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The CU2.0 Podcast
This podcast explores contemporary, critical thinking and issues impacting the nation's credit unions. What do they need to be doing to not just survive but prosper?
The CU2.0 Podcast
CU 2.0 Poscast Episode 368 Kirk Drake on AI and LAUNCH
AI - is it real or a mirage?
What is certain is that today AI is on the lips on just about everyone in credit union land.
What also is real is a recent MIT finding that 95% of large companies with AI initiatives are getting zilch out of them.
Ouch.
That does not have to be the reality for credit unions.
On the show today is Kirk Drake, CEO of CU 2.0, author of FINANCIAL, a 2020 book that envisions the rise of AI in our lives and in credit unions, and he also is hosting LAUNCH, a September 23-25 event in Oregon which wears this tagline: This isn’t a conference. It’s a launchpad.
In prior years the event was labeled CU 2.0 Live. This year it is held in partnership with Community Financial Credit Union’s ROOM (39)a initiative. Thus the name change.
A huge focus in the event will be launching good stuff in AI.
Drake tells all about the event in this show and the website has a small checklist - Are You in the Right Place? - that helps you decide if this event is for you or that it isn’t.
Listen up.
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Find out more about CU2.0 and the digital transformation of credit unions here. It's a journey every credit union needs to take. Pronto
Welcome to the CU2.0 podcast.
SPEAKER_00:Hi and welcome to the CU2.0 podcast with big new ideas about credit unions and conversations about innovative technology with credit union and fintech leaders. This podcast is brought to you by Quillo, the real-time loan syndication network for credit unions, and by your host, long-time credit union and financial technology journalist, Robert McGarvey. And now, the CU2.0 podcast with Robert McGarvey.
SPEAKER_03:AI, is it real or a mirage? What is certain is that today AI is on the lips of just about everyone in credit union land. What also is real is a recent MIT finding that 95% of large companies, 95% with AI initiatives are getting zilch out of those initiatives. Ouch. That does not have to be the reality for credit unions. On the show today is Kirk Drake, CEO of CU2.0, author of Financial, a 2020 book that envisions the rise of AI in our lives and in credit unions. And he's also hosting LAUNCH, a September 23 to 25 event in Oregon, which wears this tagline. This isn't a conference, it's a launchpad. In prior years, the event was labeled CU2.0 Live. This year, it is held in partnership with Community Financial Credit Unions, Room 39A Initiative, thus the name change. He We're going to talk about the magic of computers today. Exactly. I was reading before this call about this MIT study that came out a week or two ago that said 95% of big company AI initiatives were producing bupkis. Now, it did say most of them were throwing their AI money into sales and marketing, and the ones that were getting something were actually putting it into back office procedures. And mind you, this was big companies, and perhaps one to three credit unions would count as big companies. It's not the world of big companies, though. And I've talked to many credit unions in the last year or two who kind of admit shamefacedly that, yeah, we're playing with AI. Okay, what results are you getting? Eh, not really much. But you're going to tell me there are some bright lights that are getting results.
SPEAKER_01:Yeah, I think there's sort of four levels that I see. So the first phase is the one that got everybody all hyped up, which is I can use it to do my job description or my email writing. So how do I make me more efficient? And I think people got pretty excited about that and saw some decent gains. The next one where I think credit unions struggle is where they're trying to get something to work better between a couple departments or for a call center rep or something like that. So job specific, agentic type things. The third level is how do I buy a tool or an optimized process end to end? And I've got some good examples of that. And then the fourth is how do I start creating new value that I couldn't do before because I had time constraints somewhere in that system. And so I don't think many are getting to the fourth stage. Some are getting to third. A bunch are on the first and a couple are on the second. The second stage ones that I've seen are Senso, the credit unions that have used it for back office, front office procedure and optimization. You know, they're seeing a call center rep, instead of having to spend 15 minutes tracking down an answer, they get it in three, right? And so they saw a significant improvement in front office to back office.
