WEBVTT 1 00:00:00.160 --> 00:00:04.120 People trained and placed in open positions in public service, 2 00:00:04.200 --> 00:00:08.119 clerical work, and in jobs in the logistics industry. This 3 00:00:08.400 --> 00:00:12.080 is a new opportunity to advance your career and raise 4 00:00:12.160 --> 00:00:16.719 standards across the region. Visit nineteen thirty two Training Center 5 00:00:16.879 --> 00:00:22.000 dot org to enroll today. That's nineteen thirty two Trainingcenter 6 00:00:22.239 --> 00:00:22.960 dot org. 7 00:00:24.719 --> 00:00:28.359 Listina KCAA Lomolinda at one O six point five FM 8 00:00:28.519 --> 00:00:30.519 K two ninety three c F Brito Valley. 9 00:00:31.239 --> 00:00:34.799 The information economy has a rod. The world is teeming 10 00:00:34.880 --> 00:00:39.079 with innovation as new business models reinvent every industry industry. 11 00:00:39.159 --> 00:00:43.479 Inside Analysis is your source of information and insight about 12 00:00:43.479 --> 00:00:46.079 how to make the most of this exciting new era. 13 00:00:46.320 --> 00:00:50.560 Learn more at Inside Analysis dot cosideanalysis dot com. And 14 00:00:50.640 --> 00:00:52.880 now here's your host, through Eric. 15 00:00:52.880 --> 00:00:59.600 Kavanaugh, and keep all right, ladies and gentlemen, Hello and 16 00:00:59.640 --> 00:01:03.119 welcome back once again to the only nationally syndicated show 17 00:01:03.200 --> 00:01:07.000 all about the information economy. It's called Inside Analysis. Your 18 00:01:07.000 --> 00:01:10.599 host Eric Kavanaugh here very excited to kick off twenty 19 00:01:10.680 --> 00:01:14.760 twenty five with world renowned influencer analysts that all around 20 00:01:14.840 --> 00:01:18.280 cool guy Jim Harris. Look him up on Jim Haarris 21 00:01:18.400 --> 00:01:22.319 dot com. Just like it sounds we're at cees all 22 00:01:22.319 --> 00:01:25.640 week and we're talking to lots of really cool companies 23 00:01:25.719 --> 00:01:28.079 like Weepower and my Trust and a whole bunch of 24 00:01:28.120 --> 00:01:30.799 other folks are to get to handle on what's going 25 00:01:30.840 --> 00:01:33.879 on out there in the world of consumer technology, and Jim, 26 00:01:34.040 --> 00:01:37.439 there is no shortage of cool stuff to talk about, 27 00:01:37.799 --> 00:01:40.920 but there are some caveats, of course. This era of 28 00:01:41.000 --> 00:01:43.680 open data and sharing data, it's great stuff. We can 29 00:01:43.799 --> 00:01:45.760 learn a lot about each other what's going on, but 30 00:01:45.840 --> 00:01:49.599 some things maybe we want to keep private. And there's 31 00:01:49.640 --> 00:01:53.400 a story in the news today about a car company 32 00:01:53.480 --> 00:01:56.920 that had a breach and all kinds of data got out, 33 00:01:56.959 --> 00:02:00.599 and lots of people are very unhappy about that. Connected 34 00:02:00.640 --> 00:02:04.239 cars world, if you can tap into someone's information system, 35 00:02:04.719 --> 00:02:07.959 you can find a way to get their information, and 36 00:02:08.000 --> 00:02:11.360 that is a big problem for many companies. So privacy 37 00:02:11.400 --> 00:02:13.680 is still an issue. Security is always going to be 38 00:02:13.719 --> 00:02:16.439 an issue. Anytime you're connected to the Internet, you're connected 39 00:02:16.479 --> 00:02:18.120 to all the good guys and all the bad guys. 40 00:02:18.159 --> 00:02:20.639 The IoT world, as they call it, Internet of Things. 41 00:02:21.000 --> 00:02:24.280 Of course, there's also the IIoT, the industrial Internet of Things. 42 00:02:24.280 --> 00:02:26.919 We'll talk about that at some point, I'm sure. But 43 00:02:27.120 --> 00:02:30.599 Jim first of all great stuff is happening, but some caveats. 