ton of other models, but just thinking about open AI's progress, what you could ask and expect to get back from those early models. 06:05.561 --> 06:13.090 [SPEAKER_00]: I mean, that's remedial stuff that you wouldn't even think about today and it's just tiny little 24 month window that we've been working in. 06:13.070 --> 06:35.668 [SPEAKER_00]: If you're a lot of leaders, a lot of wills call them regional and main street businesses listen to our show, tunnel leaders, tunnel entrepreneurs, but it tends to be outside safe the Fortune 500 Fortune 1000 group that kind of come to this show and I know the question that they're asking themselves right now is, Nicholas, what's 06:35.648 --> 06:56.813 [SPEAKER_00]: What should it be working on and what shouldn't it be working on because I'm positive what you just said scared the crap out of them because efficiency and the productivity of each task is so incredibly important to smaller organizations, not that it's not the large organizations as well, but you can really feel that pain if you have even one employee working on tasks that aren't moving the needle. 06:57.147 --> 07:00.151 [SPEAKER_01]: Yeah, it's a great question, and that's why I wrote my book with B.S. 07:00.171 --> 07:01.353 [SPEAKER_01]: E. Callion at a bonsai. 07:01.774 --> 07:11.648 [SPEAKER_01]: The books about how companies can really grow a lot faster without more innovation, without new products, without hiring new people. 07:11.728 --> 07:15.314 [SPEAKER_01]: It's just changing your mindset and asking the AI to do something different. 07:15.594 --> 07:16.736 [SPEAKER_01]: So I'll give you a couple of examples. 07:17.116 --> 07:19.780 [SPEAKER_01]: If you look at 90% of companies, 07:19.760 --> 07:21.483 [SPEAKER_01]: And you look at how they're doing their marketing. 07:21.563 --> 07:33.926 [SPEAKER_01]: Just usually a canary in a coal mine, because if you marketing team isn't doing the right thing, the odds are your product development team, your customer service team, other teams aren't doing the right thing, because marketing is very data-driven. 07:33.986 --> 07:36.391 [SPEAKER_01]: It's pretty obvious what you should be doing. 07:36.471 --> 07:39.456 [SPEAKER_01]: So I'll give you my favorite example of doing the wrong thing. 07:39.737 --> 07:42.021 [SPEAKER_01]: I can imagine you and I started charity. 07:42.001 --> 07:42.341 [SPEAKER_01]: Right. 07:42.742 --> 07:45.165 [SPEAKER_01]: And we want to advertise on Google and Facebook to raise money. 07:45.526 --> 07:46.787 [SPEAKER_01]: It's a very simple equation. 07:46.807 --> 07:48.449 [SPEAKER_01]: We put cash into Google and Facebook. 07:48.830 --> 07:49.671 [SPEAKER_01]: We get cash back. 07:50.472 --> 07:50.652 [SPEAKER_01]: Right. 07:51.193 --> 07:53.055 [SPEAKER_01]: So, and then the AI is doing all that work. 07:53.096 --> 07:56.139 [SPEAKER_01]: It's doing all the targeting, all of the optimization for the advertising. 07:56.160 --> 07:57.641 [SPEAKER_01]: So, what do you ask the AI to do? 07:57.681 --> 08:04.971 [SPEAKER_01]: Well, if you look at what charities are doing today, and I'm not 10 years ago, but today, 90% have the same KPR. 08:05.120 --> 08:12.657 [SPEAKER_01]: Russia's the most popular key behind marketing, which is return on that spend, which basically the amount of money you raise divided by the amount of money you put it. 08:12.997 --> 08:19.652 [SPEAKER_01]: Right, hey, if I put a bucket to Google and I get three dollars back, I'll do this all the time, because I just found this money printing machine. 08:19.767 --> 08:36.449 [SPEAKER_01]: Turns out, there's something, you know, it's fine, you make it money, but that's not the right KPI, like, what you should really focus on is how much money in my raising, minus how much money in my putting in how much cash do I have left at the end of the day, right, but very, very few, you know, not profits do this. 08:36.509 --> 08:46.703 [SPEAKER_01]: And so, St. Jude's for example, I met St. Jude's about seven years ago, the amazing organization, right, the most respected organization in the US, the number one place college graduates want to work. 08:46.683 --> 09:04.097 [SPEAKER_01]: does amazing, amazing work to help children for free throughout their entire, you know, cancer, you know, well, the title, if the cancer comes back five years later, right, Sanju's will still be there for you, still for free parents can no fly in and have a hotel for free to eat for free. 09:04.678 --> 09:09.467 [SPEAKER_01]: And also Sanju's does, you know, a huge amount of research for childhood cancer worldwide. 09:09.447 --> 09:12.672 [SPEAKER_01]: So what I met them, they're raising a billion dollars, right? 09:12.712 --> 09:16.076 [SPEAKER_01]: And they were kind of thinking a little bit of this kind of efficiency mindset, right? 09:16.136 --> 09:18.800 [SPEAKER_01]: As long as we're efficient, we raise money efficiently, we'll do more. 09:20.422 --> 09:25.369 [SPEAKER_01]: But I like luck, you know, you should just care about how much money you've raised minus how much money you gave Facebook and Google. 09:26.010 --> 09:31.658 [SPEAKER_01]: And within a year of making that change, it does some other things as well, but need to raise 46% more money. 09:31.638 --> 09:34.391 [SPEAKER_01]: right, and now there is a 2.5 billion dollars. 09:34.913 --> 09:39.233 [SPEAKER_01]: So that's simple, simple change of like my marketing key, P.I. 09:39.314 --> 09:40.298 [SPEAKER_01]: is wrong. 09:40.463 --> 09:44.409 [SPEAKER_01]: Right, let me change it to the right one, but on my 46% better off overnight. 09:45.030 --> 09:47.033 [SPEAKER_01]: Right, and then so, so that's an example. 09:47.053 --> 09:54.965 [SPEAKER_01]: There's a very simplistic example, and I'm making you into more complex examples, but this is happening all over everywhere, every advertising I've met. 09:55.405 --> 10:00.273 [SPEAKER_01]: And then if you dig into, you know, there are HR policies, what's to keep you eye, time to hire? 10:00.733 --> 10:01.475 [SPEAKER_01]: Like, why? 10:01.695 --> 10:03.217 [SPEAKER_01]: Like, why do you have to hire fast? 10:03.237 --> 10:04.499 [SPEAKER_01]: Don't you want to hire the best people? 10:05.000 --> 10:07.804 [SPEAKER_01]: What about customer service, average handle time? 10:07.936 --> 10:09.538 [SPEAKER_01]: No, who cares, right? 10:09.558 --> 10:23.739 [SPEAKER_01]: I mean, so if you're CEO of a small business and you look at the KPIs that your team is following, I would bet you a lot that you can find three or four really important KPIs that are incorrect. 10:24.280 --> 10:26.864 [SPEAKER_01]: And if you change them, your company would be way, way better off. 10:27.445 --> 10:34.956 [SPEAKER_00]: In working with AI as much as I have and thinking about my own clients that I work with and having similar conversations, 10:37.096 --> 10:51.097 [SPEAKER_00]: One of the things that's kind of hitting the face is how many of our KPIs, how many of our work flows have been determined based on the restrictions of the system we use, not based on them actually being what's best for our business. 10:51.137 --> 11:06.040 [SPEAKER_00]: It's like, well, we can only track time to hire Nicholas, I have no ability to understand the long term, you know, return on, you know, this, you know, spending an extra four months bringing in an employee and adding these extra layers. 11:06.020 --> 11:18.499 [SPEAKER_00]: Because they didn't have the ability to either handle the amount of data or the systems couldn't do that, et cetera, you get these best practices in these KPIs that aren't necessarily as you said best for the business, but it's what they can do. 11:18.599 --> 11:33.462 [SPEAKER_00]: And to me, it feels like with AI and the ability to build these agents, build your own, your own LLMs to mind data out of databases or multiple databases and pull them together, 11:33.442 --> 11:40.933 [SPEAKER_00]: How would you grow the business in a perfect world with no restrictions and actually have that be a reality? 11:40.953 --> 11:43.717 [SPEAKER_00]: And those are really different conversations in some cases. 11:44.258 --> 11:45.560 [SPEAKER_01]: Yeah, I think that's exactly right. 11:45.580 --> 11:52.851 [SPEAKER_01]: I would add another complication, which is most executives aren't comfortable making decisions based on a protection. 11:53.371 --> 12:02.847 [SPEAKER_01]: Right, so I'll give you a really great case study from the book, a company called Syrex, which is in online, crying, insurance, not company out of Canada. 12:03.709 --> 12:08.557 [SPEAKER_01]: No, if you're online, crying, insurance company, what you care about is typically historically was leads. 