SPEAKER_03:Now, I talked with Steve at One Nevada about that. And what he told me me, and this was pretty exciting, was that basically the Senso tools allowed them to take all the documents that the call center staff consulted when a call came in, throw them all into a pot, and they found interesting things, like there are contradictions in the documents. So they cleaned all that up, and basically, it was like throwing everything into a washing machine and cleaning the dirt out and making it a lot more efficient. And I'm not trying to say it It's simple, but it was exciting, really. If I were a call center guy and I'm looking there, a guy calls up and says, you've got to tow my car, repo my car. This document says, damn right, by noon today. This other one says, no, you have 30 days.
SPEAKER_01:Right, right. I think that's definitely what it finds in the inconsistencies, the cleaning up of policy and procedure and knowing what people are actually asking with frequency. I think it really nails that kind of front office, back office intersection of things. I haven't seen a lot of other examples of that where I am starting to see it like in the case of CU2 we built about 30 or 40 GPTs that then other people are able to use in their workload so that Zeke can now do the pricing analysis, but it's done in the style or methodology that Kirk would do it. So it comes out consistent in that mind versus me having to teach him and get him to read 23 books that influenced how I think about pricing, right? And so when we're able to build specific you know, things in that regard. We've seen a lot of success in that. And then they've seen a bunch of the tools like CASAP is one where they've used AI, generative AI and machine learning to build a completely autonomous dispute, credit card dispute resolution process. And so the member can say, I don't like this transaction. And it does all the back and forth interaction, figures it out, gets this, you know, the affidavits etc in that process and then makes the go-no-go decision on refunding the item or fighting it with fraud or whatever it is and they've been able to automate you know 95 of those transactions you know kind of in that mode so that's that third layer you know um in that and yeah the I'll give you another example where a couple of credit unions have built RepGen, I joke and call it the RepGenerator, you know, so it's building scripts and power-ons for Scimitar by training it on Scimitar training manuals and examples of prior RepGens. It can, you know, now a layperson can ask for a script and it will give it a pretty good first shot at being able to query teller transactions or something else in that regard. And so very specific credit union kind of use case again probably only used by a couple of people not kind of everybody so i think there's still that and then you know some of my work with a couple credit unions is getting into much more specific things like uh i know we've got a draft of what we're calling like a merger bot that will look at the source credit union and then credit unions in their region that have declining financial performance and identify key targets for them to kind of go after um in that merger piece of things, or we're working with a couple credit unions on building a series of GPTs that optimize core deposit generation through marketing tactics, data analytics, detection, and building automation into the process around consumer engagement. There's a whole bunch of things that go into core deposits. It's not like there's just, oh, we want core deposits it's therefore we're going to do this one activity and get to it. Sometimes there's 20 or 30 things that you would never be good at doing all 20 or 30 things, or you'd really have to change that focus. And so we're using AI to both identify the optimization path, the metrics, the scorecard, but then the automation around doing all of those things that would improve cost of funds in a credit unit scale.
SPEAKER_03:Now, are you working collaboratively with credit unions? Or are you building the tool yourself and then taking it door to door?
