KCAA: Inside Analysis with Eric Kavanagh (Sun, 5 Jan, 2025)

KCAA: Inside Analysis with Eric Kavanagh on Sun, 5 Jan, 2025

Episode Transcript

Speaker 1 (00:00):
Six point five FMK two ninety three cf Brino Valley.

Speaker 2 (00:04):
The information economy has a ride. The world is teeming
with innovation as new business models reinvent every industry industry.
Inside Analysis is your source of information and insights about
how to make the most of this exciting new eraic
learn more at Inside Analysis dot Coastsideanalysis dot com. And
now here's your host, through Eric Kavanaugh.

Speaker 3 (00:29):
FI.

Speaker 4 (00:30):
All right, ladies and gentlemen, Hello and welcome back once
again to the only nationally syndicated show all about the
information economy. It's called Inside Analysis. Your host Eric Kavanaugh
here very excited to kick off twenty twenty five with
world renowned influencer analysts that all around cool guy Jim Harris.
Look him up on Jim Harris dot com. Just like

(00:52):
it sounds, we're at cees all week, and we're talking
to lots of really cool companies like Weepower and my
and a whole bunch of other folks and already get
a handle on what's going on out there in the
world of consumer technology and Jim, there is no shortage
of cool stuff to talk about, but there are some caveats,

(01:12):
of course. This era of open data and sharing data.
It's great stuff. We can learn a lot about each
other what's going on. But some things maybe we want
to keep private. And there's a story in the news
today about a car company that had a breach and
all kinds of data got out, and lots of people
are very unhappy about that. In the connected cars world,

(01:34):
if you can tap into someone's information system, you can
find a way to get their information, and that is
a big, a big problem for many companies. So privacy
is still an issue. Security is always going to be
an issue. Anytime you're connected to the Internet, you're connected
to all the good guys and all the bad guys.
The IoT world, as they call it, Internet of Things.
Of course, there's also the IIoT, the industrial Internet of Things.

(01:57):
We'll talk about that at some point, I'm.

Speaker 5 (01:59):
Sure, Jim.

Speaker 4 (02:00):
First of all, great stuff is happening, but some caveats.
What are your thoughts going into CEES twenty twenty five.

Speaker 6 (02:07):
Well, it is such an exciting show. There'll probably be
one hundred and fifty thousand people there. Eric, They're going
to be thousands of companies exhibiting great keynotes, all sorts
of panel discussions. I'm shairing a panel on AI and
Predictive AI. So it is just an incredible conference. It's

(02:32):
every year in January in Las Vegas, and it's the
most influential tech show in the world. So we're going
to see a lot of exciting things, a lot of
really interesting discussions. And last year the number one trending
topic was AI and it is AI again on Twitter

(02:56):
in advance now called X. But this year there's a
slight twist to it. The number one trending topic for
CEOs in advance of the conference is agentic AI. Sure,
so we'll talk about that in the show. So a
little teaser agentic AI.

Speaker 4 (03:17):
Yeah, And just to explain to our audience what Jim
is talking about. Our AI agents and these are little
semi autonomous applications that can do all kinds of different things.
It's not just traditional automation where you will telecomputer do
this set of processes whenever this happens. That's standard automation.
It's been around for decades quite frankly, but AI agents

(03:41):
are very very interesting little buggers because they can reconcile,
they can make different decisions based on dynamic and fluid situations.
Now what I've heard, i'd be curious to know. What
you've heard is that by and large, AI agents are
designed to do one thing very well, whether that's grab data,
analyze data, delivered data, process data, run some algorithm, figure

(04:06):
something out. But some people are suggesting AI agents could
be more versatile. And the most compelling story I've heard
out of companies like Variant. For example, we were talking
about a buddy, Tom Augenhaler. He works with Variant. Their
CEO told me that they like to have these agents
that are very purpose built and specific.

Speaker 5 (04:25):
But there are other.

Speaker 4 (04:26):
Companies now and one of them I think of is Mistral,
which is one of the new large language models. They
have this mixture of experts process where they have an
orchestrator that kind of sits on top of the agents.
So just like a manager with a bunch of direct reports,
that's how these multi layered AI agent architectures work, and
that is pretty cool stuff. So the little orchestrator sits

(04:48):
there and goes, okay, Bob, you do this, Sue, you
now do that, Fred, you do this, And that's their
job is to orchestrate, which is basically what Kubernetes does.
From Google for a system use case, which is fascinating stuff.
I mean, kubernetesay is absolutely amazing. But I think this,
this orchestration layer is going to be really important for

(05:09):
AI agents.

Speaker 5 (05:10):
What do you think?

Speaker 6 (05:11):
Absolutely, And we often think about AI as a single entity,
but here you're talking about AI as a team, a
team of skilled agents AI agents. And so let's take
for instance, accounts, receivable, accounts payable. Every year in a

(05:32):
large company, there are thousands upon thousands of conflicts that exists,
like we invoiced you for this ten thousand dollars and
you only paid US nine thousand, nine hundred and fifty dollars.
And there's a difference in this. Well, you know, historically

(05:52):
a person had to get involved in all this. Well
imagine agent to agent, account payable to account receivable, discuss
it and say, well, we actually when we received the
shipment there was one broken piece which was worth fifty dollars.
So these agents go back into their ERPs, their respective

(06:18):
systems and say, oh, the shipping manifest at the shipping
dock shows one broken thing which was worth fifty dollars.
That's why we're paying you fifty dollars less. And it
goes oh, yeah, okay, I'll accept that and boom that
arap dispute is closed with no human getting involved. And

(06:41):
the two humans on either side of this transaction to
get involved would probably cost three hundred dollars of person time.
So we've actually solved a fifty dollars dispute with two
agents AI agents at basically next to nothing in cost.

(07:03):
So how can we take eighty percent of the disputes
in ARP and solve them?

Speaker 5 (07:10):
Yeah?

