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A podcast that explores how AI is transforming careers, businesses, and industries. Hosts Greg Boone and Erica Rooney deliver real-world use cases and actionable AI strategies to help professionals stay ahead of the curve.
If you really break it down, it's
about language and knowing where,
knowing how to become an expert
at using language to expand your
horizons and the quality of your life.
How much money you make, how
good your relationships are.
Et cetera.
Now, if you take this, we're talking
about it conceptually in an abstract,
if you really reflect on it, so you've
got a synthetic brain that's been
trained and customized and everything,
you know how to ask it questions,
you're gonna cut tasks by 90%.
AI isn't the future.
It's now, and whether you're in hr,
sales, operations, or leadership.
The choices you make today will determine
whether you thrive or get left behind.
Welcome to ai, voice or victim.
I'm Greg Boone, marketing
executive and AI series.
And I'm Eric Rooney, author, speaker,
and gender equality advocate.
And I'm AI curious and we are here
to cut through the noise and show you
how to leverage AI in your career,
your business, and your brand.
In every episode, we will break down real
world use cases and give you AI driven
strategies that you can apply immediately
ready to stay ahead of the curve.
Let's jump in.
All right, y'all.
We are here at Venture Connect
in Raleigh, North Carolina.
This is such an exciting time, and we are
sitting here with Fadi Hindi, who is an
AI and digital transformation advisor.
He is a professor, a senior executor.
Founder and y'all, he is really
into Porsches too, so I am
excited to talk to this man today.
How are you?
I'm doing great.
I mean, it'd be awesome if we can make
the podcast about Porsches, if you want.
Hey, listen.
A Im Porsches, we could
correct.
Would that work for you?
Or, oh, I
love that.
I love that.
I have like a, a fake Porsche.
It's a Ford Mustang Mach.
But one of my colleagues said, oh,
it kind of looks like a Porsche.
I'll say, all right,
I'll take it.
Yeah, yeah.
We'll take it man.
All we'll
take it.
You can print up a little
logo and throw it on there.
That's right.
But Fadi, tell us a bit about
yourself and what's got you so excited
about AI transformation these days.
Sure.
Um, so I've, um, I was raised here
in North Carolina, um, since I
about, I was about five, I guess.
Um, and we, maybe six or seven,
whatever the, the number is.
But I went to NC State and, um.
I come from a fairly technical background.
I studied computer engineering,
robotics, and AI back in 89.
So we're doing AI work in 89.
Um, before, you know, all this craze
that came about since I guess 2023.
Um, I think it started
a little bit earlier.
We could talk about it
if you're interested.
When I graduated, I actually
joined the consulting, uh, house.
It was, um, Anderson Consulting back then.
Which is, uh, now Accenture.
Yep.
So I remained technical, uh, throughout
the, you know, 12 or plus, you know,
years that I worked domestically before
I got hired to start doing global roles.
I. And, uh, I always joke, joke about
this because I continued technical
for about 15, 16 years, give or take.
And then my friends always
gimme a hard time about this.
I think you and I talked about it.
They call me the Darth Vader
of engineering because I
switched over to the dark side.
Oh.
And they, uh, they're like, oh,
when I, we meet and I'm walking into
the room, they're like, oh, stop.
He's here.
You know, they just start, they
just keep busting on me because,
uh, they say I'm a turn code.
So half of my career was business.
It, it was interesting
that transition because.
Uh, I was very passionate
about engineering and software
and hardware and everything.
I could care less about p and
ls and business and Strat.
It's like just a waste of
time because I'm an engineer.
And then I went over to the other
side and it's like, oh my God, this
is like, okay, now it makes sense.
We gotta worry about p and l. We gotta
worry about profitability and I'm not
gonna listen to all the engineers.
I just wanna play with tools.
That's not gonna work.
Um, but the 31 years have been
predominantly digital transf.
It's all transformation.
So.
Um, doing, looking at the
transformation of the organization,
whether it's technical, whether it's
business, whether it's strategic.
And as I got older and more like got
more senior roles, it was more about,
alright, how do we build a strategy that
makes sense from a business standpoint?
But then I had the advantage of.
Been like being technical, so we
could actually look at what could
be done within this particular
timeframe, this particular budget.
But it was a lot of automation, a
lot of digital, and I would say a lot
of AI from 2010, probably onwards.
I. So That's incredible.
Yeah, it's been, it's been interesting.
I love that we're talking on that.
It's like 1989 because I was
four years old at the time.
Oh man.
I know.
Never heard of ai.
Me too.
I was also, also four years old.
Never heard ai.
Hey y'all, I just joined the 40.
The 40 and up club this year.
But people are, people do seem
to be just terrified of it.
Right.
And Greg and I kind of have this
scale of AI adoption where we go
from anxious to curious to serious.
Right.
And it's a long scale, but.
So many of those people are residing
in the anxious area and they think
it's this new thing that's taking over.
So to hear that it's been around
since 1989 should be a bit
reassuring, don't you think?
It's been
around since 1950.
There we go.
Even better.
Right?
So, um, I always end up talking about
this when I talk to executives or board
members because they need that context to
really come to grips with what's going on.
Um, and I just say, look, this
is a, an overnight explosion
that's been in the making for.
Um, what is it, fif?
80 years?
Yeah.
Yeah.
That's a long time, man.
Right?
That is a long time.
Right?
So if you, if you think about it that
way, and, and the, the, the grandfather or
the founder of AI was Alan Touring, and I
always say for people that are intrigued
by it, go watch the imitation game, right?