SPEAKER_01:No, no, it's definitely collaborative where we're on a call and I'm having them identify what's working, what's not working, where they're stuck, and I'll jump in and teach them how to write a prompt better, teach them how to add another data source, teach them how to actually build a GPT in that thing or train the model or figure out which model you want to use, give them real examples, use cases. Sometimes I'll go back and research things between calls. Most of the time I'll be able to kind of do it live on the call. And then second part of that is after about three or four calls, we usually have about 20 or 30 ideas. So then we'll do some prioritization among those ideas and really understand which of these do we think are likely to develop in the industry without us doing anything that we can just buy later on, which of these are really defensible moats that would create a long-term competitive advantage for the credit union and in And of those, which ones are most important? And so we'll prioritize it down to two or three, and then we'll do a 90-day sprint on seeing how far we can get using AI to do or improve one of those two or three things with the understanding that we may epically fail, right? And all we may come out of at the end of 90 days is, well, that was super interesting. What did we learn? What would we do differently the next time? And then we'll pick a different one. And my belief is that, you know, if you tackle two or three of those a quarter, you know, you're going to land on one or two of those that become a permanent part of the organizations. But the other real rub for, you know, let's say they did, were successful in two of those strategies, the impact on the reps of the organization to implement the 20 things that go into making, you know, a really robust core deposit, low-cost core deposit model are pretty impactful on everyone in the organization. And so, you know, I think part of the challenge here is you can identify what needs to be done pretty quickly, getting the team, the various team members to be able to implement and make the changes in their parts of the business and recognize that, you know, whatever you implement today, there's going to be a better version of in three or six months. Then you're going to have to go back and adjust those things again, right? But if you don't start on that journey of understanding what, you know, can or will change, you know, then you won't even have a chance to make those adjustments next time. I
SPEAKER_03:think part of it is picking the right area to pursue inside the credit union. Right. So the CENSO document thing is a good area. I talked with LTP recently. They have an AI-driven collections tool. And one of the beauties of that is very few people doing collections in credit unions really like their job. You come up to me and you say, hey, we're not letting you go. We've got work for you here, but that machine's going to do your collection calls. I say, wow, really? Cool. Cool. What's the machine's name? I send her the birthday card. And you pick the right thing, the internal staff will just jump up and applaud.
SPEAKER_01:To me, what's really going to shift is the repetitive stuff will move entirely to AI, right? And the reality is in any, in a smaller credit union, there's a lot less repetitive stuff. In a larger credit union, you have those pockets of repetitive stuff. And so it'll be more impactful there. And for the smaller credit union, it will allow them to stay lean and grow. I mean, at the end of the day, 20 years ago when I worked there, 25, 30 years ago when I worked at AgFed, they had about 75, 80 employees as an$80 million credit union. Today, they've got 45 employees as a$500 million credit union. credit union, right? So, you know, we're just going to continue to see it go in that direction.
SPEAKER_03:My sense from talking with credit union people is that a lot of them are playing with chat GPT, a handful are playing with Gemini or Claude, but they don't understand that to really unleash power, they need specially developed tools. You don't just pay the 20 bucks a month to Google to play with Gemini. Say, okay, I can automate my entire billion dollar credit union. No, it doesn't work that way. What do they need?
SPEAKER_01:Well, I think the notion that there is going to be one AI is flawed, right? Even
SPEAKER_03:Sam Altman has said recently that he expects a lot of the AI initiatives to fail, companies to go out of business. He's denying that this is another dot-com meltdown, but just as in the dot-com meltdown, it will be a ton of failure. and it's going to come quick.
SPEAKER_01:Yeah. I don't think we'll get anywhere near as painful as the dot-com stuff. Well,
SPEAKER_03:those companies were selling stupid things. I'm going to ship you 100 pounds of dog food. Now, how the hell can I do that and beat the price of Petco down the street with the shipping costs included? I could sell it to you for less, but then my shipping costs are going to kill both of us.
SPEAKER_01:Right.
SPEAKER_03:I mean, it's just silly ideas.
SPEAKER_01:Yeah, yeah, totally. I mean, I think that's just it. is the strategies that I work with the credit unions on is, hey, I don't want you to have nine AI strategies. What I want you to do is tell me what your strategies are that you're already working on, and let's figure out how to make them a lot faster and better and make a lot more progress using AI, not let's go define new things to be trying, right? Like sure, have one or two crazy things that you want to go see if you can do, but in general, you're going to get a much better ROI chasing the things you already have. Right. right, that they're able to be really methodical about it. The executives are using ChatGPT in their own work along with Microsoft Copilot to do different things and using it that way. And then they've got some point-specific solutions that they're deploying where they come across something that's already been built and optimized and has kind of a, I mean, I kind of think of this as a little bit like open source. 20 years ago where just downloading the open source software doesn't solve anything. You still have to figure out how to implement it and tweak it for what you need. And sometimes you're going to need a corporation behind that open source stuff to package up the training and package up the use cases and support in order to make it a sustainable thing that you can actually apply in a business. Other times, your business will have the sophistication that they can just plug in the widget Behind the scenes, no one even knows that it's going on there, and it just is a better way of making those decisions in that mode. And so I think understanding that most of this is human adoption, not the AI piece of it, and that what you're really solving for is pace of change and innovation and adaptability, not the core idea.