44 00:02:30.599 --> 00:02:33.240 What are your thoughts going into CEES twenty twenty five. 45 00:02:34.719 --> 00:02:38.360 Well, it is such an exciting show. There'll probably be 46 00:02:38.400 --> 00:02:42.199 one hundred and fifty thousand people there, Eric, There're going 47 00:02:42.280 --> 00:02:48.919 to be thousands of companies exhibiting great keynotes, all sorts 48 00:02:48.960 --> 00:02:54.080 of panel discussions. I'm shairing a panel on AI and 49 00:02:54.319 --> 00:02:59.639 predictive AI. So it is just an incredible conference. It's 50 00:02:59.680 --> 00:03:03.639 every year in January in Las Vegas, and it's the 51 00:03:03.680 --> 00:03:08.120 most influential tech show in the world. So we're going 52 00:03:08.199 --> 00:03:12.199 to see a lot of exciting things, a lot of 53 00:03:12.400 --> 00:03:19.319 really interesting discussions. And last year the number one trending 54 00:03:19.360 --> 00:03:23.719 topic was AI and it is AI again on Twitter 55 00:03:23.840 --> 00:03:27.520 in advance now called X. But this year there's a 56 00:03:27.560 --> 00:03:31.919 slight twist to it. The number one trending topic for 57 00:03:32.120 --> 00:03:38.280 CEOs in advance of the conference is agentic AI. Sure, 58 00:03:38.680 --> 00:03:42.199 so we'll talk about that in the show. So a 59 00:03:42.199 --> 00:03:44.319 little teaser agentic AI. 60 00:03:44.919 --> 00:03:47.159 Yeah, And just to explain to our audience what Jim 61 00:03:47.280 --> 00:03:51.479 is talking about. Our AI agents and these are little 62 00:03:51.680 --> 00:03:55.599 semi autonomous applications that can do all kinds of different things. 63 00:03:55.639 --> 00:03:59.319 It's not just traditional automation where you will tell a 64 00:03:59.360 --> 00:04:03.560 computer do this set of processes whenever this happens. That's 65 00:04:03.680 --> 00:04:07.680 standard automation. It's been around for decades, quite frankly. But 66 00:04:07.840 --> 00:04:12.400 AI agents are very very interesting little buggers because they 67 00:04:12.479 --> 00:04:16.720 can reconcile, they can make different decisions based on dynamic 68 00:04:16.759 --> 00:04:20.079 and fluid situations. Now, what I've heard, I'd be curious 69 00:04:20.120 --> 00:04:22.639 to know what you've heard, is that by and large, 70 00:04:23.040 --> 00:04:26.800 AI agents are designed to do one thing very well, 71 00:04:27.160 --> 00:04:31.920 whether that's grab data, analyze data, delivered data, process data, 72 00:04:32.040 --> 00:04:35.879 run some algorithm, figure something out. But some people are 73 00:04:35.920 --> 00:04:39.399 suggesting AI agents could be more versatile. And the most 74 00:04:39.439 --> 00:04:42.519 compelling story I've heard out of companies like Variant. For example, 75 00:04:42.519 --> 00:04:44.759 we were talking about a buddy, Tom Augenhaler. He works 76 00:04:44.800 --> 00:04:49.600 with Variant. Their CEO told me that they like to 77 00:04:49.680 --> 00:04:52.600 have these agents that are very purpose built and specific. 78 00:04:53.000 --> 00:04:55.120 But there are other companies now, and one of them 79 00:04:55.160 --> 00:04:57.720 I think of is Mistral, which is one of the 80 00:04:57.759 --> 00:05:01.360 new large language models. They have this mixture of experts 81 00:05:01.519 --> 00:05:04.600 process where they have an orchestrator that kind of sits 82 00:05:04.639 --> 00:05:06.839 on top of the agents. So just like a manager 83 00:05:06.879 --> 00:05:10.120 with a bunch of direct reports. That's how these multi 84 00:05:10.279 --> 00:05:14.120 layered AI agent architectures work, and that is pretty cool stuff. 85 00:05:14.160 --> 00:05:16.639 So the little orchestrator sits there and goes, okay, Bob, 86 00:05:16.720 --> 00:05:19.160 you do this, Sue, you now do that, Fred, you 87 00:05:19.240 --> 00:05:22.399 do this, And that's their job is to orchestrate, which 88 00:05:22.439 --> 00:05:27.160 is basically what Kubernetes does from Google for a system's 89 00:05:27.720 --> 00:05:31.480 use case, which is fascinating stuff. I mean, kubernetesays absolutely amazing. 90 00:05:32.319 --> 00:05:35.480 But I think this orchestration layer is going to be 91 00:05:35.560 --> 00:05:37.920 really important for AI agents. What do you think? 92 00:05:38.720 --> 00:05:43.519 Absolutely? And we often think about AI as a single entity, 93 00:05:44.120 --> 00:05:49.079 but here you're talking about AI as a team, a 94 00:05:49.240 --> 00:05:54.120 team of skilled agents AI agents. And so let's take 95 00:05:54.199 --> 00:05:59.079 for instance, accounts, receivable, accounts bayable. Every year in a 96 00:05:59.199 --> 00:06:05.079 large company, there are thousands upon thousands of conflicts that exists, 97 00:06:05.160 --> 00:06:10.839 like we invoiced you for this ten thousand dollars and 98 00:06:10.920 --> 00:06:14.959 you only paid US nine thousand, nine hundred and fifty dollars, 99 00:06:15.040 --> 00:06:19.959 and there's a difference in this. Well, you know, historically 100 00:06:20.000 --> 00:06:22.879 a person had to get involved in all this. Well, 101 00:06:23.439 --> 00:06:30.000 imagine agent to agent, account payable to account receivable, discuss 102 00:06:30.040 --> 00:06:33.519 it and say, well we actually when we received the shipment, 103 00:06:33.560 --> 00:06:38.480 there was one broken piece which was worth fifty dollars. 