And you'll see why that triggered.
He's this brilliant mathemat mathematician
from the uk, uh, English mathematician,
and he worked on the codebreaker.
It was the first machine that actually
did, um, heavy compute to, uh, be able to,
to break, like break the cipher basically.
But then you kind of move,
move forward from there.
So Aour defined the, the touring test,
which we all hear about, which is been the
golden standard for how will you be able
to tell if this is a machine or a human.
Yeah.
And we're blown past
that by now, of course.
Right.
You can't, you can't really tell anymore.
Um, but then it progressed from
that into an age of neural networks
and fuzzy logic which disappeared.
It might have been embedded in some
of the things that we're seeing
now, but that's a lot of the things
that we were working on back then.
And, um, you know, God bless him, my
professor, his name was, uh, Dr. John
Sutton at NC State, uh, hardcore man.
Just like he's awesome guy, but it's
just hardcore AI even back then.
Um, and, um.
That we were always, we would
get excited when we can get a
hundred images of something, right?
'cause you can actually train
the network, do some prediction.
Is this a flower?
Is it not a flower?
Right.
It's like, just to kind of crack a
joke, you guys have seen Silicon Valley?
The Yeah.
The show.
Yeah, yeah, yeah.
The show.
You like the one hot dog?
Not hot dog.
Dog hot.
Yeah.
Just that's a, not the hotdog.
Yeah.
It was almost like that basic
back then, or, you know, but
then it evolved from there.
So.
Then you started getting, they
supervised learning and machine
learning and unsupervised learning.
We can talk about those if, if you want.
Uh, but the real, uh, interesting
moment was when DeepMind, which, uh,
is a, a British company, um, that built
the algorithm that ultimately became
Google acquired them, became Alpha Go.
And that was the moment where.
It, the, it defeated the, the, uh,
go champion in China and it was an,
we, we call it an alpha go moment
because that's when it was like the
Sputnik moment for us in America.
Right, right.
And, um, from that point onwards, it's
just been an acceleration, an exponential
acceleration of, uh, the technology.
'cause Google acquired DeepMind
and they invested in the AlphaGo
project to, to kind of create.
Um, and we can talk about it Again, I
don't want to spend too much time on,
on, uh, one particular thing, but if
you're interested, we can drill into it.
That was a turning point, I think, for
ai, because they saw moves they have never
seen before, and the, the, the go champion
in China was flabbergasted because
there's like, I don't know, the number
of permutations it's like equals the
number of atoms in the universe or some
ridiculous, you know, number like that.
And they saw unconventional moves
that the AlphaGo machine was making.
And they're like, wow, okay,
this is, this could be a problem.
And this was back in 2000, um,
I'm trying to remember the date.
2015, 2016. And then, uh, Google started
using that because it was so successful.
Google started using that
for language translation.
So when you go to translate that
google.com, it's actually a lot of the,
the, um, the algorithms form DeepMind.
Um, and it was the birth
of large language models.
If you really, that that's
when it happened, right?
And then it was a convergence
of things, the advancement of
compute, the availability of data.
You got all the data you could
ever dream of, um, and then
advancements in the algorithms itself.
All of that convergence just kind of
gave birth to open AI and the chat bots.
And um, and it's actually interesting
you have time, go back and look at
G GT one and compare to GT four.
Mm-hmm.
It's like a massive
nine, massive difference.
I don't know the exact numbers.
Something like a hundred thousand
websites or something, or 10,000.
That was GT one and now GPT-4 and
Beyond is consuming everything
that is of a certain date.
Yeah.
So I know this is like a long, um,
primer in ai, but I think this is really
what's, what's going on and then the, I
believe my theory right now is that we
are, I'm gonna say this, I'm gonna stop.
Sure.
You have can ask whatever
questions you want.
We are, you know, about exponential.
Mm-hmm.
Technologies, right?
So I think we're past the
near of the curve now.
I really do.
I think we're gonna hit
singularity in a couple
years.
Yeah.
I mean there's a lot of folks that
are talking about that, so we, we
appreciate the story arc and the
backstory as I, we talk about this a
lot and you, you referenced it, AI's
been around for a very long time.
Yeah, right.
There's two moments, and I would say
in the last five or six years that
have really advanced the adoption
at the corporate level of ai.
I would say first is the pandemic.
Yep.
When, uh, everyone was saying, Hey, it's
gonna take two years to get a vaccine out.
And that's when like, uh, the
pharmaceutical company started whispering.
It was like, Hey, we kind of
got this secret weapon if we are
actually allowed to use it now.
Yeah.
That we can come up with
things a lot faster.
Right.
To your point, whether it's
chest moves or go moves, right.
There's opportunities there.
The second was the, uh, the
launch of, of open AI and chat
GPT in, uh, November of, of 22.
Right.
So late near the end of 2022.
And what always describe to folks is
this is finally the moment where they
start to see wide scale adoption.
Right?
You know, the other GPT acronym is
for general purpose technology, right?
Once folks started to define it as
general purpose technology and everybody
had it in their hands, now, that's when
you saw all the folks come out and say,
all right, there's enough air cover.
Not only can we use gen AI and then
we can use some of the, the ML and
some of the deep learning, some of
the other things that we've been
working on for the last 20 years.
Right, right.
Because now we have the air cover.
Now it's socially
acceptable enough, right?
Yeah.
Everyone's not adopting it, but
people recognize that it's there.