12:08.577 --> 12:10.681 [SPEAKER_01]: How many leads do I get and what's my cost per lead? 12:11.122 --> 12:16.912 [SPEAKER_01]: Another set of really bad caveats, right, because who cares how many leads you got and who cares how much they cost, right? 12:16.932 --> 12:18.855 [SPEAKER_01]: These are these, these leads are good. 12:18.835 --> 12:19.076 [SPEAKER_01]: Right. 12:19.116 --> 12:23.666 [SPEAKER_01]: So imagine using, you know, an AI to predict the quality of each lead. 12:23.746 --> 12:26.111 [SPEAKER_01]: So you just, you know, you get a new lead, right? 12:26.191 --> 12:35.612 [SPEAKER_01]: It's a 35 year old man out of Memphis, Tennessee, who drives, you know, this kind of car has this kind of credit score, dot, dot, dot, dot, dot, dot. 12:35.632 --> 12:36.253 [SPEAKER_01]: Right. 12:36.233 --> 12:37.815 [SPEAKER_01]: And then it's actually not that hard. 12:37.855 --> 12:47.205 [SPEAKER_01]: You can build the model that is quite accurate that can forecast the profitability of that individual customer based on all on they can stick around and pay at the monthly premiums. 12:47.925 --> 12:49.347 [SPEAKER_01]: And they're going to have a car crash. 12:49.747 --> 12:52.090 [SPEAKER_01]: And so imagine, you know, you can build the models. 12:52.130 --> 12:54.572 [SPEAKER_01]: I can rate, you know, we'll make 500 bucks from Ryan. 12:55.073 --> 12:57.876 [SPEAKER_01]: We'll make, you know, 100 bucks from Nick because he's going to have a car crash. 12:58.296 --> 13:00.999 [SPEAKER_01]: And you can do this for every single customer that you require. 13:01.700 --> 13:05.464 [SPEAKER_01]: You feed the simple one piece of data back to Facebook 13:05.444 --> 13:05.765 [SPEAKER_01]: Right. 13:05.845 --> 13:10.052 [SPEAKER_01]: And there AI figure out how to find more people that are the most profitable. 13:10.733 --> 13:20.590 [SPEAKER_01]: And so this company did this and within a few months, next thing you know, they were acquiring 90% more customers who were the top 20% customers. 13:21.011 --> 13:24.116 [SPEAKER_01]: And 60% fewer customers were the bottom 20% customers. 13:24.777 --> 13:27.402 [SPEAKER_01]: And they made four times more money than before. 13:27.382 --> 13:35.910 [SPEAKER_01]: right and so this whole idea of not only do I have the right KPI and I can think about using data on AI to maximize that. 13:36.711 --> 13:38.172 [SPEAKER_01]: But now I can build a crystal ball. 13:38.692 --> 13:41.395 [SPEAKER_01]: I can predict the future, even though it's not 100% accurate. 13:41.815 --> 13:46.680 [SPEAKER_01]: But if I could predict the future, that's the data I should be using, not what happened yesterday. 13:47.180 --> 13:55.768 [SPEAKER_01]: So too many companies also look in the rearview 13:55.748 --> 14:01.233 [SPEAKER_01]: So the best companies in the world are the ones who are using predictive models to make decisions today. 14:01.273 --> 14:05.717 [SPEAKER_01]: And the cool thing about AI is that now this is available for even the smallest company. 14:06.218 --> 14:08.180 [SPEAKER_01]: Anybody can build a predictive model. 14:08.660 --> 14:19.230 [SPEAKER_01]: Anybody can plug it in to Google and Facebook and other places and dramatically improve their customer acquisition, dramatically, you know, lower their churn, dramatically improve, you know, their cross selling. 14:19.751 --> 14:23.494 [SPEAKER_01]: So this is all available to everyone, 14:24.335 --> 14:26.358 [SPEAKER_01]: with making decisions based on forecasts. 14:26.919 --> 14:32.329 [SPEAKER_01]: No, it's just like a mindset shift where you're like, man, I know the leads I got today. 14:32.369 --> 14:33.451 [SPEAKER_01]: I know my cost to lead. 14:34.052 --> 14:37.478 [SPEAKER_01]: I don't know exactly what my profits to to be for Ryan in the next five years. 14:37.518 --> 14:40.523 [SPEAKER_01]: I'm only guessing what I'm actually comfortable with that guess. 14:40.563 --> 14:42.106 [SPEAKER_01]: I'm gonna optimize using that guess. 14:42.687 --> 14:44.189 [SPEAKER_01]: And I'm gonna improve that guess over time. 14:44.229 --> 14:45.111 [SPEAKER_01]: I'm gonna get better and better. 14:45.632 --> 14:49.819 [SPEAKER_01]: But I'm basically building a business that is running based on the future. 14:50.069 --> 14:55.796 [SPEAKER_00]: I know you may not know this, but the industry that I grew up in was actually insurance industry and the property cashity industry. 14:56.677 --> 15:02.064 [SPEAKER_00]: And one of the things that to this day, 20 plus years in the business, I've sold thousands of policies. 15:02.705 --> 15:05.288 [SPEAKER_00]: I started my own agency, grew it, sold it. 15:05.568 --> 15:14.760 [SPEAKER_00]: In 2021, we are actually the fastest growing small commercial agency in the country outside of the top 200, which are like the marshes in the Willow style of Watson's in those guys. 15:14.740 --> 15:18.686 [SPEAKER_00]: Um, who I would have loved to compete against, but we were not. 15:18.746 --> 15:37.375 [SPEAKER_00]: We were significantly smaller than them, but I, but my, my point saying all that is, Well, most people would be shocked to know is how, little and how, broad and fuzzy the data is that most carriers make their pricing decisions on. 15:37.415 --> 15:38.677 [SPEAKER_00]: And that's not a knock on the carriers. 15:38.757 --> 15:40.680 [SPEAKER_00]: Honestly, until AI, 15:40.930 --> 15:42.612 [SPEAKER_00]: became what it is today. 15:42.672 --> 15:50.784 [SPEAKER_00]: The option to actually pull in all the data wasn't even really there because of all these old pipes and there's a whole bunch of stories there which we don't know, I don't wanna bore the audience. 15:50.804 --> 16:06.766 [SPEAKER_00]: And my point is with some of the clients that I work with in that space, something that I have found eye opening as well as them is the ability to run multiple predictive models synchronously along side each other or asignancy. 16:06.786 --> 16:10.431 [SPEAKER_00]: Sorry, so you could essentially say, 16:10.411 --> 16:16.984 [SPEAKER_00]: what if we say 40 is the cutoff range for this top tier instead of 35 what is that do? 16:17.004 --> 16:31.052 [SPEAKER_00]: What if we say people who only drive who's commute is less than 15 miles instead of 20 miles and you could literally instead of having to deploy a team of actuaries who you know pull up a room full of chalkboards and start doing all these calculations. 16:31.032 --> 16:37.041 [SPEAKER_00]: you can play with all these different variables at one time and run out these scenarios years into the future. 16:37.061 --> 16:48.619 [SPEAKER_00]: And again, we're doing as you said, predictive modeling, but think about it, guys, even in your your your your smaller business, it even like a bakery or a plumbing a vertical, you can start to play with, hey, 16:48.599 --> 16:49.540 [SPEAKER_00]: what would it look like? 16:49.580 --> 16:50.502 [SPEAKER_00]: Here's what we're doing today. 16:50.902 --> 16:55.269 [SPEAKER_00]: What would it look like if I added two new estimators to my plumbing business, right? 16:55.369 --> 17:05.704 [SPEAKER_00]: If they were going after these guys and here, what could I predict and the model can give you all these scenarios so that you're not just guessing or using gut feeling. 17:05.744 --> 17:07.206 [SPEAKER_00]: Not that gut feeling isn't still there. 17:07.267 --> 17:17.121 [SPEAKER_00]: And I guess this is where my question is going is like if I can have 50 different scenarios laid out in front of me 17:18.029 --> 17:45.977 [SPEAKER_00]: It still feels like there is a gut decision at some point to know which ones to trust, which ones to give them the most way to et cetera, how are you advocating or consulting the people that you talk to around this balance or harmony between the gut feeling of a leader with experience and the information they're getting out of these predictive models in this case, not necessarily looking analytics, but the predictive models that they're 17:45.957 --> 17:47.100 [SPEAKER_01]: Yeah, a couple of thoughts on that. 17:47.120 --> 17:47.942 [SPEAKER_01]: It's a great, great question. 17:47.962 --> 17:51.931 [SPEAKER_01]: Ryan, one is, it's just going to predict the future a little bit, right? 17:51.951 --> 17:53.675 [SPEAKER_01]: And assume AI agents worked. 17:53.695 --> 17:57.063 [SPEAKER_01]: It's assumed that the number of hallucinations goes down to near zero. 17:57.665 --> 18:01.654 [SPEAKER_01]: And you get all these processes of agents doing a bunch of work for you. 18:01.634 --> 18:03.918 [SPEAKER_01]: Um, okay, so that's interesting. 18:03.958 --> 18:06.322 [SPEAKER_01]: So, and that's going to get pretty commoditized, right? 