Speaker 1: Six point five FMK two ninety three cf Brino Valley.
Speaker 2: The information economy has a ride. The world is teeming
Speaker 2: with innovation as new business models reinvent every industry industry.
Speaker 2: Inside Analysis is your source of information and insights about
Speaker 2: how to make the most of this exciting new eraic
Speaker 2: learn more at Inside Analysis dot Coastsideanalysis dot com. And
Speaker 2: now here's your host, through Eric Kavanaugh.
Speaker 3: FI.
Speaker 4: All right, ladies and gentlemen, Hello and welcome back once
Speaker 4: again to the only nationally syndicated show all about the
Speaker 4: information economy. It's called Inside Analysis. Your host Eric Kavanaugh
Speaker 4: here very excited to kick off twenty twenty five with
Speaker 4: world renowned influencer analysts that all around cool guy Jim Harris.
Speaker 4: Look him up on Jim Harris dot com. Just like
Speaker 4: it sounds, we're at cees all week, and we're talking
Speaker 4: to lots of really cool companies like Weepower and my
Speaker 4: and a whole bunch of other folks and already get
Speaker 4: a handle on what's going on out there in the
Speaker 4: world of consumer technology and Jim, there is no shortage
Speaker 4: of cool stuff to talk about, but there are some caveats,
Speaker 4: of course. This era of open data and sharing data.
Speaker 4: It's great stuff. We can learn a lot about each
Speaker 4: other what's going on. But some things maybe we want
Speaker 4: to keep private. And there's a story in the news
Speaker 4: today about a car company that had a breach and
Speaker 4: all kinds of data got out, and lots of people
Speaker 4: are very unhappy about that. In the connected cars world,
Speaker 4: if you can tap into someone's information system, you can
Speaker 4: find a way to get their information, and that is
Speaker 4: a big, a big problem for many companies. So privacy
Speaker 4: is still an issue. Security is always going to be
Speaker 4: an issue. Anytime you're connected to the Internet, you're connected
Speaker 4: to all the good guys and all the bad guys.
Speaker 4: The IoT world, as they call it, Internet of Things.
Speaker 4: Of course, there's also the IIoT, the industrial Internet of Things.
Speaker 4: We'll talk about that at some point, I'm.
Speaker 5: Sure, Jim.
Speaker 4: First of all, great stuff is happening, but some caveats.
Speaker 4: What are your thoughts going into CEES twenty twenty five.
Speaker 6: Well, it is such an exciting show. There'll probably be
Speaker 6: one hundred and fifty thousand people there. Eric, They're going
Speaker 6: to be thousands of companies exhibiting great keynotes, all sorts
Speaker 6: of panel discussions. I'm shairing a panel on AI and
Speaker 6: Predictive AI. So it is just an incredible conference. It's
Speaker 6: every year in January in Las Vegas, and it's the
Speaker 6: most influential tech show in the world. So we're going
Speaker 6: to see a lot of exciting things, a lot of
Speaker 6: really interesting discussions. And last year the number one trending
Speaker 6: topic was AI and it is AI again on Twitter
Speaker 6: in advance now called X. But this year there's a
Speaker 6: slight twist to it. The number one trending topic for
Speaker 6: CEOs in advance of the conference is agentic AI. Sure,
Speaker 6: so we'll talk about that in the show. So a
Speaker 6: little teaser agentic AI.
Speaker 4: Yeah, And just to explain to our audience what Jim
Speaker 4: is talking about. Our AI agents and these are little
Speaker 4: semi autonomous applications that can do all kinds of different things.
Speaker 4: It's not just traditional automation where you will telecomputer do
Speaker 4: this set of processes whenever this happens. That's standard automation.
Speaker 4: It's been around for decades quite frankly, but AI agents
Speaker 4: are very very interesting little buggers because they can reconcile,
Speaker 4: they can make different decisions based on dynamic and fluid situations.
Speaker 4: Now what I've heard, i'd be curious to know. What
Speaker 4: you've heard is that by and large, AI agents are
Speaker 4: designed to do one thing very well, whether that's grab data,
Speaker 4: analyze data, delivered data, process data, run some algorithm, figure
Speaker 4: something out. But some people are suggesting AI agents could
Speaker 4: be more versatile. And the most compelling story I've heard
Speaker 4: out of companies like Variant. For example, we were talking
Speaker 4: about a buddy, Tom Augenhaler. He works with Variant. Their
Speaker 4: CEO told me that they like to have these agents
Speaker 4: that are very purpose built and specific.
Speaker 5: But there are other.
Speaker 4: Companies now and one of them I think of is Mistral,
Speaker 4: which is one of the new large language models. They
Speaker 4: have this mixture of experts process where they have an
Speaker 4: orchestrator that kind of sits on top of the agents.
Speaker 4: So just like a manager with a bunch of direct reports,
Speaker 4: that's how these multi layered AI agent architectures work, and
Speaker 4: that is pretty cool stuff. So the little orchestrator sits
Speaker 4: there and goes, okay, Bob, you do this, Sue, you
Speaker 4: now do that, Fred, you do this, And that's their
Speaker 4: job is to orchestrate, which is basically what Kubernetes does.
Speaker 4: From Google for a system use case, which is fascinating stuff.
Speaker 4: I mean, kubernetesay is absolutely amazing. But I think this,
Speaker 4: this orchestration layer is going to be really important for
Speaker 4: AI agents.
Speaker 5: What do you think?
Speaker 6: Absolutely, And we often think about AI as a single entity,
Speaker 6: but here you're talking about AI as a team, a
Speaker 6: team of skilled agents AI agents. And so let's take
Speaker 6: for instance, accounts, receivable, accounts payable. Every year in a
Speaker 6: large company, there are thousands upon thousands of conflicts that exists,
Speaker 6: like we invoiced you for this ten thousand dollars and
Speaker 6: you only paid US nine thousand, nine hundred and fifty dollars.
Speaker 6: And there's a difference in this. Well, you know, historically
Speaker 6: a person had to get involved in all this. Well
Speaker 6: imagine agent to agent, account payable to account receivable, discuss
Speaker 6: it and say, well, we actually when we received the
Speaker 6: shipment there was one broken piece which was worth fifty dollars.
Speaker 6: So these agents go back into their ERPs, their respective
Speaker 6: systems and say, oh, the shipping manifest at the shipping
Speaker 6: dock shows one broken thing which was worth fifty dollars.
Speaker 6: That's why we're paying you fifty dollars less. And it
Speaker 6: goes oh, yeah, okay, I'll accept that and boom that
Speaker 6: arap dispute is closed with no human getting involved. And
Speaker 6: the two humans on either side of this transaction to
Speaker 6: get involved would probably cost three hundred dollars of person time.
Speaker 6: So we've actually solved a fifty dollars dispute with two
Speaker 6: agents AI agents at basically next to nothing in cost.
Speaker 6: So how can we take eighty percent of the disputes
Speaker 6: in ARP and solve them?
Speaker 5: Yeah?
Speaker 4: That is an excellent use case for lots of different reasons. One,
Speaker 4: it's a task that these AI agents can do very well.
Speaker 4: It's a fairly simple thing. It's fairly easy to understand.
Speaker 4: There's a discrepancy between this number and that number. Where
Speaker 4: did it come from? And of course, these agents and
Speaker 4: the new IT world in general operates on what are
Speaker 4: called declarative programs or declarative functionality, where instead of the
Speaker 4: old way of doing computing with imperative programming, where you
Speaker 4: tell the computer line by line what to do, with
Speaker 4: declarative you set you declare an objective that you want accomplished,
Speaker 4: and you let the system figure it out on its own.