104 00:06:38.920 --> 00:06:46.240 So these agents go back into their ERPs, their respective systems, 105 00:06:46.279 --> 00:06:50.360 and say, oh, the shipping manifest at the shipping dock 106 00:06:50.519 --> 00:06:53.600 shows one broken thing which was worth fifty dollars. That's 107 00:06:53.600 --> 00:06:57.759 why we're paying you fifty dollars less. And it goes oh, yeah, okay, 108 00:06:57.839 --> 00:07:03.879 I'll take I'll accept that, and boom that ARAP dispute 109 00:07:04.040 --> 00:07:09.600 is closed with no human getting involved, and two humans 110 00:07:09.639 --> 00:07:12.319 on either side of this transaction to get involved will 111 00:07:12.360 --> 00:07:18.120 probably cost three hundred dollars of person time. So we've 112 00:07:18.160 --> 00:07:24.920 actually solved a fifty dollars dispute with two agents AI 113 00:07:25.079 --> 00:07:31.360 agents at basically next to nothing in cost. So how 114 00:07:31.399 --> 00:07:35.160 can we take eighty percent of the disputes in ARP 115 00:07:35.879 --> 00:07:37.240 and solve them? 116 00:07:37.800 --> 00:07:40.759 Yeah? That is an excellent use case for lots of 117 00:07:40.759 --> 00:07:44.319 different reasons. One, it's a task that these AI agents 118 00:07:44.360 --> 00:07:46.920 can do very well. It's a fairly simple thing. It's 119 00:07:46.959 --> 00:07:51.199 fairly easy to understand. There's a discrepancy between this number 120 00:07:51.240 --> 00:07:53.600 and that number. Where did it come from? And of 121 00:07:53.639 --> 00:07:57.560 course these agents and the new IT world in general 122 00:07:57.639 --> 00:08:02.399 operates on what are called declare itative programs, or declarative 123 00:08:02.439 --> 00:08:06.120 functionality where instead of the old way of doing computing 124 00:08:06.160 --> 00:08:09.480 with imperative programming, where you tell the computer line by 125 00:08:09.600 --> 00:08:13.399 line what to do, with declarative you set you declare 126 00:08:13.759 --> 00:08:16.240 an objective that you want accomplished, and you let the 127 00:08:16.279 --> 00:08:19.199 system figure it out on its own. It's really fascinating stuff. 128 00:08:19.279 --> 00:08:22.800 Kubernetes is declarative. A lot of these new models are declarative, 129 00:08:23.120 --> 00:08:25.560 and so it's a good way to just get what 130 00:08:25.600 --> 00:08:29.360 you want done without having to micromanage the computer. That's 131 00:08:29.360 --> 00:08:31.680 really what it falls down to is it's not micromanaging 132 00:08:31.720 --> 00:08:34.960 the computer. So this is great stuff because the agency 133 00:08:35.000 --> 00:08:37.519 go back and forth that oh, okay, no, we got it, 134 00:08:38.039 --> 00:08:40.480 and then they'll they'll give some report on that, and 135 00:08:40.519 --> 00:08:42.720 then a human will go once a day or once 136 00:08:42.759 --> 00:08:44.639 an hour and look at all the different things that 137 00:08:44.679 --> 00:08:47.759 have been resolved and just make sure that they're correct, 138 00:08:47.879 --> 00:08:51.000 and then throw in some curation, throw in some commentary. 139 00:08:51.480 --> 00:08:53.559 And of course they're not always going to be right, 140 00:08:53.960 --> 00:08:56.799 but people aren't always right either. So all we really 141 00:08:56.840 --> 00:08:58.840 want for these things to do is get to where 142 00:08:58.840 --> 00:09:00.960 they're good enough or better at the people at doing 143 00:09:01.039 --> 00:09:04.919 things and much more efficient and Besides, what a dreadful 144 00:09:05.000 --> 00:09:08.120 job for a human to do. I mean, who wants 145 00:09:08.159 --> 00:09:10.240 to do that all day? Nobody does. 146 00:09:12.399 --> 00:09:18.320 I remember that my accountants, for instance, feel they need 147 00:09:18.360 --> 00:09:21.279 to balance to the penny right, And you know you're 148 00:09:21.279 --> 00:09:25.039 paying your accountant to three hundred bucks an hour to 149 00:09:25.159 --> 00:09:29.519 find three pennies that's not balanced, and it drive me crazy. 