18:06.342 --> 18:09.366 [SPEAKER_01]: You get customer service agents and this and that pricing agents. 18:10.108 --> 18:17.239 [SPEAKER_01]: So, we're back to, you know, asking the agents to do the right thing and we're back to giving it the right data and then using your judgment as well. 18:17.279 --> 18:18.441 [SPEAKER_01]: So, there's three things that matter. 18:18.962 --> 18:19.923 [SPEAKER_01]: What do you ask the AI to do? 18:19.943 --> 18:20.885 [SPEAKER_01]: We talked a little bit about this. 18:21.586 --> 18:22.488 [SPEAKER_01]: What did it do you feed it? 18:22.528 --> 18:23.750 [SPEAKER_01]: Right? 18:23.770 --> 18:25.713 [SPEAKER_01]: And then what do you do with these decisions? 18:25.733 --> 18:27.135 [SPEAKER_01]: So, um, 18:27.115 --> 18:36.859 [SPEAKER_01]: You know, one of the thoughts there is that these AI models are actually extraordinarily powerful in most companies are actually very worried about using today. 18:36.879 --> 18:43.977 [SPEAKER_01]: So for example, in the current trends business, we went back to product property casualty, and this is a public case study that's seven years old. 18:43.957 --> 18:46.181 [SPEAKER_01]: I think it's even eight years old now. 18:46.782 --> 18:48.525 [SPEAKER_01]: So Google worked with Axa, right? 18:48.585 --> 18:55.897 [SPEAKER_01]: The large European car insurance company, and their accessories to look at their predictive model for large loss car accidents. 18:55.957 --> 18:58.462 [SPEAKER_01]: Our car accident is a cost of $10,000 to ensure. 18:58.502 --> 19:02.809 [SPEAKER_01]: And Axa had 72 pieces of data that were using legally. 19:02.789 --> 19:05.833 [SPEAKER_01]: They've solved this plumbing problem and they've had the data, right? 19:05.853 --> 19:09.217 [SPEAKER_01]: They had the data and the raptures were using the data. 19:09.818 --> 19:13.763 [SPEAKER_01]: And with that data, they were about to predict who's going to have a car crash. 19:13.823 --> 19:17.127 [SPEAKER_01]: At about 38%, 40% accuracy rate. 19:17.488 --> 19:19.951 [SPEAKER_01]: Google's Cloud Team took the exact same data. 19:20.571 --> 19:22.193 [SPEAKER_01]: No new data, the exact same data. 19:22.834 --> 19:30.123 [SPEAKER_01]: And using machine learning as opposed to human math, they're able to improve the prediction to 80% accuracy. 19:30.144 --> 19:31.345 [SPEAKER_01]: They doubled it. 19:31.443 --> 19:31.663 [SPEAKER_01]: Right? 19:32.264 --> 19:43.496 [SPEAKER_01]: Yet, if you go around the P and C world, despite the fact that this thing is public, this case that is public, you'll be hard-pressed, that's the C, and how many property and casualty, you know, companies actually use AI to predict rest. 19:44.117 --> 19:58.733 [SPEAKER_01]: So that's one massive opportunity, it's just, you know, you're predictive models should be using the latest and greatest technology, because if you get agents competing against other agents from your competitors, and one is twice as accurate as yours, you're gonna lose. 19:59.017 --> 20:11.524 [SPEAKER_01]: right and then solve, for example, you're going back to the marketing example, if Syrex has got a predictive engine, that's twice as accurate as mine, and we're trying to acquire the best customers in the industry, they're going to get all of it. 20:11.858 --> 20:14.641 [SPEAKER_01]: because mine can't really predict who the best customers are. 20:15.181 --> 20:18.824 [SPEAKER_01]: So there's a huge amount of work for most companies, even small companies to do. 20:19.385 --> 20:20.706 [SPEAKER_01]: So just predict things better. 20:20.726 --> 20:22.708 [SPEAKER_01]: I'd go back to your break, big example, right? 20:23.168 --> 20:25.170 [SPEAKER_01]: I mean, you can predict a man at the product level. 20:25.671 --> 20:29.214 [SPEAKER_01]: You can predict how much flour and sugar you need. 20:29.514 --> 20:32.957 [SPEAKER_01]: You can try to predict prices of these things you want for it when to buy, when not to buy. 20:33.238 --> 20:34.258 [SPEAKER_01]: There's a lot that you can do. 20:34.679 --> 20:35.820 [SPEAKER_01]: That's really exciting. 20:36.561 --> 20:39.283 [SPEAKER_01]: But in the end to your point, that's just data. 20:39.449 --> 20:41.976 [SPEAKER_01]: and the AI is as giving you subtracting foundations. 20:42.637 --> 20:45.204 [SPEAKER_01]: But the AI doesn't have a lot of wisdom today, right? 20:45.244 --> 20:50.579 [SPEAKER_01]: And so I think it's really important to also have a human in the loop for the really important decisions. 20:50.619 --> 20:54.910 [SPEAKER_01]: Like the data day stuff, the agents are probably going to be able to handle loss a bit. 20:54.890 --> 21:05.541 [SPEAKER_01]: But when you try to make a direction shift in your business, right, hey, I'm a baker and I want to open a second, you know, bakery, or I want to add, you know, three new items to the menu. 21:06.141 --> 21:07.843 [SPEAKER_01]: You can discuss with AI what it's things. 21:07.883 --> 21:09.204 [SPEAKER_01]: It can give you, you know, it's opinion. 21:09.264 --> 21:11.206 [SPEAKER_01]: But in the end, you know, you're the baker. 21:11.927 --> 21:17.252 [SPEAKER_01]: So I guess my answer is for day-to-day things that AI can do really, really well. 21:17.353 --> 21:20.796 [SPEAKER_01]: I think we're getting pretty close to being able to remove the human in the loop. 21:20.776 --> 21:25.003 [SPEAKER_01]: Right, or with the agents not making mistakes, you know, hopefully, you know, year and a half to years from now. 21:25.744 --> 21:31.855 [SPEAKER_01]: But that means that, you know, what executives should do is really focus on the areas of the required judgment. 21:32.355 --> 21:34.299 [SPEAKER_01]: Because the AI is not going to have a lot of that. 21:34.780 --> 21:36.863 [SPEAKER_01]: But the AI can help you scenario plan. 21:37.043 --> 21:41.170 [SPEAKER_01]: You can help, you know, give you a whole bunch of different outcomes that are possible. 21:42.533 --> 21:44.015 [SPEAKER_01]: But, you know, you have to pick it up. 21:44.836 --> 21:45.197 [SPEAKER_00]: Yeah. 21:45.683 --> 21:50.390 [SPEAKER_00]: Guys, I'll give you a quick example of this, just so you understand a thought process here. 21:50.530 --> 21:58.762 [SPEAKER_00]: I actually built, I'm using OpenClaw and essentially, his name is Maximum Effort, as an homage to Deadpool. 21:59.302 --> 22:06.112 [SPEAKER_00]: I call Max, and he is essentially my chief of staff of this podcast and my consulting business, Finding Peak. 22:06.132 --> 22:09.717 [SPEAKER_00]: And I'm gonna give you exactly what happened with you, Nicholas. 22:09.697 --> 22:15.584 [SPEAKER_00]: So someone from your PR team reached out to me and emailed me and said, hey, we'd love for Nicholas to come on the show. 22:15.604 --> 22:19.308 [SPEAKER_00]: I, uh, Max goes through my inbox every day. 22:19.428 --> 22:22.972 [SPEAKER_00]: Looks for podcasts, um, outreach emails and scans them. 22:23.312 --> 22:36.767 [SPEAKER_00]: Then goes and does a full research report on that individual uses a set of filters and guidelines that I gave him and comes back with a quality score as to whether or not not did this person is smarter or not smart, but do they fit what 22:36.747 --> 22:48.995 [SPEAKER_00]: If it's, um, eight or above, an automatically clap craft, so response email puts in the calendar links and sends it back to the person and says, hey, we'd love to have Nicholas on the show, which is what happened with you because you were in eight and a half out of ten. 22:49.616 --> 22:53.625 [SPEAKER_00]: Not as quality of a person or knowledge, just, you know, what, what, what he came up with. 22:53.605 --> 23:11.288 [SPEAKER_00]: So, and then you got the email, your PR person forwarded to you, you scheduled your time, you showed up here, as soon as you scheduled your time, Max goes into Riverside, pulls out a link, creates the link, then edits the calendar so that the link for you to come into the show is ready for you. 23:11.328 --> 23:22.482 [SPEAKER_00]: I got a notification that you were booked, but outside of that, I didn't do anything until about an hour ago, I started doing some research on how I wanted to approach the conversation. 23:22.462 --> 23:22.943 [SPEAKER_00]: Right? 23:23.043 --> 23:24.926 [SPEAKER_00]: So like think about that guys. 23:24.966 --> 23:26.668 [SPEAKER_00]: That's that's that's one process. 23:26.728 --> 23:29.933 [SPEAKER_00]: Now think of processes in your business, right? 23:29.993 --> 23:35.661 [SPEAKER_00]: That you can set up some logic around that are things that you don't necessarily have to be there for. 23:35.942 --> 23:36.102 [SPEAKER_00]: Right? 