WEBVTT 1 00:00:00.080 --> 00:00:03.439 Six point five FMK two ninety three cf Brino Valley. 2 00:00:04.200 --> 00:00:07.759 The information economy has a ride. The world is teeming 3 00:00:07.799 --> 00:00:12.039 with innovation as new business models reinvent every industry industry. 4 00:00:12.119 --> 00:00:16.440 Inside Analysis is your source of information and insights about 5 00:00:16.440 --> 00:00:19.079 how to make the most of this exciting new eraic 6 00:00:19.239 --> 00:00:23.519 learn more at Inside Analysis dot Coastsideanalysis dot com. And 7 00:00:23.559 --> 00:00:26.359 now here's your host, through Eric Kavanaugh. 8 00:00:29.480 --> 00:00:29.600 FI. 9 00:00:30.960 --> 00:00:33.560 All right, ladies and gentlemen, Hello and welcome back once 10 00:00:33.600 --> 00:00:36.799 again to the only nationally syndicated show all about the 11 00:00:36.880 --> 00:00:41.039 information economy. It's called Inside Analysis. Your host Eric Kavanaugh 12 00:00:41.079 --> 00:00:44.719 here very excited to kick off twenty twenty five with 13 00:00:44.880 --> 00:00:49.679 world renowned influencer analysts that all around cool guy Jim Harris. 14 00:00:49.840 --> 00:00:52.200 Look him up on Jim Harris dot com. Just like 15 00:00:52.240 --> 00:00:56.679 it sounds, we're at cees all week, and we're talking 16 00:00:56.719 --> 00:00:59.719 to lots of really cool companies like Weepower and my 17 00:01:00.359 --> 00:01:02.320 and a whole bunch of other folks and already get 18 00:01:02.359 --> 00:01:04.560 a handle on what's going on out there in the 19 00:01:04.599 --> 00:01:08.400 world of consumer technology and Jim, there is no shortage 20 00:01:08.640 --> 00:01:12.280 of cool stuff to talk about, but there are some caveats, 21 00:01:12.280 --> 00:01:15.439 of course. This era of open data and sharing data. 22 00:01:15.480 --> 00:01:17.640 It's great stuff. We can learn a lot about each 23 00:01:17.680 --> 00:01:19.959 other what's going on. But some things maybe we want 24 00:01:20.040 --> 00:01:23.640 to keep private. And there's a story in the news 25 00:01:23.640 --> 00:01:27.760 today about a car company that had a breach and 26 00:01:28.120 --> 00:01:30.799 all kinds of data got out, and lots of people 27 00:01:30.799 --> 00:01:34.319 are very unhappy about that. In the connected cars world, 28 00:01:34.359 --> 00:01:38.000 if you can tap into someone's information system, you can 29 00:01:38.040 --> 00:01:41.280 find a way to get their information, and that is 30 00:01:41.319 --> 00:01:44.280 a big, a big problem for many companies. So privacy 31 00:01:44.319 --> 00:01:46.599 is still an issue. Security is always going to be 32 00:01:46.640 --> 00:01:49.400 an issue. Anytime you're connected to the Internet, you're connected 33 00:01:49.400 --> 00:01:51.079 to all the good guys and all the bad guys. 34 00:01:51.079 --> 00:01:53.599 The IoT world, as they call it, Internet of Things. 35 00:01:53.959 --> 00:01:57.200 Of course, there's also the IIoT, the industrial Internet of Things. 36 00:01:57.239 --> 00:01:59.280 We'll talk about that at some point, I'm. 37 00:01:59.120 --> 00:02:00.400 Sure, Jim. 38 00:02:00.519 --> 00:02:03.519 First of all, great stuff is happening, but some caveats. 39 00:02:03.560 --> 00:02:06.200 What are your thoughts going into CEES twenty twenty five. 40 00:02:07.640 --> 00:02:11.319 Well, it is such an exciting show. There'll probably be 41 00:02:11.319 --> 00:02:15.159 one hundred and fifty thousand people there. Eric, They're going 42 00:02:15.240 --> 00:02:21.840 to be thousands of companies exhibiting great keynotes, all sorts 43 00:02:21.879 --> 00:02:27.039 of panel discussions. I'm shairing a panel on AI and 44 00:02:27.280 --> 00:02:32.599 Predictive AI. So it is just an incredible conference. It's 45 00:02:32.680 --> 00:02:36.599 every year in January in Las Vegas, and it's the 46 00:02:36.639 --> 00:02:41.080 most influential tech show in the world. So we're going 47 00:02:41.120 --> 00:02:45.159 to see a lot of exciting things, a lot of 48 00:02:45.360 --> 00:02:52.240 really interesting discussions. And last year the number one trending 49 00:02:52.319 --> 00:02:56.680 topic was AI and it is AI again on Twitter 50 00:02:56.759 --> 00:03:00.439 in advance now called X. But this year there's a 51 00:03:00.520 --> 00:03:04.879 slight twist to it. The number one trending topic for 52 00:03:05.039 --> 00:03:11.199 CEOs in advance of the conference is agentic AI. Sure, 53 00:03:11.599 --> 00:03:15.120 so we'll talk about that in the show. So a 54 00:03:15.159 --> 00:03:17.319 little teaser agentic AI. 55 00:03:17.879 --> 00:03:20.120 Yeah, And just to explain to our audience what Jim 56 00:03:20.199 --> 00:03:24.400 is talking about. Our AI agents and these are little 57 00:03:24.639 --> 00:03:28.520 semi autonomous applications that can do all kinds of different things. 