Transforming B2B Commerce with AI and Data Strategy, With Guest Lin Chase

[00:00:00] Welcome to Around the Horn in Wholesale Distribution with Kevin Brown and Tom Burton.

[00:00:10] Sponsored each week by Leedsmart Technologies, Tom, Kevin and their guests review the news

[00:00:15] of the week and dive deep into the topics impacting manufacturers, wholesale distribution, independent

[00:00:21] sales agents, and the global wholesale supply chain.

[00:00:25] Whether it's M&A, SaaS and cloud computing, B2B e-commerce or supply chain issues, we

[00:00:31] peel back the onion with our guests into the topics that impact your business the most.

[00:00:37] Lynn Chase, Tom Burton, Kevin Brown here. Great to see you both. Everybody okay?

[00:00:43] Oh good. We've got the country covered. We've got Tom in Florida, Lynn in Minnesota, and I am in Southern

[00:00:50] California. Raining here. What's it like where you guys are? Beautiful.

[00:00:59] Yeah, and if you're online, let us know where you are, fill in the gaps because yeah, we pretty

[00:01:03] much got the edges of the country covered. We do. This is good. So hey, I want to dive in before we

[00:01:08] even get started with today's news and chatting with Lynn. We'll do a little bit of what we call

[00:01:14] kind of our housekeeping that we do each week. So we wanted to welcome everybody. I'm Kevin Brown.

[00:01:20] I'm here nearly every Friday with my lifelong friend and co-founder of Leedsmart Technologies,

[00:01:26] Tom Burton. We get together on what we call around the horn and wholesale distribution

[00:01:32] and manufacturing. We try and take the news of the week and the hot topics that are going on

[00:01:38] in the economy, in e-commerce and mergers and acquisitions and supply chain and sales and

[00:01:45] marketing, AI technology, and all the different things that are impacting manufacturing and

[00:01:51] wholesale distribution. We put them into a newsletter. We send that out to 8,

[00:01:55] 9,000 people now each week and we get that information. We try and put a little bend on

[00:02:01] that each week and we talk about as we so-called peel back the onion, so to speak, talk about how

[00:02:07] that news impacts manufacturing wholesale distribution. So a lot to talk about today.

[00:02:13] We do that newsletter. If you don't get that newsletter and you would like to,

[00:02:17] you can reach out to us very simply. You can email us at hello at leedsmarttech.com

[00:02:23] or you can go to the website for the podcast, which is www.aroundthehornpod.com.

[00:02:30] And you'll see all of the ways that you can listen in. You can also get subscribed. So we do two

[00:02:35] things here. We get together live again Friday morning, 9 o'clock Pacific and on LinkedIn Live,

[00:02:43] Facebook Live, and YouTube Live. You can watch the recordings there anytime as well and any of

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[00:03:01] podcast, you can do that. The last thing that we'll do, two last quick last things that we'll

[00:03:05] do is we'll ask you that if you enjoy the content that you get each week from us,

[00:03:12] click the subscribe button, click the follow button, let us know even in an email or

[00:03:17] some comments about your thoughts about our broadcast each week, we would love to hear it.

[00:03:21] The sponsor of our show that happens each week, has to be the company that Tom and I work for.

[00:03:26] We work for Leedsmart Technologies. Leedsmart is the developer of the Channel Cloud AI-enabled

[00:03:32] Customer Intelligence and CRM solution solely developed for wholesale distributors and

[00:03:38] manufacturers. So as many people know, traditional off-the-shelf software doesn't

[00:03:43] work real well. We've taken my 30 plus years of background in wholesale distribution manufacturing.

[00:03:49] Tom with a similar number of years in the technology world brought our thoughts together to

[00:03:54] develop technology that's built solely as a baseline for your AI journey, as well as

[00:04:00] current needs today with CRM and Customer Intelligence. So let's dive in. I covered a lot there.

[00:04:07] Lynn, thank you so much for being with us. I'm not even going to do a big intro for you.

[00:04:12] I'm just going to say I had the privilege of meeting you last October in Chicago

[00:04:17] at the Distribution Strategy Group's AI-in-distribution. I know Ian is going to

[00:04:23] clobber me. I'm not going to say this exactly right, but Distribution Strategy Group, Jonathan

[00:04:27] and Ian's great event that they kind of... It was I think the inaugural event for wholesale

[00:04:33] distributors to be able to come in. Here are some industry experts like yourself,

[00:04:39] like Zac Cass, a number of other speakers and talk about where we're at with AI,

[00:04:45] kind of where we're going. We had an opportunity to meet and chat a little bit and get to know each

[00:04:51] other and our love for sailing and different things like that. So before we dive in, give us...

[00:04:56] We're going to get your take on some of the news and what's going on in the world of not just

[00:05:01] wholesale distribution but in the technology world. I think there's a few topics that

[00:05:06] I could not even be here because you and Tom will go down a great road. But

[00:05:10] you have such a diverse background. Tell us what you're doing today but maybe how you got there

[00:05:14] just as important. Well, the basic idea is that I have from a very early age been very interested

[00:05:22] in math and science and computing but also music. So I've kind of had two parallel careers.

[00:05:29] I earned a very fancy PhD from Carnegie Mellon in artificial intelligence.

[00:05:33] You know, when you talk to a speech recognition system and it goes,

[00:05:36] I'm sorry, I didn't quite understand that. Could you say it again? That really annoying

[00:05:40] thing? Well, I invented that. That's called confidence annotation. So anytime you're talking

[00:05:45] to an AI system and it asks you to repeat yourself, you can blame me. But the idea was

[00:05:50] that it was necessary to sort of situate that artificial intelligence listening and talking

[00:05:55] system in an error feedback loop so that it could learn when it knew that it was understanding

[00:06:01] you and when it wasn't. So it's not perfect but that's kind of my background. I've spent 35 years

[00:06:07] in industry working on artificial intelligence, in particular machine learning type applications

[00:06:13] all around the world in North America and Europe. Spent a lot of time in Japan and Korea,

[00:06:17] a lot of time in India and have just worked with enterprise scale clients globally for many,

[00:06:24] many years helping people understand how to apply AI, helping people get their underlying data in

[00:06:31] order in order to be able to even attempt AI. So that's kind of been my techie career. I did that

[00:06:38] for 35 years in my dotage. I decided that I would become a university professor and try to

[00:06:44] manufacture some more people to kind of come out into the world as participants in the STEM

[00:06:49] communities. And so I ended up in Minnesota where I was the founding director of a new

[00:06:53] computer science program here at Minnesota State University in Mankato. And I did that for a couple

[00:06:58] of years and then I got tapped by the president of the university to work on data and analytics and

[00:07:03] planning. And eventually we're going to get to AI rollout as well at the university. So now I'm

[00:07:09] of all things a higher education university administrator. So that's how I look at it.

[00:07:15] So three more things I want you to if you don't mind briefly, meet reference.

[00:07:20] You've got quite a background too with working with some startups as well.

[00:07:24] That's right.

[00:07:25] You lived on a sailboat in the San Francisco Bay.

[00:07:27] For quite a long time.

[00:07:29] Yes.

[00:07:29] And you play like 27 instruments.

[00:07:31] I do, yes.

[00:07:32] So a quick overview on those things and we'll dive into the news.

[00:07:36] Let's do the sailing first. So there is nothing better than a blue water experience

[00:07:42] where you're offshore for weeks and just it just changes my spiritual connection to the world and my

[00:07:52] sense of what it means to be a part of the planet. I think it's really, really cool.

[00:07:56] I know you're more of a racer, more of a cruiser.

[00:07:59] I think we kind of figured that out over the last year.

[00:08:02] Yeah.

[00:08:02] But I've done some distance racing. So I, you know,

[00:08:06] the red sky at night sailor delight stuff, right?

[00:08:09] It's just nothing better than watching the sunset and sailing with a moonlight throughout the night.

[00:08:17] And I've done that Pacific Ocean, Atlantic Ocean, Mediterranean, Adriatic Sea, and the

[00:08:23] major dangerous sea of Lake Michigan.

[00:08:27] It's the scariest I've ever been on a boat, Lake Michigan.

[00:08:30] Yeah.

[00:08:30] I think the Great Lakes are terrifying.

[00:08:33] Absolutely.

[00:08:33] I live on, I live very close to Lake Superior now and it's absolutely terrifying.

[00:08:38] I think those overnight experiences, my favorite crossing was from Southern California to Catalina

[00:08:44] and having the midnight watch and being at the helm overnight everybody else is

[00:08:49] asleep and it's just me and the fog and sometimes the stars and wondering if I'm

[00:08:53] going to hit a sunken shipping container.

[00:08:56] So I'll tell you before we jump onto your startup and your music quickly before we

[00:09:00] get to the news is my first experience offshore was at 14 years old.

[00:09:06] My memory, I was 16, 17 years old.

[00:09:09] My dad told me recently I was barely 14 was bringing a racing sailboat from Long Beach

[00:09:16] to San Francisco and you know, we transited quite a ways offshore and I didn't realize

[00:09:22] I'd done that at 14.