23:36.262 --> 23:40.809 [SPEAKER_00]: I gave now now there have been scenarios where the person has come back and said, well, this person's a five. 23:41.189 --> 23:48.520 [SPEAKER_00]: Like, um, I had a guy who I really wanted to have on the show after I read his bio, um, but he was in the blockchain space and Max. 23:48.500 --> 24:07.673 [SPEAKER_00]: thought well hey we don't really talk about blockchain that much so he gave him a five out of 10 and I came back and I said well look I know like blockchain isn't the point of this podcast however it's a major story it's a developing technology and the integration of blockchain and AI technology has made it 24:07.653 --> 24:11.800 [SPEAKER_00]: kind of brought it back to the forefront of what I think should be on leader's minds. 24:12.121 --> 24:13.283 [SPEAKER_00]: Let's talk to this guy, right? 24:13.523 --> 24:19.815 [SPEAKER_00]: So now we're slowly iterating to where, eventually, I won't have to touch any of it. 24:20.115 --> 24:26.487 [SPEAKER_00]: And I'll just have great guests that show up that I then can just do what I like to do, which is the research and the interview part. 24:27.007 --> 24:27.889 [SPEAKER_00]: And 24:27.869 --> 24:43.404 [SPEAKER_00]: That, you know, I tracked it, you know, in the last month that saved me about two and a half hours a week of time, just removing this process and it's smoother for my guess, because now they're not waiting for me to go back into my email and find the, oh, did they respond yet? 24:43.484 --> 24:45.047 [SPEAKER_00]: Like, all like, it just gets handled. 24:45.368 --> 24:47.633 [SPEAKER_00]: And like, this is where, 24:47.798 --> 24:49.561 [SPEAKER_00]: I think this is where my questions coming from. 24:50.262 --> 24:56.631 [SPEAKER_00]: That long die tribe is to say, I feel like we're looking at AI and we're looking for these big home runs. 24:57.131 --> 25:13.114 [SPEAKER_00]: And to me, especially in these early days, a lot of the winds are just in finding half hour in your day here, an hour in your week here, two hours here that like these little functions that just give you small chunks of time back that when you start to stack them up, 25:13.094 --> 25:36.891 [SPEAKER_00]: Man, now I have the time to sit back and go, you know what Nicholas, I'm going to, I'm going to think about this decision right where before because I had 10 bazillion things going on and I was so hassled I'm just making these snap decisions, it actually allows us to be leaders again is what I'm saying is like it's it's like, is this, do you believe that what AI is really going to do is kind of give give leaders and people and leadership positions. 25:37.394 --> 25:44.263 [SPEAKER_00]: their jobs back to a certain extent, which is actually dissecting and making decisions and then actually advocating for them, right? 25:44.463 --> 25:50.510 [SPEAKER_00]: This busy work, I feel like is so much of the reason that we don't do those things, but I can solve that. 25:50.891 --> 25:54.075 [SPEAKER_01]: Yeah, I want to see, I think, and then take your example, right? 25:54.095 --> 26:02.305 [SPEAKER_01]: You can even, you know, use AI to do a lot of the research, and then, you know, find some questions, and then, 26:02.285 --> 26:06.770 [SPEAKER_01]: I mean, the EI could have read my book, you know, summarized it for you. 26:06.830 --> 26:16.201 [SPEAKER_01]: And so, so, yes, I think, you know, it's not that hard to create a 15 to 20, 25 percent, 30 percent at space more than what you have today. 26:16.241 --> 26:24.550 [SPEAKER_01]: And then I think, you know, then the EI calls, so, you know, yes, to like the productivity piece and the small ball, right, hitting a budget singles. 26:25.331 --> 26:31.498 [SPEAKER_01]: But also, like, if you read my book, right, there's a lot of home runs that you 26:32.018 --> 26:32.318 [SPEAKER_01]: Right? 26:32.338 --> 26:42.769 [SPEAKER_01]: And so, you know, for example, like, in 15% of the customers drive 90% to 100% of the profits are almost every industry. 26:42.789 --> 26:42.989 [SPEAKER_01]: Right? 26:43.610 --> 26:47.153 [SPEAKER_01]: And so, and we talk about this, right? 26:47.173 --> 26:51.077 [SPEAKER_01]: We, you know, large companies don't talk about this, most entrepreneurs don't talk about this. 26:51.117 --> 26:54.280 [SPEAKER_01]: When I ran my companies, I didn't talk about this, right? 26:54.300 --> 26:59.005 [SPEAKER_01]: I focused on acquiring customers and making sure 26:58.985 --> 27:02.510 [SPEAKER_01]: I just didn't spend enough time creating customer value, right? 27:02.530 --> 27:17.230 [SPEAKER_01]: So the whole idea of customer lifetime value, where you're acquiring the most valuable customers, which was like, sure, I said, but then after that, you're using a to develop these customers, to make them more valuable, right, going back to the property and casualty example, right? 27:17.210 --> 27:26.202 [SPEAKER_01]: If the customer has two products, if they have home and current insurance, they'll stay twice the three times longer than before. 27:26.903 --> 27:32.010 [SPEAKER_01]: So, and I'll give you an example of a use of AI that I think is really fascinating. 27:32.931 --> 27:38.899 [SPEAKER_01]: I was working with a fashion retailer, and it used a lot of AI to make a lot of decisions faster. 27:38.959 --> 27:40.181 [SPEAKER_01]: They were saving time. 27:40.241 --> 27:43.285 [SPEAKER_01]: They were doing that productivity piece. 27:43.265 --> 27:49.237 [SPEAKER_01]: But then, you know, one of the merchants was like, luck, you know, I've got some insights, I think, on customer behavior. 27:49.277 --> 27:55.871 [SPEAKER_01]: And I think that if we can not drive customer to do certain things, they'll buy a lot more, they'll be even happier. 27:55.931 --> 28:00.801 [SPEAKER_01]: For the customer lifetime value, the profit of a customer over time is going to go up a lot. 28:00.781 --> 28:08.313 [SPEAKER_01]: So, okay, like what's your hypothesis and the hypothesis was that if you look at customers, you've only shopped in one category, right? 28:08.834 --> 28:25.320 [SPEAKER_01]: You've only shopped in, you know, blouses or shirts, and you can get them to buy a different category, like pants or skirts in my experience, people just start shopping a lot more, which funny, by the way, was the opposite of what AI was doing. 28:25.975 --> 28:26.376 [SPEAKER_01]: Right? 28:26.436 --> 28:31.651 [SPEAKER_01]: If you go into a fashion vitally by a lot of shirts, it's going to just set up more shirts for you. 28:32.473 --> 28:35.361 [SPEAKER_01]: And the reason for that is because it's optimizing the wrong thing. 28:36.263 --> 28:36.444 [SPEAKER_01]: Right? 28:36.524 --> 28:39.833 [SPEAKER_01]: It's optimizing to a shorter metric called conversion rate. 28:40.623 --> 28:45.830 [SPEAKER_01]: Right, so it's like, it by the way, like 100%, almost 100% of websites optimize for conversion rate. 28:46.651 --> 28:54.201 [SPEAKER_01]: Right, it's like, hey, customer comes in and 6% buy and the next page, 4% buy the first page is better than the second page. 28:54.221 --> 28:56.103 [SPEAKER_01]: It turns out not to be the case, right? 28:56.724 --> 29:09.280 [SPEAKER_01]: And so we did this test and we showed that if you can get a person to start shopping at cross categories, their customer lifetime value dolls, which you 29:09.260 --> 29:09.600 [SPEAKER_01]: Right. 29:09.661 --> 29:18.693 [SPEAKER_01]: And so back to your point about thinking and not just rely on the AI, but this was a very senior merchant who had insights that the AI didn't have. 29:19.174 --> 29:21.317 [SPEAKER_01]: And she challenged the AI, she's like the AI is wrong. 29:22.538 --> 29:23.600 [SPEAKER_01]: Now the AI was wrong. 29:23.620 --> 29:24.881 [SPEAKER_01]: The AI was just training properly. 29:25.222 --> 29:33.293 [SPEAKER_01]: It was trying to do the wrong thing, which again is my experience with almost every, you know, meaningful the AI project. 29:33.357 --> 29:44.694 [SPEAKER_01]: And so, once we retrain it and say, hey, look, you know, if somebody's bought a lot of shirts, yes, the recommendation, I just should have some shirts and to it for sure, but this should also have some pants and some bells and some shoes. 29:45.335 --> 29:48.399 [SPEAKER_01]: And if you look at Netflix, like, that's, this is a really good example of this. 29:48.500 --> 29:55.530 [SPEAKER_01]: Like, if you look at Netflix seven, eight years ago, it would just recommend stuff, like it exactly like what you've already watched. 29:55.730 --> 29:58.234 [SPEAKER_01]: And so, because they were trying to increase watch time. 29:58.214 --> 30:06.242 [SPEAKER_01]: Presumably, I haven't worked with Netflix on this topic, but now if you get in Netflix, you see that recommendations and engines are like all over the place, right? 