Speaker 4 (07:11):
That is an excellent use case for lots of different reasons. One,
it's a task that these AI agents can do very well.
It's a fairly simple thing. It's fairly easy to understand.
There's a discrepancy between this number and that number. Where
did it come from? And of course, these agents and
the new IT world in general operates on what are

(07:31):
called declarative programs or declarative functionality, where instead of the
old way of doing computing with imperative programming, where you
tell the computer line by line what to do, with
declarative you set you declare an objective that you want accomplished,
and you let the system figure it out on its own.
It's really fascinating stuff. Kubernetes is declarative. A lot of

(07:54):
these new models are declarative, and so it's a good
way to just get what you want done without having
to micro manage the computer. That's really what it boils
down to is it's not micromanaging the computer. So this
is great stuff because the agency go back and forth
that oh, okay, no, we got it, and then they'll
sty'll give some report on that, and then a human
will go once a day or once an hour and

(08:16):
look at all the different things that have been resolved
and just make sure that they're correct, and then throw
in some curation, throw in some commentary. And of course
they're not always going to be right, but people aren't
always right either, So all we really want for these
things to do is get to where they're good enough
or better than people at doing things and much more efficient.
And besides, what a dreadful job for a human to do, right,

(08:40):
I mean, who wants to do that all day? Nobody does.

Speaker 6 (08:45):
I remember that. My accountants, for instance, feel they need
to balance to the penny right, And you know you're
paying your accountant to three hundred bucks an hour to
find three pennies that's not balanced, and it drives me crazy.
So how can we just say Okay, well we're pretty close.

(09:08):
I guess the principle is underneath it. Well, maybe there's
two transactions that are skewed that are three cents often
are really highlighting a problem. But I think what we're
to your point, we're going to end up automating things
that are dull, dirty, and dangerous. So anything that's a

(09:28):
really repetitive task will get automated by agentic AI. And
people worry about job loss, but there are two million
job openings right now in the US healthcare system. Two
million job openings, and it's estimated there's about three hundred
billion dollars of waste and inefficiency in the US healthcare system.

(09:51):
So agentic AI could really help improve the efficiency of
the healthcare system and us this two million gap of
people that we can't find to fill US healthcare jobs.

Speaker 4 (10:08):
Yeah, and we're going to go through a very tumultuous
and disruptive period of time, but for those who have
their sea legs under them, it should be a bit
of a fun and challenging time. I think the key
piece of advice I give to anyone in the working
world who wants to keep going out there and doing
cool things start using these technologies use JENAI. Understand it's

(10:30):
not perfect, but learn the ins and outs of these systems.
And Jenna, of course is just one facet of AI,
and it's very interesting. It does cool stuff. There are
many other facets of AI. Traditional or as I call it,
old fashioned AI is still out there doing cool things
where you build models to do predictions. As a company

(10:50):
I interviewed not recently called Safebooks AI, which is doing
really interesting stuff in the accounting space you're talking about,
and they can help companies avoid major problems like at
Macy's where this big chunk of money was not reported
as an expense, causing their accountants lots of trouble and
causing a lot of trouble across the company. That kind

(11:11):
of thing will be found by AI. And I think
we should also spin into the whole world of manufacturing
and how automation and even AI is coming into play there.
And you brought up the stat that I saw Alvin
Fu mentioned the other day, which is that China is
now dominating the automotive industry, not just the EV space,
which they are.

Speaker 5 (11:31):
They're number one electric vehicles.

Speaker 4 (11:33):
They are crushing it in terms of being able to
export cars they're number one in the world. The US
stinks compared to them these days. It's almost unbelievable, but
you have to look at the charts to see it.

Speaker 3 (11:46):
Right.

Speaker 6 (11:47):
So, for forty four years, Japan was the number one
exporter of cars in the world, and then China began
focusing on production capacity in ev and in twenty twenty
three it became the largest exporter of cars globally, just
blowing past everyone else Japan, Mexico, Germany, South Korea, and

(12:10):
the US. And part of it is the focus on
electric vehicles called evs. Seventy six percent of all EV's
in the world are produced in China. It has just
doubled down on this, and so while Europe and the
US are I don't know about evs, China is all

(12:35):
in on it, and this is in part leading to
their domination globally. And what we're seeing is a discussion
of tariffs in response. Tariffs are one of the items
that Trump has been talking about pretty prevalently, putting one
hundred percent tariffs on imported Chinese cars and so so

(13:00):
wherever there's a domestic auto industry, you're seeing governments talk
about tariffs. But for instance, in Australia, there's no domestic
auto industries, so what what is the benefit for them?
They get inexpensive cars like cars, right, So there's no
protectionism in any country that doesn't have its own auto industry.

(13:23):
So China is going to end up dominating global trade
with every country that doesn't have a domestic auto industry.
Right until finally, and then you're seeing news like Onto
announcing it's going to merge with Nissan and Mitsubishi.

Speaker 5 (13:46):
So I saw that that's.

Speaker 6 (13:48):
Three Japanese car companies who are being crushed by the
growth of Chinese evs. There have been japan Japanese car
companies very very slow to shift to evs, so they're
getting crushed and is their solution to merge?

Speaker 4 (14:08):
Right And these days, speaking of technology and data and
analytics and ERPs and things of this nature, it is
a lot easier thanks to AI and machine learning and
automation to pull off mergers and acquisitions, and therefore I
predict we will see more of these coming down the
pike because it's much easier now to get an analysis,

(14:29):
a real brass tax look at what our assets are,
how do they align with the assets of this company
or that company? And you can get a real clear
view because hitherto you were just kind of guessing. I
mean you're guessing on the personnel side, on the manufacturing side,
and all kinds of things, and hoping it's going to
work out, and it could take years to really make

(14:52):
that acquisition or that merger. Gel that's not going to
be the case in the future, as I see many
organizations are going to do. When you just amtan on,
they're going to merge to stay alive. Like I said,
we're going to go through a very tumultuous period here,
But for the agile I think it's going to be
a win win.

Speaker 5 (15:08):
What do you think I'd agree?

Speaker 6 (15:10):
I think I made a prediction that we're going to
see half of the traditional car companies, legacy automakers of
gas cars disappear over the next ten years. They're going
to either go bankrupt they're going to merge. As in
the case of Honda, Nissan and Mitsubishi. There you have

(15:32):
three car companies combining into one. You've lost two car companies.
So we're going to see a shrinking of the legacy
auto industry. And this next stat is shocking to me, Eric,
but Tesla right now today is worth more than the
rest of the auto industry added together.

Speaker 5 (15:54):
I mean, it's amazing. That blows me away.