[00:09:23] So kind of a cool experience.

[00:09:25] But so a little bit about startups that you've done as well quickly and

[00:09:29] will kind of dive ahead.

[00:09:31] Well, at Carnegie Mellon you can't help but get involved in the entrepreneurial mindset.

[00:09:36] It's kind of built into the bloodstream there.

[00:09:39] And so the first thing I did when I graduated was join a startup and then I did a series

[00:09:44] of startups in ever increasing positions of authority until I raised my own money

[00:09:48] and started my own company with a very dear friend named Yun Kim.

[00:09:52] And so, you know, I've been CEO of a Silicon Valley startup company in the software space.

[00:09:59] What an experience, right?

[00:10:01] What a life-forming, life-changing, just incredible.

[00:10:07] I can't tell you how grateful I am to have had those years.

[00:10:11] Yeah.

[00:10:11] That's good.

[00:10:12] Good to hear.

[00:10:13] Hey, so the reason I mentioned that, go ahead.

[00:10:15] Dane, what would you say, Tom?

[00:10:16] Just the reason I wanted to get that out is you have a broad level experience

[00:10:20] that you bring to our discussions today.

[00:10:21] Go ahead, Tom.

[00:10:23] As we get into it, Lynn, obviously kind of an OG here in the AI world, right, for 35 years,

[00:10:30] did you think that, you know, if you look back 30-35 years, did you ever think it would take

[00:10:36] this long for AI to kind of enter the mainstream and become the popular part of that?

[00:10:42] Yeah, we've been through a bunch of cycles of AI winters.

[00:10:45] I actually, yeah, that's what everybody calls it.

[00:10:50] Like the winter, right?

[00:10:51] Yeah.

[00:10:51] Up and down in the adopt crash cycles.

[00:10:55] I'm actually astounded at how far we've made it.

[00:10:58] I did not expect...

[00:10:59] I mean, when I started, you know, when I was 19, I'm 61 now.

[00:11:03] When I was 19, this is where you guys go, you couldn't possibly be 61.

[00:11:07] Look how beautiful you are.

[00:11:08] That's right.

[00:11:08] That's right.

[00:11:09] So, you know what, I actually see that earlier.

[00:11:13] Well, I started when I was 19.

[00:11:15] I'm 61 now.

[00:11:17] We didn't even have statistics.

[00:11:18] We didn't have...

[00:11:19] We weren't using statistical models.

[00:11:22] We weren't...

[00:11:22] We had statistics.

[00:11:23] We weren't using statistical models.

[00:11:24] We were building closed form solutions and building kind of direct symbolic

[00:11:29] representations of what we thought an artificially intelligent creature would do.

[00:11:33] We didn't switch to statistics until I was in my 20s.

[00:11:37] And I genuinely thought that this would just go on and on forever

[00:11:43] and that we would never cross the line.

[00:11:45] I just typed into sueno.ai last night a request for it to write me a song,

[00:11:54] a country song about meatloaf.

[00:11:58] And if...

[00:11:59] A food or the musician?

[00:12:01] No, I didn't tell it, which I said,

[00:12:03] write me a country ballad about meatloaf.

[00:12:06] And it wrote, recorded and produced for me in less than a minute

[00:12:13] a really funny, well-produced song about meatloaf.

[00:12:21] And I mean, I'm like, wow, AI, the AI part of me just put the musician part of me out of work.

[00:12:30] It's done.

[00:12:31] Game over.

[00:12:32] Yeah, interesting.

[00:12:34] Very, very interesting.

[00:12:36] And it was a funny song with weird like word play in it that was right on target

[00:12:43] and actually very entertaining.

[00:12:46] Well, not to go off, but then there's all these things that I don't think you can copyright

[00:12:51] AI generated music and or at least there's discussion on that.

[00:12:54] So it'll be interesting to see in the future if that meatloaf song becomes a best-selling hit.

[00:13:00] What does that mean?

[00:13:02] Do you own the rights to it or no or whatever?

[00:13:04] It's going to be anyway, very particular commercial license that you sign up for.

[00:13:09] So, you know, but you're right.

[00:13:10] It's a very the shifting ground is very new.

[00:13:13] I'm thinking if you've got a hot new country ballad, you could pitch it to Beyonce.

[00:13:19] Kevin and I had a long conversation about the Beyonce Country Carter album.

[00:13:23] I'm a huge fan of that album.

[00:13:25] I had never been a Beyonce fan before, but I have listened to the whole album again last night after we talked to him.

[00:13:33] Very good.

[00:13:33] All right, Tom, take us to the news.

[00:13:35] William, a friend.

[00:13:37] All right.

[00:13:38] So we're back to our weekly discussion and we're going to let Lynn in on this bed if she wants.

[00:13:46] Tom, you in fact, I'm going to ask John maybe later today when he,

[00:13:50] John, if you hear this when you're editing things, maybe cut out of last week,

[00:13:54] the bet that Tom and I made about Fed cuts this year.

[00:13:58] So Tom says, what did you say?

[00:14:00] Zero.

[00:14:01] I said one.

[00:14:01] No, I said one.

[00:14:02] And I'm doubling down on that despite what this says here.

[00:14:07] I mean, you look at the job numbers that just came out today way above what was expected.

[00:14:13] The economy is steaming along.

[00:14:14] I don't see any way honestly that they can certainly start cutting in the next few months.

[00:14:22] And I think we're going to see one.

[00:14:23] We may see a half a point on that line just to kind of appease people if it happens.

[00:14:29] But I'm doubling down on my one.

[00:14:31] Good.

[00:14:32] So you're doubling down based upon and we've talked about this a little bit already is

[00:14:38] you're doubling down on that.

[00:14:39] So we're talking about two stakes set the gig.

[00:14:43] Whatever you whatever you like.

[00:14:46] Lynn, we have a place that when we have a small office up at Santa Barbara where Tom

[00:14:49] normally is at it.

[00:14:51] And when I'm up there, we have got a little local steakhouse,

[00:14:54] Chuck's Chuck's steakhouse of Hawaii in Santa Barbara that we go to.

[00:14:58] And so when you're in Santa Barbara next, we'll take you to dinner there.

[00:15:00] Wonderful.

[00:15:02] So I decided in this discussion, Tom, I think the here should be the deal is

[00:15:07] the winner buys the stakes.

[00:15:09] The loser buys the wine.

[00:15:12] Okay.

[00:15:13] Okay.

[00:15:14] Right.

[00:15:15] Because whatever whatever works has a pretty good wine list.

[00:15:18] And I've got some of my eye on a couple things there when we get two cuts.

[00:15:22] So I just say this right in back to the reasonableness in this discussion is

[00:15:29] I think there's two factors that we have to think about what's going on with this.

[00:15:34] And I'm going to ask Lynn if she's got a take on this and once in on the bet in a moment.

[00:15:37] But the first is all of the economic data that they're using, right?

[00:15:43] That's the statistical model.

[00:15:46] The other side of it is I think that then we they don't talk about it and we don't talk about

[00:15:51] is there is a whole I don't know what else to call it other than an emotional side of

[00:15:55] this or a human side of this.

[00:15:57] The stock market has already baked in some of these changes, right?

[00:16:03] There's going to be potentially more of that happening.

[00:16:05] I think what the stock market rather than baking in these anticipated changes is saying is

[00:16:09] we don't care.

[00:16:11] We're just going to charge ahead and do what we do as a stock market because stock markets

[00:16:15] are following what's really happening in the world versus 30 or 40 years old or 50 year old

[00:16:21] data points, right?

[00:16:22] And I've talked about this on a soap box.

[00:16:24] And maybe the statistics that we use to track inflation are not the right statistics anymore.

[00:16:29] Don't know, not an economist, not a anything like that.

[00:16:34] But I believe there's an emotional component to it and the people of this country

[00:16:39] are expecting something.

[00:16:41] And I think we're at a place right now where there are already maybe you win and it's one

[00:16:47] because of that emotional factor.

[00:16:50] I think what we just see in this article is that there's multiple Fed officials who are voting

[00:16:55] officials who are saying they still see that we have a window for three.

[00:17:00] I think two might be the number, but I think there is that human factor that we're not talking about.

[00:17:07] Just one guy's thoughts.

[00:17:09] Well, I do agree there's an emotional aspect to it or maybe even a political

[00:17:13] aspect to it.

[00:17:13] Yeah, good point.

[00:17:15] That's right.

[00:17:16] The election, I think.

[00:17:18] Yeah.

[00:17:20] Well, and there's also, again, as we have higher rates that then means the debt,

[00:17:25] the interest we're paying on our national debt is that much higher as well.

[00:17:29] So there's a lot of variables that factor into it.

[00:17:32] Like I said, I think we could end up getting one half point cut, which might be kind of,

[00:17:37] like you said, handling the emotional or the political aspect of it because it's a greater cut.