150 00:09:29.840 --> 00:09:32.879 So how can we just say, Okay, well we're pretty close. 151 00:09:35.480 --> 00:09:38.759 I guess the principle is underneath it. Well, maybe there's 152 00:09:39.080 --> 00:09:43.480 two transactions that are skewed, that are three cents offen 153 00:09:43.960 --> 00:09:47.759 are really highlighting a problem. But I think what we're 154 00:09:47.960 --> 00:09:51.720 to your point, we're going to end up automating things 155 00:09:51.759 --> 00:09:55.840 that are dull, dirty and dangerous. So anything that's a 156 00:09:55.879 --> 00:10:02.279 really repetitive task will get automated by agentic AI. And 157 00:10:03.159 --> 00:10:06.519 people worry about job loss, But there are two million 158 00:10:06.679 --> 00:10:10.240 job openings right now in the US healthcare system, two 159 00:10:10.279 --> 00:10:13.960 million job openings, and it's estimated there's about three hundred 160 00:10:14.000 --> 00:10:18.559 billion dollars of waste and inefficiency in the US healthcare system. 161 00:10:18.679 --> 00:10:25.440 So agentic AI could really help improve the efficiency of 162 00:10:25.480 --> 00:10:30.000 the healthcare system and address this two million gap of 163 00:10:30.320 --> 00:10:35.480 people that we can't find to fill us healthcare jobs. 164 00:10:35.879 --> 00:10:38.759 Yeah, and we're going to go through a very tumultuous 165 00:10:38.799 --> 00:10:41.960 and disruptive period of time. But for those who have 166 00:10:42.080 --> 00:10:44.799 their sea legs under them, it should be a bit 167 00:10:44.799 --> 00:10:47.080 of a fun and challenging time. I think the key 168 00:10:47.120 --> 00:10:49.440 piece of advice I give to anyone in the working 169 00:10:49.440 --> 00:10:51.799 world who wants to keep going out there and doing 170 00:10:51.799 --> 00:10:57.120 cool things. Start using these technologies, use GENAI. Understand it's 171 00:10:57.159 --> 00:11:00.879 not perfect, but learn the ins and outs of these systems. 172 00:11:00.919 --> 00:11:04.200 And Jenny, of course, is just one facet of AI, 173 00:11:04.519 --> 00:11:07.000 and it's very interesting. It does cool stuff. There are 174 00:11:07.039 --> 00:11:10.600 many other facets of AI. Traditional or as I call it, 175 00:11:10.799 --> 00:11:13.639 old fashioned AI is still out there doing cool things 176 00:11:13.679 --> 00:11:17.320 where you build models to do predictions. As a company 177 00:11:17.320 --> 00:11:20.320 I interviewed not recently called Safebooks AI, which is doing 178 00:11:20.399 --> 00:11:22.960 really interesting stuff in the accounting space you're talking about, 179 00:11:23.320 --> 00:11:27.480 and they can help companies avoid major problems like at 180 00:11:27.519 --> 00:11:30.519 Macy's where this big chunk of money was not reported 181 00:11:30.960 --> 00:11:34.559 as an expense, causing their accountants lots of trouble and 182 00:11:34.639 --> 00:11:38.200 causing a lot of trouble across the company. That kind 183 00:11:38.200 --> 00:11:42.000 of thing will be found by AI, and I think 184 00:11:42.000 --> 00:11:44.879 we should also spin into the whole world of manufacturing 185 00:11:44.960 --> 00:11:47.519 and how automation and even AI is coming into play there. 186 00:11:47.559 --> 00:11:49.919 And you brought up the stat that I saw Alvin 187 00:11:49.960 --> 00:11:52.879 fuh mentioned the other day, which is that China is 188 00:11:52.960 --> 00:11:58.039 now dominating the automotive industry, not just the EV space, 189 00:11:58.080 --> 00:12:00.879 which they are. They're number one electric via goals. They 190 00:12:00.960 --> 00:12:05.720 are crushing it in terms of being able to export cars. 191 00:12:05.799 --> 00:12:09.279 They're number one in the world. The US stinks compared 192 00:12:09.320 --> 00:12:12.080 to them these days. It's almost unbelievable, but you have 193 00:12:12.080 --> 00:12:13.440 to look at the charts to see right. 