Speaker 6 (15:57):
If you don't think electrification is going to change the
ten trillion a year transportation and logistics market that stats
for you has the value of the industry.

Speaker 5 (16:09):
Yeah, that's amazing.

Speaker 4 (16:10):
And it goes back to innovation and to doing things
in new ways.

Speaker 5 (16:15):
You know.

Speaker 4 (16:15):
I attended the Reuters Next conference. I guess it was
two Novembers ago. I spoke last November and two Novembers
of guy I attended, and a girl from Rutters was
interviewing the Alex P. Morgan I think his name is.
He's the CEO of JP Morgan or one of these companies.
I think that was Gorman was his name. Very very
smart guy. And she's hounding him basically, trying to get

(16:38):
him to admit that they made a bad bet on
Twitter because they threw some money in to help him
buy Twitter, and he just he shook his head for
a couple of seconds and then he said, we're not stupid.

Speaker 5 (16:49):
I couldn't believe it.

Speaker 4 (16:50):
I was like, Wow, what an answer he goes. Anyone
who doubts Elon Musk should go visit a Tesla factory
and then you'll understand because these folks thought through the
entire end to end process and they re envisioned everything,
and that's why they're able to achieve these amazing numbers.
And that's what innovation takes. It takes unlearning your biases

(17:13):
and then relearning something new, and that I think is
going to be a hallmark of success in twenty twenty five.

Speaker 5 (17:18):
Final thoughts from you, Well, I love.

Speaker 6 (17:20):
That that Tesla is vertically integrated. In other words, it
makes the engines, it makes the chassis, it makes the batteries,
it makes the wheels, it makes the seats, and therefore,
to your point, it can control the production process from
end to end and drive efficiencies out of it. Legacy

(17:41):
auto no, they have thousands of suppliers and so to
actually change is very hard since there isn't that vertical integration.
So I agree with you absolutely, We're going to see
big changes in the auto industry in basically every industry
with AI and automation.

Speaker 4 (18:00):
And retail, we'll see a lot of cool retail stuff, healthcare,
of course, financial services.

Speaker 5 (18:06):
The pinch is everywhere.

Speaker 4 (18:07):
Folks will send me an email if you want to
be on this show Inside analysis dot Com.

Speaker 5 (18:11):
That comes right to me.

Speaker 4 (18:12):
We'll be right back here listening to Inside Analysis.

Speaker 2 (18:22):
Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.

Speaker 4 (18:29):
All right, folks, back here on Inside Analysis, and I'm
so excited today is part of our cees preview to
get a newbie, a guy who is not new to media.
He's not new to technology. He's not new to the
sports and fitness or interviews or tech or understanding what's
going on in the real world out there. None other
than John Meyer of the podcast bearing his name, and

(18:52):
we're pretty excited. He's a fellow Pennsylvanian. And John here
in Pittsburgh they call folks yinzers or like yin's hey,
yins guy, and it was like.

Speaker 5 (19:01):
Yins, What on earth is yins? What are they talking about?

Speaker 4 (19:03):
I have a theory on that which I throw at
Pennsylvanians and they like it because if you're from Pennsylvania,
you're a pennsylvani yin, right, So multiple pennsylvani yins yins.

Speaker 5 (19:13):
It's at the end of the word. That's my theory.

Speaker 4 (19:16):
I don't know if it's true or not, but hey,
you're on the other side of the state. I don't
hold that against you Eagles fan, tough team Stullers.

Speaker 5 (19:23):
You know we're usually a tough team at a bad
few games lately.

Speaker 4 (19:26):
But tell me a bit about your passion for sports
and fitness and where that dovetails with technology like wearables
and all that kind of fun stuff.

Speaker 7 (19:35):
Eric, thank you for inviting me on to the show.
As you mentioned John Meyer from Meyer Media also to
John Meyer Podcast, I've got a theory now that you
mentioned for your yins it could be going along with
ying ling beer and brewery, just saying I'm sure you're
familiar with it.

Speaker 4 (19:51):
Someone else said that too, So yins ying's yin's yings ye.
I could see after a few yings agains, you know,
it starts to slur a little bit.

Speaker 7 (19:59):
That's after a few yinglings it will slur just a
little bit. So, Eric, my huge passion is around media
content and creating. As you mentioned, I am a nube
to CEES, the first one we've been accepted as media
joining at totally overwhelming right now. I mean, I've been
to Vegas so many times, but coming to this event,
I just realized this is the like the super Bowl

(20:21):
event of technology for consumers. You mentioned fitness and everything.
Obviously a huge Eagles span, die hard, don't hold.

Speaker 8 (20:29):
It against you.

Speaker 7 (20:30):
On the other side of state, I'm actually hoping for
maybe a PA Bowl.

Speaker 5 (20:33):
Would be really cool to watch. Wouldn't that be great?
That would be the first time in history.

Speaker 7 (20:37):
And yeah, obviously I'm rooting for that, you know, and
I'll root for the Eagles to win, but you know, hey,
we'll have a good time, and they're going down Broad Street.
We'll definitely see some of the Steelers fans talking about fitness.
Been in fitness all my life, started out at a
very young age, and I just it's a natural thing
to do. Some of the technology that's out there to

(20:58):
track it. I saw some of the things that are
going to be happy in a CEES the wearables, not
only the headsets, but the gear, the recovery stuff, the masade,
everything around them. Just looking at how to improve my
game but also recover from injuries or from workouts much faster.

Speaker 5 (21:15):
Yeah, that's good stuff.

Speaker 4 (21:16):
And you know, I talk with someone else just today
about the wearables, and here's what excites me about that
stuff is the data that you can capture for individual health,
for population health, to understand how is the workout affecting you,
what is.

Speaker 5 (21:32):
Good for you to eat, what's not good for you.
I mean, I think.

Speaker 4 (21:35):
Hitherto most healthcare has been boiled down to very generic basics.
I mean they take your vitals, your heart rate, your temperature,
stuff like that.

Speaker 5 (21:46):
Okay, that's useful.

Speaker 4 (21:48):
But when you can start absorbing massive amounts of data
about how your body acts, when you sleep, when you run,
when you walk, when you're sitting, all these different things,
I think we're at the beginning of a fascinating age
in which we can get a much better understanding of ourselves,
how we sleep, and how to improve our lives every day,

(22:08):
how to improve our health. What do they say, if
you don't have your health, you don't have anything, right.
So I think that in sports, of course you have
to be very fit, and so in that world it's
a whole other level of intensity and practice and work,
and you want to know what's working with not working.
I think we're at the beginning of a golden age
because of that.