[00:17:42] But man, if you just look at the data and you look at what's happening,

[00:17:45] it's hard for me to, again, I'm not an economist either.

[00:17:48] And Lynn, very interesting your take on this.

[00:17:51] It's hard to justify three.

[00:17:53] It's just given what I'm seeing right now, it's hard to see that that's going to happen

[00:17:59] if unless there's like a just a completely political reason, which they say over and over,

[00:18:03] the Fed is not political, but we'll see.

[00:18:06] Yeah, whatever.

[00:18:07] Yeah.

[00:18:09] Lynn, do you have any thoughts before we?

[00:18:12] No, I'm on this one.

[00:18:15] I'm just like, I would like to see what happens.

[00:18:18] I still have this in poet probably completely irrational wonderment about what is really

[00:18:27] going on in the economy post COVID.

[00:18:30] I think we've seen a lot of cleanup and the supply chain.

[00:18:33] I think we've seen a lot of uptake in certain kinds of employment sectors.

[00:18:38] But the impact of AI is real in the domains where I work.

[00:18:46] I am helping to run an undergraduate program in computer science and allegedly there are

[00:18:53] 8,000 computing jobs in Minnesota that have gone unfilled for the last few years.

[00:18:59] But the doubt about what the role of AI is going to be in that employment sector is so large

[00:19:05] that not all of my students are getting jobs as quickly or at the level of pay that we would

[00:19:09] have expected.

[00:19:10] So there's some kind of something changing under the surface.

[00:19:16] Well, there's a disconnect with what you just described.

[00:19:18] Right?

[00:19:19] I mean, we're hearing about all these jobs, but if people aren't getting jobs

[00:19:23] or at the rate that they had expecting, that's an interesting factor.

[00:19:27] But it's probably sector specific and I think AI is impacting that in some sectors more than

[00:19:32] others.

[00:19:33] Good.

[00:19:34] Okay.

[00:19:34] Well, we'll have to see.

[00:19:35] So here's the closing question, Tom.

[00:19:40] We've been having these little wagers for close to probably about 50 years,

[00:19:47] probably at some level now.

[00:19:48] Tom and I have, and I think you know, Lynn, that we've been friends since kindergarten.

[00:19:53] Yes.

[00:19:53] Some days closer friends than others.

[00:19:55] But it won't be the first time one of us had to buy the other guy a beer.

[00:20:00] So Tom, you'd mentioned maybe there's a single half point cut versus multiple quarter point cuts.

[00:20:07] So if it's a single half point cut, is it a wash?

[00:20:11] Do you win?

[00:20:13] No, of course I win.

[00:20:14] Of course you win.

[00:20:15] It's just the number of pets that we're saying.

[00:20:17] We didn't say the amount.

[00:20:18] It's the number.

[00:20:19] Okay.

[00:20:20] So yeah.

[00:20:21] And if they do three half point cuts, you win.

[00:20:25] If they do three quarter point cuts, you win.

[00:20:27] Doesn't matter.

[00:20:28] Now do you guys need an arbitrator?

[00:20:29] That's what I was looking for here.

[00:20:32] As we get closer, as we get closer.

[00:20:33] No, no.

[00:20:34] I mean because I'm saying two maybe three, Tom is saying zero one.

[00:20:41] And I never said zero.

[00:20:43] I always said one.

[00:20:44] Are you okay?

[00:20:45] Okay.

[00:20:45] Tom's saying one.

[00:20:46] I'm saying two or three.

[00:20:48] But I've always had a-

[00:20:49] Just call me if you need a mediator or a judge.

[00:20:51] You will be.

[00:20:53] We should agree right now that Lynn is the mediator on this.

[00:20:57] That's fair enough.

[00:20:57] I'm good.

[00:20:58] Whatever you say, just follow me on all this.

[00:21:00] Yeah.

[00:21:00] All right.

[00:21:01] So we talked last week was we talked about in our-

[00:21:04] We're in our supply chain and in the economy section of our newsletter.

[00:21:08] And we talked last week about this huge bridge collapse

[00:21:12] in Baltimore or in the harbor there.

[00:21:15] What a horrible thing.

[00:21:16] You know what I've been thinking about related to this a number of times is

[00:21:22] Lars Nussland from 3M made a great comment on the discussion.

[00:21:27] Because this is a factor if you only looked at this bridge collapse related to supply chain,

[00:21:33] probably not huge global news.

[00:21:36] But when you think about this happening and this is the major port in the US for cars to be unloaded,

[00:21:42] cars can go other places easier.

[00:21:44] But this is the main spot that farming and agriculture equipment really comes in.

[00:21:48] And it's not as easy to move where that stuff is going through

[00:21:52] because there's some specialized skills and some things that go through that.

[00:21:56] So just saying, hey, we're going to go to New York or Savannah or wherever it might be is not as simple as that.

[00:22:01] Lars made a great comment in this discussion last week and he said,

[00:22:05] it's not just any one of these things about the Houthi attacks and the Suez Canal

[00:22:10] that are supposedly Iranian backed in the turmoil.

[00:22:18] What would you call it?

[00:22:19] I guess political turmoil that's going on in those arenas.

[00:22:24] And then you've got we post in our an article today.

[00:22:28] One of the last sections of the newsletter is our, we call it a second look.

[00:22:32] It's articles that we may have discussed before and follow information.

[00:22:35] And that's about the Panama Canal, right?

[00:22:38] And the lake, the far side of the Panama Canal is hugely impacted by the drought in Panama.

[00:22:44] It's not changing quickly.

[00:22:45] So there's fewer ships getting through and they're smaller size.

[00:22:49] And to Lars's point on our discussion last week is when you put these three things together,

[00:22:54] we have a major global impact that's happened out of this.

[00:22:57] Good news is they're getting a few small ships through right now.

[00:23:02] But just quick thoughts from either of you before we jump into meaty technology stuff on that?

[00:23:11] No.

[00:23:13] I'm the only guy that cares.

[00:23:15] No, it's a complete side note though.

[00:23:17] I've gotten, you know, Lynn and Kevin, you're all, your guys are, you know,

[00:23:21] have a lot more knowledge of nautical things than I do.

[00:23:24] But I've watched a few YouTube videos about just sort of what happened there and the fact

[00:23:29] that there were nothing around the, like the whatever they call it.

[00:23:33] No bumpers on the pylons.

[00:23:34] Yeah, no bumpers on the pylons.

[00:23:35] And just what, and the sequence of events that happened with that

[00:23:40] cause that kind of perfect storm, I guess for that to, I mean, just really, really,

[00:23:45] man, I had no idea how much intricacy there is in all the nautical things and the

[00:23:49] navigation and all of that.

[00:23:51] And I just think it looks very unfortunate that all of these sort of

[00:23:55] unfortunate problems came together at one time in the wrong place.

[00:23:58] So,

[00:23:59] Well, it's to go with that Tom in, you know, I'm a double geek on this, right?

[00:24:04] Because I'll call myself a mariner lack of a better term related to this.

[00:24:09] I just love things, all things marine.

[00:24:12] And, but having been a firefighter for many years, I was very interested in the emergency

[00:24:18] response site.

[00:24:19] And I ran across on YouTube last week, the first like 15 minutes of the 911 traffic

[00:24:26] or the radio traffic from the Baltimore fire department.

[00:24:29] And then as the state got involved and listening to the voices of the first

[00:24:33] responders to realize the bridge is gone.

[00:24:37] Then it was, there's a ship into the bridge.

[00:24:40] So it's quite an intriguing piece there and on that.

[00:24:43] And so anyways, cutting to the chase, we have a lot to talk about today.

[00:24:49] Something to pay attention to good news is some of the trapped vessels are getting out,

[00:24:54] but it's really only smaller ships.

[00:24:56] It's going to be a long months before there is transiting of cargo ships through there again.

[00:25:02] We'll keep following up on it.

[00:25:04] We're paying attention to these things as a key.

[00:25:06] So let's jump in and talk about the next article we have here and talking about what's happening

[00:25:11] in just south of the border in Mexico, a business insider article about the borderlands,

[00:25:18] Mexico, it talks about investment surge in Mexico's as company supply chains

[00:25:23] plan for new factories and so forth.

[00:25:26] All of this ties full circle, right?

[00:25:29] Tougher to get goods moving around the world right now.

[00:25:32] We've got huge increases in fuel costs, delays in shipments and container prices are through the roof.

[00:25:39] So we talk and then you may have heard in the past when you've joined us for the show is

[00:25:44] we talk oftentimes about on-shoreing and near-shoring.

[00:25:47] This is the first piece where we've seen actual statistics related to that as well.

[00:25:54] Yeah.

[00:25:54] Then you were just there.

[00:25:55] What did you see?

[00:25:56] And what are you saying?

[00:25:58] I spent about a week in Querétaro, which is a city and a state just northwest of Mexico City.

[00:26:06] And I was flabbergasted and astounded at how strong the economy is there

[00:26:13] and how much investment is going into, in fact, supply chain in that region.