Speaker 4: It's really fascinating stuff. Kubernetes is declarative. A lot of
Speaker 4: these new models are declarative, and so it's a good
Speaker 4: way to just get what you want done without having
Speaker 4: to micro manage the computer. That's really what it boils
Speaker 4: down to is it's not micromanaging the computer. So this
Speaker 4: is great stuff because the agency go back and forth
Speaker 4: that oh, okay, no, we got it, and then they'll
Speaker 4: sty'll give some report on that, and then a human
Speaker 4: will go once a day or once an hour and
Speaker 4: look at all the different things that have been resolved
Speaker 4: and just make sure that they're correct, and then throw
Speaker 4: in some curation, throw in some commentary. And of course
Speaker 4: they're not always going to be right, but people aren't
Speaker 4: always right either, So all we really want for these
Speaker 4: things to do is get to where they're good enough
Speaker 4: or better than people at doing things and much more efficient.
Speaker 4: And besides, what a dreadful job for a human to do, right,
Speaker 4: I mean, who wants to do that all day? Nobody does.
Speaker 6: I remember that. My accountants, for instance, feel they need
Speaker 6: to balance to the penny right, And you know you're
Speaker 6: paying your accountant to three hundred bucks an hour to
Speaker 6: find three pennies that's not balanced, and it drives me crazy.
Speaker 6: So how can we just say Okay, well we're pretty close.
Speaker 6: I guess the principle is underneath it. Well, maybe there's
Speaker 6: two transactions that are skewed that are three cents often
Speaker 6: are really highlighting a problem. But I think what we're
Speaker 6: to your point, we're going to end up automating things
Speaker 6: that are dull, dirty, and dangerous. So anything that's a
Speaker 6: really repetitive task will get automated by agentic AI. And
Speaker 6: people worry about job loss, but there are two million
Speaker 6: job openings right now in the US healthcare system. Two
Speaker 6: million job openings, and it's estimated there's about three hundred
Speaker 6: billion dollars of waste and inefficiency in the US healthcare system.
Speaker 6: So agentic AI could really help improve the efficiency of
Speaker 6: the healthcare system and us this two million gap of
Speaker 6: people that we can't find to fill US healthcare jobs.
Speaker 4: Yeah, and we're going to go through a very tumultuous
Speaker 4: and disruptive period of time, but for those who have
Speaker 4: their sea legs under them, it should be a bit
Speaker 4: of a fun and challenging time. I think the key
Speaker 4: piece of advice I give to anyone in the working
Speaker 4: world who wants to keep going out there and doing
Speaker 4: cool things start using these technologies use JENAI. Understand it's
Speaker 4: not perfect, but learn the ins and outs of these systems.
Speaker 4: And Jenna, of course is just one facet of AI,
Speaker 4: and it's very interesting. It does cool stuff. There are
Speaker 4: many other facets of AI. Traditional or as I call it,
Speaker 4: old fashioned AI is still out there doing cool things
Speaker 4: where you build models to do predictions. As a company
Speaker 4: I interviewed not recently called Safebooks AI, which is doing
Speaker 4: really interesting stuff in the accounting space you're talking about,
Speaker 4: and they can help companies avoid major problems like at
Speaker 4: Macy's where this big chunk of money was not reported
Speaker 4: as an expense, causing their accountants lots of trouble and
Speaker 4: causing a lot of trouble across the company. That kind
Speaker 4: of thing will be found by AI. And I think
Speaker 4: we should also spin into the whole world of manufacturing
Speaker 4: and how automation and even AI is coming into play there.
Speaker 4: And you brought up the stat that I saw Alvin
Speaker 4: Fu mentioned the other day, which is that China is
Speaker 4: now dominating the automotive industry, not just the EV space,
Speaker 4: which they are.
Speaker 5: They're number one electric vehicles.
Speaker 4: They are crushing it in terms of being able to
Speaker 4: export cars they're number one in the world. The US
Speaker 4: stinks compared to them these days. It's almost unbelievable, but
Speaker 4: you have to look at the charts to see it.
Speaker 3: Right.
Speaker 6: So, for forty four years, Japan was the number one
Speaker 6: exporter of cars in the world, and then China began
Speaker 6: focusing on production capacity in ev and in twenty twenty
Speaker 6: three it became the largest exporter of cars globally, just
Speaker 6: blowing past everyone else Japan, Mexico, Germany, South Korea, and
Speaker 6: the US. And part of it is the focus on
Speaker 6: electric vehicles called evs. Seventy six percent of all EV's
Speaker 6: in the world are produced in China. It has just
Speaker 6: doubled down on this, and so while Europe and the
Speaker 6: US are I don't know about evs, China is all
Speaker 6: in on it, and this is in part leading to
Speaker 6: their domination globally. And what we're seeing is a discussion
Speaker 6: of tariffs in response. Tariffs are one of the items
Speaker 6: that Trump has been talking about pretty prevalently, putting one
Speaker 6: hundred percent tariffs on imported Chinese cars and so so
Speaker 6: wherever there's a domestic auto industry, you're seeing governments talk
Speaker 6: about tariffs. But for instance, in Australia, there's no domestic
Speaker 6: auto industries, so what what is the benefit for them?
Speaker 6: They get inexpensive cars like cars, right, So there's no
Speaker 6: protectionism in any country that doesn't have its own auto industry.
Speaker 6: So China is going to end up dominating global trade
Speaker 6: with every country that doesn't have a domestic auto industry.
Speaker 6: Right until finally, and then you're seeing news like Onto
Speaker 6: announcing it's going to merge with Nissan and Mitsubishi.
Speaker 5: So I saw that that's.
Speaker 6: Three Japanese car companies who are being crushed by the
Speaker 6: growth of Chinese evs. There have been japan Japanese car
Speaker 6: companies very very slow to shift to evs, so they're
Speaker 6: getting crushed and is their solution to merge?
Speaker 4: Right And these days, speaking of technology and data and
Speaker 4: analytics and ERPs and things of this nature, it is
Speaker 4: a lot easier thanks to AI and machine learning and
Speaker 4: automation to pull off mergers and acquisitions, and therefore I
Speaker 4: predict we will see more of these coming down the
Speaker 4: pike because it's much easier now to get an analysis,
Speaker 4: a real brass tax look at what our assets are,
Speaker 4: how do they align with the assets of this company
Speaker 4: or that company? And you can get a real clear
Speaker 4: view because hitherto you were just kind of guessing. I
Speaker 4: mean you're guessing on the personnel side, on the manufacturing side,
Speaker 4: and all kinds of things, and hoping it's going to
Speaker 4: work out, and it could take years to really make
Speaker 4: that acquisition or that merger. Gel that's not going to
Speaker 4: be the case in the future, as I see many
Speaker 4: organizations are going to do. When you just amtan on,
Speaker 4: they're going to merge to stay alive. Like I said,
Speaker 4: we're going to go through a very tumultuous period here,
Speaker 4: But for the agile I think it's going to be
Speaker 4: a win win.
Speaker 5: What do you think I'd agree?
Speaker 6: I think I made a prediction that we're going to
Speaker 6: see half of the traditional car companies, legacy automakers of
Speaker 6: gas cars disappear over the next ten years. They're going
Speaker 6: to either go bankrupt they're going to merge. As in
Speaker 6: the case of Honda, Nissan and Mitsubishi. There you have
Speaker 6: three car companies combining into one. You've lost two car companies.