194 00:12:14.440 --> 00:12:18.720 So for forty four years, Japan was the number one 195 00:12:19.000 --> 00:12:23.639 exporter of cars in the world, and then China began 196 00:12:23.879 --> 00:12:29.039 focusing on production capacity in evs, and in twenty twenty 197 00:12:29.039 --> 00:12:33.559 three it became the largest exporter of cars globally, just 198 00:12:33.639 --> 00:12:37.360 blowing past everyone else Japan, Mexico, Germany, South Korea, and 199 00:12:37.399 --> 00:12:41.720 the US. And part of it is the focus on 200 00:12:41.960 --> 00:12:47.559 electric vehicles called evs. Seventy six percent of all EV's 201 00:12:47.639 --> 00:12:51.120 in the world are produced in China. It has just 202 00:12:51.360 --> 00:12:57.240 doubled down on this. And so while Europe and the 203 00:12:57.360 --> 00:13:01.960 US I don't know about ev is, China is all 204 00:13:02.080 --> 00:13:05.759 in on it and This is in part leading to 205 00:13:05.840 --> 00:13:10.600 their domination globally. And what we're seeing is a discussion 206 00:13:10.679 --> 00:13:17.399 of tariffs in response. Tariffs are one of the items 207 00:13:17.440 --> 00:13:21.799 that Trump has been talking about pretty prevalently, putting one 208 00:13:21.840 --> 00:13:28.399 hundred percent tariffs on imported Chinese cars. And so wherever 209 00:13:28.480 --> 00:13:33.120 there's a domestic auto industry, you're seeing governments talk about tariffs. 210 00:13:33.679 --> 00:13:37.440 But for instance, in Australia, there's no domestic auto industries, 211 00:13:37.519 --> 00:13:41.399 so what is the benefit for them? They get inexpensive 212 00:13:41.480 --> 00:13:47.360 cars like cars, right, So there's no protectionism in any 213 00:13:47.440 --> 00:13:51.399 country that doesn't have its own auto industry. So China 214 00:13:51.679 --> 00:13:56.879 is going to end up dominating global trade with every 215 00:13:56.919 --> 00:14:03.399 country that doesn't have a domestic auto industry. Right until finally, 216 00:14:05.279 --> 00:14:10.039 and then you're seeing news like Onto announcing it's going 217 00:14:10.120 --> 00:14:13.320 to merge with Nissan and Mitsubishi. 218 00:14:13.480 --> 00:14:15.639 So I saw that that's. 219 00:14:15.600 --> 00:14:20.039 Three Japanese car companies who are being crushed by the 220 00:14:20.039 --> 00:14:26.279 growth of Chinese evs. There have been japan Japanese car 221 00:14:26.279 --> 00:14:30.840 company is very slow to shift to EV's, so they're 222 00:14:30.840 --> 00:14:34.399 getting crushed and their solution. 223 00:14:34.399 --> 00:14:39.639 To merge, right, and these days, speaking of technology and 224 00:14:39.840 --> 00:14:43.679 data and analytics and ERPs and things, of this nature. 225 00:14:43.720 --> 00:14:46.960 It is a lot easier thanks to AI and machine 226 00:14:47.039 --> 00:14:50.399 learning and automation to pull off mergers and acquisitions, and 227 00:14:50.440 --> 00:14:52.720 that therefore, I predict we will see more of these 228 00:14:52.759 --> 00:14:55.679 coming down the pike because it's much easier now to 229 00:14:55.720 --> 00:14:59.039 get an analysis, a real brass tax look at what 230 00:14:59.200 --> 00:15:01.759 our assets are are, how do they align with the 231 00:15:01.799 --> 00:15:05.240 assets of this company or that company, and you can 232 00:15:05.240 --> 00:15:08.799 get a real clear view because hitherto you were just 233 00:15:08.919 --> 00:15:12.600 kind of guessing. I mean you're guessing on the personnel side, 234 00:15:12.679 --> 00:15:15.279 on the manufacturing side, and all kinds of things, and 235 00:15:15.360 --> 00:15:17.039 hoping it's going to work out, and it could take 236 00:15:17.159 --> 00:15:21.360 years to really make that acquisition or that merger. 237 00:15:21.519 --> 00:15:21.799 Jeil. 238 00:15:22.240 --> 00:15:24.159 That's not going to be the case in the future, 239 00:15:24.519 --> 00:15:26.600 as I see many organizations are going to do what 240 00:15:26.679 --> 00:15:29.639 you just commented on. They're going to merge to stay alive. 241 00:15:30.039 --> 00:15:31.440 Like I said, we're going to go through a very 242 00:15:31.480 --> 00:15:34.320 tumultuous period here, But for the agile I think it's 243 00:15:34.360 --> 00:15:35.799 going to be a win win. What do you think 244 00:15:36.639 --> 00:15:37.399 I'd agree? 245 00:15:37.519 --> 00:15:40.399 I think I made a prediction that we're going to 246 00:15:40.440 --> 00:15:46.559 see half of the traditional car companies, legacy automakers of 247 00:15:46.759 --> 00:15:53.440 gas cars disappear over the next ten years, they're going 248 00:15:53.480 --> 00:15:55.879 to either go bankrupt, they're going to merge. As in 249 00:15:55.919 --> 00:15:59.120 the case of Honda, Nissan and Mitsubishi. There you have 250 00:15:59.320 --> 00:16:04.240 three car companies combining into one. You've lost two car companies. 