SPEAKER_03:And to go back to what I was saying, ChatGPT alone isn't going to solve your problem. You need to have tools that work off or inside ChatGPT.
SPEAKER_01:Yeah, absolutely. Now, there are some pretty cool... There's an agent mode in ChatGPT now that you can use. So I was trying to figure this out for trying to use it to figure out a really complex flight hotel and kid logistics problem where Violet was coming from Ojai, Kim and the kids were coming from Medford. My sister and her family are going from San Francisco. I needed to leave with them, but I need to fly separately back on a different day and then They need to get back and forth in time to meet Violet's return to boarding school parameters. And so I had it go analyze all of that, look at those pieces, and then it built and then it created an agent that spun up and actually booked the tickets on the different websites as part of that process. So I do think there is going to be a shift where it starts to build or even like Warren this summer was working on a project for Quillo to be able to analyze a loan tape for which loans were CDFI and which loans weren't. And so he used ChatGPT, never done any program before, built a JSON tool that will look at the list of records and go to the census website after some geomapping and then check the census website to find out if they are or aren't CDFI and ran into limitations where the census website will only work 199 times in a row before it decides that you're a bot. And so he had to build a timeout feature that after 199, it pauses for three minutes and then starts over so that it could go through a list of 10,000 addresses. And in about four or five weeks, he had this thing working where you can give a list of 10,000 addresses. And then five hours later, he comes back and says, these 42% are CDFI. These 42% were PO boxes. These 18% were not, right? And so I think that's the type of thing where, okay, it started off as a research project to see if you could do it. And then once you've figured it out, then there's a whole nother rework of turning that into permanent IP that is built into the Quillow platform that now anytime someone uploads a list, they can run with.
SPEAKER_03:Now, is there, and I hear this from credit unions, is there data that they shouldn't put into ChatGPT? Data they want analyzed, but they shouldn't put that data in. This is deja vu because we heard the same thing about data like a dozen or dozen years ago.
SPEAKER_01:Yeah. Yeah. Um, and so, you know, so I, I think, you know, it's, it's, it's still very frothy. And I think where, you know, I would say I'm like my coaching, a third is ideation, showing them better techniques and approaches. A third is prioritization and understanding what actually matters to the credit union. Cause sometimes they have a really hard time figuring out, you know, of these 12 different ways to grow members, which one's the most important. They have a hard time and answering that question. And then a third of it is operationalizing some piece of software or some change or some GPT that you've built so that it can become a permanent solution.
SPEAKER_03:And what about data privacy? In other words, can I put this loan information into ChatGPT?
SPEAKER_01:Yeah,
SPEAKER_03:yeah, exactly. What's the answer to that?
SPEAKER_01:I mean, my general recommendation is no, remove any PII. But it's pretty easy to remove and obfuscate the data so that you can use it in some format.
SPEAKER_03:You don't say Joe Schmo, social security number, blah, blah, blah, applying for a$100,000 loan to buy an expensive BMW. The FICO score is 480. Should I approve it or decline it? You can put in all the data. Just take out Joe Schmo's name and his social security.