[00:26:20] So I went there as a part of a delegation from our university to negotiate exchange

[00:26:27] relationships with a constellation of six technical universities in that state,

[00:26:33] in that city and state.

[00:26:35] And they have, I'm just so impressed with the state government and the federal government

[00:26:42] in Mexico in their forward thinkingness.

[00:26:45] They have developed something called a triple helix model,

[00:26:48] which aligns government both at the state and federal level and industry, major industry players,

[00:26:56] and the universities.

[00:26:58] And they have co-located technical universities in industrial hubs.

[00:27:04] And one of them is in a major supply distribution hub in Querétaro.

[00:27:08] And it's, I think, the third or fourth largest supply chain hub in North America now.

[00:27:14] And I heard, it was all in Spanish.

[00:27:17] And so they were talking about Fedex and De Achele, which is DL.

[00:27:22] DL, sure.

[00:27:24] Upe Ache, right?

[00:27:25] Which is UPS.

[00:27:26] And I visited and went to the university that's situated right in the middle of these huge

[00:27:33] corporate complexes where there are trucks and trains and airplanes coming in and out with

[00:27:38] with material all the time.

[00:27:41] And I was just so impressed with how clean and safe Querétaro is.

[00:27:48] And with the youth and vigor and sort of rigor that they're applying to not just supply chain,

[00:27:58] but we also visited the aeronautical complex and the automotive manufacturing complex.

[00:28:02] But I mean, they're just doing it right.

[00:28:05] And boy, I was super impressed.

[00:28:08] And my provost who was with me had never been to Mexico before.

[00:28:13] And he turned to me about halfway through the visit we were there for five or six days.

[00:28:18] And he's like, this is not the way Mexico is portrayed in the United States.

[00:28:22] No, that is a great statement.

[00:28:25] You know, and I think most people in the U.S. have two perceptions of Mexico

[00:28:30] is poverty and beach resorts.

[00:28:33] And there's a whole country in between that that is thriving in so many ways.

[00:28:41] And if you look at the manufacturing side of it, you know, it's what's funny is

[00:28:44] if you drive a Volkswagen or a Ford, there's a high likelihood that there was a whole lot of

[00:28:49] either components for your car or your car was assembled there.

[00:28:53] And look inside some of your clothes.

[00:28:55] And it's astounding.

[00:28:57] I've had some involvement with manufacturing in Mexico in a couple of different roles

[00:29:01] I've played in the past.

[00:29:02] And if you get into some cities like, I mean, Monterey is a good example,

[00:29:07] just thriving bustling city of commerce.

[00:29:11] And to your provost's point, our perception, I think,

[00:29:15] I think you kind of nailed it.

[00:29:16] Our perception in this country is not what that country really is when you get down in it.

[00:29:23] Funny enough, my next door neighbor that just about the house next door from us about a year ago

[00:29:29] is a retired corporate telecom attorney from Mexico City.

[00:29:33] And I have some conversations with him.

[00:29:35] It's like people need to hear the deals that are being done, you know, and in your piece there.

[00:29:41] But triple helix you mentioned.

[00:29:44] Yeah.

[00:29:44] To me, that sounds like something I would like here during the Winter Olympics figure skating.

[00:29:49] Well, they mean that the completely intertwined and centrally planned

[00:29:57] interactions between the academic institutions, the government institutions,

[00:30:02] and the industry partners.

[00:30:03] The industry partners help the universities make sure that the content and curriculum

[00:30:07] that's being delivered is absolutely appropriate so that the graduates are really ready to hit

[00:30:11] the ground running when they come into their jobs.

[00:30:14] And the government is funding, it's funding initiatives that allow industry and universities

[00:30:21] to be very closely tied and to be working together very effectively.

[00:30:25] So in wrapping this up, here's the gig of what's going on there, right?

[00:30:30] We've been talking for a year and a half on this show about nearshoring,

[00:30:35] onshore, and the increase of that happening.

[00:30:37] And the reason geopolitical is the term I was looking for earlier,

[00:30:41] geopolitical tension in China, Taiwan, Asia, pressure, U.S.-Iran relationships with what's

[00:30:49] going on in the Suez Canal.

[00:30:50] Here's what happened in Mexico this year.

[00:30:53] $31 billion has been invested in manufacturing infrastructure by specific companies

[00:31:01] year to date, $31 billion.

[00:31:04] Last year, 2023 all total, $36 billion.

[00:31:09] So, boy, I don't want to say you heard it here first because you heard it a lot of places.

[00:31:15] But what we were talking about early last year, late 2022 about this coming around, here we are.

[00:31:21] So anyways, that's good.

[00:31:23] I just wanted to hit Bob's comment real quick before we leave here.

[00:31:27] And I'm, Lynn, I'd like your take on this.

[00:31:31] He's talking about the approach of building redundancy in local sourcing.

[00:31:35] Do you see, or maybe it's not 100% clear, do you see that companies are using Mexico as a

[00:31:43] redundancy or kind of an alternative?

[00:31:45] Or do you see that a lot of companies are just saying, hey, I'm moving a lot of my supply

[00:31:50] chain to Mexico or in South America, Central America and going all in.

[00:31:56] I'm not sure I can comment on it so much from the supply chain point of view,

[00:32:01] but from the aviation point of view,

[00:32:04] Keretero is now the fourth largest aviation parts manufacturing and assembly hub in the world.

[00:32:12] And it's only behind Seattle, which is Boeing and Toulouse, which is the European consortium

[00:32:19] manufacturer.

[00:32:21] And one other location that I can't remember, it might have been in Asia somewhere.

[00:32:24] So it is very clearly in the aviation industry anyway, a fourth leg, not a redundancy play.

[00:32:31] So, but I don't know from a DHL and UPS and FedEx point of view.

[00:32:36] I do know that one of the things they're taking advantage of is good land and rail

[00:32:41] connection to their seaports.

[00:32:43] Keretero is kind of centrally located between the east and the west coasts of Mexico.

[00:32:47] So that might be more of a redundancy from a network, a supply network point of view.

[00:32:53] I think there's a mix of that, which I've seen in some of my experiences.

[00:32:58] We've got in, I've worked in the textile or for me, in protective clothing arena,

[00:33:04] where plants in the US, plants in Mexico, some very highly skilled workers in Mexico that work at a

[00:33:11] lower rate.

[00:33:13] And with NAFTA, it's easy to move things back and forth.

[00:33:16] And so I think we're going to see much more, Tom, one guy's opinion here,

[00:33:21] I think we're going to see much more of the mainstay manufacturing and kind of co-manufacturing

[00:33:30] tied up into this.

[00:33:31] But I think we're going to see a growth of not just redundancy, but new technology there as

[00:33:36] well or new factories there as well.

[00:33:38] The key thing that I think we've got to watch really closely in this and we need to move

[00:33:42] on because we're going to not have taken advantage of Lynn's background and our guests

[00:33:47] here if we don't get into some of our technology discussions deeper quickly.

[00:33:50] But last piece on this is watch closely what China does here.

[00:33:57] China of the investments that have come in 57, this year today, 57% are from the US,

[00:34:04] followed by 17%, Germany, 14% Argentina, but 6% of it's from China.

[00:34:12] If we start seeing that number from China move up, you might start seeing specifically with this,

[00:34:18] well, this political, I was almost going to say regime, but political group that's in charge

[00:34:26] of the country right now from the president side of things is we may see some pressure

[00:34:31] on what we're doing in Mexico as a country if China's stake there increases too much.

[00:34:37] So let's watch that in coming days, weeks, months and so forth.

[00:34:41] So jumping into our manufacturing distribution segment,

[00:34:45] six industrial metaverse use cases for manufacturing. Anybody want to lead us off on that?

[00:34:53] Yeah, I asked Lynn earlier, you know, I asked her, is the metaverse really kind of

[00:34:59] a thing in manufacturing yet and then I thought your answer was really good is that

[00:35:04] virtual reality, virtual augmentation has been around for a long time, but it's still

[00:35:08] kind of working its way through the system in a lot of different places.

[00:35:12] Yeah. So I remember years ago working when we were first doing virtual and augmented reality,

[00:35:20] working with a manufacturer in the Pacific Northwest who had a line, a manufacturing line

[00:35:28] that included a lot of radioactive materials. And before they could, in good faith, put

[00:35:34] a new worker on the line, they had to go through a significant amount of training safety

[00:35:38] training and operational training. And they were doing it all with virtual reality and augmented

[00:35:42] reality. And I thought that was a really, really great application. But that's admittedly from a

[00:35:49] total dollars in the manufacturing industry. That's a niche case. So there's that. I also saw

[00:35:56] some really cool work at Boeing before the word metaverse came online where they were

[00:36:03] using augmented reality to speed up parts placement and harness connectivity in their wiring line,

[00:36:10] in their wiring production line. So yes, I think the metaverse is a very real thing in manufacturing,

[00:36:18] but I think that a big play, a big general play, a big application in general

[00:36:26] is probably going to have to end up being something more along the lines of facility

[00:36:31] layout or facility and process planning, something that everybody can use. And I think we're still in

[00:36:37] the early adoption phase with those kind of broader use cases. I think those are great points.