251 00:16:05.279 --> 00:16:08.399 So we're going to see a shrinking of the legacy 252 00:16:08.559 --> 00:16:13.000 auto industry. And this next stat is shocking to me, Eric, 253 00:16:13.159 --> 00:16:17.679 but Tesla right now today is worth more than the 254 00:16:17.720 --> 00:16:20.200 rest of the auto industry added together. 255 00:16:21.360 --> 00:16:22.919 I mean, it's amazing. 256 00:16:22.960 --> 00:16:27.120 That blows me away. If you don't think electrification is 257 00:16:27.200 --> 00:16:30.399 going to change the ten trillion a year transportation and 258 00:16:30.519 --> 00:16:35.279 logistics market. That stats for you half the value of 259 00:16:35.320 --> 00:16:36.039 the industry. 260 00:16:36.559 --> 00:16:40.600 Yeah, that's amazing. And it goes back to innovation and 261 00:16:40.679 --> 00:16:42.320 to doing things in new ways. 262 00:16:42.440 --> 00:16:42.639 You know. 263 00:16:42.720 --> 00:16:46.279 I attended the Reuters Next conference. I guess it was 264 00:16:46.399 --> 00:16:49.879 two novembers ago. I spoke last November and two Novembers 265 00:16:49.879 --> 00:16:53.039 of I tended and a girl from Routers was interviewing 266 00:16:53.480 --> 00:16:56.480 the Alex P. Morgan I think his name is. He's 267 00:16:56.519 --> 00:16:58.759 the CEO of JP Morgan or one of these companies. 268 00:16:58.759 --> 00:17:01.360 I think that was Gorman was his name. Very very 269 00:17:01.360 --> 00:17:05.079 smart guy, and she's hounding him, basically trying to get 270 00:17:05.160 --> 00:17:07.039 him to admit that they made a bad bet on 271 00:17:07.079 --> 00:17:09.319 Twitter because they threw some money in to help him 272 00:17:09.319 --> 00:17:12.400 buy Twitter, and he just he shook his head for 273 00:17:12.440 --> 00:17:15.720 a couple of seconds and then he said, we're not stupid. 274 00:17:16.960 --> 00:17:19.279 I couldn't believe it. I was like, Wow, what an answer. 275 00:17:19.319 --> 00:17:22.920 He goes, anyone who doubts Elon Musk should go visit 276 00:17:22.960 --> 00:17:27.279 a Tesla factory and then you'll understand because these folks 277 00:17:27.359 --> 00:17:30.039 thought through the entire end to end process and they 278 00:17:30.079 --> 00:17:33.480 re envisioned everything and that's why they're able to achieve 279 00:17:33.519 --> 00:17:37.680 these amazing numbers. And that's what innovation takes. It takes 280 00:17:37.759 --> 00:17:42.720 unlearning your biases and then relearning something new, and that 281 00:17:42.759 --> 00:17:44.440 I think is going to be a hallmark of success 282 00:17:44.480 --> 00:17:48.039 in twenty twenty five. Final thoughts from you, Well, I love. 283 00:17:47.880 --> 00:17:52.319 That that Tesla is vertically integrated. In other words, it 284 00:17:52.480 --> 00:17:56.920 makes the engines, it makes the chassis, it makes the batteries, 285 00:17:57.000 --> 00:18:00.000 it makes the wheels, it makes the seats, and there 286 00:18:00.119 --> 00:18:03.720 forward to your point, it can control the production process 287 00:18:03.799 --> 00:18:07.240 from end to end and drive efficiencies out of it. 288 00:18:07.720 --> 00:18:11.720 Legacy Auto, No, they have thousands of suppliers and so 289 00:18:12.279 --> 00:18:15.559 to actually change is very hard since there isn't that 290 00:18:15.720 --> 00:18:18.960 vertical integration. So I agree with you absolutely. We're going 291 00:18:19.039 --> 00:18:23.839 to see big changes in the auto industry, in basically 292 00:18:23.880 --> 00:18:26.359 every industry with AI and automation. 293 00:18:27.039 --> 00:18:30.720 Yeah, and retail, we'll see a lot of cool retail stuff. Healthcare, 294 00:18:30.759 --> 00:18:35.079 of course, financial services. The pinch is everywhere. Folks will 295 00:18:35.079 --> 00:18:36.319 send me Neil if you want to be on this 296 00:18:36.359 --> 00:18:39.359 show Inside Analysis dot com. That comes right to me. 297 00:18:39.599 --> 00:18:43.319 We'll be right back. You're listening to Inside Analysis. 