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Right, folks, hello, and welcome to the only coast to coast radio
show in the US of A that's all about the information economy. It's time
for Inside Analysis, or truly Eric Kavanaugh is here part of the dm Radio
Broadcasting Network, and folks, I'm very excited to have my good buddy and
broadcasting legend Jim Harris on the line. He is quite the social media influencer
these days, has been for a while. He's been going to Davos for
years, he's been going to the World Economic Forum, he's been going to
CEES, the Consumer Electronics Show for years, and in fact, he is
almost always one of the top voices individual voices for social media amplification at these
events. I get his tweets all the time, and they're always very carefully
crafted. He always finds really interesting trends to talk about, really cool stuff
like solar panels or new technologies that are coming out. Of course, drones
are everywhere, and today we wanted to talk about what we saw at CEES
and what he saw at DeVos. I've never been the DeVos, at least
not yet, but maybe next year I'll be rubbing elbows with the world shakers
and movers out there. But Jim, welcome to the show. Thanks so
much for doing our program today, And real quick thoughts on CS. What
were your impressions from the show show this year? Well, Eric, great
to be on the show. And for both CEES and for DAVOS, the
top topic was AI. And every year Google has its Google Io Developer Conference,
and you know, it's a multi day event. The CEO has a
couple hour kenote and somebody summarized the entire keynote and it's into about twelve seconds
and it goes something like this AI AI, we're embedding AI, taking generative
AI, AI, AI AI AI. And really that is what came out
of CEES and out of Davos. The top trending hashtag for both events was
AI, and I'm sure it will be for Mobile World Congress at the end
of I Burian start of March in Barcelona, which is the top global mobile
connectivity event. So AI is the hottest topic, and it has been since
chat gptt launched November thirtieth, twenty twenty two. Now, I've been talking
about AI for a decade and people just didn't get it. But once chat
GPT had this conversational interface, people understood it, and it is the hottest
topic. Yeah, I mean, it absolutely is. And it's funny because
chat GPT was not new. There were earlier versions of it that some people
were already playing around with, so it had been there. But I think
it's it's just the fact that they did this rollout and so many people jumped
on to check it out, and their minds were just blown, right,
I mean, their minds were absolutely blown. And even if Sam Altman says
strange things now and again, like you said, the year of large language
models is over, and everyone was like, uh, are you are you
talking? Who are you again? I think he was talking about small models,
which is very interesting. There's mistral, there's this sort of they call
it a combination of experts or mixture of experts or something, which are smaller
models. And my buddies from the Northeastern University, the Institute for Experiential AI,
are saying that you don't have to be bigger to be better with these
language models, right, and so there's a tremendous amount of innovation in that
space. I mean, it's really it's kind of blowing my mind. It
reminds me really of the had Dupe days. For those of you who did
not know about had Dupe. It came out of Yahoo. It was their
engine for indexing the web. It was turned over to Google. They open
sourced it, and then companies like Cloudera jumped on and Horton Works and map
r and at one point there were like seven distributions of headdoup of the headdoup
distributed file system hdfs, and then there were six, and there were five
of four and three and two and one, and they're like, Okay,
that was too much. I feel that way about these models, all these
foundational models. But if you look at where it's all going, I mean,
these AI models will absolutely revolutionize consumer software, enterprise software. They're being
baked into everything. I mean when I walked around at CEES, many of
the booth were talking about AI and their smart devices and their smart systems.
Right, So it's already permeated the electronics industry, right. Yeah. AI
is being embedded in hardware, in software, in internal company processes, in
external customer facing applications, so it's being embedded everywhere. And I go to
more than sixty conferences a year ERIC and at everyone, AI is the top
trending topic. And if you're a startup seeking to raise money from a VC
and you don't have the words AI jen ai in your pitch deck, you're
not getting funded. So it is this relentless drum beat. And I believe
that the innovation we're seeing right now around AI and jen ai is like the
birth of the Web in ninety three. And in nineteen ninety three, we
had the Internet beforehand, but it was hard to use, it was cryptic,
it was user vicious, and you know, you had to remember Eunuch's
command. So it was there, but nobody in the general public knew about
it. Researchers in academ and in the military knew about it, but the
people didn't know about it. And once the web was born and you could
surf around just by clicking with your mouse, it exploded. And so I
was telling my clients in ninety three, this is going to change everything,
and they looked at me, some of them like I had two heads,
you know. But here today we can see that it is baked into everything
thirty years later, and the same is going to happen with AI and generative
AI. But the pace of change is infinitely faster. We're turbocharging change right
now. Yeah, yeah, that's exactly right. And I should say there
are lots of other traditional kinds of technologies that were at the show. I
stopped by the Logitech suite and I checked out some of their new stuff.