Speaker 7 (22:28):
What do you think, Eric, You actually just touched on
a bunch of things, because that actually crosses over into
healthcare if you think about it. You talked about you know,
going into a doctor and they just take care of
simple vitals, your heart rate, do your blood pressure. But
how would you love to walk in there with all
the data and say, hey, listen, here's what I got
from my fitness, here's what I got from allmine, and they're.

Speaker 5 (22:50):
Like, wow, I wish we had this.

Speaker 7 (22:53):
Now you can present them. Problems are easily solved. You
can avoid injury, you can avoid long term illness. There's
so many things that you can understand as a person
to do much better, so you actually avoid the doctor.

Speaker 5 (23:05):
Right, that's right. An apple a day keeps the doctor away.

Speaker 7 (23:08):
Baby.

Speaker 5 (23:09):
I'm all for that.

Speaker 4 (23:10):
And you know, I remember, I'm originally from Chicago. Saw
as a huge Michael Jordan fan and the guy who's
just a machine, I mean, honest to goodness, he's just
easily taught players of all time and he would stretch
before every game. I think it was like an hour
or so he would take the time to stretch. And
because of that and maybe because of his physique, he

(23:31):
was rarely injured. You didn't see that guy get injured
very often at all because he took care of himself.
And see, I think again with wearables you're gonna have
a You need software too to analyze the data understand
what it all means. Because telemetry data, when you look
at it, the raw data, it's like ones and zeros
and very incomprehensible stuff. But in a certain application you
can kind of start to understand how that's interesting and

(23:53):
compare yourself to other people, the people, your size, your weight,
your height, whatever. The more we can understand how our
body is responding to the world and to what we eat,
the foods we eat, how much we drink, what we drink,
I think it's going to really change and give a
lot more awareness and power to individual people to live
better lives. What do you think, Eric, the wearables?

Speaker 7 (24:17):
I saw one on. Therefore, you're wearing a pair of
pants right and ahead. All the RFID and all the telemetry,
all the data being pulled from it's almost like the stuff,
like the copper stuff you wear. But here you put
it on and you can actually start to read your
body and the metrics as are happening on your body,
your muscles, where they're tightening, where they're not working, where

(24:37):
you're performing, where you're not you're at the gym and
you do something you're actually doing an exercise, and if
you do it incorrectly, you're going to get hurt at
some point, not all the time, right away. This can
tell you how well you're doing immediately while you're doing it.
You can read all this data efficiently right on your
phone while you're at the gym, be like, oh man,

(24:59):
I need to stop running now. Normally you like run
and you do thirty minutes of cardio while this is
already reading immediately what's happening to you and be like, hey, listen,
you need to you know, shut it down and go
into a soft mode so that you don't injure yourself.

Speaker 5 (25:12):
Right, No, that's right.

Speaker 4 (25:14):
Feedback, I mean, everyone loves a feedback loop, whether that's
from your colleagues or friends or your boss say hey,
good job, or okay, maybe better next time, whatever it is,
it helps to have feedback. And now we have digital
feedback about what's really happening in our lives.

Speaker 6 (25:29):
You know.

Speaker 5 (25:30):
I came up with this concept a few.

Speaker 4 (25:31):
Years ago about big data, and I talk about something
I call real world data at scale, and that's where
we are these days. You can capture the data, you
can analyze the data, process the data, I think population helps.
You know, in San Antonio they have one of the
highest heart attack rates.

Speaker 5 (25:47):
Well, why is that? You know it's probably because.

Speaker 4 (25:49):
Of all the food and tied hide fat for example.
You don't really know that, But I think, like I say,
until now, we've kind of been guessing for a lot
of stuff, and now we're going to be able to
tell and validate and understand from the data itself, how
this affects us, How this makes me feel better? What
doesn't look when I'm feeling down, Why is that We're
going to have a lot more information? And to me,

(26:11):
that's just supercharging this life.

Speaker 6 (26:14):
Eric.

Speaker 7 (26:14):
When you talk about feedback, think about the feedback that
you receive from colleagues, right, constructive criticism. It's very tough
for a lot of people to take and to receive.
But if you have a device telling you one hundred
percent honest, this is what's going on and this is
what's happening, you're more have to believe it and adjust
to it, rather than somebody telling you you might take

(26:35):
a while to you adjust or until you experience the injury.
And like I should have listened to them here you
listen immediately.

Speaker 5 (26:43):
That's funny.

Speaker 4 (26:44):
Well, Another one of my fun lines is machines don't lie, right.
I mean, machines are just saying what's on the information
that they're giving, They're just giving it back to you.
And even simple things like your steps or your screen
time or whatever it is. It's like if you take
a minute to look at that how your behavior is
day to day, and then you can start to improve things.

Speaker 5 (27:05):
And people want to improve. Like let's say you want
to lose some weight.

Speaker 4 (27:08):
While you can weigh yourself every day and that helps,
but it's just once a day, right, or maybe twice
a day or something. But to have this constant feed
giving you information and give you insights and giving you
a nudge perhaps. I mean you're starting to see some
of that in the tech world too, like, hey, starting
to get laid.

Speaker 5 (27:23):
Maybe you should go to bed.

Speaker 4 (27:24):
I mean, personally, I don't like the school marm apps
on my phone, but I do understand it's good to
get feedback and to understand what's happening.

Speaker 5 (27:32):
Right, Yep.

Speaker 7 (27:33):
I'm definitely looking forward to not only the sports, the fitness,
the wearables, just the information the things that I can
do to not improve my personal fitness in life, but
see what's out there?

Speaker 5 (27:44):
Mm hmm, well and CS.

Speaker 4 (27:46):
I mean it's I've only been to one last year,
and it's just massive. I mean it's so big that
they don't just fill up the exhibit halls. They fill
up with the Venetian Lots of companies will rent hotel
rooms for demo and things of that nature. So it's
just like five convention centers strung together. You're talking like
one hundred and fifty thousand people gathering.

Speaker 5 (28:07):
And there's a lot to learn.