Speaker 6: So we're going to see a shrinking of the legacy
Speaker 6: auto industry. And this next stat is shocking to me, Eric,
Speaker 6: but Tesla right now today is worth more than the
Speaker 6: rest of the auto industry added together.
Speaker 5: I mean, it's amazing. That blows me away.
Speaker 6: If you don't think electrification is going to change the
Speaker 6: ten trillion a year transportation and logistics market that stats
Speaker 6: for you has the value of the industry.
Speaker 5: Yeah, that's amazing.
Speaker 4: And it goes back to innovation and to doing things
Speaker 4: in new ways.
Speaker 5: You know.
Speaker 4: I attended the Reuters Next conference. I guess it was
Speaker 4: two Novembers ago. I spoke last November and two Novembers
Speaker 4: of guy I attended, and a girl from Rutters was
Speaker 4: interviewing the Alex P. Morgan I think his name is.
Speaker 4: He's the CEO of JP Morgan or one of these companies.
Speaker 4: I think that was Gorman was his name. Very very
Speaker 4: smart guy. And she's hounding him basically, trying to get
Speaker 4: him to admit that they made a bad bet on
Speaker 4: Twitter because they threw some money in to help him
Speaker 4: buy Twitter, and he just he shook his head for
Speaker 4: a couple of seconds and then he said, we're not stupid.
Speaker 5: I couldn't believe it.
Speaker 4: I was like, Wow, what an answer he goes. Anyone
Speaker 4: who doubts Elon Musk should go visit a Tesla factory
Speaker 4: and then you'll understand because these folks thought through the
Speaker 4: entire end to end process and they re envisioned everything,
Speaker 4: and that's why they're able to achieve these amazing numbers.
Speaker 4: And that's what innovation takes. It takes unlearning your biases
Speaker 4: and then relearning something new, and that I think is
Speaker 4: going to be a hallmark of success in twenty twenty five.
Speaker 5: Final thoughts from you, Well, I love.
Speaker 6: That that Tesla is vertically integrated. In other words, it
Speaker 6: makes the engines, it makes the chassis, it makes the batteries,
Speaker 6: it makes the wheels, it makes the seats, and therefore,
Speaker 6: to your point, it can control the production process from
Speaker 6: end to end and drive efficiencies out of it. Legacy
Speaker 6: auto no, they have thousands of suppliers and so to
Speaker 6: actually change is very hard since there isn't that vertical integration.
Speaker 6: So I agree with you absolutely, We're going to see
Speaker 6: big changes in the auto industry in basically every industry
Speaker 6: with AI and automation.
Speaker 4: And retail, we'll see a lot of cool retail stuff, healthcare,
Speaker 4: of course, financial services.
Speaker 5: The pinch is everywhere.
Speaker 4: Folks will send me an email if you want to
Speaker 4: be on this show Inside analysis dot Com.
Speaker 5: That comes right to me.
Speaker 4: We'll be right back here listening to Inside Analysis.
Speaker 2: Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All right, folks, back here on Inside Analysis, and I'm
Speaker 4: so excited today is part of our cees preview to
Speaker 4: get a newbie, a guy who is not new to media.
Speaker 4: He's not new to technology. He's not new to the
Speaker 4: sports and fitness or interviews or tech or understanding what's
Speaker 4: going on in the real world out there. None other
Speaker 4: than John Meyer of the podcast bearing his name, and
Speaker 4: we're pretty excited. He's a fellow Pennsylvanian. And John here
Speaker 4: in Pittsburgh they call folks yinzers or like yin's hey,
Speaker 4: yins guy, and it was like.
Speaker 5: Yins, What on earth is yins? What are they talking about?
Speaker 4: I have a theory on that which I throw at
Speaker 4: Pennsylvanians and they like it because if you're from Pennsylvania,
Speaker 4: you're a pennsylvani yin, right, So multiple pennsylvani yins yins.
Speaker 5: It's at the end of the word. That's my theory.
Speaker 4: I don't know if it's true or not, but hey,
Speaker 4: you're on the other side of the state. I don't
Speaker 4: hold that against you Eagles fan, tough team Stullers.
Speaker 5: You know we're usually a tough team at a bad
Speaker 5: few games lately.
Speaker 4: But tell me a bit about your passion for sports
Speaker 4: and fitness and where that dovetails with technology like wearables
Speaker 4: and all that kind of fun stuff.
Speaker 7: Eric, thank you for inviting me on to the show.
Speaker 7: As you mentioned John Meyer from Meyer Media also to
Speaker 7: John Meyer Podcast, I've got a theory now that you
Speaker 7: mentioned for your yins it could be going along with
Speaker 7: ying ling beer and brewery, just saying I'm sure you're
Speaker 7: familiar with it.
Speaker 4: Someone else said that too, So yins ying's yin's yings ye.
Speaker 4: I could see after a few yings agains, you know,
Speaker 4: it starts to slur a little bit.
Speaker 7: That's after a few yinglings it will slur just a
Speaker 7: little bit. So, Eric, my huge passion is around media
Speaker 7: content and creating. As you mentioned, I am a nube
Speaker 7: to CEES, the first one we've been accepted as media
Speaker 7: joining at totally overwhelming right now. I mean, I've been
Speaker 7: to Vegas so many times, but coming to this event,
Speaker 7: I just realized this is the like the super Bowl
Speaker 7: event of technology for consumers. You mentioned fitness and everything.
Speaker 7: Obviously a huge Eagles span, die hard, don't hold.
Speaker 8: It against you.
Speaker 7: On the other side of state, I'm actually hoping for
Speaker 7: maybe a PA Bowl.
Speaker 5: Would be really cool to watch. Wouldn't that be great?
Speaker 5: That would be the first time in history.
Speaker 7: And yeah, obviously I'm rooting for that, you know, and
Speaker 7: I'll root for the Eagles to win, but you know, hey,
Speaker 7: we'll have a good time, and they're going down Broad Street.
Speaker 7: We'll definitely see some of the Steelers fans talking about fitness.
Speaker 7: Been in fitness all my life, started out at a
Speaker 7: very young age, and I just it's a natural thing
Speaker 7: to do. Some of the technology that's out there to
Speaker 7: track it. I saw some of the things that are
Speaker 7: going to be happy in a CEES the wearables, not
Speaker 7: only the headsets, but the gear, the recovery stuff, the masade,
Speaker 7: everything around them. Just looking at how to improve my
Speaker 7: game but also recover from injuries or from workouts much faster.
Speaker 5: Yeah, that's good stuff.
Speaker 4: And you know, I talk with someone else just today
Speaker 4: about the wearables, and here's what excites me about that
Speaker 4: stuff is the data that you can capture for individual health,
Speaker 4: for population health, to understand how is the workout affecting you,
Speaker 4: what is.
Speaker 5: Good for you to eat, what's not good for you.
Speaker 5: I mean, I think.
Speaker 4: Hitherto most healthcare has been boiled down to very generic basics.
Speaker 4: I mean they take your vitals, your heart rate, your temperature,
Speaker 4: stuff like that.
Speaker 5: Okay, that's useful.
Speaker 4: But when you can start absorbing massive amounts of data
Speaker 4: about how your body acts, when you sleep, when you run,
Speaker 4: when you walk, when you're sitting, all these different things,
Speaker 4: I think we're at the beginning of a fascinating age
Speaker 4: in which we can get a much better understanding of ourselves,
Speaker 4: how we sleep, and how to improve our lives every day,
Speaker 4: how to improve our health. What do they say, if
Speaker 4: you don't have your health, you don't have anything, right.