298 00:18:49.240 --> 00:18:54.160 Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh. 299 00:18:56.799 --> 00:18:59.279 All right, folks, back here on Inside Analysis, and I'm 300 00:18:59.319 --> 00:19:02.640 so excited today is part of our cees preview to 301 00:19:02.720 --> 00:19:05.839 get a newbie, a guy who is not new to media, 302 00:19:05.880 --> 00:19:08.640 he's not new to technology. He's not new to sports 303 00:19:08.640 --> 00:19:12.240 and fitness or interviews or tech or understanding what's going 304 00:19:12.279 --> 00:19:14.359 on in the real world out there. None other than 305 00:19:14.480 --> 00:19:19.200 John Meyer of the podcast bearing his name, and we're 306 00:19:19.200 --> 00:19:22.799 pretty excited. He's a fellow Pennsylvanian and John here in 307 00:19:22.920 --> 00:19:26.599 Pittsburgh they call folks yinzers or like Yin's, Hey, Yin's 308 00:19:26.680 --> 00:19:28.960 the guys, And I was like, yins what on earth 309 00:19:29.000 --> 00:19:30.720 is yeans. What are they talking about. I have a 310 00:19:30.759 --> 00:19:33.200 theory on that which I throw at Pennsylvanians and they 311 00:19:33.319 --> 00:19:38.160 like it because if you're from Pennsylvania, you're a pennsylvani yin, right, 312 00:19:38.240 --> 00:19:41.680 So multiple Pennsylvanians yins. It's at the end of the word. 313 00:19:42.119 --> 00:19:44.519 That's my theory. I don't know if it's true or not, 314 00:19:44.680 --> 00:19:46.200 but hey, you're on the other side of the state. 315 00:19:46.200 --> 00:19:50.000 I don't hold that against you. Eagles fan. Tough team Stullers. 316 00:19:50.160 --> 00:19:52.240 You know we're usually a tough team at a bad 317 00:19:52.319 --> 00:19:55.079 few games lately. But tell me a bit about your 318 00:19:55.200 --> 00:19:59.519 passion for sports and fitness and where that dovetails with technology, 319 00:19:59.640 --> 00:20:01.440 like where and all that kind of fun stuff. 320 00:20:02.279 --> 00:20:04.119 Eric, thank you for inviting me on to the show. 321 00:20:04.279 --> 00:20:06.880 As you mentioned. John Meyer from Meyer Media also to 322 00:20:07.000 --> 00:20:09.880 John Meyer Podcast. I've got a theory now that you 323 00:20:10.000 --> 00:20:13.160 mentioned for your yins. It could be going along with 324 00:20:13.480 --> 00:20:17.599 yingling beer and brewery, just saying I'm sure you're familiar 325 00:20:17.640 --> 00:20:17.960 with it. 326 00:20:18.440 --> 00:20:22.119 Someone else said that too, so yins ying's yin's yangs ye. 327 00:20:22.200 --> 00:20:24.400 I could see after a few yings agains, you know, 328 00:20:25.160 --> 00:20:26.759 it starts to slur a little bit. 329 00:20:26.839 --> 00:20:29.440 That's Okay, after a few yinglings, it will slur just 330 00:20:29.480 --> 00:20:32.640 a little bit. So, Eric, my huge passion is around 331 00:20:32.640 --> 00:20:35.480 media content and creating. As you mentioned, I am a 332 00:20:35.599 --> 00:20:38.920 nube to ces, the first one we've been accepted as 333 00:20:39.039 --> 00:20:42.680 media joining at totally overwhelming right now. I mean, I've 334 00:20:42.680 --> 00:20:45.519 been to Vegas so many times, but coming to this event, 335 00:20:45.720 --> 00:20:48.960 I just realized this is the like the Super Bowl 336 00:20:49.000 --> 00:20:53.599 event of technology for consumers. You mentioned fitness and everything. Obviously, 337 00:20:53.720 --> 00:20:57.160 huge Eagles fan, die hard, don't hold it against you. 338 00:20:57.200 --> 00:20:59.200 On the other side of state, I'm actually hoping for 339 00:20:59.319 --> 00:21:01.480 maybe a PA would be really cool to watch. 340 00:21:01.519 --> 00:21:02.359 Wouldn't that be great? 341 00:21:02.720 --> 00:21:04.799 That would be the first time in history. And yeah, 342 00:21:04.880 --> 00:21:07.880 obviously I'm rooting for that, you know, and I'll root 343 00:21:07.920 --> 00:21:10.680 for the Eagles to win, but you know, hey, we'll 344 00:21:10.680 --> 00:21:13.119 have a good time and they're going down Broad Street. 345 00:21:13.119 --> 00:21:14.960 We'll definitely see some of the Steelers fans. 346 00:21:15.480 --> 00:21:15.599 Uh. 347 00:21:15.839 --> 00:21:19.480 Talking about fitness, been in fitness all my life, started 348 00:21:19.519 --> 00:21:21.920 out at a very young age, and I just it's 349 00:21:21.920 --> 00:21:24.720 a natural thing to do. Some of the technology that's 350 00:21:24.759 --> 00:21:26.519 out there to track it. I saw some of the 351 00:21:26.559 --> 00:21:29.920 things that are going to be happening. I sees the wearables, 352 00:21:30.000 --> 00:21:34.359 not only the headsets, but the gear, the recovery stuff, 353 00:21:34.440 --> 00:21:37.759 the massade, everything around them. Just looking at how to 354 00:21:37.880 --> 00:21:40.920 improve my game but also recover from injuries or from 355 00:21:40.960 --> 00:21:42.079 workouts much faster. 