Speaker 4 (28:09):
I mean I try to keep an open mind every
day and learn about new technologies. And we are in
an era where because of AI, to a certain extent,
we're able to do things that we were not able
to do before. We're able to optimize things right, we
can crunch the numbers and understand you know, again, like
with media.

Speaker 5 (28:26):
What what do people want to watch? What do they
I want to watch? Now?

Speaker 4 (28:28):
I will say I have some pet peeves with software
as a service and some of the data we get
from these engines. In fact, we're going to do a
show next year called Lies, Damn Lies and SaaS Statistics
because it's all about like, all right, is this real?

Speaker 5 (28:42):
What are you talking about?

Speaker 4 (28:43):
You throw some money at YouTube or LinkedIn or Google, like, oh, well,
look at all these impressions you got, And I like,
who are these people?

Speaker 5 (28:51):
Are you sure like you have new subscribers? Do I really?

Speaker 9 (28:54):
Yeah?

Speaker 7 (28:54):
Yeah, are they real? Let's just put it that way,
or they're a new one. But then they just they're
not there anymore. It's a dummy count.

Speaker 5 (29:01):
You have to wonder.

Speaker 4 (29:02):
I mean, I'll just give you a stat The other
day I saw because I threw some money at some
of these YouTube videos, and there were two hundred and
sixty one views. And they look at the demographics and
every last one of them was a male.

Speaker 5 (29:14):
Over the age of sixty five.

Speaker 4 (29:16):
Now, when you have like eleven strata of years, like
you know, eleven to fifteen, I can't remember what they were.
And then the last one is sixty five plus and
I got two hundred and sixty one viewers. You need
to tell me every last one of these viewers. When
I go viral and old folks home somewhere, what the
hell's going on here?

Speaker 5 (29:33):
I don't think that's real. So I do think there
is a.

Speaker 4 (29:36):
You know, a bit of a hedge on the excitement
around the data because we need transparency. That's one of
my mantras is, you know, the more transparency we get,
the better because we can all see what's really happening,
then we can all improve it.

Speaker 5 (29:48):
What do you think about that?

Speaker 7 (29:49):
I agree, And you got two hundred and sixty one views,
but you probably got two hundred and eleven likes, and
they're like, Okay, is that really possible that these people
all likes my video? Because I'll tell you what, when
I go and watch a video, I try to do that,
but I don't do that all the time.

Speaker 4 (30:06):
So right, Yeah, when I saw one the other day
where it's it gave a keynote and at first it
was like twenty thirty four, and then over like two
weeks it was ten thousand, ten thousand views and zero
likes and no comment. Obviously it wasn't zero. There was
zero comments and just like twelve likes or something. I'm like, hmm,
to ten thousand people really watch this and no one commented.

Speaker 5 (30:27):
It was pretty forward looking stuff.

Speaker 4 (30:28):
I basically said that before too long, AI is going
to take over the judicial system, at least for civil cases.
I think you're going to be able to just upload
your case into a folder. The defense uploads their case
into the folder. You do an interview on camera to
an AI engine, a chatbot, and then it goes all right,
sting guilty, paying five thousand dollars next, and like no

(30:52):
one commented on that, like wow, wow.

Speaker 7 (30:54):
I can see a lot of comments on that. It
will definitely clear a backlog, but I think it will
also create a some frick.

Speaker 4 (31:01):
My theory is that people will have a harder time
lying to a cold computer camera than to a person.

Speaker 5 (31:08):
That's my theory. I think it's going to be hard.

Speaker 4 (31:09):
I don't know why I think that, but something tells
me it's like oh, because the other side of AI
is that these models are getting so good they can
tell like your behavior, like is the acting shifting? I
mean there are little tells you can learn just from experience,
Like you know, when you ask our question, someone looks
down and blinks as they talk, Well, you know, they
probably did something wrong or they're probably not being straight

(31:30):
with you, and these algorithms are going to pick that
stuff up.

Speaker 5 (31:33):
I mean that's I guess.

Speaker 4 (31:34):
The last question I'll throw at you is, as we
face this world, this AI overthrow as I call it,
because it's going to be everywhere it's already everywhere. It's
going to be in every major cloud based application that
you use. There's going to be some aspect, whether under
the covers or right there on the surface. What are
your thoughts about this changing dynamic and how we as
humans can remain in charge.

Speaker 7 (31:56):
I think one of the things about remaining in charge
for is validating it and always having a governance around it.
Not AI governing AI, because that's like the government governing
the government.

Speaker 5 (32:09):
It just doesn't work anything.

Speaker 7 (32:11):
Because I think for us with AI, let's test a
little bit on the audio and video stuff that are
happening that cees and AI is a huge part of that. Now,
a lot of the videos that are created today, some
of the stuff is spoofed from video from AI generating
on it. How do you tell the difference? I think

(32:31):
there needs to be a governance around it in order
a tag or something within the video, even if it's remade, recaptured,
you know, whatever screenshot, there needs to be a way
to tag that that is AI generated in order for
us to understand what deep fake is and what is
happening out there. But I also believe there's so many

(32:51):
positives for AI and around video and audio because it
has helped us in our business nowadays be more efficient
not only creating the video, but to fine tune in
editing and cleaning up some of the stuff. So, circling
back to your question, I just think there needs to
be a way that we got to put something in
place to monitor what we're creating from AI and to

(33:12):
validate it, whether it is using a third party AI,
to validate that that is happening, because not a human.
It's suffer a human to understand that that's AI generated
in some cases.

Speaker 4 (33:25):
Right, I think you're right, and I'm sure some smart
people are working on this right now. My theory is
that you can do a number of different things. One,
you can use the metadata that is baked into cameras.
For example, when you take a picture with all these
new iPhones and Samsungs, there's metadata layer that captures where
you were at the time, what time you took this thing.

(33:47):
All this information is captured in the video. So to
be able to capture that and say, ah, this is
a clean video, this has not been edited, this has
not been doctored.

Speaker 5 (33:55):
I think that's the key.

Speaker 4 (33:56):
In probably QR codes too, like a watermark of some
kind of qr CO. But I think you're right. We
do need to have some governance around this because otherwise evidence,
even in courts, is going to be out the window.

Speaker 5 (34:05):
But folks, look this gentleman.