30:06.262 --> 30:13.389 [SPEAKER_01]: Like, you know, stuff you may lie because of what you saw, but then you get like a lot of weird stuff that, you know, why are they showing me horror movies? 30:13.429 --> 30:15.351 [SPEAKER_01]: I don't really watch any horror movies. 30:15.711 --> 30:19.975 [SPEAKER_01]: It's if you like a show and you watch that show and you're done with the show, maybe you'll turn. 30:20.355 --> 30:23.579 [SPEAKER_01]: But if you watch a show and then watch a show on a different category, right? 30:23.599 --> 30:28.103 [SPEAKER_01]: Because if you run out of runcums, but then you have a horror show or an action show, 30:28.083 --> 30:48.538 [SPEAKER_01]: And so this kind of really deep knowledge of customers is what we have to train AI on and in most companies to do a pretty bad job at one understanding, you know, they're on customers a few, you know, these thousand CEOs that I met, one of the first questions I would ask them is what percentage of your profits come from your top 20 percent customers. 30:48.923 --> 30:51.647 [SPEAKER_01]: And only about 20 to 25% of CEOs knew that. 30:52.348 --> 30:56.735 [SPEAKER_01]: And then if you asked a follow-up, which is okay, what did these customers eat for breakfast? 30:57.016 --> 30:57.757 [SPEAKER_01]: Like who are they? 30:58.117 --> 30:58.818 [SPEAKER_01]: Tell me more, right? 30:59.219 --> 31:03.606 [SPEAKER_01]: Because if I know that, I'm gonna train the AI to find you more people like that. 31:04.127 --> 31:06.951 [SPEAKER_01]: She'd then develop your current customers to be more like that. 31:07.512 --> 31:12.720 [SPEAKER_01]: And then if you think this way, everything is around improving customer lifetime value. 31:12.700 --> 31:15.243 [SPEAKER_01]: as opposed to every other metric you can think of. 31:15.343 --> 31:18.387 [SPEAKER_01]: Right, churn, churn is important, but it's not the most important thing. 31:19.228 --> 31:21.390 [SPEAKER_01]: Right, number of customers acquired doesn't really matter. 31:22.592 --> 31:24.914 [SPEAKER_01]: Like, how is my customer lifetime value? 31:24.935 --> 31:26.376 [SPEAKER_01]: Am I increasing it every day? 31:26.937 --> 31:31.582 [SPEAKER_01]: You know, I thought Ryan was worth $2,000 in the next five years, and I was worth $3,000 in books. 31:31.602 --> 31:33.164 [SPEAKER_01]: I've created a thousand books of value. 31:33.945 --> 31:36.328 [SPEAKER_01]: If they train a AI to do that, 31:36.308 --> 32:03.065 [SPEAKER_01]: All right, acquired the most valuable customers and then find the next best thing you can do to get a customer to be more valuable for you, all of that while also checking that you're delighting customers, but so customer lifetime value goes up that promoter score goes up right and so that's the trick and and it sounds a little bit, you know, theoretical if you're a small business owner, but I'm one of my good friends runs a gym and he started thinking this way and looking at customer lifetime value. 32:03.085 --> 32:04.847 [SPEAKER_01]: I don't come any members I have. 32:04.827 --> 32:11.437 [SPEAKER_01]: I don't care how many I acquire, I want these people to stay forever and invest a lot more money into your health. 32:12.178 --> 32:17.607 [SPEAKER_01]: So now you just get this business, you know, you used to be a 20,000 dollar business, that's a 1.2 million dollar business. 32:18.168 --> 32:23.095 [SPEAKER_01]: It's churn is like 3% and what people join these stay forever. 32:23.135 --> 32:27.983 [SPEAKER_01]: So what did he do while he started targeting 55 year old customers in the bottom? 32:28.182 --> 32:32.847 [SPEAKER_01]: because they have health issues that are more difficult to deal with. 32:33.628 --> 32:35.911 [SPEAKER_01]: You hire different types of trainers and train them differently. 32:35.991 --> 32:39.475 [SPEAKER_01]: You build a whole set of practices like nutrition. 32:39.495 --> 32:44.521 [SPEAKER_01]: They also have things around, as members and members love it and never quit. 32:45.743 --> 32:54.433 [SPEAKER_01]: So just imagine going from a typical gym where you just like cost and training to this really kind of customer centric high customer lifetime value business. 32:54.453 --> 32:56.215 [SPEAKER_01]: So it's average customer. 32:56.195 --> 32:59.461 [SPEAKER_01]: is worth 28 times more than the average customer of an older. 33:00.342 --> 33:01.785 [SPEAKER_01]: This can be done by anybody. 33:02.506 --> 33:15.790 [SPEAKER_01]: Don't focus on just the average customer, try to understand who your best customers are, try to find more like that and try to make everyone of your customers your best customers and AI can help you do all that stuff even if you're a very small business. 33:16.259 --> 33:16.639 [SPEAKER_00]: Yeah. 33:17.180 --> 33:27.394 [SPEAKER_00]: And the best part about this, you know, is so you figure out who your highest lifetime value customer is and you get a good feel for that avatar or avatars. 33:28.215 --> 33:33.922 [SPEAKER_00]: And then it's not like you can't then go back and now optimize for conversion of those specific people. 33:34.323 --> 33:38.368 [SPEAKER_00]: So, you know, guys, what Nicholas is not saying is just 33:38.348 --> 33:43.035 [SPEAKER_00]: stop at what's the best lifetime value for a customer and how do you provide that product? 33:43.115 --> 33:52.169 [SPEAKER_00]: It's now that we have a better feel for what we need to optimize for, we can give that to our conversion optimization agent. 33:52.450 --> 33:54.573 [SPEAKER_00]: And now they have the proper target. 33:54.613 --> 33:57.758 [SPEAKER_00]: So what I love about this what you're saying here is 33:57.738 --> 34:10.375 [SPEAKER_00]: figure out the right target first, spend the time figuring out what the right target is before we go into these optimization cycles because we know AI is amazing at optimization, creating efficiency, et cetera. 34:10.755 --> 34:14.921 [SPEAKER_00]: But if we're for shooting at the wrong target, we're no further along. 34:14.981 --> 34:24.994 [SPEAKER_00]: We just have this engine optimizing for the wrong things, and we're just as, you know, maybe miserable or frustrated with our results as we always 34:24.974 --> 34:33.505 [SPEAKER_00]: And that part of it to me is it's so the granularity of AI is another part of it, right? 34:33.525 --> 34:40.014 [SPEAKER_00]: Like we may be able to say like people over 55, but I'm positive that what your friend didn't stop at was people over 55. 34:40.234 --> 34:54.272 [SPEAKER_00]: It was probably, you know, men who are 55 and, you know, want to play golf more and, you know, this, you know, maybe I've tried TRT in the past but didn't like it and 34:54.252 --> 35:02.783 [SPEAKER_00]: And then what I love about it is about AI in particular and how we find places using our business is now the things that I'm not good at. 35:02.883 --> 35:06.047 [SPEAKER_00]: Like I am not good at creating ad copy. 35:06.487 --> 35:14.337 [SPEAKER_00]: I've been writing and doing marketing my entire life and to be honest with you, creating like ad copy for like an Instagram ad or a Facebook ad, it just like, 35:14.317 --> 35:15.038 [SPEAKER_00]: breaks my brain. 35:15.778 --> 35:17.420 [SPEAKER_00]: I know all the stupid copy hacks. 35:17.460 --> 35:20.243 [SPEAKER_00]: I've read David Ogobie, or whatever reason it breaks my brain. 35:20.603 --> 35:39.582 [SPEAKER_00]: But what I can do is say to the AI, I want you to give me, you know, Alex from OZ copy with a David Ogobie hook with a bubbubbub, you know, and then give me 50 different iterations that speak to men over the age of 55 who like to play golf, have tried TRT and are frustrated with the results bang. 35:39.562 --> 35:41.485 [SPEAKER_00]: that comes out in 15 minutes. 35:41.785 --> 35:48.415 [SPEAKER_00]: So now the parts of the process that you normally would have had outsource or just not in your zone of genius, those things are taken care of. 35:48.836 --> 35:54.985 [SPEAKER_00]: And as you described, you can spend your time in the spots where your zone of genius actually matters, right? 35:55.446 --> 36:01.235 [SPEAKER_00]: Talking to your customers, figuring out why they decided to stay, you know, what was, you know, was it this exercise? 