It's very interesting the Logitech Reach. I did want to give some time to
those folks. Very simple product. The announcement date was, I guess back
in September, and what's cool with this? So it's basically it's just it's
called the Logitech Reach. It's just a boom device for your camera, basically.
But they did a couple of really clever things, one of which is
a kind of handle you can grab onto such that the perspective doesn't change when
you go around a table, like if you can imagine going around like this
showing different objects on the table, it will keep the perspective the same instead
of turning things upside down. Right, you have to kind of see it
to appreciate it. But you think about how many creators there are out there
for this whole creator economy. You think how many people are trying to do
training online all this stuff. And what's really cool about this is how they
launched it. So Logitech went out and they did a go fundbe campaign when
they basically said, hey, this is a new product we're going to develop.
If we can raise this money, this is what it'll do, and
if you want to be the first one to get it, just go ahead,
donate some money to this gofundb page and we'll have you on the top
of the list. They sawd out in like five minutes. They raised all
the money in five minutes. I'm like, what, that's amazing, What
a clever plan. Now, this is really interesting. This is innovating in
how we innovate. Historically, companies spent two million dollars to develop a new
product and then launched it and it might go well, it might flop or
whatever. With a GoFundMe campaign with crowd sourcing, hey do you really want
this? They totally de risk innovation. That's right. And I was on
a program with a global leader from Lego. You know, Lego's pretty classic.
It's made out of plastic. It comes in little squares and shapes and
as pegs and you walk it together. We all played with it as kids.
Well, what they've done is they've said to kids, hey, if
you want a certain type of Lego set, you tell us what it is,
and you get ten thousand of your friends to say they want it to
and you do the marketing, like show us what a campaign would look like.
They make all the little children do the work, and then they once
if they get ten thousand kids excited about it, the product launches. That's
amazing. Any guesses on how big this crowdsource business is? Yeah, I
feel like Doctor Evil. A billion dollars a year, A billion dollars a
year off of child labor, right, yeah, right, that's that's pretty
funny. Well, and they're not the only ones, right, So this
is a trend. And I noticed that the Consumer Electronics Show at CS and
it was so cool to see you there. And folks, we had our
own booth, our own recording studio for DM Radio. So we'll be back
next year if you want to be recorded. Eric who for the other media
that we're in our block, Oh, Reuters and CNN and Bloomberg and the
Associated Press and DM Radio. Yeah, baby hit the big time. But
one of the other companies we looked at was LG Life's Good and they gave
me a whole tour of their technologies and they did something very similar to what
the folks at Logitech are doing, and that they bring out all kinds of
prototype technologies and put them on the show floor and then they watch and see
who likes this, who likes that. It's a brilliant strategy for knowing what
to work on right And these days, man, when margins are tight,
when the economy is you know, it's not in recession, but it's teetering.
It's just strange. It's a very strange economy right now. You have
to be careful about stuff. And I think you nailed it when you said
they have d risked innovation and that is a big deal, right, big
big deal. Yeah, you know, because knowing where to place your bets
is so important, especially in manufacturing because it's got such a long process from
concept, ideation all the way through manufacturing, production, distribution, et cetera.
Well, Logitech with this deal, they expert. They greased those tracks,
just greased tracks when right into production. Bang this thing out and it's
great. It's very simple, but it's something that wasn't out there before,
and they're catering to what the audience wants, right, So I just want
to stress how de risk is. Imagine I went to any executive, any
CEO, and said, hey, if I could take your innovation pipeline and
guarantee you one hundred percent that every new product would be successful out of the
gate, well, right, and it would make you a billion dollars?
Would you pay attention? You should? We should? We really look at
how do we innovate, and how do we innovate and how we innovate?
Yeah, innovation innovation. You gotta love that. It's very meta. It
is very meta innovation innovation. It says crazy and the who else did we
see? I mean I walked around quite a bit on the show floor.