58 00:03:28.560 --> 00:03:33.479 It's not just traditional automation where you will telecomputer do 59 00:03:33.639 --> 00:03:37.479 this set of processes whenever this happens. That's standard automation. 60 00:03:37.520 --> 00:03:41.840 It's been around for decades quite frankly, but AI agents 61 00:03:41.879 --> 00:03:46.560 are very very interesting little buggers because they can reconcile, 62 00:03:46.639 --> 00:03:51.039 they can make different decisions based on dynamic and fluid situations. 63 00:03:51.520 --> 00:03:53.400 Now what I've heard, i'd be curious to know. What 64 00:03:53.439 --> 00:03:57.080 you've heard is that by and large, AI agents are 65 00:03:57.120 --> 00:04:01.560 designed to do one thing very well, whether that's grab data, 66 00:04:01.599 --> 00:04:06.439 analyze data, delivered data, process data, run some algorithm, figure 67 00:04:06.520 --> 00:04:10.400 something out. But some people are suggesting AI agents could 68 00:04:10.400 --> 00:04:13.560 be more versatile. And the most compelling story I've heard 69 00:04:13.560 --> 00:04:15.919 out of companies like Variant. For example, we were talking 70 00:04:15.919 --> 00:04:19.399 about a buddy, Tom Augenhaler. He works with Variant. Their 71 00:04:19.560 --> 00:04:23.480 CEO told me that they like to have these agents 72 00:04:23.480 --> 00:04:25.560 that are very purpose built and specific. 73 00:04:25.959 --> 00:04:26.800 But there are other. 74 00:04:26.680 --> 00:04:29.560 Companies now and one of them I think of is Mistral, 75 00:04:29.959 --> 00:04:32.639 which is one of the new large language models. They 76 00:04:32.639 --> 00:04:36.079 have this mixture of experts process where they have an 77 00:04:36.199 --> 00:04:38.680 orchestrator that kind of sits on top of the agents. 78 00:04:38.720 --> 00:04:41.199 So just like a manager with a bunch of direct reports, 79 00:04:41.600 --> 00:04:45.680 that's how these multi layered AI agent architectures work, and 80 00:04:45.759 --> 00:04:48.360 that is pretty cool stuff. So the little orchestrator sits 81 00:04:48.360 --> 00:04:50.959 there and goes, okay, Bob, you do this, Sue, you 82 00:04:51.079 --> 00:04:53.560 now do that, Fred, you do this, And that's their 83 00:04:53.639 --> 00:04:57.600 job is to orchestrate, which is basically what Kubernetes does. 84 00:04:58.040 --> 00:05:02.680 From Google for a system use case, which is fascinating stuff. 85 00:05:02.680 --> 00:05:06.360 I mean, kubernetesay is absolutely amazing. But I think this, 86 00:05:06.360 --> 00:05:09.399 this orchestration layer is going to be really important for 87 00:05:09.519 --> 00:05:10.240 AI agents. 88 00:05:10.279 --> 00:05:10.879 What do you think? 89 00:05:11.639 --> 00:05:16.439 Absolutely, And we often think about AI as a single entity, 90 00:05:17.040 --> 00:05:22.040 but here you're talking about AI as a team, a 91 00:05:22.160 --> 00:05:27.079 team of skilled agents AI agents. And so let's take 92 00:05:27.120 --> 00:05:32.040 for instance, accounts, receivable, accounts payable. Every year in a 93 00:05:32.160 --> 00:05:38.040 large company, there are thousands upon thousands of conflicts that exists, 94 00:05:38.079 --> 00:05:43.800 like we invoiced you for this ten thousand dollars and 95 00:05:43.839 --> 00:05:47.959 you only paid US nine thousand, nine hundred and fifty dollars. 96 00:05:48.000 --> 00:05:52.920 And there's a difference in this. Well, you know, historically 97 00:05:52.959 --> 00:05:55.839 a person had to get involved in all this. Well 98 00:05:56.360 --> 00:06:02.920 imagine agent to agent, account payable to account receivable, discuss 99 00:06:03.000 --> 00:06:05.959 it and say, well, we actually when we received the 100 00:06:06.000 --> 00:06:09.839 shipment there was one broken piece which was worth fifty dollars. 