Speaker 4: So I think that in sports, of course you have
Speaker 4: to be very fit, and so in that world it's
Speaker 4: a whole other level of intensity and practice and work,
Speaker 4: and you want to know what's working with not working.
Speaker 4: I think we're at the beginning of a golden age
Speaker 4: because of that.
Speaker 7: What do you think, Eric, You actually just touched on
Speaker 7: a bunch of things, because that actually crosses over into
Speaker 7: healthcare if you think about it. You talked about you know,
Speaker 7: going into a doctor and they just take care of
Speaker 7: simple vitals, your heart rate, do your blood pressure. But
Speaker 7: how would you love to walk in there with all
Speaker 7: the data and say, hey, listen, here's what I got
Speaker 7: from my fitness, here's what I got from allmine, and they're.
Speaker 5: Like, wow, I wish we had this.
Speaker 7: Now you can present them. Problems are easily solved. You
Speaker 7: can avoid injury, you can avoid long term illness. There's
Speaker 7: so many things that you can understand as a person
Speaker 7: to do much better, so you actually avoid the doctor.
Speaker 5: Right, that's right. An apple a day keeps the doctor away.
Speaker 7: Baby.
Speaker 5: I'm all for that.
Speaker 4: And you know, I remember, I'm originally from Chicago. Saw
Speaker 4: as a huge Michael Jordan fan and the guy who's
Speaker 4: just a machine, I mean, honest to goodness, he's just
Speaker 4: easily taught players of all time and he would stretch
Speaker 4: before every game. I think it was like an hour
Speaker 4: or so he would take the time to stretch. And
Speaker 4: because of that and maybe because of his physique, he
Speaker 4: was rarely injured. You didn't see that guy get injured
Speaker 4: very often at all because he took care of himself.
Speaker 4: And see, I think again with wearables you're gonna have
Speaker 4: a You need software too to analyze the data understand
Speaker 4: what it all means. Because telemetry data, when you look
Speaker 4: at it, the raw data, it's like ones and zeros
Speaker 4: and very incomprehensible stuff. But in a certain application you
Speaker 4: can kind of start to understand how that's interesting and
Speaker 4: compare yourself to other people, the people, your size, your weight,
Speaker 4: your height, whatever. The more we can understand how our
Speaker 4: body is responding to the world and to what we eat,
Speaker 4: the foods we eat, how much we drink, what we drink,
Speaker 4: I think it's going to really change and give a
Speaker 4: lot more awareness and power to individual people to live
Speaker 4: better lives. What do you think, Eric, the wearables?
Speaker 7: I saw one on. Therefore, you're wearing a pair of
Speaker 7: pants right and ahead. All the RFID and all the telemetry,
Speaker 7: all the data being pulled from it's almost like the stuff,
Speaker 7: like the copper stuff you wear. But here you put
Speaker 7: it on and you can actually start to read your
Speaker 7: body and the metrics as are happening on your body,
Speaker 7: your muscles, where they're tightening, where they're not working, where
Speaker 7: you're performing, where you're not you're at the gym and
Speaker 7: you do something you're actually doing an exercise, and if
Speaker 7: you do it incorrectly, you're going to get hurt at
Speaker 7: some point, not all the time, right away. This can
Speaker 7: tell you how well you're doing immediately while you're doing it.
Speaker 7: You can read all this data efficiently right on your
Speaker 7: phone while you're at the gym, be like, oh man,
Speaker 7: I need to stop running now. Normally you like run
Speaker 7: and you do thirty minutes of cardio while this is
Speaker 7: already reading immediately what's happening to you and be like, hey, listen,
Speaker 7: you need to you know, shut it down and go
Speaker 7: into a soft mode so that you don't injure yourself.
Speaker 5: Right, No, that's right.
Speaker 4: Feedback, I mean, everyone loves a feedback loop, whether that's
Speaker 4: from your colleagues or friends or your boss say hey,
Speaker 4: good job, or okay, maybe better next time, whatever it is,
Speaker 4: it helps to have feedback. And now we have digital
Speaker 4: feedback about what's really happening in our lives.
Speaker 6: You know.
Speaker 5: I came up with this concept a few.
Speaker 4: Years ago about big data, and I talk about something
Speaker 4: I call real world data at scale, and that's where
Speaker 4: we are these days. You can capture the data, you
Speaker 4: can analyze the data, process the data, I think population helps.
Speaker 4: You know, in San Antonio they have one of the
Speaker 4: highest heart attack rates.
Speaker 5: Well, why is that? You know it's probably because.
Speaker 4: Of all the food and tied hide fat for example.
Speaker 4: You don't really know that, But I think, like I say,
Speaker 4: until now, we've kind of been guessing for a lot
Speaker 4: of stuff, and now we're going to be able to
Speaker 4: tell and validate and understand from the data itself, how
Speaker 4: this affects us, How this makes me feel better? What
Speaker 4: doesn't look when I'm feeling down, Why is that We're
Speaker 4: going to have a lot more information? And to me,
Speaker 4: that's just supercharging this life.
Speaker 6: Eric.
Speaker 7: When you talk about feedback, think about the feedback that
Speaker 7: you receive from colleagues, right, constructive criticism. It's very tough
Speaker 7: for a lot of people to take and to receive.
Speaker 7: But if you have a device telling you one hundred
Speaker 7: percent honest, this is what's going on and this is
Speaker 7: what's happening, you're more have to believe it and adjust
Speaker 7: to it, rather than somebody telling you you might take
Speaker 7: a while to you adjust or until you experience the injury.
Speaker 7: And like I should have listened to them here you
Speaker 7: listen immediately.
Speaker 5: That's funny.
Speaker 4: Well, Another one of my fun lines is machines don't lie, right.
Speaker 4: I mean, machines are just saying what's on the information
Speaker 4: that they're giving, They're just giving it back to you.
Speaker 4: And even simple things like your steps or your screen
Speaker 4: time or whatever it is. It's like if you take
Speaker 4: a minute to look at that how your behavior is
Speaker 4: day to day, and then you can start to improve things.
Speaker 5: And people want to improve. Like let's say you want
Speaker 5: to lose some weight.
Speaker 4: While you can weigh yourself every day and that helps,
Speaker 4: but it's just once a day, right, or maybe twice
Speaker 4: a day or something. But to have this constant feed
Speaker 4: giving you information and give you insights and giving you
Speaker 4: a nudge perhaps. I mean you're starting to see some
Speaker 4: of that in the tech world too, like, hey, starting
Speaker 4: to get laid.
Speaker 5: Maybe you should go to bed.
Speaker 4: I mean, personally, I don't like the school marm apps
Speaker 4: on my phone, but I do understand it's good to
Speaker 4: get feedback and to understand what's happening.
Speaker 5: Right, Yep.
Speaker 7: I'm definitely looking forward to not only the sports, the fitness,
Speaker 7: the wearables, just the information the things that I can
Speaker 7: do to not improve my personal fitness in life, but
Speaker 7: see what's out there?
Speaker 5: Mm hmm, well and CS.
Speaker 4: I mean it's I've only been to one last year,
Speaker 4: and it's just massive. I mean it's so big that
Speaker 4: they don't just fill up the exhibit halls. They fill
Speaker 4: up with the Venetian Lots of companies will rent hotel
Speaker 4: rooms for demo and things of that nature. So it's
Speaker 4: just like five convention centers strung together. You're talking like
Speaker 4: one hundred and fifty thousand people gathering.