[00:36:42] And you made the comment about training with that. And that was number five on that list of six.

[00:36:49] And that one was the one that really stood out to me. And my background being in starting

[00:36:54] my career in distribution world was with safety equipment. And if you start thinking about,

[00:37:01] the great example in that nuclear setting is pretty niche as you say, but you know what?

[00:37:07] There's thousands of people just in the US being trained every day on working at heights.

[00:37:13] How to wear fall protection correctly. How simple would it be rather than

[00:37:19] putting somebody up on a large piece of scaffolding and assume that they're comfortable with that.

[00:37:26] Why not replicate that with virtual reality or augmented reality so they feel they're standing

[00:37:32] two feet off the ground on a piece of scaffolding, but they feel like they're 40 stories up.

[00:37:39] And how do they work in that environment? Is that the right person to continue even training?

[00:37:44] Right. And I think that if you think about military pilots and even commercial pilots have

[00:37:49] been training in simulators for, I don't know, 50 years probably at some level. And I know that's

[00:37:55] how the military kind of weeds people out as well. I see some phenomenal pieces. I mean,

[00:38:02] the facility design and engineering piece that you were mentioning is great. But

[00:38:06] the training application for me with this seems phenomenal. Not just in that nuclear

[00:38:11] setting or the safety thing that I said, but just using different tools and equipment in

[00:38:17] that setting. I think it could be really interesting. Yeah. I love your idea of screening

[00:38:26] possible applicants or early stage. It's great. What a great idea.

[00:38:33] If you take something as simple as working at heights, right? There's hundreds of thousands

[00:38:40] of people around the country that probably millions around the world that put a harness on,

[00:38:47] that have to connect to a welded anchor of 5,000 that can hold 5,000 pound rating and

[00:38:55] all of these things to it. And sometimes companies will hire somebody or somebody in their job

[00:39:01] now needs to go be involved in that. You don't know how they're going to react to that

[00:39:05] until you put them in the setting. So I think what we're going to see is over time, there's

[00:39:09] going to be so many of these things that we can do. And it just opens the discussion broader for

[00:39:18] this idea of how we're using the metaverse, digital twins with AI and robots. Other things,

[00:39:26] all the other things we're going to discuss today. But I think what we're going to,

[00:39:30] our workplace today, and this is, I'm kind of passionate about this because

[00:39:34] as we talk so often about people that are concerned about their jobs or whatever,

[00:39:38] and we were talking about this earlier today before we went live. And even with the song

[00:39:44] you talked about, right? Is you're out of business. Well, I think when we look into

[00:39:48] specific wholesale distribution and manufacturing, these technologies are just going to make us

[00:39:54] so much more efficient. Now maybe we won't need to hire as many people, but I don't see a net loss

[00:40:01] of jobs in that many places. So anyways, that's my takeaway is I think when you talk about the

[00:40:08] metaverse, digital twins and some of the things that we can do with this, we're in a great place.

[00:40:17] All right, let's jump ahead. We had another article about small manufacturers

[00:40:22] building a case for robotics. That article, if you have the newsletter, take a look at it,

[00:40:27] it ties right into this previous discussion and what we're going to talk about a little bit more.

[00:40:32] One quick thing, Tom, before we move ahead into our e-commerce and marketing segment is

[00:40:37] if you're listening to us on the recorded podcast, we're reviewing and you can't see that if you're

[00:40:42] on the podcast on our screen next to the, today we truly have a rose between two thorns

[00:40:50] with Lynn joining us here. You can't see our faces nor the articles that we're looking at,

[00:40:55] but we publish the newsletter every week. It's called Around the Horn and Wholesale Distribution.

[00:41:00] It's sponsored by Leedsmart Technologies. And if you would like that newsletter,

[00:41:04] just reach out to us at www.aroundthehornpod.com. You can sign up for it there. Send us an email

[00:41:11] at hello at leedsmarttech.com. So e-commerce and marketing segment four ways AI will reshape

[00:41:18] B2B commerce. Mr. Burton, why don't you start us off?

[00:41:22] I don't know. I mean, I'm just going to start, you know, there's a few good points in here,

[00:41:27] but I'm going to bring this over to Lynn because I think we can have a good conversation about this.

[00:41:31] Should I just go to the restroom and come back and it seems like data might have something

[00:41:36] to do with this. What do you think? Yeah, I do. So even though like I've had this big fancy

[00:41:45] large scale career and artificial intelligence, I am really, really have for the last decade anyway

[00:41:53] been really focused on earlier stage adopters and getting people, getting companies and helping small

[00:42:02] and medium businesses and larger businesses get into the digital realm. Really just kind of

[00:42:09] getting on to the digital platform and getting out of initially it was getting off of paper and into

[00:42:17] digitized formats, but now it's getting out of spreadsheets and getting into platforms where

[00:42:24] there's a certain amount of what I might call semantic framing going on, meaning

[00:42:28] thinking about what is the meaning of the data that we're working with and planning for

[00:42:35] or what is the data that we need? How do we want to be organizing it and accessing it

[00:42:43] with always in mind, what are the decisions that we want to be able to support?

[00:42:48] And what are the actions and initiatives we want to be able to undertake by standing on this data

[00:42:53] platform? And that is kind of the gong I've been banging on for the last decade because

[00:43:03] it's everywhere. I mean people can feel really overwhelmed with the amount of data they have,

[00:43:09] they can feel overwhelmed with what they think of as they're not clean data or the lack of

[00:43:13] quote unquote cleanliness in their data or the aging out of contents that they have content

[00:43:18] that they have in their data and that can be overwhelming so I'll stop there for a second.

[00:43:24] Tom, Kevin, any thoughts?

[00:43:31] I like what you said about semantic planning. I think that is not something that's in most

[00:43:36] vocabulary of a lot of companies and data when they think of bad data that's predominantly

[00:43:43] looking at data hygiene but there's a lot of things like I see this every day and the work

[00:43:48] that we're doing. The data hygiene, yeah there's some data hygiene issues but to me that's not

[00:43:53] what we really run into as being the showstopper. It's getting that semantic planning and looking

[00:43:59] and saying okay how can we take even some data that we have and start utilizing that and then

[00:44:05] let's start seeing the analytics or the things that we can use with that and then keep adding to

[00:44:11] that incrementally and I see that it sounds like you're seeing the same thing because there's a

[00:44:16] lot that can be done incrementally. It doesn't have to be a big bang, we have to spend five

[00:44:21] years doing data hygiene and all of that but no you can start moving things over and taking

[00:44:26] small steps and kind of getting a minimum viable data strategy in place. That's exactly right and

[00:44:32] I'll give just a simple example. Some organizations have just for historical reasons just because

[00:44:40] of how things have operated especially during the growth phases of maybe a family-owned business

[00:44:46] and the operational data around who the customers are and what they've been ordering might be separate

[00:44:58] from important financial data related to the costs of the raw materials or the costs of the

[00:45:05] SKUs that are being shipped and that happens all the time and it happens in every industry. In my

[00:45:11] world in the university, in running a university it's that the catalog data meaning what are all the

[00:45:17] courses and what are all the prerequisites for all the courses and who can teach them is very

[00:45:23] traditionally separate from any kind of cost center information about what does it cost to

[00:45:28] actually hire and teach you know have a faculty member or pay for the lights to be on in

[00:45:34] the classroom that those are typically siloed from each other and when it comes down to

[00:45:40] being able to do really important strategic planning some semantic planning around the data

[00:45:46] has to be done and you have to take small steps to say well let's connect this chunk of the operational

[00:45:52] data to this chunk of the financial data and let's plan to do this going forward. Let's plan

[00:45:57] to how we're going to keep that data aligned and let's put some auditing in place to make

[00:46:02] sure that it stays clean once we've cleaned it up. And just to tie it back to this article

[00:46:08] right the four ways and one of the things they were talking about is product catalogs kind of

[00:46:12] going away but using AI to basically you know just use that for search or conversation or so

[00:46:18] forth. The all the stuff that we're talking about with data can absolutely be applied to the

[00:46:24] things they were talking about in B2B commerce here. So it can be applied in B2B commerce,

[00:46:28] it can be applied to your sales team, it could be applied to your operations team.

[00:46:32] So taking the data semantic planning you're talking about has a lot of different tentacles and where

[00:46:39] it actually can create benefits in the business. And it sounds like Lynn you're doing a lot of that

[00:46:43] with helping companies kind of get that first as we are too. And again like I said data hygiene

[00:46:51] to me what I'm seeing that's the least of the issues compared to some of the other things

[00:46:56] that are and I think what you know it goes with that right is I think sometimes people are you

[00:47:01] know in our world we're talking to wholesale distributors and manufacturers every day is

[00:47:08] sometimes I think they underestimate the data in general. You've made this comment so many times

[00:47:16] over the years is your deep experience in business is you know I think you've said you

[00:47:22] 've never seen an industry that has the amount of data that wholesale distribution has right.