101 00:06:11.879 --> 00:06:18.639 So these agents go back into their ERPs, their respective 102 00:06:18.720 --> 00:06:23.000 systems and say, oh, the shipping manifest at the shipping 103 00:06:23.079 --> 00:06:26.319 dock shows one broken thing which was worth fifty dollars. 104 00:06:26.360 --> 00:06:29.000 That's why we're paying you fifty dollars less. And it 105 00:06:29.079 --> 00:06:33.959 goes oh, yeah, okay, I'll accept that and boom that 106 00:06:35.360 --> 00:06:41.160 arap dispute is closed with no human getting involved. And 107 00:06:41.240 --> 00:06:44.399 the two humans on either side of this transaction to 108 00:06:44.439 --> 00:06:50.079 get involved would probably cost three hundred dollars of person time. 109 00:06:50.399 --> 00:06:55.920 So we've actually solved a fifty dollars dispute with two 110 00:06:56.639 --> 00:07:02.519 agents AI agents at basically next to nothing in cost. 111 00:07:03.279 --> 00:07:07.120 So how can we take eighty percent of the disputes 112 00:07:07.199 --> 00:07:10.399 in ARP and solve them? 113 00:07:10.720 --> 00:07:10.959 Yeah? 114 00:07:11.000 --> 00:07:15.000 That is an excellent use case for lots of different reasons. One, 115 00:07:15.439 --> 00:07:18.399 it's a task that these AI agents can do very well. 116 00:07:18.439 --> 00:07:22.000 It's a fairly simple thing. It's fairly easy to understand. 117 00:07:22.279 --> 00:07:25.199 There's a discrepancy between this number and that number. Where 118 00:07:25.240 --> 00:07:28.160 did it come from? And of course, these agents and 119 00:07:28.240 --> 00:07:31.600 the new IT world in general operates on what are 120 00:07:31.680 --> 00:07:37.519 called declarative programs or declarative functionality, where instead of the 121 00:07:37.600 --> 00:07:40.959 old way of doing computing with imperative programming, where you 122 00:07:41.000 --> 00:07:44.120 tell the computer line by line what to do, with 123 00:07:44.240 --> 00:07:48.560 declarative you set you declare an objective that you want accomplished, 124 00:07:48.600 --> 00:07:51.000 and you let the system figure it out on its own. 125 00:07:51.000 --> 00:07:54.120 It's really fascinating stuff. Kubernetes is declarative. A lot of 126 00:07:54.120 --> 00:07:57.079 these new models are declarative, and so it's a good 127 00:07:57.079 --> 00:08:00.399 way to just get what you want done without having 128 00:08:00.439 --> 00:08:02.920 to micro manage the computer. That's really what it boils 129 00:08:02.959 --> 00:08:05.959 down to is it's not micromanaging the computer. So this 130 00:08:06.000 --> 00:08:08.519 is great stuff because the agency go back and forth 131 00:08:08.959 --> 00:08:11.600 that oh, okay, no, we got it, and then they'll 132 00:08:11.639 --> 00:08:14.000 sty'll give some report on that, and then a human 133 00:08:14.079 --> 00:08:16.199 will go once a day or once an hour and 134 00:08:16.240 --> 00:08:18.439 look at all the different things that have been resolved 135 00:08:18.720 --> 00:08:21.759 and just make sure that they're correct, and then throw 136 00:08:21.800 --> 00:08:25.120 in some curation, throw in some commentary. And of course 137 00:08:25.120 --> 00:08:27.720 they're not always going to be right, but people aren't 138 00:08:27.720 --> 00:08:30.319 always right either, So all we really want for these 139 00:08:30.360 --> 00:08:32.399 things to do is get to where they're good enough 140 00:08:32.480 --> 00:08:35.720 or better than people at doing things and much more efficient. 141 00:08:35.759 --> 00:08:39.960 And besides, what a dreadful job for a human to do, right, 142 00:08:40.200 --> 00:08:43.159 I mean, who wants to do that all day? Nobody does. 143 00:08:45.320 --> 00:08:51.279 I remember that. My accountants, for instance, feel they need 144 00:08:51.320 --> 00:08:54.200 to balance to the penny right, And you know you're 145 00:08:54.240 --> 00:08:57.960 paying your accountant to three hundred bucks an hour to 146 00:08:58.120 --> 00:09:02.440 find three pennies that's not balanced, and it drives me crazy. 147 00:09:02.799 --> 00:09:05.799 So how can we just say Okay, well we're pretty close. 