There's LG of course, well MasterCard, right if you look at the interesting
stuff MasterCard was working on. We interviewed Raja Raja Manar from MasterCard. He
is the communications VP, as ever called, and they've done something very interesting
which I think you're going to see in other iterations in other ways where they
got together with a bunch of trusted content creators and they used existing content to
train a model to be a small to mid sized business AI assistant to help
you launch your business. How do you launch a business, how do you
set up your books, how do you get clients? All these little things
that are very useful. And what's cool about this is again like people have
to understand these publicly trained models like chat, GPT, there's a ton of
information in there. I mean, it's going to take us years just to
figure out what's in there. That's how much is in there, right,
But the problem from a business perspective is that's too obtuse. There's too much
information, it's too broad. Now these systems use waitings basically, that's what
they're really doing. And there are tokens, which token is like I think
half a word they say. And in the early days, the AI could
see one token behind it and one token ahead and that's how it actually cranks
out the right. It might blow people's minds to know that. And then
these transformer models came out where now you have almost like a Greek chorus of
recommendation components, little bots, and then there's still one decision maker that goes,
Okay, I'm gonna go with this token decative. But the point is
now they can see like five six tokens right, five six tokens left,
and that's how they develop things. But the point is the more focused you
can be. And this is what they talk about when they talk about embeddings,
for example, and training your model. You can really do it one
of two ways. You can fine tune the model itself on the data you
train it on, or you can put all your embeddings into a vector database
and get this RAG model. What they call regenerative augmented or a retrieval augmented
generation, that's what it stands for, came out of Facebook. And those
embeddings are your moorings basically that those are your anchors of truth that allow that
optimize the quality of the the content that you get back. But that's how
these things work, right, And as you get more and more narrowly focused
on things. That's when you get tremendous value. That's what my buddy,
doctor Sama Fayad says from the Institute for Experiential AI Northeastern University. The narrow
the better. In fact, one guy said, narrow AI is the only
AI. Is that what you're hearing as well? Well, I want to
take it and comment on something else first, to go to a big picture,
which is many people don't know this, but more than ninety nine percent
of companies in the US are SMEs, small and medium sized enterprises small under
one hundred people, medium under five hundred people more than ninety nine percent,
and SMEs by definition can't afford an AI data scientist on staff. So MasterCard
is aiming at a market that really is the engine of our economy globally and
in North America definitely to help them. So a friend of mine is engaged
in a startup. He works, he's working with a hospital and he's used
chat ept the developer model to ingest all their policy manuals, you know,
and so it's a very tightly defined base of knowledge. And you're a frontline
manager in the hospital and you have somebody who's got poor performance. You don't
have to go read sixty pages of manuals on how to you know, do
a performance improvement program. You just say I have a poorly performing employee,
what do I do? And the model will just spit out one You write
them a letter saying your performance is poorer to you develop a PIP performance improvement
program. You know, you do this this, and if after three warnings
they're still bad, you fire them. So but who has time to try
and figure out what to do? Or imagine all government documents for the US
government being ingested, so you don't have to figure out right about how to
fill out this form or do I need that form and this form? So
imagine just making everything simple for people. So this is an amazing set of
outcomes and MasterCard is aiming to do this for the SME market, which I
think is really cool. Yeah, and we're I said, We're going to
see more things like this, And you know, Frankly, I'll be giving
a speech a talk at the Data Universe conference in New York April tenth and
eleventh, and I'll be talking about the death of journalism. And what I'm
talking about is how traditional journalism models organizational structures are out the window. It's
gonna be impossible for them to outflank these large language models. They're gonna have
to join them and they can't beat him. You got to join them.
But folks, don't touch out. Dog'll be right back. You're listening to
Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
All right, folks, back here on Inside Analysis with my good buddy
Jim Harris, broadcaster, extraordinary social media master. He's he's very good at
that kind of stuff. It's spreading the word amplification. Reach out to us
if you want your event amplified, send me an email info at inside analysis
dot com. And we're talking about AI as the top topic at the Consumer
Electronics Show and it was also the top topic at Davos. And you know,
in the last segment, Jim, you were just outlining some of the
most awesome use cases for AI, which is you feed these models. And
I think most companies are going to want their own private model to not have
to run the risk of putting their PII or their IP intellectual property out into
some public model because we really don't know what happens. I mean, chat
GPT is a black box at this point in time. Now, Facebook,
I think Lama is open source. So people were screaming buddy murder about that.
But hey, man, I say, hats off for going open source
with this stuff. But you were giving on this really fascinating model use case
for AI, which is take volumes of policy documents. So think how governments
actually run the laws, the regulations, the policies, all these things.
You can feed them into a large language model and then you don't need to
go pouring over details to try to understand things anymore. To get the policies,
you just ask your little interactive chatbot, your little buddies, say hey,
how do I fire this person? Or even simple stuff like how do
I registered to be a foreign agent? That's a big thing in the US
these days. Ooh did you be a foreign agent or not? What does
the process look like? Well, hitherto that is something that only the highest
paid lawyers in the country could help you do. Now you don't need that.