[00:47:29] We work with companies that are 100 years old and you know they've got you know 20 years of

[00:47:35] invoice data and they're pumping out 200 invoices a day you know whatever it might be.

[00:47:41] So but I think sometimes they get people get wrapped around the axle thinking about oh well I have

[00:47:47] emails in the website field on our ERP system or you know or I have three addresses that are

[00:47:57] simple when we see all the time right is we have ACME manufacturing, ACME MFG and ACME

[00:48:04] all in our ERP system so my data is bad. Well it's like yeah that's a you know

[00:48:11] a fly on an elephant right when we're talking about the bigger picture of what we need to

[00:48:16] look to accomplish. Yeah we completely agree with that one of the things that I often have

[00:48:25] I often spend time with clients and collaborators on is helping people get used to the idea

[00:48:33] that the data doesn't have to be perfectly clean you can refer to ACME three different ways

[00:48:39] but still be able to make the connections between the data silos in ways that lead you to understand

[00:48:46] answer questions like which customer of segments should I be focusing on right now?

[00:48:53] Who should I be upselling what to? Which which skews need a little love right now because

[00:49:02] if we don't start moving it now it's going to time out in the warehouses and we're never

[00:49:06] going to be able to move it. You can answer questions like that even on top of data that

[00:49:10] isn't perfectly clean as long as you're thinking about okay what kind of noise am I willing to

[00:49:19] deal with once these things are combined? Great great comment so if you take back to the

[00:49:25] you're obviously both familiar with it but I've not heard that phrase was it semantic

[00:49:30] planning is that the term? I think we just made it up I was just talking about it.

[00:49:35] I love it but I've never heard and I feel like odd man out. No no semantic just means the meaning

[00:49:40] of the data instead of just thinking about the overwhelming. No I get it yeah I love it I

[00:49:45] thought maybe it was something they taught in computer science courses or something that

[00:49:49] wouldn't have let me even in that building but let alone the class but I think when you

[00:49:56] talk about that Lin it's so powerful because you mentioned something that might be the single

[00:50:01] biggest issue that most wholesale distributors and manufacturers both faces silo data right and I

[00:50:08] think this is one of the the biggest values that AI is going to bring is when we can start

[00:50:14] thinking about data lakes that have every valuable spreadsheet that they've had all their live

[00:50:22] ERP data their marketing automation their e-commerce other connected systems maybe there's some

[00:50:27] supply chain data coming in and then we have every main from a distributor I've got you know

[00:50:32] every spec sheet from every manufacturer that I've had I've got all of that across the board

[00:50:38] and I have yet to ever meet anybody in wholesale distribution manufacturing that has

[00:50:43] really figured out how forget about AI have found a way to really take advantage of

[00:50:50] their silo data and there's ways to do that with proper CRM tool planning right today without AI

[00:50:58] and then the things that we're going to be looking at you know Tom's team Tom runs the

[00:51:02] development side in our company and the things that we're going to we're building within our

[00:51:05] company is being able to talk to your data from all these sources and getting those

[00:51:10] singular silos from many silos and it's astounding to me what's available to us

[00:51:17] and in your future so yeah the bottom line for me is always it doesn't really matter how deep into

[00:51:26] your AI journey you are whether you've not gotten started yet or you're just having your folks

[00:51:31] play around with chat GPT to kind of get used to it or whether you're really deep into it

[00:51:35] the the notion that we have to be stewards and apply husbandry to our data is really critical

[00:51:43] and the kinds of things that we have to constantly be thinking about are

[00:51:48] what are the decisions and choices we're trying to make given what's in this data given what we

[00:51:53] know is in this data from there skies the limit you can do very simple things that are very

[00:51:59] powerful or you can throw everything into a data lake and bring in the biggest large language

[00:52:03] model and have it solve all the problems you can be anywhere on that spectrum but still get

[00:52:07] a lot of value out of it and look at that across the organization not just accounting

[00:52:13] has all this data and then marketing is on an island over here and you know planning is over there

[00:52:18] so I think that's good stuff but again again I know I just want to say one thing I just

[00:52:23] we could spend probably the whole afternoon on this is you know the quality right what you're

[00:52:28] saying Lynn is a little bit of good quality data from a semantic perspective and produce a

[00:52:34] lot of big insights just with a small amount and those insights can then be used to determine

[00:52:38] where you go next you don't have to throw the kitchen sink in to think you're going to get something

[00:52:43] back that is a value anyway I think we need to do a session like the five myths of data or something

[00:52:48] and and kind of go through that because I do think it's something that a lot of companies

[00:52:53] are putting attention on we've talked about it Kevin I think a week or two ago what was

[00:52:57] 60 some percent of companies saying that data data projects are on there yeah so I think what we

[00:53:04] should do is if we can convince uh convince Lynn to come back later in the year she can then adjudicate

[00:53:11] the stake that you owe me uh and we can do a separate session where we're just talking about

[00:53:16] data okay I think that would be that would be let's see that would be a service to humankind

[00:53:22] yes very good okay uh so to cover a couple of other things there's we have another article

[00:53:28] there about manufacturer sales to rebound via digital commerce great article some good good

[00:53:34] I was going to kind of jump ahead to some of the other pieces past this one but there's

[00:53:38] some good charts in there if you're getting the newsletter uh look at those charts in there

[00:53:43] they've got some good data there if you're not getting the newsletter let us know we'll get that

[00:53:47] to you but a couple of pieces that I wanted to talk about since we're talking about data right

[00:53:51] the next article as we get into our technology cyber security and AI segment is this article

[00:53:57] from info security magazine about a report of a seven-fold increase in data theft cases so

[00:54:03] we're having all these discussions about data right we've got all these cyber security issues

[00:54:08] as well because you know think of the way it comes across in my mind is the better I get

[00:54:14] with my data the more valuable that might be to somebody else or somebody else wanting to

[00:54:18] hold me hostage with my data did you either have you have any takeaways from that article

[00:54:23] well this was more personal devices it looked like you know taking me out they mentioned

[00:54:28] personal but then they talked about business as well yeah but yeah of course I mean is

[00:54:33] it's anything that's got value right is potentially a risk for some sort of criminal activity

[00:54:38] right um and you know data what we think about data that's you know social security numbers

[00:54:44] and personal data and all that stuff well that's just but all these business analytic data

[00:54:49] that we're talking about and you know the quality of your semantic planning and how you put that

[00:54:54] together there's some potential value and yeah of course you're going to have to treat it as an asset

[00:55:00] because it is an asset so yep yep that's um positive stuff Lynn any thoughts on that before

[00:55:07] we move away uh it's just real quick there was a major earthquake in the software system

[00:55:14] security I emailed you about it earlier in the week Kevin the xz back door yeah talk to us about

[00:55:20] that because I just realized we did not include that and I had planned super quick about it but

[00:55:26] so in the software development world which is where I spent a lot of my time there's the

[00:55:32] notion of the software supply chain which is a little different than the way you guys use

[00:55:37] the word supply chain but the basic idea is that over the last let's say 15 years

[00:55:43] um we've shifted entirely from a software development approach as as a world this is on the world level

[00:55:50] not just in the united states we've shifted from companies writing proprietary software that they

[00:55:56] have absolute 100 control over and every line of code is written by the people who are paid to be

[00:56:03] in the room writing the code to having a very open software supply chain meaning components of

[00:56:12] software that get built into the systems that companies are building and selling as products

[00:56:16] are coming from the open source community and it's about 90 percent of software now that is sold

[00:56:22] and delivered um has about 90 percent of the componentry is is from the open source community

[00:56:28] and so this open source approach to developing software has many many benefits it's uh it's a

[00:56:35] very interesting almost socialist model or almost communistic model of how people are applied how

[00:56:41] software engineering talent is applied to the creation of software and built into it is

[00:56:48] uh a system by which developers have developer reputations and you have the right to contribute

[00:56:58] to code that gets produced and then sent out and used and this xz backdoor which you can

[00:57:05] read all about it um is the first major expose of social engineering happening in a way where

[00:57:15] fake it was a long game multi-year fakery around who the developers were and pretending to be trusted

[00:57:25] people in a very core piece of software that affects many many operating systems and therefore

[00:57:31] many computing systems and so it's kind of shaken the world dramatically to think that

[00:57:38] social engineering which used to be just phishing and calling up someone and asking for their

[00:57:43] password is now fundamental it's like in the foundational levels of how all the software that

[00:57:49] we run is being built and now everybody's looking left and looking right and going do i really know

[00:57:57] who joe schmidt three from chicago is or is that guy a russian military infighter so is that mostly

[00:58:08] tied to open source software then or yeah that's most of the software in the world yeah right okay

[00:58:14] yeah so it's pretty pretty pretty interesting times in the software supply chain world yeah

[00:58:20] and it directly relates to this uptick in data security and data violations and