Speaker 4 (34:06):
Up online, John Meyer. That's m ye er the John
Meyer Podcast. Look forward to seeing it at CES.

Speaker 5 (34:11):
We'll be right back. You're listening to Inside Analysis.

Speaker 2 (34:13):
Excited, Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.

Speaker 4 (34:27):
All Right, folks, welcome back to Inside Analysis and our
CEES preview for twenty twenty five. And folks, I'm super
excited to have a fellow Yinzer, a fellow Pennsylvanian on
the call here today, doctor Frank Vigiano. What's new, doc?
That's his brand, that's what he talks about. He has
been a judge at CEES for a very long time now.
It has been focused on the healthcare industry for years

(34:49):
and years and as a regular TV and radio personality.
So I got to ask you, Frank, what's new?

Speaker 5 (34:55):
Doc?

Speaker 3 (34:56):
So much you know with CES, I mean I've been
going for it'll be my believe it or not, my
fifty seventh year attending CS. So they started in New
York City. I'll send you a clip. What was interesting
is that CBS in Las Vegas did a special when
I arrived there seven years ago. My badge said fifty

(35:17):
years and had a special ribbon, and I saw this
is great. So we started, you know, networking with a
bunch of different people talking about badges, and I learned
that I was only one of two people that have
attended every csin since the origination in New York City.

Speaker 9 (35:34):
Yeah, pretty crazy.

Speaker 4 (35:36):
Well, and as you know here in Pennsylvania, the number
fifty seven has a certain meaning, right HEINZ fifty seven.

Speaker 5 (35:42):
So it's all coming full circle.

Speaker 4 (35:44):
Now what do you see what excites you about CEES
twenty twenty five.

Speaker 3 (35:49):
Well, there's so many new technologies. I think it's branched
out into We talked a little earlier about healthcare, and
that's a real big important category. People are finding there
were rings, they're wearing, watches, they're wearing, they're wearables to
give them insight into their body and what's happening. And
there's more and more apps being created. So that's a

(36:10):
big growth area for CES. And you still the standards,
you know, you know, television appliances, I mean, it's all
still there, but these other new categories. For example, in
the automotive section, a lot of these manufacturers are exhibiting
now at cs where they, you know, had never done

(36:31):
so before, and the reason being is that they had Detroit.
So when the pandemic came, everything stopped and nobody went
to the Detroit show because they canceled it. They had to,
they had no choice. So because of that and kind
of the combination of CEES growing and inviting more auto

(36:51):
automobile manufacturers.

Speaker 9 (36:53):
To exhibit there, they had a whole.

Speaker 3 (36:55):
Area on the in the north hole where the majority
of it was all automobiles, different things to light, our
radar sensors, all different technology, and the vehicles themselves.

Speaker 4 (37:07):
You know. You know what really excites me because I'm
a data guy. I focus on data, artificial intelligence, big data, leveraging, data, telemetry.
We have all this data these days, and we now
have the compute capacity and the algorithms to analyze data
at scale and really understand what's going on. So when
I think about population health ory, even individual healthcare. Yeah,

(37:30):
a few years ago we had a few things. They
take your vitals, they can do blood draws, they can
look at you through different lenses. But still for those
who have paid attention. It's a relatively opaque window. I
mean it's hard. I mean it's translucent a little bit,
but you really have to know what you're doing to
figure out what these scores mean on blood tests and

(37:50):
things of this nature. But now, if you have one
of these wearables and it's keeping track of your pulse,
of your blood pressure, of your steps, of just everything
about you what you're doing, I have to believe that
in the near future, if not already, we're going to
have tremendously improved capabilities to understand what is my health,

(38:11):
what are my problems, what are the because once you
understand leading indicators, you can start watching for things like diabetes,
for example, or respiratory problems or whatever.

Speaker 5 (38:22):
And I think we're.

Speaker 4 (38:23):
Going to enter in age and as long as we
can hack through hippo regulations and security and compliance and
all the concerns about those things, I think we're going
to enter an absolutely golden era of personal health care
and population health.

Speaker 3 (38:36):
What do you think, Well, I agree with you totally absolutely,
and it's being welcome. I think initially, for example, when
light arc came out and some of the technologies for automobiles,
the biggest concern from consumers was, oh, that's recording me
while I'm driving, and the rest is recorded, and many
things do not record. The only thing they do is

(38:58):
they process the data for if I'm driving and I'm sleepy,
and i start to drows a little bit sensus that
I'm not awake from and I'm not driving the vehicle
or at least be paying attention to the vehicle as
it's moving. So therefore that's going to create all kinds
of issues. It's going to slow that car down.

Speaker 9 (39:19):
They could even bring.

Speaker 3 (39:20):
It to a stop until I, you know, wake up
and say, oh no, I'm here, I've got the steering
WM in my hand, that type of thing. So I
think what's important is that the fear that people have
of their privacy. And honestly, Eric, there is no more privacy.

Speaker 5 (39:37):
Yeah.

Speaker 4 (39:38):
Well, I hosted a stage at the Data Universe conference
in New York back in April, and they had me
host the privacy stage, and the first thing I said
is privacy is dead.

Speaker 5 (39:48):
Forget about it.

Speaker 4 (39:49):
I mean not that we shouldn't aspire towards respecting privacy.
I think that's still a very worthwhile goal. Yes, big
companies want to protect your sensitive data. But to think
that your data is not everywhere is just a naive perspective,
it seems to me, right.

Speaker 3 (40:05):
Oh, Relliant, I mean, if you have a cell phone, really,
are you kidding me? They know where you are every
minute of every second regards to where you go.

Speaker 4 (40:13):
Right, that's right, And sharing data is valuable, I mean,
especially in the healthcare space again with all these devices.
Now you have to normalize the data. You have to
attend to lapses in data. That's one of the issues
with IoT, right, is that you do have lapses of telemetry.
You have to watch out for that and model for that.

(40:35):
But nonetheless, I mean, I think at some significant scale,
and I'm sure universities are working on this and major
manufacturers are looking into this, there is just an absolute
treasure trove of information that we can now finally access
and analyze to figure out absolutely what are the behavioral
patterns that lead to better health?

Speaker 5 (40:55):
Right.