Yeah.
Right.
So those things I think have
advanced it in the last few years.
To your point though, but it's
been around for a very long time.
This is your AI curious girl here.
Are there any stats on like the percentage
of adoption, like a, I don't know, not
just men or women, but people in general?
Who are actually using
Very low.
Yeah, very low.
Very low.
I mean, it's shocking to me,
but I run into it every day.
Yeah.
And, and, and a lot of the
consulting work that I do, and
this is very like, specifically
targeted over the past six months.
Actually, my post this
morning was about this.
I'm disappointed with the level
of adoption and the level of
the fud, the fear, uncertainty,
and doubt that we see from, I.
Executives, you know, from business owners
and it's, um, it's just unfounded because
I think of the fear mongering that goes
around around ai, it, you know, it's
news and bad news, you know, brings on,
brings home the cash kind of a thing.
Um.
But I think the reality is if you, if you,
if I look at the past six months, it's
been, it hasn't been as, it's not what you
expect it to be like everybody's doing it.
Actually, it's quite the opposite.
I mean, if you had a pie this
big, I think people that are
doing AI might be like 5% of it.
It's tech companies that are using ai,
the pioneers of individuals, you know, but
then you've got maybe a 20%, 25% kind of,
if you look at a Venn diagram or whatever,
that's actually people talking about it.
And then the rest are not
doing anything about it.
Right?
We don't want it, we don't like it.
Take it away.
I don't wanna deal with it.
Keep it at an arm's length.
I'm gonna fight it, I'm gonna block it.
You know, all these things
that are really happening.
Uh, but to, to Greg's point,
if you actually look back, um,
the machine learning aspect of
it, so as we got out of the.
The winter freeze from the, whatever,
from the eighties and nineties, and we
got into more of, of these algorithms
that can identify patterns into two
separate, uh, branches, supervised
learning or unsupervised learning.
And I, I was, I've studied, uh, the course
of Dr. Andrew, uh, in from Stanford for
like six or eight months as I was doing
an, an AI consultancy, uh, uh, overseas.
It was very powerful.
Right.
And that is more, that has more of
a hold today than generative ai.
It's just, I'm talking
about enterprise now.
Mm-hmm.
Right.
So you've got, and you could classify,
you've got like the big Boys Enterprises,
bank of America and so on and so forth.
They have dedicated programs that
are looking at future ventures.
So they are gonna, they are
investing in this right now.
How much of this is getting adopted?
I'm not talking about bank
Medicare, I'm just talking in
general about bigger enterprises.
How much of that.
Research getting integrated
back into the enterprise.
I would say it's very low.
It's just my take on it.
Then there's a, a group of companies
that have been using this machine
learning stuff for a long time because
it's predicting engine failures,
like, you know, very, very expensive
engine failures like airplane engines,
rolls Royce or ge, whatever it is.
And being able to do unsupervised
learning to actually detect, use
thousands of parameters to be able to.
Hone in on one or two engines that are
having a problem out of this production.
You know, it's extremely valuable
and it's, people see the light.
There's really no convincing.
Those have been around for a long time.
Right.
I mean, it's not new and that's
been the case all the way through
to, I would say, I mean probably
the until GPT one came out.
Yep.
But the mass adoption
was around GPT-3 or 3.5,
right?
Correct.
Yeah.
And I would say that the, uh,
you know, so to your point,
corporations have been using it.
You know, folks say,
well, I don't want it.
I'm not adopting it.
You've been using it for years.
That's right.
Right.
The fact is you're unaware.
Right.
Okay.
That's a different topic.
I. Right.
But say that you're anti ai.
Okay, we'll stop using
Google Maps in ways.
Yeah.
If you're gonna cut it off,
then cut off all the things that
you're getting to benefit from.
Right.
You just aren't recognizing
that that is also in the same
category as machine learning,
just like Gen ai and then, uh, Dr.
Uh, Andrew Eng. Right?
Yeah.
I think they have videos
on, uh, deep learning.
Oh, at ai.
Oh,
yeah.
That's the latest venture he's working on.
So I, I listen to and watch a lot of
the videos, part of the coursework
that I, I do, and talking about.
Agen AI and this and that, right?
But we can go, that's a different
topic for a different day.
I think, you know, when we talk more
broadly about AI adoption, I say this
all the time, folks, is my sincere
hope that more people start to wake
up and understand how they can use
this to, to get productivity gains.
How they can use this to, you know, have,
uh, basically it's a PhD in your pocket.
Right.
So, and people ask me all the time
about, you know, I'm getting all these
certificates and things, this coursework.
It's like, yeah, because
I'm very inquisitive.
I want to, it's the first time in
my history where I can learn so many
different things at a level that that
doesn't take me months and years.
To learn.
Right.
And so even if you're just using it to
get smarter about whatever it is, it could
be just getting smarter about the world.
Yeah.
Right.
And so for me, the adoption far
exceeds the, the technical folks and
then some of the marketing folks.
I was just at a conference in Vegas
last week and one of the things
I kept saying to folks, I'm like.
So you guys keep talking about AI
adoption, but you're talking to a
crowd that already believes in ai.
That's right.
I said, but this is the first technology
in our lifetime that affects every
single employee in an organization.
That's right.
So if we talk about it in historical,
digital transformation moments, right?
Most of the time it's contained
to this is only gonna impact it.
So you're only concerned about one
or two potential saboteurs, right?
Or this is only gonna impact the
marketing organization, right.