148 00:09:08.440 --> 00:09:11.720 I guess the principle is underneath it. Well, maybe there's 149 00:09:12.039 --> 00:09:16.399 two transactions that are skewed that are three cents often 150 00:09:16.919 --> 00:09:20.720 are really highlighting a problem. But I think what we're 151 00:09:20.919 --> 00:09:24.639 to your point, we're going to end up automating things 152 00:09:24.679 --> 00:09:28.799 that are dull, dirty, and dangerous. So anything that's a 153 00:09:28.840 --> 00:09:35.240 really repetitive task will get automated by agentic AI. And 154 00:09:36.120 --> 00:09:39.480 people worry about job loss, but there are two million 155 00:09:39.639 --> 00:09:43.200 job openings right now in the US healthcare system. Two 156 00:09:43.240 --> 00:09:46.840 million job openings, and it's estimated there's about three hundred 157 00:09:46.919 --> 00:09:51.440 billion dollars of waste and inefficiency in the US healthcare system. 158 00:09:51.600 --> 00:09:58.240 So agentic AI could really help improve the efficiency of 159 00:09:58.440 --> 00:10:03.039 the healthcare system and us this two million gap of 160 00:10:03.240 --> 00:10:08.240 people that we can't find to fill US healthcare jobs. 161 00:10:08.799 --> 00:10:11.679 Yeah, and we're going to go through a very tumultuous 162 00:10:11.720 --> 00:10:14.919 and disruptive period of time, but for those who have 163 00:10:15.039 --> 00:10:17.720 their sea legs under them, it should be a bit 164 00:10:17.759 --> 00:10:20.039 of a fun and challenging time. I think the key 165 00:10:20.039 --> 00:10:22.360 piece of advice I give to anyone in the working 166 00:10:22.399 --> 00:10:24.720 world who wants to keep going out there and doing 167 00:10:24.759 --> 00:10:30.080 cool things start using these technologies use JENAI. Understand it's 168 00:10:30.080 --> 00:10:33.799 not perfect, but learn the ins and outs of these systems. 169 00:10:33.879 --> 00:10:37.159 And Jenna, of course is just one facet of AI, 170 00:10:37.480 --> 00:10:39.960 and it's very interesting. It does cool stuff. There are 171 00:10:40.000 --> 00:10:43.519 many other facets of AI. Traditional or as I call it, 172 00:10:43.720 --> 00:10:46.600 old fashioned AI is still out there doing cool things 173 00:10:46.639 --> 00:10:50.240 where you build models to do predictions. As a company 174 00:10:50.279 --> 00:10:53.320 I interviewed not recently called Safebooks AI, which is doing 175 00:10:53.360 --> 00:10:55.879 really interesting stuff in the accounting space you're talking about, 176 00:10:56.279 --> 00:11:00.399 and they can help companies avoid major problems like at 177 00:11:00.440 --> 00:11:03.480 Macy's where this big chunk of money was not reported 178 00:11:03.919 --> 00:11:07.480 as an expense, causing their accountants lots of trouble and 179 00:11:07.559 --> 00:11:11.080 causing a lot of trouble across the company. That kind 180 00:11:11.159 --> 00:11:14.960 of thing will be found by AI. And I think 181 00:11:14.960 --> 00:11:17.879 we should also spin into the whole world of manufacturing 182 00:11:17.919 --> 00:11:20.480 and how automation and even AI is coming into play there. 183 00:11:20.480 --> 00:11:22.840 And you brought up the stat that I saw Alvin 184 00:11:22.919 --> 00:11:25.840 Fu mentioned the other day, which is that China is 185 00:11:25.919 --> 00:11:30.960 now dominating the automotive industry, not just the EV space, 186 00:11:31.000 --> 00:11:31.600 which they are. 187 00:11:31.679 --> 00:11:33.279 They're number one electric vehicles. 188 00:11:33.639 --> 00:11:37.279 They are crushing it in terms of being able to 189 00:11:37.320 --> 00:11:41.080 export cars they're number one in the world. The US 190 00:11:41.279 --> 00:11:44.840 stinks compared to them these days. It's almost unbelievable, but 191 00:11:44.840 --> 00:11:46.080 you have to look at the charts to see it. 192 00:11:46.120 --> 00:11:46.360 Right. 