Speaker 3 (40:56):
Well, a good example too would be a device Let's
say that send a message to a physician and they're
monitoring in a certain let's say, a certain part of
the body for a certain reason, and here's an alert
that comes over. So my goodness is this is like,
we got to take care of this immediately. When it
saves someone's life and that information is shared with others,

(41:17):
that's huge. So people don't worry any more about privacy
because guess what it's superseded what's needed to save a life.

Speaker 9 (41:24):
I mean, how could you be upset with that?

Speaker 4 (41:28):
Well, and you know, I'll make an analogy here to
maintenance of machines like trains on automobiles and things of
this nature. We've had predictive analytics in the industries where
they could afford it thirty years ago, even possibly forty
years ago, like in aeronautics for example, with airplanes. And
what they've learned is that if you capture all the data,
the censored data, you could tell hey, if this part breaks,

(41:51):
that part's going to break shortly thereafter. So you sense
and wait for this part to you know, to reach
a certain threshold. And I was fascinated to learn that
sound is a big part of that whole game, because
when they start squeaking, they make little sounds like a
timing dolt making a sound like, hm, I got a
problem here, you have to address it. Well, if you
think about how that changes the attitude of the person

(42:13):
monitoring this information beforehand, the guy on the train would
just have to go check every door, look at every wheel,
and you're never ever going to find it, not never,
but almost never going to find anything. But if you're
acting on data from the system that's telling you a
problem is coming, then you're attentive and you're focused and
you know where to focus your attention.

Speaker 5 (42:33):
So to me, that is a.

Speaker 4 (42:34):
Massive sea change in operational efficiency and engagement with the user,
with the human right and as the old saying goes,
what gets measured gets managed. So I think it really
changes the game for how people can pay it and
they don't want to pay too much attention, don't want
to get all worked up about it, but still to
know that you have this problem and have some way

(42:56):
of measuring it, that's going to be huge for improving
healthcare overall.

Speaker 9 (43:00):
Right, no question, absolutely, Yeah.

Speaker 5 (43:04):
That's good stuff. Well, so what are you excited to
see at CEES?

Speaker 9 (43:07):
Are you going to be see a couple of things
that are interesting? Now?

Speaker 3 (43:09):
LG has come up with some great technology. I mean
they have for years, but they just launched about a
week ago. They teased it at CEES twenty twenty four.
But now they've launched it and it's the first seat
through old LED television. Oh wow, you see through it now?
People say why or what is so the idea is

(43:31):
that many I think you know that from interior design perspective.
Many people say, I don't want that TV in my bedroom.
I don't want it in the living room. I don't
want to see it, citty now, I don't want it
hanging on the wall whatever. And we understand, we get that.
So what LG has done is they've taken an ol
ed television and you can see right through it. So
you put it in the picture window of your home,

(43:52):
let's say, in front of an ocean view, a mountain view.

Speaker 9 (43:55):
It's incredible.

Speaker 3 (43:55):
And then what happens there is a black screen that
rolls up when you want to see TV, and when
you do that, it blocks a life from going through
and therefore the image is just like it would be
if you were watching a regular television.

Speaker 9 (44:10):
It's amazing.

Speaker 4 (44:11):
Probably, yeah, it's it's fun to watch all these innovations.
In my goodness, you've seen many of them. You're fifty
seventh cees going to.

Speaker 3 (44:23):
When I was on you know, seven years ago. Of
course this was big because everybody's like CES fifty years.
So they said, doctor Frank, what was the deal the
first CES? What was it all about? And I said
color television. It was in New York City. Wow, And
they showed color TV. That was such a breakthrough. I mean,

(44:46):
you know, and I mean I remember all this was
like my father, He's the one that really got me
into electronics and technology.

Speaker 9 (44:53):
He was a physician, but he loved tech, loved it.

Speaker 3 (44:56):
He bought a Hoffman solar radio from Hoffman Electronics in California.

Speaker 9 (45:01):
I still have it.

Speaker 3 (45:02):
It was AM batteries in the back and I took it.
I said, can I take it to the pool? He said, well,
this is not cheap, and you know if you take
you better be careful. I said, okay, so, mister magician.
You know, I get it to the pool. All my
friends are standing around, We're listening to the radio, and
I pulled the battery out. Everybody's like, what you know,
it's still playing and it's not plugged in. That was

(45:24):
solar Yeah. I was nineteen. Let me think what that
would be about nineteen fifty seven.

Speaker 4 (45:33):
Nineteen fifty seven, Oh my goodness, and solar power. You
look at I mean, I just I wrote it on
LinkedIn today about and we'll look for solutions at CEES.
What just happened in Puerto Rico. Eighty percent of the
power is down now in Puerto Rico. And you know,
we've seen the horrible things happening over in Ukraine where
the Russians target the energy infrastructure, and it just.

Speaker 5 (45:55):
Tells me we got to get off the grid, man.

Speaker 4 (45:57):
I mean, the grid is there, it's very powerful, and it
served society very very well. But nonetheless, I mean I
love to push for solar, I love to push for
alternative energy forms, and you know, let's let's try to
focus on that and really incentivize people to get off
the grid and just be more more sustainable. And that's
what sustainability is all about, right, I mean, we absolutely

(46:20):
numbers have to come together and make sense. But nonetheless
ces is the place to find that stuff too, right
of course.

Speaker 3 (46:25):
And you know the problem is, you know what I
never understood the iPhone, great phone, love the technology, love software.

Speaker 9 (46:32):
The people.

Speaker 3 (46:32):
I see them at airports everywhere, tethered to a wall
to plug it into charge, and it's like, hey, this
makes no sense. I mean, why can't we just replace
a battery. And Kia Sarah, by the way, is the
only company that I'm aware of currently they have a
they are products some model seventy two hundred. But it
is the only phone that I know that you can

(46:56):
replace the battery. So take the pack off.

Speaker 4 (47:00):
I remember, I remember being very annoyed about that with
the with the iPhone, and you know, you have to
ask yourself, yes. And I just had a guy from
Apple on the show moments ago who we were talking about,
Steve Jobs and his commitment to design being a philosophy,
because there's a great quote I read from him where

(47:20):
he said, you know, a lot of people view design
as a sort of veneer that you put on at
the end of the of the process of building something.
With us, it's central, it's the beginning, it's everything. Design
is form and function combined. And so I do respect
that and I understand it. But nonetheless, the batteries man
like to be able to get a new battery because
these phones, when they get older, the battery life gets

(47:41):
shorter and shorter and shorter and just gets more annoying,
you know, And so let's let's move in that direction, right, Yeah, Well, the.