This impacts everyone.
And so it's not surprising to me
that when I look at organizations
and companies that are saying, Hey, I
have these great business use cases,
but I can't get the team to adopt it.
It's like you haven't addressed
the fact that these people
think it's gonna take their job.
That's right.
And you seem highly surprised.
That's right.
That they don't wanna lean into
this business case that you have.
Yeah.
When you haven't solved for them
individually in their careers.
Right.
And so that's where I'm kind of
my head is at, and that's why.
Recently I, I got a c uh, certificate in
ai, uh, specialization in AI for business,
uh, from from University of Penn because
it was focused on non-data scientists.
It was focused on people management.
It was focused on functions that
aren't highly technical by design.
That's why I took that, that course.
So I was trying to understand how
we get more people to adopt this.
Well, and that tees it up perfectly.
I was at a AI networking event and I
heard the best quote ever, and it's
like, AI will not take your job, but
people who know AI will take your job.
And so I've been using that when
I've just been working with the
women that I coach and it really
kind of clicks for them there.
It's like, okay, it's not this robot
that's, that's taking over what I do.
It's not removing the human.
But with all that being said, what
are the different roles that you
are seeing being impacted by ai?
You know, um, you said
something that is spot on.
This is a general exponential
technology, so there nothing is safe.
I mean, I can't think of
a, including consultants.
I can't think of any single job
that will not be distracted.
Absolutely.
Consultants.
Oh yeah.
Actually consultants or lawyers
probably are gonna be at the
top of the list for disruption.
Um, and talking about like, uh, you know,
big firms, the big firms, they know it.
They have the, they have the
appetite to say, if we don't do
something, we're gonna wipe out.
Like Baker McKinsey, you know, um,
Alan Ovary and those guys, right?
So they're onto that and they've realized
that they've got to fundamentally change.
'cause somebody, you know, switched on.
Others are just, you know, they
just, they've just resisted because
of the, you know, think about cost
factor, um, the privacy, security.
There's just so many
things that come into play.
But there's, if you guys are interested
in some insights, I'd love to share some.
Please do.
You know, so if I look at the span of the
31 years and then the early, um, AI work.
And by the way, we're embedding
AI algorithms in robots.
We, I was the, uh, uh, I was the captain
and a team leader for a period of two,
two to three years for the, for the
robots that NC State were building
for the Mars Mission Research Lab.
We worked on a ed robot and we worked
on a rover and it was, the mission of it
was sponsored by GM and Motorola and like
a lot of money that was getting pulled
into research and it was looking at.
Building robots this again, back
in like nine, like 1990 timeframe.
Um, they, we were looking at the best
way to be able, there wasn't, um,
enough satellite and real time feedback.
And so there's a lot of work around
embedding intelligence using sonars to
be able to map, uh, for the robot to
actually figure out from sonar readings.
This is before lidar, that sonar
readings from all the sensors around.
It's got a, it's got a map of a
certain typographical area of Mars,
like a spot, and it will use, it
will do the navigation, but motor
errors in a long run over a two
hour period is gonna throw it off.
It could be, you know, miles off
from where it originally started.
So use sonar sensors to get data
and compare the projected, uh,
where it currently thinks it's
at versus what it's reading from.
That sonar sensors and it would do the
math and say, okay, I'm actually off
by five degrees and it will adjust.
So yeah, it was fairly intelligent
for 1990 if you really think about it.
And there were other robots.
We had a bi bed, like a, a robot that
uses two legs to climb stairs and go,
there's just a lot of embedded systems
and it was super cool way back then.
So if you look at the progression of
that and then you get into more of the
advanced machine learning that we got,
that is more, that's more complicated.
You're gonna have to do derivatives,
you're gonna have to do no calculus.
You know, one, two, and three.
Most likely.
Even beyond that, you
have yet linear algebra.
I'm not throwing these to.
Make it sound like I'm actually
throwing these out to say this
is the insight that was a barrier
to entry for the average user.
1998, I worked on a robotics
project at North Carolina a
and t that shout out to Dr. Yu.
There you go.
I, I was chosen to work with, uh, her
grad students and basically my role
was to write the code to keep basically
this robot in this contained area.
Right.
So in my mind, very cool.
I was, I was making this Land
Rover type thing for Mars Rover,
whatever you wanna call it, right?
But in reality, I think what I was
working on was the Roomba of 1998.
I was like, oh, this is how people
took that kind of information
and what they did with it.
But anyway, I understand.
I had to take the linear algebra
and all the math courses in
computer science and then,
but keep going.
And then the algorithms you get
out of there are things like, uh,
linear and logistics regressions.
Gradient descent and blah, blah, blah.
And you gotta do matrices and
multiplication and it's just too much man.
Right?
You, it's just too much
for a business user.
It's too much for even a, a techie user.
And that's why I ended up being,
uh, relegated to ai, uh, data
scientists and AI engineers.
'cause no one wanted to touch it, right?
It's very complicated.
So that's one, that's Insight one.
Now, insight two, generative
ai, like let's say any chat
bot or chat, GPT and LLM.
The reason this is inside too,
the reason for the explosion.
There's, there's one more inside.
After this, the reason for the
explosion is that they gave a window
to AI similar when they gave us
the browser back in 1992, right.
The internet was this, you
know, ubiquitous weird thing
that people didn't understand.
They got Netscape browser.
It's like, oh wow.
Okay.
Now I get it.