Speaker 5: And there's a lot to learn.
Speaker 4: I mean I try to keep an open mind every
Speaker 4: day and learn about new technologies. And we are in
Speaker 4: an era where because of AI, to a certain extent,
Speaker 4: we're able to do things that we were not able
Speaker 4: to do before. We're able to optimize things right, we
Speaker 4: can crunch the numbers and understand you know, again, like
Speaker 4: with media.
Speaker 5: What what do people want to watch? What do they
Speaker 5: I want to watch? Now?
Speaker 4: I will say I have some pet peeves with software
Speaker 4: as a service and some of the data we get
Speaker 4: from these engines. In fact, we're going to do a
Speaker 4: show next year called Lies, Damn Lies and SaaS Statistics
Speaker 4: because it's all about like, all right, is this real?
Speaker 5: What are you talking about?
Speaker 4: You throw some money at YouTube or LinkedIn or Google, like, oh, well,
Speaker 4: look at all these impressions you got, And I like,
Speaker 4: who are these people?
Speaker 5: Are you sure like you have new subscribers? Do I really?
Speaker 9: Yeah?
Speaker 7: Yeah, are they real? Let's just put it that way,
Speaker 7: or they're a new one. But then they just they're
Speaker 7: not there anymore. It's a dummy count.
Speaker 5: You have to wonder.
Speaker 4: I mean, I'll just give you a stat The other
Speaker 4: day I saw because I threw some money at some
Speaker 4: of these YouTube videos, and there were two hundred and
Speaker 4: sixty one views. And they look at the demographics and
Speaker 4: every last one of them was a male.
Speaker 5: Over the age of sixty five.
Speaker 4: Now, when you have like eleven strata of years, like
Speaker 4: you know, eleven to fifteen, I can't remember what they were.
Speaker 4: And then the last one is sixty five plus and
Speaker 4: I got two hundred and sixty one viewers. You need
Speaker 4: to tell me every last one of these viewers. When
Speaker 4: I go viral and old folks home somewhere, what the
Speaker 4: hell's going on here?
Speaker 5: I don't think that's real. So I do think there
Speaker 5: is a.
Speaker 4: You know, a bit of a hedge on the excitement
Speaker 4: around the data because we need transparency. That's one of
Speaker 4: my mantras is, you know, the more transparency we get,
Speaker 4: the better because we can all see what's really happening,
Speaker 4: then we can all improve it.
Speaker 5: What do you think about that?
Speaker 7: I agree, And you got two hundred and sixty one views,
Speaker 7: but you probably got two hundred and eleven likes, and
Speaker 7: they're like, Okay, is that really possible that these people
Speaker 7: all likes my video? Because I'll tell you what, when
Speaker 7: I go and watch a video, I try to do that,
Speaker 7: but I don't do that all the time.
Speaker 4: So right, Yeah, when I saw one the other day
Speaker 4: where it's it gave a keynote and at first it
Speaker 4: was like twenty thirty four, and then over like two
Speaker 4: weeks it was ten thousand, ten thousand views and zero
Speaker 4: likes and no comment. Obviously it wasn't zero. There was
Speaker 4: zero comments and just like twelve likes or something. I'm like, hmm,
Speaker 4: to ten thousand people really watch this and no one commented.
Speaker 5: It was pretty forward looking stuff.
Speaker 4: I basically said that before too long, AI is going
Speaker 4: to take over the judicial system, at least for civil cases.
Speaker 4: I think you're going to be able to just upload
Speaker 4: your case into a folder. The defense uploads their case
Speaker 4: into the folder. You do an interview on camera to
Speaker 4: an AI engine, a chatbot, and then it goes all right,
Speaker 4: sting guilty, paying five thousand dollars next, and like no
Speaker 4: one commented on that, like wow, wow.
Speaker 7: I can see a lot of comments on that. It
Speaker 7: will definitely clear a backlog, but I think it will
Speaker 7: also create a some frick.
Speaker 4: My theory is that people will have a harder time
Speaker 4: lying to a cold computer camera than to a person.
Speaker 5: That's my theory. I think it's going to be hard.
Speaker 4: I don't know why I think that, but something tells
Speaker 4: me it's like oh, because the other side of AI
Speaker 4: is that these models are getting so good they can
Speaker 4: tell like your behavior, like is the acting shifting? I
Speaker 4: mean there are little tells you can learn just from experience,
Speaker 4: Like you know, when you ask our question, someone looks
Speaker 4: down and blinks as they talk, Well, you know, they
Speaker 4: probably did something wrong or they're probably not being straight
Speaker 4: with you, and these algorithms are going to pick that
Speaker 4: stuff up.
Speaker 5: I mean that's I guess.
Speaker 4: The last question I'll throw at you is, as we
Speaker 4: face this world, this AI overthrow as I call it,
Speaker 4: because it's going to be everywhere it's already everywhere. It's
Speaker 4: going to be in every major cloud based application that
Speaker 4: you use. There's going to be some aspect, whether under
Speaker 4: the covers or right there on the surface. What are
Speaker 4: your thoughts about this changing dynamic and how we as
Speaker 4: humans can remain in charge.
Speaker 7: I think one of the things about remaining in charge
Speaker 7: for is validating it and always having a governance around it.
Speaker 7: Not AI governing AI, because that's like the government governing
Speaker 7: the government.
Speaker 5: It just doesn't work anything.
Speaker 7: Because I think for us with AI, let's test a
Speaker 7: little bit on the audio and video stuff that are
Speaker 7: happening that cees and AI is a huge part of that. Now,
Speaker 7: a lot of the videos that are created today, some
Speaker 7: of the stuff is spoofed from video from AI generating
Speaker 7: on it. How do you tell the difference? I think
Speaker 7: there needs to be a governance around it in order
Speaker 7: a tag or something within the video, even if it's remade, recaptured,
Speaker 7: you know, whatever screenshot, there needs to be a way
Speaker 7: to tag that that is AI generated in order for
Speaker 7: us to understand what deep fake is and what is
Speaker 7: happening out there. But I also believe there's so many
Speaker 7: positives for AI and around video and audio because it
Speaker 7: has helped us in our business nowadays be more efficient
Speaker 7: not only creating the video, but to fine tune in
Speaker 7: editing and cleaning up some of the stuff. So, circling
Speaker 7: back to your question, I just think there needs to
Speaker 7: be a way that we got to put something in
Speaker 7: place to monitor what we're creating from AI and to
Speaker 7: validate it, whether it is using a third party AI,
Speaker 7: to validate that that is happening, because not a human.
Speaker 7: It's suffer a human to understand that that's AI generated
Speaker 7: in some cases.
Speaker 4: Right, I think you're right, and I'm sure some smart
Speaker 4: people are working on this right now. My theory is
Speaker 4: that you can do a number of different things. One,
Speaker 4: you can use the metadata that is baked into cameras.
Speaker 4: For example, when you take a picture with all these
Speaker 4: new iPhones and Samsungs, there's metadata layer that captures where
Speaker 4: you were at the time, what time you took this thing.
Speaker 4: All this information is captured in the video. So to
Speaker 4: be able to capture that and say, ah, this is
Speaker 4: a clean video, this has not been edited, this has
Speaker 4: not been doctored.
Speaker 5: I think that's the key.
Speaker 4: In probably QR codes too, like a watermark of some
Speaker 4: kind of qr CO. But I think you're right. We
Speaker 4: do need to have some governance around this because otherwise evidence,
Speaker 4: even in courts, is going to be out the window.
Speaker 5: But folks, look this gentleman.