Speaker 3 (47:48):
Keith Sarah, which I like about it. You can, like
I say, again, swalk the battery. Here's the best part.
Three hundred and fifty dollars. That's it only available through
ver Ozon. But when you have a few young people
saw my phone and said wow because I dropped it
and I said, well, you can drop this in six
feet water and nothing happens. And the glass on the front,

(48:09):
it's got gorilla glass, so you can't crack it, you
can't scratch it.

Speaker 9 (48:12):
It's unbelievable.

Speaker 3 (48:13):
I mean, it's really yeah. And so and they said
three hundred and fifty dollars. I just paid thirteen fifty
for an iPhone. I said, well, what the market will bear.

Speaker 4 (48:25):
Yeah, that's that's a lot of money. And you know,
the cool thing about cees is that it is a showcasing.
One of the things I learned last year when I
went to my first one. So I don't have nearly
as many in my history as you do. But one
of the cool things I like. I think it was
LG in fact, who were showing me around and all
their cool little things. And what they do is they
have prototypes that they bring to the conference and then

(48:45):
they'll have cameras watching who goes by which object and
how long they stay there. So they're using data about
how sticky these these technologies are to determine which ones
to push into production, because obviously it's a big risk
pushing some prototype be a production. You got to spend
a whole bunch of money, get a whole bunch of parts,
hire people, all this stuff. So you want to know
what the winners are and what the losers are or

(49:07):
the ones that probably will win. I thought that was
a brilliant strategy and it just shows a real comprehensive
view of the of the problem space and the opportunities.

Speaker 5 (49:17):
Right.

Speaker 3 (49:18):
Well, that's exactly what they did last year was they
called old ed T for transparency, so they headed out.
People were like just blown away, Oh my goodness, this
is incredible technology. So they got feedback and here we
are a year later and now you can buy it.

Speaker 1 (49:32):
Wow.

Speaker 5 (49:33):
I love that.

Speaker 4 (49:34):
Well, look this gentleman up online, folks, Doctor Frank Vigiano.
Sounds like a good Italian name. It was from down
the road here. I'm up in Gibsonia, Pennsylvania. He's down
the road in Indiana, Pennsylvania. But what's new, Doc that's
his whole tagline. He's been doing this stuff for a
long time. We look forward to seeing you at CES
in Las Vegas. Don't touch that del folks will be

(49:54):
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let workers set their own pay Since the nineteen seventies,

(53:22):
when the idea began taking hold in corporate America, pay
levels have zoomed up by more than one thousand percent. Well,
not for you. This set your own pay movement has
only been available to top corporate executives, whose medium paychecks
now top sixteen million dollars a year. But since it's
been such a boon for this test group, I say

(53:43):
it's time to expand the no hassle compensation concept to
all employees. This would greatly boost grassroots purchasing power, economic growth,
and fairness for all. Oh my god, no squawk corporate
apologists rushing to say that technically, CEOs do not directly
set their pay. Rather, the bosses have attached their earnings

(54:05):
to their corporation's ever rising stock prices. Thus, astronomical rewards
go to those who obsessively focus on jacking up the
price of their own stock, even though that's a selfishly
narrow and false measure of a corporation's performance. Also, stock
price is no indicator of a CEO's worthiness. Even bosses

(54:27):
who are blockheads can still get a boost simply because
they've rigged the system to hitch a free ride on
inflated stock value. This is Jimhita saying still, if it's
good enough for them, why not an equal deal for
working staffs who actually deliver the products and services that
give a corporation some true value. I say each worker

(54:49):
should get the same percentage increase in pay that the
top haunch ho takes. It's a very simple process and
it's only fair. The high tier ratio lowdown is made
possible by you subscribers to GYM high Tar's lowdown on substack.
Find us at gymhiitar dot substack dot com.

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have no need to come up with additional funds.

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Speaker 17 (56:40):
NBC News Radio I'm Lisa Carton America is paying tribute
to former President Jimmy Carter. The thirty ninth president, died
last weekend in his home state of Georgia at the
age of one hundred. His motorcade arrived at the Carter
Center in Atlanta Saturday afternoon, where he will remain for
several days for public viewings. On Tuesday, the late president
and his family will travel to Washington, d C. Where

(57:03):
he will lie in state at the US Capitol and
a state funeral will be held Thursday at Washington National Cathedral.
Senator Amy Klobashar says the US capital will be secured
tomorrow for the certification of the twenty twenty four presidential election.
Appearing on CNN's State of the Union, the Minnesota Democrats said,
in the years since the January sixth insurrection, officials have

(57:25):
upgraded security measures in Washington, d C.

Speaker 11 (57:28):
We have, as you note, a new police chief, increase morale,
many hundreds of more officers, and we have a plan
and a strategy in place.

Speaker 17 (57:37):
Vice President Kamala Harris will preside over the certification of
the electoral votes for president tomorrow, marking the first time
since two thousand and one a vice president has presided
over the certification of their own election loss. Over sixty
million Americans are being impacted by the first major winter
storm of twenty twenty five. Kansas, Kentucky, Arkansas, and Virginia

(57:59):
have declared states of emergency, and southern states like Florida
and Mississippi are also warning of dangerous cold. A blizzard
warning is in effect, with hazardous driving conditions possible today
from Kansas to West Virginia. Forecasters say the storm will
impact the mid Atlantic region by this evening, dumping heavy
snow on Virginia, Baltimore and Washington, d C. Filmmaker Jeff

(58:22):
Beina is dead at the age of forty seven. The
writer and director was best known for films like Life
After Beth and The Little Hours. He was also married
to actress Aubrey Plaza. Authorities believe he died by suicide.
You're listening to the latest on NBC News Radio.

Speaker 1 (58:39):
NBC News on KCAA, Lomolada, sponsored by Teamsters Local nineteen
thirty two, Protecting the Future of Working Families Teamsters nineteen
thirty two, dot org.

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Speaker 13 (59:55):
A Welcome to the Fabulous Lifestyle radio Show.

Speaker 4 (01:00:07):
Tune in for a vibrant mix of fashion