356 00:21:42.599 --> 00:21:44.720 Yeah, that's good stuff. And you know, I talk with 357 00:21:44.759 --> 00:21:47.839 someone else just today about the wearables. And here's what 358 00:21:47.960 --> 00:21:51.279 excites me about that stuff is the data that you 359 00:21:51.319 --> 00:21:56.759 can capture for individual health, for population health, to understand 360 00:21:57.039 --> 00:21:59.599 how is the workout affecting you, what is good for you, 361 00:21:59.680 --> 00:22:01.839 to eat, what's not good for you. I mean, I 362 00:22:01.880 --> 00:22:06.839 think hitherto most healthcare has been boiled down to very 363 00:22:06.880 --> 00:22:10.880 generic basics. I mean they take your vitals, your heart rate, 364 00:22:11.359 --> 00:22:15.960 your temperature, stuff like that. Okay, that's useful. But when 365 00:22:16.000 --> 00:22:20.640 you can start absorbing massive amounts of data about how 366 00:22:20.680 --> 00:22:23.519 your body acts, when you sleep, when you run, when 367 00:22:23.599 --> 00:22:26.160 you walk, when you're sitting, all these different things, I 368 00:22:26.200 --> 00:22:28.759 think we're at the beginning of a fascinating age in 369 00:22:28.799 --> 00:22:32.680 which we can get a much better understanding of ourselves, 370 00:22:32.759 --> 00:22:35.839 how we sleep, and how to improve our lives every day, 371 00:22:35.839 --> 00:22:39.000 how to improve our health. What do they say, if 372 00:22:39.039 --> 00:22:41.000 you don't have your health, you don't have anything, right, 373 00:22:41.079 --> 00:22:43.039 So I think that in sports, of course you have 374 00:22:43.119 --> 00:22:46.240 to be very fit, and so in that world, it's 375 00:22:46.279 --> 00:22:50.640 a whole other level of intensity and practice and work, 376 00:22:50.720 --> 00:22:52.799 and you want to know what's working with it's not working. 377 00:22:53.119 --> 00:22:55.039 I think we're at the beginning of a golden age 378 00:22:55.119 --> 00:22:55.640 because of that. 379 00:22:55.680 --> 00:22:58.559 What do you think, Eric, you actually just touched on 380 00:22:58.559 --> 00:23:01.119 a bunch of things, because that actually crosses over into 381 00:23:01.200 --> 00:23:05.240 healthcare if you think about it. You talked about, you know, 382 00:23:05.279 --> 00:23:07.160 going into a doctor and they just take care of 383 00:23:07.319 --> 00:23:10.359 simple vitals your heart rate to your blood pressure. But 384 00:23:10.400 --> 00:23:12.119 how would you love to walk in there with all 385 00:23:12.119 --> 00:23:14.960 the data and say, hey, listen, here's what I got 386 00:23:15.000 --> 00:23:17.839 from my fitness here's what I got from allmine, and they're. 387 00:23:17.599 --> 00:23:19.960 Like, wow, I wish we had this. 388 00:23:20.319 --> 00:23:23.920 Now you can present him problems are easily solved. You 389 00:23:23.960 --> 00:23:27.559 can avoid injury, you can avoid long term illness. There's 390 00:23:27.559 --> 00:23:29.680 so many things that you can understand as a person 391 00:23:29.720 --> 00:23:32.519 to do much better. So you actually avoid the doctor. 392 00:23:32.640 --> 00:23:36.079 Right, that's right. An apple a day keeps the doctor away. Baby. 393 00:23:36.119 --> 00:23:38.799 I'm all for that. And you know I remember, I'm 394 00:23:38.880 --> 00:23:41.640 origually from Chicago. Saw I was a huge Michael Jordan 395 00:23:41.720 --> 00:23:44.160 fan and the guy who's just a machine I mean, 396 00:23:44.240 --> 00:23:48.319 honest to goodness, he's just easily taught players of all time. 397 00:23:48.720 --> 00:23:51.559 And he would stretch before every game. I think it 398 00:23:51.640 --> 00:23:53.720 was like an hour or so. He would take the 399 00:23:53.799 --> 00:23:57.359 time to stretch. And because of that and maybe because 400 00:23:57.359 --> 00:24:00.519 of his physique, he was rarely injured. You didn't see 401 00:24:00.519 --> 00:24:02.240