36:01.275 --> 36:04.540 [SPEAKER_00]: Or when we added the nutrition program and like, 36:04.520 --> 36:14.762 [SPEAKER_00]: Those are things we don't get, oftentimes in smaller businesses, we don't get to those things, not because we don't value them, because we have so much transactional nonsense in our business that we can't. 36:15.303 --> 36:21.516 [SPEAKER_00]: And AI is freeing that up in a way that I feel like, 36:21.732 --> 36:44.938 [SPEAKER_00]: We have to take advantage of we can't wait like there isn't if you're in the same community as your friend who has a gym and you're trying to target the same market that he is you're going to get destroyed by him because he kind of FAF owed with this stuff and figured it out and played around with it and even if it's not perfect he's so far ahead for the next iteration and and that's the part where 36:44.918 --> 36:51.728 [SPEAKER_00]: I'd love for you to talk a little bit about is, let's say they've gotten into the book and we've made some early wins and that's great. 36:52.248 --> 37:05.307 [SPEAKER_00]: But how do we set our business up because it feels like and I've gotten this feedback every week we're getting a new model and it can do this new thing and should I switch from open AI to Claude and like how do we manage 37:05.287 --> 37:16.528 [SPEAKER_00]: from a leadership perspective, this, the constant change in the constant like new features and new models like how do we work through all that and not get lost in that mess? 37:16.964 --> 37:18.707 [SPEAKER_01]: Yeah, what are your great questions? 37:18.987 --> 37:21.170 [SPEAKER_01]: And what you said, by the way, is really insightful. 37:22.492 --> 37:25.336 [SPEAKER_01]: The idea is to put customer lifetime value in the middle, right? 37:25.356 --> 37:29.522 [SPEAKER_01]: And everything, we'll still sort of answer your questions the same thing, right? 37:29.963 --> 37:35.951 [SPEAKER_01]: If you've got the right KPI as any thinking about it the right way, that you're going to get a 2x or a 3x, right? 37:36.392 --> 37:40.558 [SPEAKER_01]: And if a new model comes in and it's 10% better, that's 10%. 37:40.538 --> 37:49.806 [SPEAKER_01]: So staying ahead on the model front is useful and should try to not be too far behind, but that's not the leverage. 37:49.921 --> 37:59.974 [SPEAKER_01]: Like the leverage is just, if you use the latest model and my model is six months old, like at the right, I'm asking you to do the right things and you're asking to do the wrong, I'm gonna transfer you. 38:00.875 --> 38:15.473 [SPEAKER_01]: Right, and so again, like the, yes, wash everything, yes, you know, make sure that, you know, what you develop as a gnostic that, you know, uses, you know, the MCP protocol so that you don't have it, you can be wedded to one model. 38:15.453 --> 38:26.405 [SPEAKER_01]: And by the way, like, I love Google, I love, and I'm leaving Silicon Valley, I love these companies, but this, you know, AI agent operating system is going to run your business, right? 38:26.846 --> 38:28.428 [SPEAKER_01]: You can't be beholden to one of them. 38:29.349 --> 38:31.872 [SPEAKER_01]: You have to have some optionality if you can afford it, right? 38:31.892 --> 38:39.300 [SPEAKER_01]: And it's, it creates turn out complexity, but if you can have at least two models keep peating for your attention, that's a useful thing. 38:39.280 --> 38:45.850 [SPEAKER_01]: So staying in staying out of the models and using the best model, I think is interesting, but not the most important thing. 38:45.891 --> 38:48.915 [SPEAKER_01]: Having to write KPIs much more interesting, except for one exception. 38:49.877 --> 39:02.537 [SPEAKER_01]: I would say that if you believe like I do, that we're getting really close, pick a number, I don't know, 18 months, to having AI being able to write code with thought of human in the loop. 39:02.855 --> 39:07.303 [SPEAKER_01]: Then you have to have the AI to do that. 39:07.323 --> 39:11.951 [SPEAKER_01]: But if the cloud is better, then open AI at coding. 39:11.971 --> 39:17.200 [SPEAKER_01]: And open AI requires a human in the loop, and clon can code without that, even in the loop just a little bit of oversight. 39:18.202 --> 39:21.387 [SPEAKER_01]: Then you have an infinite innovation cycle. 39:21.367 --> 39:41.468 [SPEAKER_01]: Right, and this is actually really a critical concept to understand the data AI can write code like 99.9% of the code is the data launch for an American to have any idea right and it's live like in the week right now like how many ideas do you have right so so that you have to really pay attention to. 39:41.448 --> 39:51.762 [SPEAKER_01]: But models are the best I coding, and you have to be really on the edge of that, but, you know, one element is slightly better than another at creating, you know, ad copy. 39:52.163 --> 39:56.869 [SPEAKER_01]: If you add copies a little bit better, but your KPI sucks, you're not going to be there. 39:56.929 --> 40:02.936 [SPEAKER_01]: So I think that's the most important thing, right, is just making your customer in the middle. 40:03.597 --> 40:13.429 [SPEAKER_01]: And then, you know, even going beyond the avatar of the best customer, like you want a predictive model that can give you an an equals one. 40:13.409 --> 40:14.871 [SPEAKER_01]: Rine is worth this much. 40:15.392 --> 40:16.634 [SPEAKER_01]: Nicholas is worth this much. 40:17.235 --> 40:21.222 [SPEAKER_01]: And Rine is improving in customer lifetime value and Nicholas is not going the other way. 40:21.943 --> 40:22.644 [SPEAKER_01]: So why? 40:22.865 --> 40:23.406 [SPEAKER_01]: What's happening? 40:23.526 --> 40:24.748 [SPEAKER_01]: Why is Rine getting better? 40:24.848 --> 40:28.113 [SPEAKER_01]: And why have we lost a thousand dollars on deck in the last six weeks? 40:28.133 --> 40:28.714 [SPEAKER_01]: What have you done? 40:29.335 --> 40:32.160 [SPEAKER_01]: And then when you start thinking this way. 40:32.140 --> 40:43.316 [SPEAKER_01]: One, you learn a lot as an executive, and two, you plug in the AI agents on top of that, and they tell you, like, hey, you screwed up your pricing on this product, and you pissed Nicolas off, he's, no, that's what happened. 40:43.396 --> 40:46.600 [SPEAKER_01]: Or you had a customer service interaction with Nick, it didn't go very well. 40:46.620 --> 40:49.184 [SPEAKER_01]: And one of the most valuable customers, I said, bought from you, sends. 40:49.845 --> 40:51.347 [SPEAKER_01]: No, so, no. 40:51.327 --> 41:00.140 [SPEAKER_01]: that kind of connectivity, but focusing on customer lifetime value and customer, you know, no promoter score or some customer satisfaction. 41:00.180 --> 41:06.670 [SPEAKER_01]: And in the end, if you really analyze what a business is, it's, you know, trying to keep constant control, of course, right? 41:07.291 --> 41:11.557 [SPEAKER_01]: But then, you know, increasing customer lifetime value, and increasing customer satisfaction. 41:11.577 --> 41:13.740 [SPEAKER_01]: If you do those two things, you're going to win. 41:13.720 --> 41:21.369 [SPEAKER_01]: Right, and then yet, when people are plugging in AI agents what's up with their business, the AI is never known by these two things. 41:23.332 --> 41:25.595 [SPEAKER_01]: Right, so that's the opportunity. 41:26.035 --> 41:27.197 [SPEAKER_01]: It's just focusing on that. 41:27.257 --> 41:36.468 [SPEAKER_01]: And then there's one more I want to share, which is, I think it really massive opportunity for small businesses in meeting businesses as well. 41:37.449 --> 41:42.816 [SPEAKER_01]: It's gotten harder to compete against larger companies with big brands. 41:42.796 --> 41:48.126 [SPEAKER_01]: Right people in the consolidating to more, you know, fewer brands trust is being eroding. 41:48.166 --> 41:56.040 [SPEAKER_01]: And so that's why you have a lot of data suggesting that the biggest, most profitable companies in every industry get more profitable every year. 41:56.060 --> 41:57.422 [SPEAKER_01]: You know, faster. 41:57.763 --> 42:02.391 [SPEAKER_01]: So they're they're profitability accelerating and it's leaving, you know, others behind. 42:02.371 --> 42:18.017 [SPEAKER_01]: So, you know, historically building a brand has been hard, you know, obviously you have to have an amazing product, you have to have an amazing trust with your customers and you can build a brand through word of mouth, but also you can try to do some brand advertising. 42:18.554 --> 42:32.516 [SPEAKER_01]: But historically, like 88% of print advertising was on profitable and then you have to take a big rest right, you have to build these ads at cost $100,000, and then you have to buy a TV spot for $200,000, bucks, $300,000, bucks. 42:32.556 --> 42:37.043 [SPEAKER_01]: And now you're three to $400,000 into it before you know if anything is working or not. 42:37.984 --> 42:44.815 [SPEAKER_01]: And so now with the eye and with digital marketing, you can actually circumvent all that and build a brand of no risk. 42:45.436 --> 42:56.571 [SPEAKER_01]: So I'll just share a quick story, which is in Vizaline and Vizaline has some pretty big issues during the pandemic, because you have to go see a debt test to get in Vizaline. 