Versus the BBS and dial up
modems and all that stuff.
The same thing happened with chat GPT
By giving you a moment into the real
potential of AI and the generative AI is
totally different than machine learning.
But you know, again, we can, we
can talk about that if you're
interested, but that, that is the
reason why this thing just took off.
You know, because now the average user can
access AI and they don't have to know all
that jazz that we, I just talked about,
linear algebra and gradient descent, and
you don't have to worry about any of that.
Now Insight three, and this is
what we have to watch out for.
Well, let's stay on Insight
two for just a second.
Yeah, go ahead.
The other piece that I would add
to that is the NLP part of it.
The natural language processing.
Yeah.
The fact that you can talk
in common language Yeah.
To the machine, and it
can understand you right.
Now, one thing before we get into
Insight three, I just wanna make sure
it's, I, I make this point because
I talk about this a lot, is a lot
of people are struggling because.
People talk about gen AI
in a very abstract way.
Yeah.
Right.
And so I always use the old
analogy, the the Henry Ford.
Right?
If you would ask people what they
wanted, they would've said faster horses.
And the reason that is, is they had no
imagination to understand what a car is.
Right.
And so part of what we have to do
a better job is understanding that
we have made this natural leap to
understand, but you're basically
talking to people, not about going from
horses to cars, but going from horses
directly to space shuttles, right?
And then people are surprised
that people aren't adopting it.
It's like, I have no clue
what you're talking about.
Context, window, natural
language processing.
Could you just show me?
And so as we walk around and we
show people on our phones or on our
desktops, then people are like, oh,
that's what we're talking about.
'cause I really thought we
were talking about robot.
Yeah, great.
On
Tuesday, I was at
cheerleading practice, right?
And all the moms are hanging out in the
room and we were talking about ai, and
of course the woman next to me says,
I've just, I've never used Chad, GPT.
So I was like, all right,
we're gonna download it.
We're gonna talk about this right now.
And it was so cool because I
would say about half the room.
Were women professionals who were saying,
oh, I use it to do this in my business,
or I'm a therapist and it makes my
note takings process so much faster.
And then the other half was
just these women who were like,
what do you even use it for?
And by the end of practice, they had had.
Full meal plans, worked out all
with their allergies on what
days and their shopping lists.
And they were like, oh my
gosh, this is kind of cool.
And then another one was
doing it for a job negotiation
and how to write the email.
So it was professional, but
also, you know, felt like her and
they were just like loving it.
But it is, it's taking
it from this very weird.
Abstract to, wow, this is
how I can use it in my life.
Right?
And so we just have to get out
there and show people more.
Yeah.
So that's one of the things we wanna do.
But I cut you off.
I just wanted to make
sure I made that point.
Insight three.
The insight three, let's hear it.
Um, before moving to,
or do you want to go?
Yeah, no, because, go ahead.
It's a very good point.
Um, and by the way, the fact that computer
vision is integrated with speech, text,
and text to speech and image generation.
That's Agent Agentic AI already, right?
People don't talk about it.
They, they're trying to like position
the next big thing and everyone
is racing to coin the next term.
But agent AI is already
happening if you're just using
chat g, PT four and the voice.
Um, actually it's already happening
'cause that one neural network has
been trained on text generating, text
generating images, generating speech.
And just doing all those
conversions, which is basically an
automated workflow for AI is low.
It's any type of
deep research is going off and
basically completing a task for you
without you having to tell it to go to.
50 websites that's going in and doing it.
Yeah.
Like text, speech, and image
generation is having its moment.
Yeah.
Right, right now in the
last couple of weeks mm-hmm.
Uh, open AI has introduced,
uh, a new model.
Uh, uh, Google has introduced a new model.
Multiple others.
Yeah.
Right.
And we'll, we can talk about that if
we have a little bit of time at the
end, but there we are moving from just
the text phase of things to, you know.
Text and speech to a lot more of like,
Hey, you're not gonna be able to tell
whether it's sesame.ai or open ai.
Like you're not gonna be able to
tell that you're talking to a human.
That's right or not.
And I actually think this is a great
time to introduce you to our game
that we love to play on this podcast.
Ooh, great.
And this is really helps people
understand how they can use
AI in their everyday life.
But you have to pull out your phone
and you have to go to chat, GPT
or Gemini, whichever you're using.
And you have to tell me what
is the last thing you asked?
We called this last chatt.
Yes.
Your last chat.
Look and don't go over there trying
to figure out what is the, you know,
you, you can be vulnerable here, right?
That's right.
Oh, I have no problem man.
Alright.
Bring
it.
Bring it.
Oh, oh, oh.
Erica, what was yours?
What was mine?
I think
it's gonna get progressively
worse if it gets to me, so,
oh look, y'all.
Okay.
My husband is, um, stealing my chat
because it's literally my last chat is
Junior pm it cybersecurity interview.
So that ain't me.
That is my husband, Dan
Rooney stealing my chat login.
I'm calling him out my last chat.
But, but he looks like he, he's
trying to help himself through
an interview and what to do.
Do I think he needs
to interview somebody today?
Right.
So that's good.
Okay.
But mine was how many people fit
in the Fillmore in Charlotte?
And that is because I had to take my
10-year-old to a concert last night.
I wanted to, which
concert was that?
Conna Price.
Right.
Okay.
Alright.
Dunno who that is, but Sounds
awesome Body.
Yeah, she is super excited about it, man.
So the last one was to my,
uh, the managing partner of
ops of Ops at, uh, at my firm.