[00:58:26] what you're seeing in the data yeah so bringing that full circle too if i'm listening and i'm a

[00:58:32] wholesale distributor in middle america right is uh when ian heller was with us uh earlier this

[00:58:40] year with we called it our legends show because we had kind of three of the top probably i'll say

[00:58:47] 10 mines or so in wholesale distribution uh that were with us uh we had mike marx dirk

[00:58:54] beverage and and ian with us and as we were winding the show up down we were we were

[00:58:59] talking about cyber security and ian brought up an actual instance that's happened of a and it ties

[00:59:06] into literally what we just read about earlier in the week that is it i don't remember if it was

[00:59:11] chat gpt that can do this but within what was it 60 seconds of listening to your voice can

[00:59:16] around replicate your voice moving forward i can't remember what the the time was but there

[00:59:21] was an instance where somebody got a phone call at an electrical distributor wanting to buy

[00:59:29] x thousands of dollars and have it delivered of copper tubing wasn't the company it wasn't a

[00:59:38] standard address right so now you start thinking about this when we think about deep fakes or whatever

[00:59:43] the term becomes right is you know and we had a comment on there from on that particular

[00:59:49] show from somebody that said they won't deliver to a new location for one of their customers unless

[00:59:56] it's been very physically verified that there is a job and a project and it's that company going on

[01:00:01] there because you think about you know the price of copper and other commodities right um and how

[01:00:07] easily those can be resold this is the kind of stuff you just related it to us that that big of an

[01:00:13] issue it is potentially with software and technology you take that across the board is what happens when

[01:00:19] we get an order for something that we normally wouldn't get but it's bob's voice i've been talking to

[01:00:25] bob for 20 years right so interesting times all right tom any other closing thoughts on that

[01:00:33] piece nope nope let's move ahead so i want your take we don't need to spend a lot of time on this

[01:00:38] but all we've been talking about in the world for the last what is it 20 some or about 20 months or so

[01:00:48] has been chat gpt and and open ai and the microsoft relationship in congress said not here

[01:00:57] yeah i think it's a bit of a non-story i think that microsoft obviously is working on you know

[01:01:02] things that will pass the government scrutiny and pretty soon this will be uh a non i mean

[01:01:08] look these are early products right and doing anything with government and government level

[01:01:13] security and government level regulations and so forth is not for the faint of heart and um

[01:01:20] so i think it's going to be you know i don't think it's going to be a big deal i think microsoft

[01:01:23] obviously has the resources and a lot of the other big companies to do this and eventually it'll

[01:01:29] it'll be you know be able to pass some of the scrutiny on that side well the idea behind

[01:01:33] it is security right it's right so somebody does and they ban the free use of chat gpt but it's

[01:01:40] interesting just to use the microsoft co-pilots with this which are and i think there's a big

[01:01:46] difference here and both of you could correct me if i'm wrong here please do is there's a big

[01:01:52] difference between saying how you use the free version of chat gpt where someone could load in

[01:01:58] some legislation not realizing that they just put that on the internet right i've shared over

[01:02:03] and over an example of a security document i had for how we keep our congregations at church safe

[01:02:09] that is goes out globally from my faith and i almost did a comparison to two different documents

[01:02:17] from a previous one and caught myself but those things could happen right um versus having

[01:02:23] you know what was it called clippy uh from right in my power point if i work in congress

[01:02:30] i mean those those are pretty unique differences in my mind they're use cases

[01:02:36] yeah and i think this is this is basically a safety move um copilot is built in the the

[01:02:42] microsoft office sweet now and they're saying turn it off so that you're not accidentally

[01:02:47] drafting the next legislation for whatever and microsoft is listening just just you're not

[01:02:52] allowed to use it i don't think that they're going to end up with not allowing people who

[01:02:57] work in congressional district offices to use microsoft products it's it's a temporary it's

[01:03:03] absolutely attentive to your point into tom's point i agree there is another spin and i'm gonna

[01:03:08] i'm gonna drop a grenade before and then we need to move on but here's my grenade in all of this

[01:03:13] there will be a line of thinking and it will be talked about in lots of other podcasts

[01:03:19] out there that this is part of also this administration scrutiny and sometimes said torture

[01:03:27] of big tech so let's move on i'm just going to throw that bomb out there and we'll see what happens

[01:03:34] next we'll go into our sales and mna section um we don't need to spend a lot of time here we

[01:03:40] talked at length last week blockbuster deal done uh middle of last week i think it was

[01:03:45] actually last thursday with uh home depot buying srs distribution huge deal uh biggest scene in many

[01:03:54] many years mike hawkett who's been on our podcast with us multiple times uh senior editor at modern

[01:04:01] distribution management did a great recorded conversation with an industry expert on mna

[01:04:09] in that side of the distribution space so if you're interested in learning more about uh

[01:04:14] not just um that deal but an mna roundup for year to date so far uh mdm uh is a great resource for

[01:04:23] that so uh let's kind of jump ahead we've got there's a few articles in our people and leadership

[01:04:28] segment seeking ai's productivity potential at the ground level and um then we've got another

[01:04:34] one in there about us workers being anxious about ai use in finance and operations any take from

[01:04:41] either of you two on those two articles i think it's more of a lot of the same stuff that we've

[01:04:47] been talking about the the one about ai and finance and operations i think was maybe a bit

[01:04:53] more insightful on some of the things that people are worried about um and then rightly so and

[01:05:00] rightly so right it's um i think the risk that we have and and then i definitely want your take

[01:05:06] on this i think the risk that we have is we start to take the ai for granted and we just assume

[01:05:11] that it's all right and i think that's maybe the biggest maybe risk that we have right now it's

[01:05:17] like you know people say what's on the internet it must be true if it comes from ai then it

[01:05:21] absolutely has to be true yeah right and especially in business i think there's some you know valid

[01:05:26] points in here about just you know looking at it willy nilly yeah i agree with that assessment

[01:05:34] entirely um i don't remember which of these articles it was in but one of the articles

[01:05:40] said something along the lines of the difference between ai and previous disruptive technologies

[01:05:48] is that ai really only happens in the bits and it doesn't use up real um raw materials or real

[01:05:55] material and i just really wanted to point out that that's not true ai particularly at skill

[01:06:02] uses a huge amount of electricity and water huge and so much so that people in my world people who've

[01:06:11] been doing ai for a long time are very very concerned about who's going to control the rivers

[01:06:19] in 20 years because it could be that ai installations end up controlling all of these

[01:06:29] raw materials and natural resources in order to exist yeah so we've talked i love that you

[01:06:35] brought that up we've talked about this a few different times uh over the past year or so

[01:06:40] but it was and actually not over last year probably in the last six months um at the event

[01:06:46] that we met at lin zack cas who was can't remember his title or role but was very early

[01:06:51] employee at open ai the makers of chat g p t he were you there yet for his keynote at the

[01:06:58] yeah i saw it yeah yeah so he talked at length about um the energy needed to support ai right and he

[01:07:07] was even talking about fusion or we need we we're not going to be able to get to that place and

[01:07:13] and i'm not going to try and quote zack here i'm going to make my own statement about this but

[01:07:17] you know the thought about saying we can solve and cure cancer we probably can't get there until

[01:07:26] we have solved the issue of how we can power the computing to get to the place where we can

[01:07:32] solve now i'm oversimplifying that and maybe it's not a good use case example but all of the things

[01:07:39] that we're talking about three five seven 20 years down the road of what ai will do for mankind

[01:07:46] probably can't get there unless we figure out that the the way to power that as well yeah

[01:07:53] great point glad you brought that up good um let's just kind of kick our way ahead here there's one

[01:07:58] other article in the people leadership segment about and we referenced this a little bit earlier

[01:08:03] the discussion about people and jobs uh it says manufacturing could need 3.8 million new

[01:08:08] employees by 2033 i'm just going to say i think that number is actually low tied to our earlier

[01:08:16] discussion today about nearshoring and on shoring because we're going to be doing quite a bit

[01:08:21] of on shoring as well that goes with this and then we've got the infrastructure bill that we've

[01:08:26] got out there now and spending i think that number may go higher so i think you know we just added when

[01:08:32] you don't know this we just added our people in leadership segment a few months ago because

[01:08:36] we had one of the top speakers and writers and researchers out there related to wholesale

[01:08:41] distribution say hey you guys don't talk enough about people so now we have this segment

[01:08:48] so thank you dirk for the reminder on that so anyways any that tom you want to jump into this

[01:08:53] where you're you're at there with it i'll just if you guys don't mind i can give us a few minutes

[01:08:59] at the end we can close out with some good discussion on the day with lin but i'll just

[01:09:02] hit a couple of things our industry scuttle but um the folks from tipco in um in mariland have uh

[01:09:10] done a new deal in south uh texas which is a it's kind of a big deal for them i was actually

[01:09:16] messaging last night with the ceo of tipco i'm gonna try to work out our schedules to have lunch