Speaker 4: Up online, John Meyer. That's m ye er the John
Speaker 4: Meyer Podcast. Look forward to seeing it at CES.
Speaker 5: We'll be right back. You're listening to Inside Analysis.
Speaker 2: Excited, Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
Speaker 4: All Right, folks, welcome back to Inside Analysis and our
Speaker 4: CEES preview for twenty twenty five. And folks, I'm super
Speaker 4: excited to have a fellow Yinzer, a fellow Pennsylvanian on
Speaker 4: the call here today, doctor Frank Vigiano. What's new, doc?
Speaker 4: That's his brand, that's what he talks about. He has
Speaker 4: been a judge at CEES for a very long time now.
Speaker 4: It has been focused on the healthcare industry for years
Speaker 4: and years and as a regular TV and radio personality.
Speaker 4: So I got to ask you, Frank, what's new?
Speaker 5: Doc?
Speaker 3: So much you know with CES, I mean I've been
Speaker 3: going for it'll be my believe it or not, my
Speaker 3: fifty seventh year attending CS. So they started in New
Speaker 3: York City. I'll send you a clip. What was interesting
Speaker 3: is that CBS in Las Vegas did a special when
Speaker 3: I arrived there seven years ago. My badge said fifty
Speaker 3: years and had a special ribbon, and I saw this
Speaker 3: is great. So we started, you know, networking with a
Speaker 3: bunch of different people talking about badges, and I learned
Speaker 3: that I was only one of two people that have
Speaker 3: attended every csin since the origination in New York City.
Speaker 9: Yeah, pretty crazy.
Speaker 4: Well, and as you know here in Pennsylvania, the number
Speaker 4: fifty seven has a certain meaning, right HEINZ fifty seven.
Speaker 5: So it's all coming full circle.
Speaker 4: Now what do you see what excites you about CEES
Speaker 4: twenty twenty five.
Speaker 3: Well, there's so many new technologies. I think it's branched
Speaker 3: out into We talked a little earlier about healthcare, and
Speaker 3: that's a real big important category. People are finding there
Speaker 3: were rings, they're wearing, watches, they're wearing, they're wearables to
Speaker 3: give them insight into their body and what's happening. And
Speaker 3: there's more and more apps being created. So that's a
Speaker 3: big growth area for CES. And you still the standards,
Speaker 3: you know, you know, television appliances, I mean, it's all
Speaker 3: still there, but these other new categories. For example, in
Speaker 3: the automotive section, a lot of these manufacturers are exhibiting
Speaker 3: now at cs where they, you know, had never done
Speaker 3: so before, and the reason being is that they had Detroit.
Speaker 3: So when the pandemic came, everything stopped and nobody went
Speaker 3: to the Detroit show because they canceled it. They had to,
Speaker 3: they had no choice. So because of that and kind
Speaker 3: of the combination of CEES growing and inviting more auto
Speaker 3: automobile manufacturers.
Speaker 9: To exhibit there, they had a whole.
Speaker 3: Area on the in the north hole where the majority
Speaker 3: of it was all automobiles, different things to light, our
Speaker 3: radar sensors, all different technology, and the vehicles themselves.
Speaker 4: You know. You know what really excites me because I'm
Speaker 4: a data guy. I focus on data, artificial intelligence, big data, leveraging, data, telemetry.
Speaker 4: We have all this data these days, and we now
Speaker 4: have the compute capacity and the algorithms to analyze data
Speaker 4: at scale and really understand what's going on. So when
Speaker 4: I think about population health ory, even individual healthcare. Yeah,
Speaker 4: a few years ago we had a few things. They
Speaker 4: take your vitals, they can do blood draws, they can
Speaker 4: look at you through different lenses. But still for those
Speaker 4: who have paid attention. It's a relatively opaque window. I
Speaker 4: mean it's hard. I mean it's translucent a little bit,
Speaker 4: but you really have to know what you're doing to
Speaker 4: figure out what these scores mean on blood tests and
Speaker 4: things of this nature. But now, if you have one
Speaker 4: of these wearables and it's keeping track of your pulse,
Speaker 4: of your blood pressure, of your steps, of just everything
Speaker 4: about you what you're doing, I have to believe that
Speaker 4: in the near future, if not already, we're going to
Speaker 4: have tremendously improved capabilities to understand what is my health,
Speaker 4: what are my problems, what are the because once you
Speaker 4: understand leading indicators, you can start watching for things like diabetes,
Speaker 4: for example, or respiratory problems or whatever.
Speaker 5: And I think we're.
Speaker 4: Going to enter in age and as long as we
Speaker 4: can hack through hippo regulations and security and compliance and
Speaker 4: all the concerns about those things, I think we're going
Speaker 4: to enter an absolutely golden era of personal health care
Speaker 4: and population health.
Speaker 3: What do you think, Well, I agree with you totally absolutely,
Speaker 3: and it's being welcome. I think initially, for example, when
Speaker 3: light arc came out and some of the technologies for automobiles,
Speaker 3: the biggest concern from consumers was, oh, that's recording me
Speaker 3: while I'm driving, and the rest is recorded, and many
Speaker 3: things do not record. The only thing they do is
Speaker 3: they process the data for if I'm driving and I'm sleepy,
Speaker 3: and i start to drows a little bit sensus that
Speaker 3: I'm not awake from and I'm not driving the vehicle
Speaker 3: or at least be paying attention to the vehicle as
Speaker 3: it's moving. So therefore that's going to create all kinds
Speaker 3: of issues. It's going to slow that car down.
Speaker 9: They could even bring.
Speaker 3: It to a stop until I, you know, wake up
Speaker 3: and say, oh no, I'm here, I've got the steering
Speaker 3: WM in my hand, that type of thing. So I
Speaker 3: think what's important is that the fear that people have
Speaker 3: of their privacy. And honestly, Eric, there is no more privacy.
Speaker 5: Yeah.
Speaker 4: Well, I hosted a stage at the Data Universe conference
Speaker 4: in New York back in April, and they had me
Speaker 4: host the privacy stage, and the first thing I said
Speaker 4: is privacy is dead.
Speaker 5: Forget about it.
Speaker 4: I mean not that we shouldn't aspire towards respecting privacy.
Speaker 4: I think that's still a very worthwhile goal. Yes, big
Speaker 4: companies want to protect your sensitive data. But to think
Speaker 4: that your data is not everywhere is just a naive perspective,
Speaker 4: it seems to me, right.
Speaker 3: Oh, Relliant, I mean, if you have a cell phone, really,
Speaker 3: are you kidding me? They know where you are every
Speaker 3: minute of every second regards to where you go.
Speaker 4: Right, that's right, And sharing data is valuable, I mean,
Speaker 4: especially in the healthcare space again with all these devices.
Speaker 4: Now you have to normalize the data. You have to
Speaker 4: attend to lapses in data. That's one of the issues
Speaker 4: with IoT, right, is that you do have lapses of telemetry.
Speaker 4: You have to watch out for that and model for that.
Speaker 4: But nonetheless, I mean, I think at some significant scale,
Speaker 4: and I'm sure universities are working on this and major
Speaker 4: manufacturers are looking into this, there is just an absolute
Speaker 4: treasure trove of information that we can now finally access
Speaker 4: and analyze to figure out absolutely what are the behavioral
Speaker 4: patterns that lead to better health?
Speaker 5: Right.