193 00:11:47.360 --> 00:11:51.679 So, for forty four years, Japan was the number one 194 00:11:51.960 --> 00:11:56.559 exporter of cars in the world, and then China began 195 00:11:56.840 --> 00:12:01.960 focusing on production capacity in ev and in twenty twenty 196 00:12:02.000 --> 00:12:06.519 three it became the largest exporter of cars globally, just 197 00:12:06.559 --> 00:12:10.279 blowing past everyone else Japan, Mexico, Germany, South Korea, and 198 00:12:10.320 --> 00:12:14.679 the US. And part of it is the focus on 199 00:12:14.919 --> 00:12:20.519 electric vehicles called evs. Seventy six percent of all EV's 200 00:12:20.600 --> 00:12:24.039 in the world are produced in China. It has just 201 00:12:24.279 --> 00:12:30.159 doubled down on this, and so while Europe and the 202 00:12:30.320 --> 00:12:34.919 US are I don't know about evs, China is all 203 00:12:35.000 --> 00:12:38.720 in on it, and this is in part leading to 204 00:12:38.799 --> 00:12:43.559 their domination globally. And what we're seeing is a discussion 205 00:12:43.600 --> 00:12:50.320 of tariffs in response. Tariffs are one of the items 206 00:12:50.360 --> 00:12:54.759 that Trump has been talking about pretty prevalently, putting one 207 00:12:54.799 --> 00:13:00.120 hundred percent tariffs on imported Chinese cars and so so 208 00:13:00.840 --> 00:13:05.159 wherever there's a domestic auto industry, you're seeing governments talk 209 00:13:05.200 --> 00:13:09.440 about tariffs. But for instance, in Australia, there's no domestic 210 00:13:09.480 --> 00:13:13.000 auto industries, so what what is the benefit for them? 211 00:13:13.080 --> 00:13:18.360 They get inexpensive cars like cars, right, So there's no 212 00:13:18.480 --> 00:13:23.080 protectionism in any country that doesn't have its own auto industry. 213 00:13:23.559 --> 00:13:28.799 So China is going to end up dominating global trade 214 00:13:29.159 --> 00:13:34.279 with every country that doesn't have a domestic auto industry. 215 00:13:34.360 --> 00:13:41.679 Right until finally, and then you're seeing news like Onto 216 00:13:41.879 --> 00:13:46.320 announcing it's going to merge with Nissan and Mitsubishi. 217 00:13:46.440 --> 00:13:48.639 So I saw that that's. 218 00:13:48.519 --> 00:13:52.960 Three Japanese car companies who are being crushed by the 219 00:13:53.000 --> 00:13:59.200 growth of Chinese evs. There have been japan Japanese car 220 00:13:59.240 --> 00:14:03.759 companies very very slow to shift to evs, so they're 221 00:14:03.799 --> 00:14:08.200 getting crushed and is their solution to merge? 222 00:14:08.559 --> 00:14:13.360 Right And these days, speaking of technology and data and 223 00:14:13.440 --> 00:14:16.879 analytics and ERPs and things of this nature, it is 224 00:14:16.919 --> 00:14:20.360 a lot easier thanks to AI and machine learning and 225 00:14:20.399 --> 00:14:24.080 automation to pull off mergers and acquisitions, and therefore I 226 00:14:24.159 --> 00:14:26.320 predict we will see more of these coming down the 227 00:14:26.360 --> 00:14:29.639 pike because it's much easier now to get an analysis, 228 00:14:29.840 --> 00:14:33.120 a real brass tax look at what our assets are, 229 00:14:33.519 --> 00:14:36.000 how do they align with the assets of this company 230 00:14:36.399 --> 00:14:39.120 or that company? And you can get a real clear 231 00:14:39.200 --> 00:14:43.240 view because hitherto you were just kind of guessing. I 232 00:14:43.279 --> 00:14:46.919 mean you're guessing on the personnel side, on the manufacturing side, 233 00:14:46.919 --> 00:14:48.960 and all kinds of things, and hoping it's going to 234 00:14:49.039 --> 00:14:52.600 work out, and it could take years to really make 235 00:14:52.720 --> 00:14:55.799 that acquisition or that merger. Gel that's not going to 236 00:14:55.840 --> 00:14:58.440 be the case in the future, as I see many 237 00:14:58.559 --> 00:15:00.519 organizations are going to do. When you just amtan on, 238 00:15:00.759 --> 00:15:03.360 they're going to merge to stay alive. Like I said, 239 00:15:03.360 --> 00:15:05.799 we're going to go through a very tumultuous period here, 240 00:15:06.039 --> 00:15:07.559 But for the agile I think it's going to be 241 00:15:07.559 --> 00:15:08.080 a win win. 242 00:15:08.159 --> 00:15:10.320 What do you think I'd agree? 243 00:15:10.440 --> 00:15:13.360 I think I made a prediction that we're going to 244 00:15:13.399 --> 00:15:19.519 see half of the traditional car companies, legacy automakers of 245 00:15:19.679 --> 00:15:26.360 gas cars disappear over the next ten years. They're going 246 00:15:26.440 --> 00:15:28.799 to either go bankrupt they're going to merge. As in 247 00:15:28.840 --> 00:15:32.080 the case of Honda, Nissan and Mitsubishi. There you have 248 00:15:32.279 --> 00:15:37.159 three car companies combining into one. You've lost two car companies. 