42:57.252 --> 42:59.675 [SPEAKER_01]: And the top competitor back then was Smile Direct Club. 43:00.296 --> 43:03.500 [SPEAKER_01]: That ship directed consumers don't have to see a debt test, right? 43:03.641 --> 43:12.152 [SPEAKER_01]: So you figure during the pandemic, Smile Direct Club would just eat, you know, you know, in Vizaline alive, right? 43:12.132 --> 43:17.480 [SPEAKER_01]: And so, in Visaline was stuck, you know, trying to figure this out. 43:18.120 --> 43:21.345 [SPEAKER_01]: And in Q4 2020, that this office has started to reopen. 43:21.866 --> 43:23.648 [SPEAKER_01]: And in Visaline's revenues went up by 26%. 43:24.710 --> 43:26.252 [SPEAKER_01]: And Smell Direct Club was down by 6%. 43:26.512 --> 43:32.701 [SPEAKER_01]: And a difference in market cap that happened that day, it was about $15 billion that shifted between the two companies. 43:33.723 --> 43:34.143 [SPEAKER_01]: Why? 43:34.704 --> 43:40.452 [SPEAKER_01]: Because a number of searches for the Invisaline brand in the last three months 43:40.803 --> 43:51.087 [SPEAKER_01]: on an absolute basis and a double relative to smile direct love, so that when the dentist office is reopened, people were primed to buy from a division line because they really knew the brand. 43:51.788 --> 43:52.811 [SPEAKER_01]: So how has this happened? 43:52.831 --> 44:00.027 [SPEAKER_01]: So we did some work with a division line and then we repeated this for a number of brands and it booked explains how you can do all this, but long story short. 44:00.007 --> 44:06.399 [SPEAKER_01]: You, you know, use AI to build, you know, at six second ad or 30 second ad, that ad isn't about selling now. 44:06.459 --> 44:09.165 [SPEAKER_01]: It's about building a brand through the medium and long term, right? 44:09.405 --> 44:13.413 [SPEAKER_01]: So it's a very different ad and a typical kind of direct response, performance marketing, yeah. 44:14.334 --> 44:19.585 [SPEAKER_01]: But, and, but historically, the problem was that we couldn't tell if the thing was working or not until much later. 44:19.645 --> 44:22.470 [SPEAKER_01]: So you couldn't really optimize it using AI. 44:22.788 --> 44:35.987 [SPEAKER_01]: Well, now you can put an add on YouTube and you can put like 15 different variations of an add or even 15 different ask because it's so much cheaper with AI and you can see in real time which of the ads is driving people with search for your brand. 44:36.406 --> 45:06.413 [SPEAKER_01]: And then you double down on that add and then you only start investing money on that add and maybe just in one state or small state in Iowa for example, right, you start investing a little bit of money in and you're like, hey, look, it's searches for my brain or doubling in Iowa and maybe you wait a couple of months to see the impact on yourselves and you're like, hey, look, my sales in Iowa going through the roof, all depth of the diversity country, whoa, this thing worked, now let me scale it to the whole country. 45:06.393 --> 45:10.581 [SPEAKER_01]: That just offers a reopen and a visa line eats while the red club's lunch. 45:10.601 --> 45:11.643 [SPEAKER_01]: So anybody can do this. 45:11.943 --> 45:12.905 [SPEAKER_01]: It's very inexpensive. 45:13.065 --> 45:15.450 [SPEAKER_01]: And next thing you know, your brand is at a whole different level. 45:15.510 --> 45:17.934 [SPEAKER_01]: And then, magical things happen, right? 45:17.994 --> 45:19.417 [SPEAKER_01]: You website converts better. 45:19.437 --> 45:22.683 [SPEAKER_01]: You're, you can price 10, 15% higher. 45:22.743 --> 45:25.408 [SPEAKER_01]: And customers will be okay with that. 45:25.428 --> 45:28.213 [SPEAKER_01]: You acquire more customers more cheaply, dot, dot, dot, dot, dot, dot, right? 45:28.233 --> 45:29.295 [SPEAKER_01]: It's a second. 45:29.275 --> 45:46.963 [SPEAKER_01]: Having an amazing brand is a force multiplier for a business, but it was something that was undueable for small companies, for small companies before, and now it's AI and the ability to test before you invest any real money, anybody, a bakery, a gem can build an amazing brand. 45:47.517 --> 45:48.358 [SPEAKER_00]: Yeah. 45:48.378 --> 45:49.138 [SPEAKER_00]: I couldn't agree with you more. 45:49.158 --> 45:52.742 [SPEAKER_00]: This has been a mountain that I've been shouting off of for a few years now. 45:53.863 --> 46:07.976 [SPEAKER_00]: Basically, since I got into AI, since my fingers first got in and I first started using this thing, like, I just started telling all my clients on this show, I've said a thousand times, your brand might be your most valuable asset today. 46:08.156 --> 46:11.219 [SPEAKER_00]: Like, and what I mean by that is not, you don't need to have a good product. 46:11.239 --> 46:16.123 [SPEAKER_00]: You have to have a good product, 46:16.103 --> 46:17.986 [SPEAKER_00]: you know, you can have the best product. 46:18.106 --> 46:28.220 [SPEAKER_00]: And if you have the worst marketing, I don't know today that that build a good product and they will come thing really relates anymore because of how saturated the market is with messaging. 46:28.280 --> 46:35.009 [SPEAKER_00]: And I saw a really trite example compared to your in Vizaline example the other day I was watching a video. 46:35.029 --> 46:37.572 [SPEAKER_00]: This guy's name is Greg Eisenberg. 46:37.813 --> 46:45.283 [SPEAKER_00]: He talks 46:45.820 --> 47:06.748 [SPEAKER_00]: he started this little application where he lives in the UK and he was moving from one you're trying to find a new apartment with his girlfriend and the hardest part was they both had different visions for the apartment so they were taking pictures of these apartments but they're empty so you can't really say so we basically built this little app that you tell it your style you take a picture of the room and it kind of 47:06.728 --> 47:08.010 [SPEAKER_00]: dresses the room up for you. 47:08.030 --> 47:08.972 [SPEAKER_00]: It stages the room for you. 47:09.293 --> 47:09.533 [SPEAKER_00]: Okay. 47:09.553 --> 47:09.754 [SPEAKER_00]: Cool. 47:09.774 --> 47:10.194 [SPEAKER_00]: You know, whatever. 47:10.615 --> 47:14.483 [SPEAKER_00]: Well, that's not the that part is interesting, but not not the story. 47:14.903 --> 47:16.566 [SPEAKER_00]: The story is he had zero brand. 47:16.727 --> 47:23.860 [SPEAKER_00]: He literally created this even says I created this on a weekend for my for my girlfriend and I and then I just. 47:24.346 --> 47:27.631 [SPEAKER_00]: For fun, decided to commercialize it, okay? 47:27.651 --> 47:28.753 [SPEAKER_00]: He's like, I had zero brand. 47:29.214 --> 47:45.600 [SPEAKER_00]: So what he did was he created an AI agent, and we don't have to get into the technical details of how he created the AI agent, but essentially what it did was using TikTok, it created five to seven different versions of TikToks and TikTok ads every day. 47:45.660 --> 47:46.962 [SPEAKER_00]: And, 47:46.942 --> 48:03.246 [SPEAKER_00]: And then he would run them and what he found by giving it access to the analytics as well, is it would test words at the top words at the middle words on the bottom do I highlight certain words how long should the video's be what and basically he found this format that it was like 48:03.226 --> 48:18.906 [SPEAKER_00]: Family member plus a funny deadpan story plus value plus CTA with a kitchen scene yielded these massive returns and he was saying on this interview. 48:18.886 --> 48:45.096 [SPEAKER_00]: how would I have ever gotten to that as the, you know, hook to image to whatever, like it would take me years and it took him about six weeks of just iterating and he's like now the thing automatically builds this, it's, you know, I give it like, and this is the wild part about this stuff guys is he said 60% of his content now is in the veins that he knows works, 48:45.076 --> 48:50.972 [SPEAKER_00]: And then he has 40% of the content they create is on new tests. 48:50.992 --> 48:59.835 [SPEAKER_00]: So now he's got this wheel, just, I mean, just think about this for your business, for the gym, for, for a large organization, this is should be, I mean, this should be like common practice. 49:00.076 --> 49:00.958 [SPEAKER_00]: But like, 49:00.938 --> 49:22.058 [SPEAKER_00]: He is now on the side on this little side project he created, making an extra $4 to $500 a day in new that's not renewal, $4 to $500 a day in new signups, paid signups on a recurring loop that is putting 60% of your winners out there and doubling into them, but also constantly testing new defined new winners. 