I said, please update this agreement
by adding an, sorry, this was the
Chad GPT, and then I sent the link to
my, uh, the managing partner of ops.
Update this agreement.
It's an NDA by adding an email
field to the signature block.
Also, add a signature
block for the client.
Beautiful and updated the NDA.
Beautiful.
So we're gonna get into that in a second.
Okay.
About the impact on the legal community.
Alright, so let's, let's get
to, well, lemme get my chat.
Hold on.
Hold on.
Hold.
Could be good.
Alright, so mine was, we
know it's gonna be good.
Knowing him, it's not gonna be good.
It is not gonna be bad, but
it's be very intentional.
I can, can we agree on that?
At least I a hundred percent.
Alright.
Fair.
You know, in preparation for, for
today, one of the things I wanted to
find out was I said, Hey, give me three
examples of how HR professionals could
use Gen AI to be 30% more productive.
Right?
I want to be somewhat specific,
but I wanted to see what it came
up with, which was pretty cool.
It talked about streamlining, uh,
job descriptions and postings.
And they gave you a lot more
detail as to how one would do it,
even including the tools, right?
So you could use Jasper or copy
ai, or chatt pt Claude, whatever
the second one was automate
employee onboarding content, right?
Again, trying to make sure people
understand how they could actually
use this to be a lot more productive.
Um, and then the last one was
summarize employee feedback
and engagement data, right?
A lot of companies, especially
if you're a smaller organization,
we're at Venture Connect right now.
There's a lot of founders.
Whether it's legal things that they
need to, you know, get prepared and
then just have an attorney review, or if
there's HR related things, or there are
tasks, right, they can be a lot better.
Right.
One of the other things that I'm
looking at that's not in here is I
did something similar for CMOs, right?
How could a founder get the access to A
CMO without having to have a CMO in the
office, at least strategically, right?
So give them all the things.
So there's just a lot of different
ways, but I wanna hear what you gotta
say now about the legal community.
Oh yeah, sure.
So, um, and the impact here.
So we're gonna, we're gonna
get back to insight three.
Alright.
Let's get back to insight three.
That's the one that you really want,
like the whole reason I'm here.
Alright.
Here's the one we
really want.
The reason we're like going back and
forth all, I believe it's gonna be about
this insight because it blows my mind.
Okay.
Like the discovery of this.
Um, so we talked about the, the machine
learning side of it and the complexity
of it and why people don't do it.
Right.
Even though it's giving you
the biggest value, right?
Right.
And then we talked about what was Insight
two is about the, um, the explosion.
It was the context,
the text.
Yeah.
And the, the whole thing about the
explosion of it, because of giving
you a window, it gave you a face.
Right.
It gives you a channel that was not there.
So we can say, oh, we want
about open ai, good or bad.
I'm not here to judge, but it
was a stroke of genius to give
it, give us an internet browser.
Give us the Netscape for ai.
Yep.
Right.
And they've been able
to maintain dominance.
Good for them.
Right?
So far, because Netscape actually
wasn't able to keep it up for,
just pause there for a second.
For those of you that don't
know what Netscape is, oh God.
Right?
You're talking about two folks that came
through computer science in the nineties.
It was an original browser.
Yeah.
So it was, uh, I
didn't even know what Netscape was.
Well,
we were the same age thing.
That was the first one.
Yeah.
So, um, yeah, Netscape was the
first like introduction to.
The browser environment, I guess, or,
or an experience for, for regular users.
Um, so now for Insight three, I'm
saying is that now that these things
have converged and, uh, chat, GPT,
like LLMs are getting trained on code.
Yeah.
And you, we, we were
just talking about this.
Yeah.
So what's gonna happen is that
we're gonna start demolishing that
barrier to the productive machine
learning stuff, and the two are
going to converge into a single LLM.
And then you have no limits.
Yeah.
You can build supervised, unsupervised,
you can have it do linear algebra
and derivatives and find the
gradient descent, and K means, and
it'll do all that stuff for you by.
You knowing how to prompt it
properly and hence brings insight.
Number four.
If you guys are not doing a
prompt engineering, you gotta
start doing prompt engineering.
And to do some, um, I guess some promotion
for the class that I'm teaching at NC
State in for working professionals,
homemakers, students, anybody who
wants to, my argument's a 10 x factor.
You're saying about 30%, it's
more like a thousand percent.
A hundred percent, right?
Yeah.
It's all about prompt engineering.
Whether you take that course or you
don't take that course, you invest
on your own, you go get a Coursera.
My advice to you is start
working in prompt engineering.
That's gonna be the singular
most important skill.
Um, I think in the coming
two to five years, you know, so, so to
that point right in, there's two points.
One, um, I'm on Coursera a lot.
Awesome.
I'm looking, looking for like
some points or something.
I don't know what I get as
you get more free courses.
I have no idea.
Gold star.
I, I mean it, go start.
I get Apple.
I don't know what I'm gonna get.
I gonna get something, but.
Uh, I spent a lot of time there.
Right.
So that's thing one.
Thing two is, so I was reading an article.
I like to go back in time a little bit
back in time, meaning like two years ago,
because what I'm trying to figure out is
how right or wrong people have been Yeah.
Over that timeframe.
And there was two points
from two or three years ago.
That are so fundamentally
off, in my opinion.
And there was a Harvard business,
uh, review Harvard Business.