249 00:15:38.200 --> 00:15:41.360 So we're going to see a shrinking of the legacy 250 00:15:41.519 --> 00:15:45.919 auto industry. And this next stat is shocking to me, Eric, 251 00:15:46.120 --> 00:15:50.639 but Tesla right now today is worth more than the 252 00:15:50.679 --> 00:15:53.159 rest of the auto industry added together. 253 00:15:54.320 --> 00:15:57.279 I mean, it's amazing. That blows me away. 254 00:15:57.360 --> 00:16:01.000 If you don't think electrification is going to change the 255 00:16:01.120 --> 00:16:05.919 ten trillion a year transportation and logistics market that stats 256 00:16:06.000 --> 00:16:08.960 for you has the value of the industry. 257 00:16:09.480 --> 00:16:10.879 Yeah, that's amazing. 258 00:16:10.919 --> 00:16:14.480 And it goes back to innovation and to doing things 259 00:16:14.519 --> 00:16:15.279 in new ways. 260 00:16:15.360 --> 00:16:15.559 You know. 261 00:16:15.639 --> 00:16:19.240 I attended the Reuters Next conference. I guess it was 262 00:16:19.320 --> 00:16:22.799 two Novembers ago. I spoke last November and two Novembers 263 00:16:22.840 --> 00:16:25.360 of guy I attended, and a girl from Rutters was 264 00:16:25.399 --> 00:16:29.159 interviewing the Alex P. Morgan I think his name is. 265 00:16:29.200 --> 00:16:31.679 He's the CEO of JP Morgan or one of these companies. 266 00:16:31.720 --> 00:16:34.279 I think that was Gorman was his name. Very very 267 00:16:34.279 --> 00:16:38.039 smart guy. And she's hounding him basically, trying to get 268 00:16:38.080 --> 00:16:40.000 him to admit that they made a bad bet on 269 00:16:40.039 --> 00:16:42.279 Twitter because they threw some money in to help him 270 00:16:42.279 --> 00:16:45.360 buy Twitter, and he just he shook his head for 271 00:16:45.360 --> 00:16:48.679 a couple of seconds and then he said, we're not stupid. 272 00:16:49.919 --> 00:16:50.720 I couldn't believe it. 273 00:16:50.759 --> 00:16:53.519 I was like, Wow, what an answer he goes. Anyone 274 00:16:53.519 --> 00:16:57.159 who doubts Elon Musk should go visit a Tesla factory 275 00:16:57.639 --> 00:17:00.879 and then you'll understand because these folks thought through the 276 00:17:01.080 --> 00:17:04.559 entire end to end process and they re envisioned everything, 277 00:17:04.920 --> 00:17:08.279 and that's why they're able to achieve these amazing numbers. 278 00:17:08.599 --> 00:17:12.960 And that's what innovation takes. It takes unlearning your biases 279 00:17:13.200 --> 00:17:16.119 and then relearning something new, and that I think is 280 00:17:16.119 --> 00:17:18.480 going to be a hallmark of success in twenty twenty five. 281 00:17:18.519 --> 00:17:21.039 Final thoughts from you, Well, I love. 282 00:17:20.839 --> 00:17:25.319 That that Tesla is vertically integrated. In other words, it 283 00:17:25.400 --> 00:17:29.880 makes the engines, it makes the chassis, it makes the batteries, 284 00:17:29.960 --> 00:17:33.200 it makes the wheels, it makes the seats, and therefore, 285 00:17:33.400 --> 00:17:36.920 to your point, it can control the production process from 286 00:17:37.160 --> 00:17:41.200 end to end and drive efficiencies out of it. Legacy 287 00:17:41.240 --> 00:17:45.319 auto no, they have thousands of suppliers and so to 288 00:17:45.480 --> 00:17:49.599 actually change is very hard since there isn't that vertical integration. 289 00:17:50.079 --> 00:17:52.279 So I agree with you absolutely, We're going to see 290 00:17:52.279 --> 00:17:57.640 big changes in the auto industry in basically every industry 291 00:17:57.759 --> 00:17:59.799 with AI and automation. 292 00:18:00.359 --> 00:18:03.680 And retail, we'll see a lot of cool retail stuff, healthcare, 293 00:18:03.720 --> 00:18:05.519 of course, financial services. 294 00:18:06.079 --> 00:18:07.400 The pinch is everywhere. 295 00:18:07.480 --> 00:18:08.960 Folks will send me an email if you want to 296 00:18:09.000 --> 00:18:11.559 be on this show Inside analysis dot Com. 297 00:18:11.559 --> 00:18:12.319 That comes right to me. 298 00:18:12.519 --> 00:18:16.039 We'll be right back here listening to Inside Analysis. 