Now I talk to someone who put together a legal document and memorandum just
using chat GPT brought in and like showed it to a layer. It's like,
wow, did you become a lawyer he's like, no, I just
used chat GPT. And yes, there was that story of the one foolish
lawyer who wrote something up and didn't bother to check his sources. But I
almost wonder if that wasn't sort of amplified as fud, you know, fear,
uncertainty and dread, like, oh, you don't want to use this
technology, it'll be dangerous. Like so people making five hundred dollars an hour
to do basic law work, right, that's dangerous to their business model.
But goodness gracious, you can learn so. And from a government perspective,
that is just the most amazing thing ever, because most people want to follow
the rules, they just may not understand the rule. And now they have
an easy on rep to understanding what that stuff does. What do you think,
absolutely we have to pay attention to this. At the Consumer Electronics Show,
the CEO of Intel had a keynote where he was being interviewed by the
Nasdaq reporter for a media outlet, and he said, we're seeing ten x
one hundred x one thousand x ten thousand x productivity increases use cases. And
I thought, is this just hyperbole or is it reality? So I went
back and I thought about it, and here's my testing of his statement.
So Kathy Wood, who's the CEO of our invest on Wall Street, who's
brilliant. I love what she says. She focuses on exponential change and disruption.
Her team has said that using generative AI, teams that do coding programming
will be ten x more productive than teams that don't use AI by twenty thirty.
And it's not in either or. It's an end. It's you can't
just give your programming to AI. Just like anything I get back from GPT,
I don't put out in the world. It's a first draft for me.
It gets me sixty to ninety percent of the way there. So the
base case of tenx is Kathy Wood. And then in Davos, I put
this statement to a panel and a woman who was a marketer said, you
know, back in the nineteen eighties in the nineties in advertising, when we
had a client, we'd sketch out all these boards by hand for an advertising
campaign. It would take us two weeks. And I thought about Madmin,
you know, very mad man, right, But today she said, using
chat, GPT and mid journey, it takes us two hours. And I
did the math on that, and that's a five hundred x increase in productivity.
Now, her next point was, that's not end to end in the
process, you still have to meet the client. There's no productivity gain there.
You still have to negotiate the final campaign and pricing and everything else.
But for part of the process, there's a five hundred x productivity improvement.
So that was kind of the mid use case. And then I began thinking
about the ten thousand X statement. And every year I go to a conference,
it's my favorite conference every year. It's called Nicks med It's looking at
the future of medicine, and a PhD student will spend a whole year studying
the interaction of a single protein with all other proteins in the human body to
try and understand the emergence of cancer. Takes a whole year to do this
study. Well, they gave this process to AI and got back six hundred
and forty four million years of PhD equivalent work. So there's a use case
that six hundred and forty four million X productivity increase, which is way bigger
than ten thousand. So in the end I agreed with the CEO of Intel
statement ten to ten thousand x productivity increases, but it won't necessarily be across
an entire chain of process. It may just be part of it. And
the other thing that the panelist in Davos said is, you know, when
we get these games, what do we get with them? So rather than
just having a couple of boards to show a client, we might actually develop
five full commercials with AI. In other words, we get five hundred x
time back, but we'll spend half of that giving them more choice, richer
choices. So all these productivity gains don't go to the bottom line. Some
of them go to more enhanced outputs. Yeah, and that makes a lot
of sense, and again it's such a game changer. I had a company
called alter X on a webinar a few months ago, and they're a business
intelligence analytics kind of company and they have something called Magic Documents, which is
their use of JENNYI and it's absolutely brilliant. I was blown away by this.
What they do is they allow you to take a report, let's say
from your ERP system or your P and L or something, some report that
is generated by a trusted system that you use a source of record, A
system of record, I should say, like your ERP for example, and
you generate your report, it's all numbers of how many products were sold in
things of this nature. Then you load that into their system and you give
it the target that you want, like PowerPoint for example, or an article.
But the PowerPoint one is quite impressive. And say, generate a fifteen
slide presentation based on this report, and it goes z and just bangs it
out, complete with graphics and bar charts and pie charts and all this kind
of stuff. And then you just go through and make sure that it got
things right. But the point is you don't have to write that thing from
scratch. Can I tell you how much time that just saved? Not just
time, but painful, unpleasant time. I mean to be honest admission here,
I've never been a fan of PowerPoint. I don't like how it was
set up, the structure. I get where it came from. I understand,
I just don't like it. It just kind of annoys me. And
here just steam rolls right through that process. And then what you could do
is say, oh, now I want to write this for the IT team,
or I want to write this for the marketing team. I want to
write this for the Board of Directors, and it changes the tone and it
changes what goes into it such that you can have five different versions of the
same report for five different groups that you banged out in like two three hours
instead of two to three weeks. That's like forty fifty x improvements and it
just, you know, it just solves these painful problems. Now you can
spend your time absorbing the information, talking to the board, talking to the
marketing team to figure stuff out, because that's still going to be a human
process. Right. But you said before the show we were talking dull,
dirty and dangerous, like all that kind of stuff is out the window.