I don't know what exactly it was something
related to Harvard, but there was someone
a couple years ago that said that prompt
engineering was gonna, was not the
future and it was gonna just go away.
How wrong could that have been?
That's right.
Right.
And I think, again, it
goes back to that whole.
Faster horses moment.
I don't think people could really imagine,
how can imagine the advancements, right?
So that's thing one where
I thought was so wrong.
Thing two was there was also a lot of
talk back then that the only people
that were gonna see 10 X productivity
gains were gonna be less experienced.
Individuals.
I mean, there were people
writing articles about that.
It's like, yes, this is great if you're
a junior employee and this and that.
And I was like, how wrong
could you have been?
That's right.
Because what they didn't understand is
what someone that was seasoned could
actually do, could imagine, could create
when they had all of this time back.
Right.
You know, we talk, uh, jokingly, Eric
and I about, you know, uh, pessimists
get to be right, optimists get to
be rich, and how you define rich.
And she'll say, well,
wealth could be defined.
It's time back or health.
I'm paraphrasing.
You know, the smart mine
is freedom of time and good health,
right?
And so what folks need to
understand always talk about
flipping that time equation.
Right.
And so if you're a good prompt
engineer and you can tell the machine
exactly what you're looking for,
like one of the greatest hacks I was
listening to recently, someone said,
and I thought this was brilliant.
Not a lot of people use the, uh, you
know, talking to, uh, chat GBT, they
don't click on the microphone, right?
Or the record button they type.
And what someone was saying was,
us as humans, we have been taught
that we paraphrase and we make
things more concise when we type.
Right when we're in a text.
So her hack was just talk to it because
you're gonna give it a lot more context
and be free flowing than you would if you
actually just typed, and it's just easier.
So people are talking
to, and it will convert
it to text for you anyway.
Right.
And it'll
convert
it to text.
Yeah.
Right.
And so I would say, look, you don't have
to be great at figuring out exactly what,
just say the things that are on your mind.
Yeah.
And then over time you'll
start to understand, now there
are patterns, there's Yeah.
Flip interactions.
There's all these other things that you
can do, persona patterns and things that
for sure they should go to your course.
And I spend a lot of time learning.
Exactly.
But again.
We have computer science background, so
all this pattern stuff made sense to me.
Yeah.
Templates and all of that.
Right.
And I think you can be very much
dumbed down, but at the end of the
day, if you could just talk to it,
you'll find that you're gonna give
it a lot more context, you know?
Yeah.
So, um, something that we talk about
in the class, because the beginning
of the class is a bit more, um, of
an expanded primer to what I just
covered at the start of the podcast.
Uh, to put things in context and dispel
myths and shine, like turn on the lights.
Yeah.
Uh, in plain English so people
don't feel dumb and they
actually say, oh, now I get it.
This is like electricity being
introduced to our life and I
need to make, I live in the dark.
I want electricity because you
can't live without it these days.
Right.
It's the same, I think it's the same
analogy, the same paradigm shift.
Right.
So having said that.
Everything that we just talked about.
There are two things for these
large language models is the term
for them, which is, I covered
it from AlphaGo into et cetera.
That became the foundation for Chad,
GPT and Claude and all these guys.
Um, my analogy for it, 'cause I
always try to find the business
way of explaining it to someone.
And it's not that business users are dumb.
No.
It's just that.
They're not excited about engineering.
Why is it called Ladderal language model?
What's the LLM?
Right?
Right.
Why is it called uh, GPT?
Right?
You know, uh, which stands for
generative pre-trained Transformer.
Transformer.
It actually means nothing to correct to
someone that's trying to figure this out.
You know, you kind, you kind of look
at these things and you, you kind of
say, okay, so in order for you, so
lemme put it in plain English, your
ability to 10 x, or I would say even.
20, 30, 40 x your productivity and
capability is based on two things.
The, the, the, the, the,
I'm gonna keep it simple.
The bot that you're using is
gonna be, uh, super awesome.
Supernatural.
If it's been trained on a large data set,
such as the internet or whatever, and
it's been, there's been reinforcement.
So that's, I call it the, the, the
one half, the one phase of the coin.
It's actually getting, it's
actually building a good.
Uh, is building a good synthetic brain.
So how are the human brain like, you know,
I'm not controlling, it's just flowing.
My neurons are firing and I'm able to
talk to you and hopefully it makes sense.
Um, it's the same with
building that synthetic brain.
It's as if you've got a fadi that you
trained over 53 years or whatever.
And you codified all of that
into that synthetic brain.
So that's one half.
If you can do that, you've created
like a digital twin for Fadi.
Yep.
And by the way, we, we have a, we
have a digital Fadi in the firm.
I'm, I'm serious.
I have a digital Erica.
Oh, there you go.
Her name is Cheryl.
Her name is Cheryl.
I named her after Cheryl Sandberg my idol.
I did not know that.
I love
it.
Yeah.
She's my idol, you know.
Oh, okay.
Okay.
Seal Facebook back in the day.
Yep.
Um, so the other half of the coin
is going to be about how can you.
How can you quiz it?
Right.
And, uh, I'm gonna borrow
from Tony Robbins here.
Um, and I know he got it from hundreds
or thousands of people he met and
hundreds of books that he read.
But he said something that was just spot
on, that actually was for me and my wife.
It changed our life because, uh, we
were both working at the same startup.
I, I was the founder and she was
a part of the team, et cetera.
Um, the, it's, by the
way, it's very profound.
I mean, just forget AI and everything.
It's just very profound.