299 00:18:22.160 --> 00:18:27.119 Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh. 300 00:18:29.720 --> 00:18:32.240 All right, folks, back here on Inside Analysis, and I'm 301 00:18:32.240 --> 00:18:35.599 so excited today is part of our cees preview to 302 00:18:35.640 --> 00:18:38.799 get a newbie, a guy who is not new to media. 303 00:18:38.839 --> 00:18:41.000 He's not new to technology. He's not new to the 304 00:18:41.119 --> 00:18:44.960 sports and fitness or interviews or tech or understanding what's 305 00:18:45.000 --> 00:18:47.119 going on in the real world out there. None other 306 00:18:47.160 --> 00:18:51.759 than John Meyer of the podcast bearing his name, and 307 00:18:52.000 --> 00:18:55.559 we're pretty excited. He's a fellow Pennsylvanian. And John here 308 00:18:55.599 --> 00:18:59.119 in Pittsburgh they call folks yinzers or like yin's hey, 309 00:18:59.240 --> 00:19:00.920 yins guy, and it was like. 310 00:19:01.000 --> 00:19:03.160 Yins, What on earth is yins? What are they talking about? 311 00:19:03.400 --> 00:19:05.039 I have a theory on that which I throw at 312 00:19:05.079 --> 00:19:08.640 Pennsylvanians and they like it because if you're from Pennsylvania, 313 00:19:08.799 --> 00:19:13.640 you're a pennsylvani yin, right, So multiple pennsylvani yins yins. 314 00:19:13.680 --> 00:19:16.000 It's at the end of the word. That's my theory. 315 00:19:16.359 --> 00:19:18.119 I don't know if it's true or not, but hey, 316 00:19:18.160 --> 00:19:19.400 you're on the other side of the state. I don't 317 00:19:19.400 --> 00:19:22.960 hold that against you Eagles fan, tough team Stullers. 318 00:19:23.079 --> 00:19:25.160 You know we're usually a tough team at a bad 319 00:19:25.240 --> 00:19:26.119 few games lately. 320 00:19:26.519 --> 00:19:29.480 But tell me a bit about your passion for sports 321 00:19:29.799 --> 00:19:33.319 and fitness and where that dovetails with technology like wearables 322 00:19:33.319 --> 00:19:34.400 and all that kind of fun stuff. 323 00:19:35.200 --> 00:19:37.039 Eric, thank you for inviting me on to the show. 324 00:19:37.200 --> 00:19:39.799 As you mentioned John Meyer from Meyer Media also to 325 00:19:39.960 --> 00:19:42.799 John Meyer Podcast, I've got a theory now that you 326 00:19:42.920 --> 00:19:46.039 mentioned for your yins it could be going along with 327 00:19:46.400 --> 00:19:50.119 ying ling beer and brewery, just saying I'm sure you're 328 00:19:50.160 --> 00:19:50.920 familiar with it. 329 00:19:51.400 --> 00:19:55.000 Someone else said that too, So yins ying's yin's yings ye. 330 00:19:55.119 --> 00:19:57.359 I could see after a few yings agains, you know, 331 00:19:58.079 --> 00:19:59.640 it starts to slur a little bit. 332 00:19:59.759 --> 00:20:02.519 That's after a few yinglings it will slur just a 333 00:20:02.519 --> 00:20:05.960 little bit. So, Eric, my huge passion is around media 334 00:20:06.079 --> 00:20:08.920 content and creating. As you mentioned, I am a nube 335 00:20:08.960 --> 00:20:12.480 to CEES, the first one we've been accepted as media 336 00:20:12.640 --> 00:20:15.759 joining at totally overwhelming right now. I mean, I've been 337 00:20:15.759 --> 00:20:18.440 to Vegas so many times, but coming to this event, 338 00:20:18.680 --> 00:20:21.920 I just realized this is the like the super Bowl 339 00:20:21.960 --> 00:20:26.200 event of technology for consumers. You mentioned fitness and everything. 340 00:20:26.200 --> 00:20:29.799 Obviously a huge Eagles span, die hard, don't hold. 341 00:20:29.640 --> 00:20:30.119 It against you. 342 00:20:30.160 --> 00:20:32.160 On the other side of state, I'm actually hoping for 343 00:20:32.279 --> 00:20:33.240 maybe a PA Bowl. 344 00:20:33.240 --> 00:20:35.400 Would be really cool to watch. Wouldn't that be great? 345 00:20:35.640 --> 00:20:37.240 That would be the first time in history. 346 00:20:37.279 --> 00:20:40.039 And yeah, obviously I'm rooting for that, you know, and 347 00:20:40.400 --> 00:20:43.400 I'll root for the Eagles to win, but you know, hey, 348 00:20:43.400 --> 00:20:46.039 we'll have a good time, and they're going down Broad Street. 