Those are amazing stories of improvements. And then you look at Davos and what
these folks you're talking about. These are CEOs, These are government leaders.
These are people who are responsible for the well being of thousands and sometimes millions
of people. Guess what that needs to be top of mind. I'm guessing
it was. Tell us it was. And you can apply AI to some
challenges that you might not think of. So we need to explode exponentially the
amount of batteries that we have globally to electrify transportation, for instance, and
get away from fossil fuels, and some people criticize EV batteries because, for
instance, some of the old ones have cobalt in them, right, which
is a conflict mineral or their child miners. Well, you can use AI
to look at new battery chemistries which completely eliminate the need for cobalt at all.
Right, And LFP lithium phosphate is lithium iron phosphate is an example of
that there's no cobalt in it. So now we had a problem with cobalt,
and it's cheaper and you can scale it faster, So we can use
AI to focus on problems. But again it's going to be the scientists and
AI working collaboratively. You can't just outsource problems to AI. People are always
going to be part of this, but they're unintended or surprising use cases.
So in Douvilas, I was talking to one executive who is really quite nervous
about speaking in front of groups, and his parents were having their fiftieth winning
anniversary and he hates like he's not a comedian or a humorist. He wanted
something that was poignant, that was funny, so he had chat gp T
right. The first draft his wedding toast for his parents, and then he
threw an iterative process. He refined it. Well. He gave this talk
at his parents' fiftieth and his mom came up and said, honey, that
was fantastic, and his son, who's eleven, says, yeah, Granny,
it was chat GPT that did it. Of course, Granny doesn't know
what the hell the eleven years talking about. We can use this. This
is a way of taking everyone and leveling up their skills in all sorts of
areas that they're not skilled at well. And you know, let's think just
real quick about the medical field where right now in the United States. I
don't know what it's like in Canada and the United States. It is so
segmented and compartmentalized, and you have different groups responsible for different things. I
mean, I've seen this up clost and personal and unpleasant ways. But the
point is you have to get disappointed with this person. They say, okay,
we should talk to that person. That takes three weeks to get to
that person. Then it takes another three weeks to get to some other person.
And this just takes time, and you're driving all around and waiting and
trying to get stuff done. Well, if you think about again, you
have to do this responsibly. But if you start, and I guarantee it's
happening already, you get these models and you start training fairly narrow focused models
on all the research that's available, and then any doctor turns into a ten
X doctor. Any generalist can understand what the specialists know based upon information that's
in the record, that's been understood, that's been research that's been shared,
frood vetted, And that's really going to be where the heavy lifting is done
now is in training the models and constantly giving them feedback. And this I'll
get back to the Institute for Experiential AI. Doctor Sama Faiad says, all
the time human in the loop, there's always going to be a human in
the loop. Don't think you're going to turn this thing on walk away and
have it do your job for you know, it is going to give you
all this additional information, all this additional context you're able to see in a
particular case. Well, actually, eighty percent of the time that we give
this medication, this happens unless there's this other medication that's contraindicated and you have
this problem. It used to be you had to sit around and read journals
to get all that stuff. Now you'll be able to just interact with your
little chatbot and you're in the hospital and say, what are the contrainteications for
this? Up? Up, up, up up up. You get them
all on the screen for you. What about that? What about for this
blood type? But up up It just all this stuff is going to come
flooding out and we'll finally be able to use this before the person had to
either remember all this stuff or have manuals at their side. And you know,
have you ever seen doctors looking at manuals when you're in the hospital.
No, they're never doing that. They're just walking around talking to people doing
their thing. Point being these technologies, this AI stuff is a force multiplier
in just about any category. And I'm glad. I'm very encouraged to hear
that. Folks at DABOS. We're talking about that one minute till the next
break, go ahead. So seven thousand new medical journal articles came out today.
So the question is, has your general practitioner as your doctor read them
all this morning? You know, So we're gonna have to use AI if
we want the best outcomes and it's not gonna be okay, the AI is
going to be your doctor. It's gonna be your doctor. Plus AI are
gonna wigh out perform doctors who don't use AI, and you'll have better health
outcomes. Yeah. No, that's exactly right. It's gonna be your doctor
plus AI. Your doctor is not going away, but you're gonna hope that
your doctor uses this stuff because they're gonna be way, way, way informed.