SPEAKER_01:Exactly. Exactly. And I think once you start doing that, then it's true with all these things. Okay, well, I need to do this employee review. Well, don't make it about Robert McGarvey, right? Like, make it about, you know, Billy Bob, you know, Thornton, and now you're fine, right? Like, and so there's very easy ways to do that, that, you know, anonymize it in that mode and just being really clear about that. Making sure you turn off the, I don't want my data training chat GPT, you know, even if you're paying, you've got to go in and set that setting to don't train on me, right? And then also being realistic, you know, about the fact that if If all of your documents are stored in Office 365 and Microsoft, who makes Office 365, also owns 50% of ChatGPT, what story are we telling ourselves about whether they're going to use or not use those data?
SPEAKER_03:interesting question. I'm sure Microsoft would say they won't, but it's...
SPEAKER_01:Right, and we trust Microsoft not to, but we don't trust Jack GPT. I mean, you can't have it both ways. Either you trust the corporation or you don't. And by the way, the PR shitstorm that Microsoft and Jack GPT would experience if it came out that they were training on corporate records would be pretty terrible, right?
SPEAKER_03:Yeah, I know. Microsoft has always known it has to protect corporate records. I think their cloud business would go to hell in a handbasket. Right. And I think I have no reason to believe they haven't done a good job. Right. ChatGPT is a different animal altogether. It doesn't have those existing lines of business. So what's going to happen at your event later this month in Oregon where AI is going to be a big focus, I believe?
SPEAKER_01:Well, I think we're definitely going to to dive into these four layers and map them out. We're going to try to get a group of credit unions to work on some common problems together. And I think we're going to bring in some deeper expertise, both inside and outside the industry, you know, like Sarup from Senso or others that can really talk about the operationalizing of it and, you know, showing that and hopefully get some of our existing credit unions to show things that they're doing and accelerate that. Because I think three parts of this adoption cycle, one, how do we get a credit union that's already winning in this regard to show another credit union an easy path forward?
SPEAKER_03:But credit unions are not competitive, and I actually believe that. Right. Now, are two credit unions in Portland, Oregon that are a mile apart competitive? Probably in some manner, shape, or form. But Navy Federal doesn't give a hoot about PECU. Right. Right. It does care about state employees, or at least the old state employees, which like to tweak bigger institutions. I don't think the new one still has that sense of humor.
SPEAKER_01:Yeah. So, you know, I think some of it is I want to find what are the common big problems that everybody's kind of looking for and what are the next steps in here to really make this actionable and improve. And I beginning to create, you know, great, we've got an AI policy. Let's start figuring out an AI scorecard to figure out if we're even being successful with some of these things, right? Like, and diving into some common frameworks and approaches to tackling these problems.
SPEAKER_03:And you touched on this earlier, but I heard the same thing from a Radon chief economist, Duffy, the mergers guy, talks about it all the time, which is operating efficiency. And Duffy has all these numbers that show that the three or four biggest banks are vastly more efficient than any credit union. Right. So that everything they do is cheaper. Right. And do credit unions have to entirely close that gap No, but I do think they have to use AI or something to whittle away at the gap.
SPEAKER_01:Totally agree. of doing a transaction or providing member service or doing these things is going to be in a very rapid race to zero, right, using these tools. And so if, yes, you can choose to wait and watch and recognize that, you know, if it costs you, you know,$100 today and it's going to go to zero in not 20 years, I think it's going to happen in seven, right, that you can, you know, let's say it goes from 100 to 80 to 60 to 40 to 20 in that mode, it's going to be infinitely harder to catch up from 100 to 20 than it is from 40 to 20, right? And the impact financially on your credit union of that delay, when it costs you an$80 more to do the thing that your competitor can do for 20, means that they're going to be stealing market share in that timeframe. right? Because they can price alone a quarter point lower or they can price, you know, we're going to get margin compression everywhere, you know, in that mode. And so, you know, if you, if you just think back to what happened with the cost of internet over the last 25 years, sure, we've added all sorts of devices. We're using more than ever or any, and things like Verizon and T-Mobile and AT&T have survived, but there were a lot of telecom companies that got their asses kicked in the in that timeframe, right? That didn't figure out the switch, the optimization, the need to move. It's not like Verizon decided they're going to put fiber over the entire country, right? They walled off a section of the world that they could get and they bet on cellular and bought broadband spectrum in that mode. And so I think we have to recognize that the shift is going to change our businesses and that margin compression will cause us to need to find other ways to make that money and at the same time require us to lower our cost structure in the core business. And the longer you wait to start tackling that, I mean, I just don't know how you catch up.