349 00:20:46.079 --> 00:20:49.880 We'll definitely see some of the Steelers fans talking about fitness. 350 00:20:50.039 --> 00:20:52.759 Been in fitness all my life, started out at a 351 00:20:52.839 --> 00:20:55.519 very young age, and I just it's a natural thing 352 00:20:55.559 --> 00:20:58.160 to do. Some of the technology that's out there to 353 00:20:58.279 --> 00:20:59.799 track it. I saw some of the things that are 354 00:20:59.799 --> 00:21:03.079 going to be happy in a CEES the wearables, not 355 00:21:03.160 --> 00:21:07.960 only the headsets, but the gear, the recovery stuff, the masade, 356 00:21:08.039 --> 00:21:11.400 everything around them. Just looking at how to improve my 357 00:21:11.480 --> 00:21:15.000 game but also recover from injuries or from workouts much faster. 358 00:21:15.519 --> 00:21:16.920 Yeah, that's good stuff. 359 00:21:16.920 --> 00:21:18.640 And you know, I talk with someone else just today 360 00:21:18.680 --> 00:21:21.880 about the wearables, and here's what excites me about that 361 00:21:21.960 --> 00:21:26.880 stuff is the data that you can capture for individual health, 362 00:21:27.119 --> 00:21:31.440 for population health, to understand how is the workout affecting you, 363 00:21:31.559 --> 00:21:32.119 what is. 364 00:21:32.039 --> 00:21:33.799 Good for you to eat, what's not good for you. 365 00:21:33.839 --> 00:21:35.240 I mean, I think. 366 00:21:35.119 --> 00:21:40.920 Hitherto most healthcare has been boiled down to very generic basics. 367 00:21:40.960 --> 00:21:45.839 I mean they take your vitals, your heart rate, your temperature, 368 00:21:45.920 --> 00:21:46.559 stuff like that. 369 00:21:46.839 --> 00:21:48.200 Okay, that's useful. 370 00:21:48.480 --> 00:21:52.480 But when you can start absorbing massive amounts of data 371 00:21:52.519 --> 00:21:56.279 about how your body acts, when you sleep, when you run, 372 00:21:56.319 --> 00:21:58.599 when you walk, when you're sitting, all these different things, 373 00:21:59.000 --> 00:22:01.599 I think we're at the beginning of a fascinating age 374 00:22:01.640 --> 00:22:05.599 in which we can get a much better understanding of ourselves, 375 00:22:05.720 --> 00:22:08.759 how we sleep, and how to improve our lives every day, 376 00:22:08.799 --> 00:22:11.920 how to improve our health. What do they say, if 377 00:22:11.960 --> 00:22:13.960 you don't have your health, you don't have anything, right. 378 00:22:14.039 --> 00:22:16.000 So I think that in sports, of course you have 379 00:22:16.039 --> 00:22:19.200 to be very fit, and so in that world it's 380 00:22:19.200 --> 00:22:23.599 a whole other level of intensity and practice and work, 381 00:22:23.680 --> 00:22:25.720 and you want to know what's working with not working. 382 00:22:26.039 --> 00:22:28.000 I think we're at the beginning of a golden age 383 00:22:28.039 --> 00:22:28.599 because of that. 384 00:22:28.599 --> 00:22:31.480 What do you think, Eric, You actually just touched on 385 00:22:31.519 --> 00:22:34.079 a bunch of things, because that actually crosses over into 386 00:22:34.119 --> 00:22:38.200 healthcare if you think about it. You talked about you know, 387 00:22:38.240 --> 00:22:40.119 going into a doctor and they just take care of 388 00:22:40.240 --> 00:22:43.279 simple vitals, your heart rate, do your blood pressure. But 389 00:22:43.359 --> 00:22:45.079 how would you love to walk in there with all 390 00:22:45.079 --> 00:22:47.920 the data and say, hey, listen, here's what I got 391 00:22:47.920 --> 00:22:50.720 from my fitness, here's what I got from allmine, and they're. 392 00:22:50.559 --> 00:22:52.880 Like, wow, I wish we had this. 393 00:22:53.240 --> 00:22:56.839 Now you can present them. Problems are easily solved. You 394 00:22:56.880 --> 00:23:00.519 can avoid injury, you can avoid long term illness. There's 395 00:23:00.519 --> 00:23:02.640 so many things that you can understand as a person 396 00:23:02.680 --> 00:23:05.119 to do much better, so you actually avoid the doctor. 397 00:23:05.559 --> 00:23:08.720 Right, that's right. An apple a day keeps the doctor away. 398 00:23:08.759 --> 00:23:09.039 Baby. 399 00:23:09.039 --> 00:23:09.720 I'm all for that. 400 00:23:10.200 --> 00:23:13.480 And you know, I remember, I'm originally from Chicago. Saw 401 00:23:13.519 --> 00:23:15.720