Folks, don't touch up that. I'll be right back. You're listening
to Inside Analysis. Expect Welcome back to Analysis. Here's your host, Eric
Tavanaugh. All right, folks, back here on Inside Analysis talking to the
legend himself, Jim Harris, broadcaster, a social media expert, world traveler,
sixty conferences a year and he does it every year. I don't know
how he does that. He jumps all off with the planet. Mobile World
Congress is coming next, folks, So if you want some amplification at Mobile
World Congress, send me an email info at Inside Analysis dot com. Email's
not going away, by the way, I just want to say everyone keeps
talking about the NB not going to happen. Email is still king for communication
for transactional from important stuff, send him an email, even if it's just
got to link in it to the zoom, like, where's he just got
my email? But everything is changing. It's changing at like rapid speed.
And I'm glad to hear that. At Davos and Cees, these were very,
very hot topics, and you know, we have to look on the
bright side. It's going to be really fun and interesting to dive into this.
And I'll give you my theory about how media is going to change dramatically.
You're going to see smart news organizations figure out that they need to leverage
their voice and train their own models on their content and their style, and
then start using this stuff to generate content. And then again the role is
going to be not so much the reporter but the curator. So you still
will want to check facts, You'll still want to want to check dates and
specifics and things of that nature, but you're going to I mean, what
I think is going to happen here is you're going to have systems of record
connected to llms spinning out useful bits of information or really persisting it in a
model somewhere, and then the reader like you, me, whoever else,
will have a much more personalized, engaging experience with the journalist, which is
going to be their own personal journalist. So you'd be like, let's say
I name my journalist Bob, Like, hey, Bob, what happened at
the city council, meaning last night with respect to the new tax cuts or
something? And I'll be like, well, Congressman so and so said,
that'll just tell you all this stuff. And then you're going to have this
interactive process where you're just asking it what's going on relevant to you. Think
about how much better that is for an individual. I think if you look
at traditional broadcast meeting, you've spent a lot of time in that world.
It was this one size fits all model where everyone who's watching the show is
watching it at the same time, stamp at the same continuums, getting the
same exact content. That's not good enough anymore. People need personalized content that
is relevant for them. And that's why I think broadcast is going to have
some trouble dealing with all this stuff. But how do you see I mean,
you're a broadcaster, how do you see the future of media panning out
here? Well, media is going to have to change. I mean,
I've been a journalist for forty years, and I've seen huge amount of change.
When I got into television, we used to have these million dollar edit
suites where we'd actually have tape to tape, the tape was edited and cut
with it zach donives and you know, with the scotch tape put back together.
And now you can do all the editing on a laptop or on your
even your mobile phone with titling and everything else. So I've seen head spinning
change over the forty years i've been a journalist. I want to give another
example that people might not think of, how chat GPT and Generative AI can
save up to three hundred and sixty billion dollars in the US healthcare system.
So if you go to your doctor and the doctor wants to order a test,
but the insurance company in the US denis paying for it, that doctor
has to write an appeal letter why I deserve to have this test done.
So there's this urologist who was writing about five appeal letters a week, taking
twenty to thirty minutes for each one. Now he just uses chat GPT without
using the patient's name or the insurance company. Basically instantly, it spits out
a letter as to why this particular condition needs this test, including citations from
medical journals. Wow, So take five appeal letters a week at thirty minutes,
and what is one hundred and fifty minutes every single week of a urologious
time worth who's on a quarter million dollar salary. You do the math.
It's big numbers. So we're going to see AI eliminating wasteful, inefficient administrative
work in the medical system. And that can be worth by one analysis of
a major consulting firm three hundred and sixty billion dollars a year. That's cool,
blows me away. I'll give you another example that comes from next Med,
which I mentioned in the first segment. It's my favorite conference. Well,
there was a doctor from the NHS in the UK National Health Service.
It is the largest single payer provider in the world. They do one hundred
and six fifty six billion pounds of healthcare funding every single year, and during
the pandemic, twenty five million people a year missed their appointments. Now this
is problematic because you know you have weight times to get an appointment with a
specialist, and then somebody misses their appointment. That's terrible. And there are
health consequences. If you're a woman and you miss two mammograms, you increase
the chance of stage three breast cancer by three hundred percent. Or if you
have a retina problem and you miss a single appointment, you can increase your
chance of blindness five thousand percent. So it's not just the twenty five missed
appointments, it's the medical outcomes of not catching things early enough. So this
doctor, very frustrated, looked at the twenty five million missed appointments and without
using any medical data, so he was only using databases with age, income,
geography, those kind of things. He was able to predict based on
history, who would miss their appointment for upcoming and then they do this eight
weeks before assigning appointments. They can then say, now here are some of
the reasons. It might be an elderly man who doesn't want to get on
the tube late at night because his eyesight is bad, right, so you've
given him a nine pm appointment and he misses it. Or it might be
a single mum. You give an appointment during the day, but she can't
afford to take time off work, so she misses it. So the system,
the AI predicts who will miss their appointment and then they get busy in
trying to mitigate that. In other words, give them the first choice of
appointments where they're going to actually show up and where this system has been tested.