SPEAKER_03:Now, go back to 1983. Personal computers are starting to gain traction in the workplace, and a business had to make a decision. Are we going to go with MS-DOS, with whatever operating system Apple was using? Digital even had an operating system called Rainbow, and then there were the mini-computer operating systems. You made a choice, and you were kind of stuck with your choice. It was expensive to move to another system. It was expensive to move your data to the other system. Are we in that same thing now? In other words, is this existentially critical to pick the right AI tool, be it Copilot or ChatGPT or Gemini?
SPEAKER_01:Yeah. I think the difference now is if someone really starts to get, like, you look at Facebook. Facebook isn't using Gemini. They're not using ChatGPT. And so for them to keep everybody honest, what do they do? They open source their IP in the AI space so that ChatGPT and Gemini don't develop such a competitive edge that they can't ever come back from it, right? You saw the same thing happen with giving away free email or, I mean, over and over again, that is the playbook in Silicon Valley is if I can't compete with you and I can't monetize it fast enough, I will then give it away. Android is a perfect example. Apple was on a tear, was going to have had like 80, 90% of mobile handset stuff. And since Android came out and was given away for free and was allowed to be used by all these different, you know,
SPEAKER_03:manufacturers. And they could modify it.
SPEAKER_01:Right. They now have.
SPEAKER_03:It's almost unheard of. You can, here's my operating system. By the way, do what you want with it.
SPEAKER_01:Yeah, it's free. Like free, modify it however you want. You can get your version and we'll keep it coming out with new versions. But, you know, and so you look at that, that was a completely defensive move by Google to make sure that they were not crowded out of the search marketplace through an operating system. So I think the same thing is going to happen in AI where, and we've already seen it happen with Llama 2 and Llama 3 and other guys where, yes, there's going to be a lot of people competing hard and wanting to one-up each other, but there's also going to be big tech companies that open source things to keep everybody honest and to keep it from becoming a complete monopoly. So
SPEAKER_03:when companies go to your event in Oregon, what are they going to leave with? What are you hoping they leave with?
SPEAKER_01:Yeah, so the credit unions are going to hopefully leave with hope and ambition around trying new things and finding good partners in the credit industry to try those things with. The fintechs are going to leave with clients to participate in those trials and good relationships. Because at the end of the day, collaboration requires trust, right? And trust is not a set of defining principles and guidelines and a treaty. Trust is, I'm going to have you over to my house for dinner next time I'm in town. I'm going to take your call on a Thursday when I'm already really busy and totally packed. It's, I recognize you. It's transparency. It's good news, bad news. There's so much more that goes into trust. And so that The number one thing we're solving for in CO2 launch is how do we build trust between credit unions and between fintechs so that they can solve hard shit, right? Because when that trust isn't there and when it's a pure vendor-to-vendor relationship, credit unions have a really hard time digesting technology quick enough just based on pure economic and contract terms.
SPEAKER_03:And how many credit unions are open to trials and how many And how many trials can one credit union run? In other words, I would tell everybody, go to One Nevada. However, there has to be a limit to how many trials One Nevada. One Nevada is a great place, and they've done trials, but they don't want to run 12 at the same time.
SPEAKER_01:No, I mean, in general, most credit unions can handle one to three, anything more than that over a long period of time. You'll see pockets where they pop up, like DCU's Innovation Lab or things like that, where they start something new, they'll try five, 50 things and then they burn themselves out, right? And they can't sustain that for 10 or 20 straight years, right? You know, generally what I find is that they can do it for short bursts and they can do it while they get to the next tier or level, and then they need some time to digest and grow organically in that before they're ready to try more things again.