49:22.578 --> 49:30.726 [SPEAKER_00]: And it becomes the self-propetuation machine that like, could we have gotten 49:30.706 --> 49:33.369 [SPEAKER_00]: But the timetable is completely different. 49:33.710 --> 49:38.175 [SPEAKER_00]: And I know we're talking about a simple app and whatever and guys, please don't do the like my business is different thing. 49:38.395 --> 49:40.418 [SPEAKER_00]: There is a version of this for every business. 49:40.919 --> 49:46.906 [SPEAKER_00]: And if we just compare, guy who started an app and then asked to do all this marketing the old way, right? 49:46.966 --> 49:56.798 [SPEAKER_00]: Of iterating and going to Canva or hiring a firm versus this iterative loop machine that is constantly learning doubling into winners while still experimenting with new stuff. 49:57.402 --> 50:06.395 [SPEAKER_00]: It doesn't even matter the comparison and product quality, the speed at which the AI driven business is able to iterate is going to win over time every time. 50:06.555 --> 50:20.035 [SPEAKER_00]: It just, it's, I mean, to me, I just look at it, I'm like, this is a no-brainer and it's fascinating and it is absolutely this leveraged unlock that any business can take advantage of. 50:20.555 --> 50:22.558 [SPEAKER_01]: Yes, so insightful, Ryan. 50:23.138 --> 50:25.121 [SPEAKER_01]: I funny that's the last chapter of my book, you know, B.C. 50:25.141 --> 50:26.002 [SPEAKER_01]: going out of Bonzai. 50:27.524 --> 50:32.230 [SPEAKER_01]: So let's just assume for a second that companies will figure this out, where they'll look customer lifetime value in the middle. 50:32.871 --> 50:40.420 [SPEAKER_01]: They'll, they'll, they'll optimize the entire business around that, right, you know, and then they'll have a software engine and they can really code a lot of stuff really, really fast. 50:40.568 --> 50:45.715 [SPEAKER_01]: Okay, so now the logical question is like, okay, so everybody could do this, right? 50:45.735 --> 50:46.996 [SPEAKER_01]: It's not going to be that hard. 50:47.757 --> 50:51.242 [SPEAKER_01]: Um, what's different, how can I maintain my competitive edge? 50:51.682 --> 50:53.024 [SPEAKER_01]: It's precisely what you said. 50:53.745 --> 51:02.095 [SPEAKER_01]: Like in the end with AI, what really is going to matter is give it the right, you know, data, give it the right thing to optimize, of course, as we discussed, you know, throughout the show. 51:02.916 --> 51:04.939 [SPEAKER_01]: Well, who moves faster? 51:06.269 --> 51:08.111 [SPEAKER_01]: I'll only test any run a year, right? 51:08.131 --> 51:14.077 [SPEAKER_01]: So like I was just, I was working with an internet company, right, we all know what it is, but I can't mention it. 51:14.137 --> 51:18.821 [SPEAKER_01]: And the CDO wasn't happy with the velocity of testing of the company. 51:18.841 --> 51:28.290 [SPEAKER_01]: And so I worked with them and he created a cross-functional growth team that I have before, and that team was given all sorts of testing tools. 51:28.311 --> 51:30.673 [SPEAKER_01]: They were also told about decoding, couldn't test without approval. 51:30.693 --> 51:32.935 [SPEAKER_01]: So they could just basically go, right? 51:32.915 --> 51:40.776 [SPEAKER_01]: And then, so they started doing a bunch of tests, they had a bunch of hypotheses themselves, right put them on a Google spreadsheet, testing, testing, testing, testing. 51:40.796 --> 51:44.105 [SPEAKER_01]: And after every test, they not only looked at, it was a test successful or not. 51:44.586 --> 51:48.797 [SPEAKER_01]: But they also looked at what was the 51:48.777 --> 51:53.383 [SPEAKER_01]: Hey, this guy stuck in legal for a while, this guy stuck in creative, whatever it was, right? 51:53.403 --> 51:55.666 [SPEAKER_01]: And then they would just break in down those barriers one by one. 51:55.706 --> 51:57.408 [SPEAKER_01]: It's how you're part time attorney for the team. 51:57.448 --> 51:58.389 [SPEAKER_01]: Let's do this to that. 51:58.910 --> 52:04.456 [SPEAKER_01]: And next thing you know, like 10x the number of tasks that we're doing really quickly, then he started running out of ideas. 52:04.937 --> 52:09.823 [SPEAKER_01]: He could have like, So they put up a Google spreadsheet for the whole company. 52:10.327 --> 52:14.594 [SPEAKER_01]: And as CEO said, look, you know, every month, we're going to give $315,000 prices. 52:14.694 --> 52:16.677 [SPEAKER_01]: This is a big amount of companies that are going to afford it. 52:17.238 --> 52:24.008 [SPEAKER_01]: And we'll give $315,000 prices and for the best idea for a test and anybody can participate. 52:24.729 --> 52:29.376 [SPEAKER_01]: And the innovation here is that we're going to give the prices out before the tests are run. 52:30.017 --> 52:31.700 [SPEAKER_01]: It gets doesn't matter if it works or not. 52:31.800 --> 52:35.085 [SPEAKER_01]: It's just, is the customer going to be thrilled. 52:35.065 --> 52:39.713 [SPEAKER_01]: and are going to be more profitable, right, customer lifetime value and that promoter score. 52:39.774 --> 52:41.016 [SPEAKER_01]: Two things that really matter here. 52:41.797 --> 52:52.938 [SPEAKER_01]: So just imagine now the whole company is listening to the customer more, and if you any customers going up on Twitter and listening to what customers are saying, I just just get better ideas. 52:54.060 --> 52:57.546 [SPEAKER_01]: And now they're doing 25 times more tests than before. 52:57.526 --> 53:00.550 [SPEAKER_01]: And now, you know, and imagine now plug it in exactly what you said. 53:00.590 --> 53:08.281 [SPEAKER_01]: Now, you know, you can start not just doing human tests, but you can start not training agents to come up with new tests and even test them, you know, by writing code. 53:09.042 --> 53:22.521 [SPEAKER_01]: And so, as long as you've got the customer lifetime value as the optimization factor, you can actually eventually run a limitless testing organization, you know, two, three, four, five years from that, but 53:22.602 --> 53:29.417 [SPEAKER_01]: Again, even if you do this, and if you got the wrong KPI, you're going to build the business drawing in the wrong direction really, really fast. 53:29.437 --> 53:35.650 [SPEAKER_01]: So KPI is right, the thing is moving at lightning speed, good luck with any competitor catching you. 53:36.085 --> 53:37.047 [SPEAKER_00]: I love it. 53:37.307 --> 53:37.988 [SPEAKER_00]: And you know what it is. 53:38.029 --> 53:59.848 [SPEAKER_00]: It's an egoless business because what you're saying is I don't know the answer all I care about is finding the answer and I just love that I mean that's that's like the first thing I say to a founder to an executive that I work with is it's not about being right it's about getting it right and if you can't get past that we can't move forward like if you have to be right this doesn't work, but if you're willing to let. 53:59.828 --> 54:06.134 [SPEAKER_00]: the systems, the machines, the test, guide you as you lay around your experience, man. 54:06.374 --> 54:07.155 [SPEAKER_00]: Such an exciting time. 54:07.375 --> 54:14.842 [SPEAKER_00]: And because I love that you are out there sharing this message of getting the KPI rate, because there's nobody talking about this. 54:14.862 --> 54:16.404 [SPEAKER_00]: I mean, obviously, I run a podcast for living. 54:16.444 --> 54:17.244 [SPEAKER_00]: I see all the pitches. 54:17.304 --> 54:18.365 [SPEAKER_00]: I talk to so many people. 54:18.686 --> 54:21.769 [SPEAKER_00]: This is such a unique perspective, and it's so incredibly important. 54:22.109 --> 54:25.973 [SPEAKER_00]: The book is BS Akoya, not a Banzai, wherever you guys get books. 54:25.993 --> 54:27.574 [SPEAKER_00]: We'll have the links in the show notes. 54:27.554 --> 54:32.183 [SPEAKER_00]: If someone wants to go beyond just the book and get deeper into your world, where's the best place to do that? 54:32.343 --> 54:33.365 [SPEAKER_01]: Yeah, we shot to me a LinkedIn. 54:33.385 --> 54:34.748 [SPEAKER_01]: I'm happy to talk to people. 54:34.989 --> 54:36.471 [SPEAKER_01]: I do a lot of consulting as well. 54:36.912 --> 54:38.736 [SPEAKER_01]: Yeah, and I started four companies myself. 54:38.816 --> 54:40.219 [SPEAKER_01]: So I love talking to founders. 54:40.800 --> 54:43.125 [SPEAKER_01]: I let it to talk to anyone. 54:43.145 --> 54:43.425 [SPEAKER_00]: Awesome. 54:43.505 --> 54:44.247 [SPEAKER_00]: I appreciate you. 54:44.267 --> 54:45.309 [SPEAKER_00]: Thank you so much for your time. 54:45.349 --> 54:45.930 [SPEAKER_00]: Have a great day. 54:46.371 --> 54:47.253 [SPEAKER_00]: Thanks for writing to it. 54:47.313 --> 54:47.453 [UNKNOWN]: Cheers.