It has increased the capacity of the NHS by six percent. Wow.
Now when you do the math on that, that's nine billion pounds of value
being created with no medical data, just normal parameters, so nothing confidential.
So this blows me away. We're going to be using AI for profound outcomes,
and if we don't use it, we're disadvantaging ourselves. Like, imagine
increasing the capacity of the healthcare system by six percent with zero cost. That's
just that's crazy. And you know, you reminded me of something. We
had Dan Bodner on the show a couple months ago. I guess he is
the CEO of Varrant v er I n T. Look those folks up online
and they've got thirty five bots now, And it was interesting. I was
talking with them about bots and you know, maybe they'll be richer, more
interesting, et cetera. And he goes, oh, actually, what our
perspective is that with a bot, you want it to be very very good
at one thing and then that's what it does. It does that one thing,
and it does it extremely well. And he was talking about scheduling and
what a nightmarre scheduling is for someone who runs a hospital, for the nurses,
for the doctors on call, for all this kind of stuff, because
hitherto the scheduling problem is highly manual, and the person in charge of scheduling
has to be like, okay, Bob is called in sick, Now who
can I call? And you have to go look up phone numbers and kind
of scratch your head, who can I get? It's painful, it's a
very unpleasant process, he said. What they did is they put a program
together. It's like a points system where if you agree to take someone's role
when they're sick, you get a few x your points. If it's a
really nasty shift, you get even extra points. And they keep track of
all this stuff. And now anytime someone's out, you just hit a button
brip, it goes ask him if you don't want to ask him. All
that stuff is automated. So now there's like that scheduling probably it's problems solve
done. You're like, what like that used to be one of the hardest
jobs for people who are an admin in hospitals and different facilities and like,
problem solved. Next, what else you got? Why don't you go talk
to the patient for a few minutes? Right? Oh, yeah, that's
right, the patients. So I mean, these are amazing examples of how
these technologies, these algorithms can tackle very complex challenges very easily for almost no
money. I mean, you have to pay for the software and get things
installed, but once it's there, that is a massive game changer. One
minute lefts what's your what's your advice to the world, Jim, Well,
we have to embrace this technology. If you look at a one hundred and
fifty years years ago, eighty percent of jobs in the US were in agriculture,
and there were huge protests when farm automation came along, tractors and combines,
like, what will happen to all the horse people, the horse people,
the horse people, the horse people. You know, here we are
one hundred and fifty, one hundred and sixty years later and agriculture employment is
less than two percent. So we survived farm automation, right, But what
is certain is that jobs changed. People had to learn and reskill, society
change. Companies had to change. So this is really a story not about
the technology, but about change, about reskilling. That's right, reskilling,
folks. Get out there, get out start using this stuff. Folks,
you've been listening to Inside Analysis. Respect. Okay, folks, time for
the podcast bonus segment here on Inside Analysis with our good buddy Jim Harris.
He's always fun to talk to, and I have to say, Jim,
there has been some unpleasant news recently. I saw and apparently you've seen it
as well. I saw City Group plans to lay off twenty thousand people.
SAP announced the layff of eight thousand employees. Eight thousand, and I think
they've got about eighty or ninety thousand employees. And I looked it up and
it said as of twenty twenty one, it's like one hundred and seven.