SPEAKER_03:Well, and you only have a credit union with the possible exception of Navy Federal only has so much staff, therefore so much bandwidth. In other words, who's going to manage this trial internally?
SPEAKER_01:Managing the change rate that comes out of innovative things is, it can take, you either have to be totally comfortable with chaos and that you've really got great team members who can digest and empower people to make the right decisions in the moment of things that we've not defined before, or you've got to have Dick dictatorship iron fist, which doesn't sustain long-term, right? It gets you short-term gains, but it doesn't, it won't, the ecosystem, once it gets out of whack and it's not healthy or the social organization causes so much friction that people leave and self-select off the island and then it doesn't work, right? So that, I mean, that's where you look at Elon Musk and his ability to burn out people, right? Like he just consistently burned through, you know, people over time. And it's a great experience. They want to work there for a short period of time, but very few people go down that road for 20, 30, 40 years.
SPEAKER_03:Wow. Same basic principle at Goldman Sachs. Yeah. Many people work five years and they either get fired or they say, I can't do these 80-hour weeks anymore and move to something else. But they've had great training. How many institutions, fintechs and credit unions do you expect at this event?
SPEAKER_01:I think we'll have about 25 to 30 institutions between fintechs and credit unions. So it's not huge. It's really designed to be kind of smaller and intimate, making sure everybody walks out of there really knowing everybody else well and understanding, you know, strengths, weaknesses, pain points, you know, opportunities in that regard.
SPEAKER_03:Well, in the past CU2 live events, you've always gotten a credit union or two or a fintech or two. I said, who the hell are these people? Where'd they come from? And it was kind of fun. You know, like, wow. I hope you have some unpredictable ones.
SPEAKER_01:There's always a level of uncertainty and chaos and we're comfortable living in that and recognizing that. You know, I would love to tell you that I can predict what is going to be the most interesting thing that occurs at these events. And I can also tell you that I have zero ability to predict that. What I can predict is if I get the formula right of the right people, people in the room with the right mindset, magic happens. If I let people in who are trying to sell or who believe that their number one thing there is to sell credit unions something or to take things from that, then that disrupts that ecosystem or that balance in that group. If I get people who really dork out, who believe in the force and believe in the power of collaboration, and recognize that they don't have to know exactly the value they're going to get to get the value, right? That those are the people that can trust in the first place, right? So it's always that kind of funny thing. I want to know exactly what's going to happen. Well, if you're the type of person that needs to know exactly what's going to happen and everything needs to be 100% predictable and there'd be no risk, then this is probably not the room for you, right? If you're the type of person that says, hey, look, I can take a leap of faith and recognize that smart people are going to be in this room. They're going to push me. out of my comfort zone, make me learn new things. I'm going to see the world differently. And by doing all of that, I will make better decisions for my organization and see a better glimpse of the future than I could see, you know, standing on my own island. Then they get a ton of stuff out of it.
SPEAKER_03:Well, this is the fourth iteration of this event. And I can tell you, I've done 400 plus of these podcasts. I very rarely get someone on here who comes in on total sales mode. And if I do, I beat them up for a while. But it really rarely happens now. Because I always say, they say, how do I prepare? I say, listen to two or three podcasts and you'll figure it out. And there's a sales That just bores me, man. Tell me something interesting. If I want to read your sales literature, I'll go to your website. And I probably have read it already.
SPEAKER_02:Exactly.
SPEAKER_03:Before we go, Get in touch with me. This is rjmcgarvey at gmail.com. Robert McGarvey again. That's rjmcgarvey at gmail.com. Get in touch. We'll figure out a way that you can help. We need your support. We want your support. We thank you for your support. The CU 2.0 Podcast.