[01:09:21] when i'm in washington dc in a couple weeks but um great move on their part congratulations to both

[01:09:26] parties involved in that deal there is uh that mdm breakdown of the mega deal with each uh

[01:09:34] home depot talking about and that kind of tied that whole discussion ties in a lot deeper to

[01:09:39] the overall building materials market so that's good and then finally in a in a an acquisition

[01:09:45] that was kind of surprising to some people that we talked about when it first hit a couple weeks

[01:09:49] goes val and distribution uh acquiring a division of west co and west co being a large um electrical

[01:09:59] distributor largest in the country of what they do and their integrated supply division was sold

[01:10:04] that's done and complete so lastly is we try and do each week that good reads this week is uh

[01:10:12] kind of a cool article about what employees think the workplace will look like in 30 years i don't

[01:10:18] know if you guys had had a chance to take a look at that but it was kind of i think as an employer

[01:10:24] it's something we should be thinking about i thought it was pretty good i thought it was yeah

[01:10:29] pretty accurate i don't i didn't um i think what's missing is potentially the impact of ai

[01:10:35] into that workplace in the next 30 years but um i think at a high level i thought it was

[01:10:39] pretty pretty good article yeah that's good that's good well we're kind of getting to uh our our

[01:10:46] timeframes right now but i i just wanted to uh kind of get a a chat from both of you right as i'll

[01:10:55] defer to you as the experts we talked about a ton of different things today but if if we had a

[01:11:02] listener here with us today that runs a wholesale distribution company uh or a manufacturing firm

[01:11:09] maybe we can kind of wrap up with some maybe simple takeaways on what i should be thinking about in my

[01:11:16] business uh what i should be i've got my marketing people wanting to buy a tool i've got my sales

[01:11:23] guys wanting to maybe get a better newer crm i've got accounting people that are talking about

[01:11:28] all this data maybe what are some takeaways from from the two of you for for today for our listeners

[01:11:34] on where maybe where do i get started go ahead land well for me it's always about the data and the

[01:11:44] framing question is given all the data that you know you have how do you want to kind of frame up

[01:11:51] the decision making what are the decision supports that you want that data to provide

[01:11:56] what are the main questions you want to be able to man answer what are the main initiatives

[01:12:00] you want to be able to undertake once you harness your data that will lead you to

[01:12:06] what's the low hanging fruit in terms of maybe combining silos or it'll lead you to to the

[01:12:12] to the things you can get done soon that's great so that's kind of sign of some actionable

[01:12:17] insights as i like to say and the reason i brought this question up is not just our

[01:12:22] discussion today but i listen into a lot of the webcasts and some of the events and so forth

[01:12:29] where i'm hearing people just consistently talking to wholesale distributors is you know the

[01:12:35] simplest example that says you know just get started just get started but they don't really

[01:12:40] talk much about what is getting started mean right and you made a comment earlier than when

[01:12:44] we were talking before we went live today about even just doing some good things with getting

[01:12:51] utilizing the cloud for different things right so maybe you can elaborate on that for us a little

[01:12:57] bit i'm so sorry i can't remember what i said you were just talking about getting started using

[01:13:03] you know cloud-based tools within our businesses right and yeah that we can oh right right no i

[01:13:09] know you mean um this is the get off of the individual spreadsheets get into get into platforms

[01:13:16] that support uh broader sharing of data broader connectivity of data i'm always very suspicious

[01:13:24] when i go in to work with a new team or a new company and they say well carol uses that on the

[01:13:31] spreadsheets on her laptop and i'm like yeah i'm thinking that that's an indicator that we're

[01:13:35] not really thinking at the organizational level about the value and the accessible worth of this

[01:13:43] data if it's sitting on one person's spreadsheet it's an indication to me that we should be

[01:13:48] thinking about okay where do we want to put the data why do we want to use it how do we

[01:13:52] want to access it and that would normally mean some kind of cloud solution yeah and i'll just

[01:13:59] finish off with one thing i realized you know i like the word semantic planning we were talking about

[01:14:04] i'm going to go one step further and call it semantic strategy and one of the things that i'm

[01:14:10] it just hit me even in this conversation today because i see it over and over and over i see

[01:14:14] similarities as we work with our different customers i think that we can and this is our

[01:14:19] responsibility right as vendors and technology providers that are out here is to provide kind of

[01:14:24] some best practices for semantic strategy because i think the fact that we are seeing a broader

[01:14:31] spectrum than what an individual would see in their company we can provide some insights into

[01:14:37] what should be some semantic strategy and i'm already seen it's like yeah we always see this

[01:14:42] and we always see this and we always see this and it's maybe obvious to ask but it's not

[01:14:46] going to be obvious to somebody in a company especially to your point if they're running stuff

[01:14:51] off of a spreadsheet somewhere so i think there's a lot we're going to be able to do with semantic

[01:14:55] strategy which can then lead to semantic planning which thing can be leading to the proper

[01:15:00] right project plan has a relate to your data so that's my last word i think i think that ties

[01:15:05] well tom you know and we kind of laugh we were talking about in a board meeting recently

[01:15:10] we were talking about this is that you know somebody it was it was kind of a joking thing

[01:15:16] but it's true right and when we look at things from from lead smart from our channel cloud

[01:15:22] customer intelligence and crm solution is you know we were talking about competitive companies

[01:15:27] and and our biggest competitor there's two of them one is email and spreadsheets

[01:15:33] and the other one is apathy yeah right because i'm afraid to go take these big leaps because

[01:15:41] you know my business has been running forever pretty well by doing what i do and that you know it's on

[01:15:47] on you know anna margaret or joe's you know laptop and you know we we talk about this even internally

[01:15:54] sometimes is like our is our data structure even in simple things like share point correct

[01:16:00] right and and we can be taking those steps up quite a bit easier in that setting but yeah

[01:16:06] interesting because we do see so much of that out there it's like where is that

[01:16:10] is it in a spreadsheet is it in a report is it you know and so that cloud-based solution

[01:16:15] you're getting involved with that earlier that you're talking about lin is good so

[01:16:18] any other takeaways on the day for either of you well i think some good good conversations

[01:16:22] lin thank you really really really added some great value loved it so that's really guys

[01:16:28] what did you call me a rose between two thorns that's right that's right i'm a delicate flower

[01:16:35] right yes we'll have the editor cut that piece out and send it to you in a clip

[01:16:44] you've been globally we get six to seven countries a week downloading the podcast and hundreds of

[01:16:52] people either listening in live or listening in later on on youtube and facebook and linkedin

[01:16:59] so this has been good lin folks want to follow your work or see what you're up to what's the

[01:17:05] best way for them to pay attention to what you're doing uh um linkedin linkedin linkedin

[01:17:14] okay there's my curveball for you for the day you knew one was going to come

[01:17:18] all right but where can they hear your music uh woofactor music com i was checking it out

[01:17:26] the other day i'm going to listen to more so that'll be great i put out a business so you know

[01:17:33] i i believe that i believe that experience you had you described earlier with was it sumo

[01:17:40] is that right sumo su no and oh and you remember the prompt the ai prompt was write me a country

[01:17:47] ballad about you know what it called the song a slice of heaven okay very good yeah i that that

[01:17:57] seems like something send us a link send us a link i'd like to put that yeah i will do that i think the

[01:18:04] that makes me think of that probably could be sold to uh we're dal yankovitch

[01:18:12] right and and to go even further back i don't know if you would remember this but the old doctor

[01:18:17] da mento show from yes right so there we go there we're aging ourselves now okay so uh

[01:18:24] lin chase find her uh woo music dot com is that right factor well fact okay and then music

[01:18:33] dot com and then linkedin is a great place to follow her we'll have lin back it's been

[01:18:37] great to have you in wrapping up thank you both for being with us and the time you both put into

[01:18:43] today again we get together every friday morning um or nearly every friday morning and we chat about

[01:18:50] the world of wholesale distribution and manufacturing and the impact of the news of the week on that

[01:18:57] if you would like to get our newsletter and you don't just let us know at hello at

[01:19:03] leedsmarttech.com or you can go to the show's website which is www.aroundthehornpod.com we're

[01:19:10] grateful for the people that join us on a consistent basis we've been doing this we didn't mention this

[01:19:14] today is number 85 that we've done this it's astounding to me we've done this 85 times i actually

[01:19:21] have a thread that i'm working on in chat gpt about what to do for your 100th episode of

[01:19:30] your podcast uh so i'm working through that with some ideas now because we've got to figure some fun

[01:19:35] and special to do so that's it for us tom lin thank you both again if you like what we do hit

[01:19:42] the like the subscribe the share whatever it is you do on the platform you're listening

[01:19:46] leave us a review more people will hear about it at that point i will wish both of you and

[01:19:50] everybody with us today a wonderful weekend we'll thank you again be kind be safe and do

[01:19:57] good things we hope you enjoyed today's episode and our guests each week we try our best to dig

[01:20:09] into the topics that are impacting your business so please reach out to us and let us know how you

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