Speaker 3: Well, a good example too would be a device Let's
Speaker 3: say that send a message to a physician and they're
Speaker 3: monitoring in a certain let's say, a certain part of
Speaker 3: the body for a certain reason, and here's an alert
Speaker 3: that comes over. So my goodness is this is like,
Speaker 3: we got to take care of this immediately. When it
Speaker 3: saves someone's life and that information is shared with others,
Speaker 3: that's huge. So people don't worry any more about privacy
Speaker 3: because guess what it's superseded what's needed to save a life.
Speaker 9: I mean, how could you be upset with that?
Speaker 4: Well, and you know, I'll make an analogy here to
Speaker 4: maintenance of machines like trains on automobiles and things of
Speaker 4: this nature. We've had predictive analytics in the industries where
Speaker 4: they could afford it thirty years ago, even possibly forty
Speaker 4: years ago, like in aeronautics for example, with airplanes. And
Speaker 4: what they've learned is that if you capture all the data,
Speaker 4: the censored data, you could tell hey, if this part breaks,
Speaker 4: that part's going to break shortly thereafter. So you sense
Speaker 4: and wait for this part to you know, to reach
Speaker 4: a certain threshold. And I was fascinated to learn that
Speaker 4: sound is a big part of that whole game, because
Speaker 4: when they start squeaking, they make little sounds like a
Speaker 4: timing dolt making a sound like, hm, I got a
Speaker 4: problem here, you have to address it. Well, if you
Speaker 4: think about how that changes the attitude of the person
Speaker 4: monitoring this information beforehand, the guy on the train would
Speaker 4: just have to go check every door, look at every wheel,
Speaker 4: and you're never ever going to find it, not never,
Speaker 4: but almost never going to find anything. But if you're
Speaker 4: acting on data from the system that's telling you a
Speaker 4: problem is coming, then you're attentive and you're focused and
Speaker 4: you know where to focus your attention.
Speaker 5: So to me, that is a.
Speaker 4: Massive sea change in operational efficiency and engagement with the user,
Speaker 4: with the human right and as the old saying goes,
Speaker 4: what gets measured gets managed. So I think it really
Speaker 4: changes the game for how people can pay it and
Speaker 4: they don't want to pay too much attention, don't want
Speaker 4: to get all worked up about it, but still to
Speaker 4: know that you have this problem and have some way
Speaker 4: of measuring it, that's going to be huge for improving
Speaker 4: healthcare overall.
Speaker 9: Right, no question, absolutely, Yeah.
Speaker 5: That's good stuff. Well, so what are you excited to
Speaker 5: see at CEES?
Speaker 9: Are you going to be see a couple of things
Speaker 9: that are interesting? Now?
Speaker 3: LG has come up with some great technology. I mean
Speaker 3: they have for years, but they just launched about a
Speaker 3: week ago. They teased it at CEES twenty twenty four.
Speaker 3: But now they've launched it and it's the first seat
Speaker 3: through old LED television. Oh wow, you see through it now?
Speaker 3: People say why or what is so the idea is
Speaker 3: that many I think you know that from interior design perspective.
Speaker 3: Many people say, I don't want that TV in my bedroom.
Speaker 3: I don't want it in the living room. I don't
Speaker 3: want to see it, citty now, I don't want it
Speaker 3: hanging on the wall whatever. And we understand, we get that.
Speaker 3: So what LG has done is they've taken an ol
Speaker 3: ed television and you can see right through it. So
Speaker 3: you put it in the picture window of your home,
Speaker 3: let's say, in front of an ocean view, a mountain view.
Speaker 9: It's incredible.
Speaker 3: And then what happens there is a black screen that
Speaker 3: rolls up when you want to see TV, and when
Speaker 3: you do that, it blocks a life from going through
Speaker 3: and therefore the image is just like it would be
Speaker 3: if you were watching a regular television.
Speaker 9: It's amazing.
Speaker 4: Probably, yeah, it's it's fun to watch all these innovations.
Speaker 4: In my goodness, you've seen many of them. You're fifty
Speaker 4: seventh cees going to.
Speaker 3: When I was on you know, seven years ago. Of
Speaker 3: course this was big because everybody's like CES fifty years.
Speaker 3: So they said, doctor Frank, what was the deal the
Speaker 3: first CES? What was it all about? And I said
Speaker 3: color television. It was in New York City. Wow, And
Speaker 3: they showed color TV. That was such a breakthrough. I mean,
Speaker 3: you know, and I mean I remember all this was
Speaker 3: like my father, He's the one that really got me
Speaker 3: into electronics and technology.
Speaker 9: He was a physician, but he loved tech, loved it.
Speaker 3: He bought a Hoffman solar radio from Hoffman Electronics in California.
Speaker 9: I still have it.
Speaker 3: It was AM batteries in the back and I took it.
Speaker 3: I said, can I take it to the pool? He said, well,
Speaker 3: this is not cheap, and you know if you take
Speaker 3: you better be careful. I said, okay, so, mister magician.
Speaker 3: You know, I get it to the pool. All my
Speaker 3: friends are standing around, We're listening to the radio, and
Speaker 3: I pulled the battery out. Everybody's like, what you know,
Speaker 3: it's still playing and it's not plugged in. That was
Speaker 3: solar Yeah. I was nineteen. Let me think what that
Speaker 3: would be about nineteen fifty seven.
Speaker 4: Nineteen fifty seven, Oh my goodness, and solar power. You
Speaker 4: look at I mean, I just I wrote it on
Speaker 4: LinkedIn today about and we'll look for solutions at CEES.
Speaker 4: What just happened in Puerto Rico. Eighty percent of the
Speaker 4: power is down now in Puerto Rico. And you know,
Speaker 4: we've seen the horrible things happening over in Ukraine where
Speaker 4: the Russians target the energy infrastructure, and it just.
Speaker 5: Tells me we got to get off the grid, man.
Speaker 4: I mean, the grid is there, it's very powerful, and it
Speaker 4: served society very very well. But nonetheless, I mean I
Speaker 4: love to push for solar, I love to push for
Speaker 4: alternative energy forms, and you know, let's let's try to
Speaker 4: focus on that and really incentivize people to get off
Speaker 4: the grid and just be more more sustainable. And that's
Speaker 4: what sustainability is all about, right, I mean, we absolutely
Speaker 4: numbers have to come together and make sense. But nonetheless
Speaker 4: ces is the place to find that stuff too, right
Speaker 4: of course.
Speaker 3: And you know the problem is, you know what I
Speaker 3: never understood the iPhone, great phone, love the technology, love software.
Speaker 9: The people.
Speaker 3: I see them at airports everywhere, tethered to a wall
Speaker 3: to plug it into charge, and it's like, hey, this
Speaker 3: makes no sense. I mean, why can't we just replace
Speaker 3: a battery. And Kia Sarah, by the way, is the
Speaker 3: only company that I'm aware of currently they have a
Speaker 3: they are products some model seventy two hundred. But it
Speaker 3: is the only phone that I know that you can
Speaker 3: replace the battery. So take the pack off.
Speaker 4: I remember, I remember being very annoyed about that with
Speaker 4: the with the iPhone, and you know, you have to
Speaker 4: ask yourself, yes. And I just had a guy from
Speaker 4: Apple on the show moments ago who we were talking about,
Speaker 4: Steve Jobs and his commitment to design being a philosophy,
Speaker 4: because there's a great quote I read from him where
Speaker 4: he said, you know, a lot of people view design
Speaker 4: as a sort of veneer that you put on at
Speaker 4: the end of the of the process of building something.