I think it's a little bit inflated. I'm guessing it's about ten percent of
their workforce that they're laying off. And they said it's a retooling for JENAI,
all right. I don't know if they buy that argument, but it
is certainly big news. Microsoft thing off nineteen hundred, so these layoffs are
coming fast and furious. What are your thoughts on that, and what does
it say about where we're going. Well, a couple things. One is
we're in a time that people have been predicting a recession for about two and
a half years now, so this is the longest time ever of a predicted
recession that has never happened. So one of the insights around this is,
yes, large companies historically are the largest net job losers, but recessions happen
when unemployment is high, and all these people who are let go have jobs
tomorrow. They're in high demand high tech work. So we have not seen
this recession come to fruition that has been predicted by economists for two and a
half years. So that's the good news. Now, if you are one
of these eight thousand people at SAP or twenty thousand or nineteen hundred at Microsoft,
what is the important message here for you? And job security today is
based on learning and changing and accepting on certainty, So people have to continually
be retooling, reskilling. That's what creates job security. Mm hmmm, yeah,
I think that's yeah, go ahead, no, no, you go
ahead. I think that's an excellent point. I do think that these new
technologies, this GENAI stuff, these are force multiplying technologies. I mean,
used correctly, one person can now be as productive as two or three or
four people just because of the time that they save and the creative juice that
they inject into the process. You look at chat ept bard is the one
that I typically use. There's clawed by anthropic. These are generative tools and
they are very powerful. I mean, it's amazing. They're very good at
poetry. By the way, if anyone is interested in writing some poetry,
they're actually quite good at that kind of stuff because all they're doing is reflecting
back what they've been trained on. Right. So, I think I was
talking to high On Park, a good buddy mind from Amalgam Insights, and
he was saying he thinks that these layoffs are prematurely announced and that these companies
really haven't yet figured out what they're going to be doing with these technologies,
and so you know what exactly is going on. It feels to me like
a cost savings measure, which is not new. But you know, his
argument was maybe they should have waited and to see how their organizations can embrace
these new technologies before going and laying people off. I mean, I think
he's right, But what do you think I would agree. One of the
things that caught Silicon Valley by surprise was Elon Musk buying Twitter and then laying
off seventy five percent, eighty percent of the workforce at Twitter, right,
and they that was head spinning and people were thinking, well, Twitter's going
to go down. It just won't be able to operate. But it's been
up the now. It has had a noted sharing there, but it had
an outage here and there before Eli bought it. But the point of this
is that we really need to look at what does an organization need in terms
of people, how does it operate optimally? And in fact, the amount
of innovation coming out of Twitter has been greater in the last six months than
the prior six years. There have been more features, and so this doesn't
seem to compute. How do you end up innovating more with seventy five to
eighty percent fewer people. So really it challenges everyone to say, our real
job is to innovate, Our real job is to keep our skills current.
And one of the insights from I know we're going to be talking about Davos
and ce Yes, but one insight was from DeVos is that when you talk
to CEOs, forty percent of them believe we're going to have to reskill forty
percent of our people within five years. But when you talk to people in
the organization on the frontline and frontline management, ninety seven percent of them say
we need training and development and reskilling right now, not in five years.
So there's this huge disconnect between what companies think they need to do in terms
of training, education and reskilling and what people on the ground are saying.
And given this earlier discussion that ultimate security is based on relearning and on continually
keeping up your skill set, we need to significantly invest more in training,
education, reskilling. Yeah, I think that's true, and I think you're
right about the fact that it is now, it's not in three to five
years. I mean, this JENAI stuff is, to be clear, focused
on a couple key segments of the enterprise software landscape, namely content creation,
namely chat bots for example, hr basic stuff that a model can be trained
on very easily or relatively easily. But there are lots of other spaces where
AI will in the future attack traditional enterprise software applications. So it's not everywhere
yet, but it's gonna be. I mean you have to think that these
new foundational models are going to subsume most of what enterprise software has been doing
for the last thirty forty years. And you look at an SAP with eight
thousand layoffs, Well, SAP's core business, that's EERP enterprise resource planning,
it's human resources with few glass you know, it's concur with expenses. I
mean, just real quick think about you see on TV now, I saw
an ad for one of these tax firms saying, oh, with AI,
now we can do your taxes so much faster. And that is true.
And I can tell you this, my friend, like pretty soon it's going
to be so much easier to do your taxes. You're gonna say, go
and just generate what you think is the case, and then I'll go through
and check and correct you on stuff. But then if your business doesn't really
change next year, it's going to be even easier. So the accounting world
is in the cross airs of jen Ai. I promise you final thoughts from
you, jib, I'd agree completely white collar work, there's profound efficiencies that
can be wrought out of our historical processes. And if you think about anything
that's dull, dirty, or dangerous, it's gonna be automated. Right.
Yeah, Well, if you just think about eighty percent of some phone calls
into call centers, it call centers are people simply resetting their password? Well,
couldn't an AI do that just as well with three point factor authentication,
your voice, the phone number you're calling in on, and then some pin
just as well as a human. Right boom, that's right, and I
think we'll close on that. Folks, Dull, dirty, or dangerous,
that's all in the crosshairs of AI. Folks, you have been listening to
Inside Analysis, don't miss admitted of the action. Check out the podcast.
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