Speaker 4: With us, it's central, it's the beginning, it's everything. Design
Speaker 4: is form and function combined. And so I do respect
Speaker 4: that and I understand it. But nonetheless, the batteries man
Speaker 4: like to be able to get a new battery because
Speaker 4: these phones, when they get older, the battery life gets
Speaker 4: shorter and shorter and shorter and just gets more annoying,
Speaker 4: you know, And so let's let's move in that direction, right, Yeah, Well, the.
Speaker 3: Keith Sarah, which I like about it. You can, like
Speaker 3: I say, again, swalk the battery. Here's the best part.
Speaker 3: Three hundred and fifty dollars. That's it only available through
Speaker 3: ver Ozon. But when you have a few young people
Speaker 3: saw my phone and said wow because I dropped it
Speaker 3: and I said, well, you can drop this in six
Speaker 3: feet water and nothing happens. And the glass on the front,
Speaker 3: it's got gorilla glass, so you can't crack it, you
Speaker 3: can't scratch it.
Speaker 9: It's unbelievable.
Speaker 3: I mean, it's really yeah. And so and they said
Speaker 3: three hundred and fifty dollars. I just paid thirteen fifty
Speaker 3: for an iPhone. I said, well, what the market will bear.
Speaker 4: Yeah, that's that's a lot of money. And you know,
Speaker 4: the cool thing about cees is that it is a showcasing.
Speaker 4: One of the things I learned last year when I
Speaker 4: went to my first one. So I don't have nearly
Speaker 4: as many in my history as you do. But one
Speaker 4: of the cool things I like. I think it was
Speaker 4: LG in fact, who were showing me around and all
Speaker 4: their cool little things. And what they do is they
Speaker 4: have prototypes that they bring to the conference and then
Speaker 4: they'll have cameras watching who goes by which object and
Speaker 4: how long they stay there. So they're using data about
Speaker 4: how sticky these these technologies are to determine which ones
Speaker 4: to push into production, because obviously it's a big risk
Speaker 4: pushing some prototype be a production. You got to spend
Speaker 4: a whole bunch of money, get a whole bunch of parts,
Speaker 4: hire people, all this stuff. So you want to know
Speaker 4: what the winners are and what the losers are or
Speaker 4: the ones that probably will win. I thought that was
Speaker 4: a brilliant strategy and it just shows a real comprehensive
Speaker 4: view of the of the problem space and the opportunities.
Speaker 5: Right.
Speaker 3: Well, that's exactly what they did last year was they
Speaker 3: called old ed T for transparency, so they headed out.
Speaker 3: People were like just blown away, Oh my goodness, this
Speaker 3: is incredible technology. So they got feedback and here we
Speaker 3: are a year later and now you can buy it.
Speaker 1: Wow.
Speaker 5: I love that.
Speaker 4: Well, look this gentleman up online, folks, Doctor Frank Vigiano.
Speaker 4: Sounds like a good Italian name. It was from down
Speaker 4: the road here. I'm up in Gibsonia, Pennsylvania. He's down
Speaker 4: the road in Indiana, Pennsylvania. But what's new, Doc that's
Speaker 4: his whole tagline. He's been doing this stuff for a
Speaker 4: long time. We look forward to seeing you at CES
Speaker 4: in Las Vegas. Don't touch that del folks will be
Speaker 4: right back. You're listening to Inside Analysis. We can't everything everyone,
Speaker 4: or can we the station that leaves no listener behind KCAA.
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Speaker 13: Here's a progressive idea I picked up from the unlikeliest
Speaker 13: of sources, corporate CEOs. For decades, these chieftains of our
Speaker 13: economic order have been steadily implementing a very visionary process
Speaker 13: for establishing corporate wage levels. The essence of it is this,
Speaker 13: let workers set their own pay Since the nineteen seventies,
Speaker 13: when the idea began taking hold in corporate America, pay
Speaker 13: levels have zoomed up by more than one thousand percent. Well,
Speaker 13: not for you. This set your own pay movement has
Speaker 13: only been available to top corporate executives, whose medium paychecks
Speaker 13: now top sixteen million dollars a year. But since it's
Speaker 13: been such a boon for this test group, I say
Speaker 13: it's time to expand the no hassle compensation concept to
Speaker 13: all employees. This would greatly boost grassroots purchasing power, economic growth,
Speaker 13: and fairness for all. Oh my god, no squawk corporate
Speaker 13: apologists rushing to say that technically, CEOs do not directly
Speaker 13: set their pay. Rather, the bosses have attached their earnings
Speaker 13: to their corporation's ever rising stock prices. Thus, astronomical rewards
Speaker 13: go to those who obsessively focus on jacking up the
Speaker 13: price of their own stock, even though that's a selfishly
Speaker 13: narrow and false measure of a corporation's performance. Also, stock
Speaker 13: price is no indicator of a CEO's worthiness. Even bosses
Speaker 13: who are blockheads can still get a boost simply because
Speaker 13: they've rigged the system to hitch a free ride on
Speaker 13: inflated stock value. This is Jimhita saying still, if it's
Speaker 13: good enough for them, why not an equal deal for
Speaker 13: working staffs who actually deliver the products and services that
Speaker 13: give a corporation some true value. I say each worker
Speaker 13: should get the same percentage increase in pay that the
Speaker 13: top haunch ho takes. It's a very simple process and
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Speaker 17: NBC News Radio I'm Lisa Carton America is paying tribute
Speaker 17: to former President Jimmy Carter. The thirty ninth president, died
Speaker 17: last weekend in his home state of Georgia at the
Speaker 17: age of one hundred. His motorcade arrived at the Carter
Speaker 17: Center in Atlanta Saturday afternoon, where he will remain for
Speaker 17: several days for public viewings. On Tuesday, the late president
Speaker 17: and his family will travel to Washington, d C. Where
Speaker 17: he will lie in state at the US Capitol and
Speaker 17: a state funeral will be held Thursday at Washington National Cathedral.
Speaker 17: Senator Amy Klobashar says the US capital will be secured
Speaker 17: tomorrow for the certification of the twenty twenty four presidential election.
Speaker 17: Appearing on CNN's State of the Union, the Minnesota Democrats said,
Speaker 17: in the years since the January sixth insurrection, officials have
Speaker 17: upgraded security measures in Washington, d C.
Speaker 11: We have, as you note, a new police chief, increase morale,
Speaker 11: many hundreds of more officers, and we have a plan
Speaker 11: and a strategy in place.
Speaker 17: Vice President Kamala Harris will preside over the certification of
Speaker 17: the electoral votes for president tomorrow, marking the first time
Speaker 17: since two thousand and one a vice president has presided
Speaker 17: over the certification of their own election loss. Over sixty
Speaker 17: million Americans are being impacted by the first major winter
Speaker 17: storm of twenty twenty five. Kansas, Kentucky, Arkansas, and Virginia
Speaker 17: have declared states of emergency, and southern states like Florida
Speaker 17: and Mississippi are also warning of dangerous cold. A blizzard
Speaker 17: warning is in effect, with hazardous driving conditions possible today
Speaker 17: from Kansas to West Virginia. Forecasters say the storm will
Speaker 17: impact the mid Atlantic region by this evening, dumping heavy
Speaker 17: snow on Virginia, Baltimore and Washington, d C. Filmmaker Jeff
Speaker 17: Beina is dead at the age of forty seven. The
Speaker 17: writer and director was best known for films like Life
Speaker 17: After Beth and The Little Hours. He was also married
Speaker 17: to actress Aubrey Plaza. Authorities believe he died by suicide.
Speaker 17: You're listening to the latest on NBC News Radio.
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