If you reflect on this little
nugget, he says the quality of your
questions, this is not about ai.
Yeah, I'm just talking in general.
In general.
Conceptually, the quality of your
questions will directly is a direct line
and influence to the quality of your life.
So if you think about that, the argument
is that if you ask better questions.
Not limiting questions, right?
That, that focus on scarcity.
But if you change your mindset and,
and look at the way that you have your
internal dialogue and start asking
yourself better questions, all of a sudden
new resources will be unlocked, uh, bad.
Um, thinking will be edited out
by simply changing the language.
So what I'm getting to is that if you
really break it down, it's about language.
And knowing where, knowing how to become
an expert at using language to expand your
horizons and the quality of your life.
How much money you make, how
good your relationships are.
Et cetera.
Now you take this, we're talking about
it conceptually in an abstract, if you
really reflect on it by then, I think
the book was, uh, awaken the Giant.
I know it's an older book, like
an old was written in the past,
but it's all applicable, you know,
today because it's concepts, right?
So now we take this concept.
Once you buy into that concept,
you say, oh, wait a minute.
If I asked myself better questions,
I would've had a better career,
more money, happier life, more
balanced, whatever it is that the
ques, the better questions for your.
For your, your, your journey, uh, is
going to be, we take this concept again.
You buy into that, take that concept, and
you apply to the second half of that coin.
So you've got a synthetic
brain that's been trained and
customized and everything.
You know how to ask it questions.
You're gonna cut tasks by 90%.
So writing that NDA instead of
it if, or updating that NDA for
the firm instead of it taking.
Four hours of my time,
it took five minutes.
That's invaluable, man.
I saw a post just this morning
on LinkedIn, um, and I think the
guy's name is Greg Eisenberg.
I'll have to go back and look, but
he posted about, I don't know how
real the story is or not, but the
point still remain, which was he
said, now I can do all of these
NDAs and all these legal documents.
That's correct.
Uh, I can provide now my
attorneys the first draft.
And now what?
That's right.
Would've taken them 10 hours
or I got billed for 10 hours,
took them one hour to review.
He said, now we're on a collision course.
And he said, basically it's gonna
be death by a thousand cuts.
'cause he's saying a lot of
these folks aren't recognizing
what's happening in real time.
They're gonna slowly but surely start
to see their billable hours in the
construct that was created before.
Radically go down right over time,
because people are gonna start to
realize that I don't need you to spend.
10 hours looking at something
I already drafted by a billion.
Yeah.
Uh, by, by a million attorneys
across the entire internet.
Have all agreed in my, my,
you know, context window.
Right.
And so I think that you're
gonna see that same situation.
You know, we work in an agency model.
I say the same thing.
It was like the hourly rate
is, is aggressively dying.
This is not a slow, right.
So you're talking about.
Professional services in general, right.
If your structure was built on this
idea that the junior folks would
do these mundane tasks, that would
take, you know, many hours, right?
One 'cause there's others concern, right.
And I'll make a point here, which
one of the concerns is that if you
have these things that can give you
all of this administrative type work,
and that's what we traditionally
have had people less experienced in
companies doing, then how do they get
trained up to do the senior thing?
And what my argument would be is.
Do you really think that having
the intern, you know, go get coffee
and move paper from one desk, that
the other one was training them on
the, the nature of the business?
How about you think about it differently
and say all of that busy work that you
gave that individual, now that time is
back for you and for that individual
and now you could actually spend time
mentoring them on what actually they
need to do to learn for their job.
Right.
You don't have to Mr. Miyagi and tell
them, you know, by picking up that piece
of paper, you're painting the fence.
Yeah, no, just show me the moves.
Right, right.
Just show 'em how to fight like that.
That's what I wanna do.
Yeah.
Like, I don't wanna paint your
fence and, and sand the floor.
Right.
And so I think that that's the part
where, you know, as whether it's in
your, I don't know if it's gonna be right
for your course, but I also a hundred
percent agree with you is, you know.
Brings us back to prompt engineering
and what you're talking about, right?
The level of specificity
and detail and context.
You get it?
There's gonna be a direct correlation and
reflection in the output that you get.
That's right.
So people in this constant motion, in the
early days of, you know, chatt, PT and
others, and always use this example and
they say, well, which number's bigger?
9.11 or 9.9.
Right?
And for us, out of context, for the
average person, they'll say 9.9 is bigger.
Right.
But in earlier days chat, GBT
would say 9.11 and people say, see?
Got you.
But what I didn't realize is
it was trained on software
release cycles and from that
context, that is a bigger number.
Yeah.
But if you didn't give it enough
information, I think what they call
that, uh, LAN ese, uh, paradox.
Right.
Us as humans, we know a lot
more than we can ever tell.
Yeah.
Right.
So if you ask someone how to ride a
bike or how do they use their phone?
There's a million steps that they
don't, that are very implicit to us.
That's right over with,
with with our lives.
Right?
So we don't say every
single thing that we do.
Right.
And so to your point, you train
up that one side of the coin, I.
To know everything that we do.
That's right.
Right.
And then you use the
other side of the coin.
Now I'm gonna engage with it.
Yeah.
Now and, and digital twin, uh,
Erica Cheryl, digital twin Fighter.
You gotta come up with a better name.
Like, I don't know what it is.
Maybe it's Robins.
I have no idea.
Right.
But you come up with the, you know,
that digital twin never gets tired.
A digital twin is always there.
Mine is AI serious.
I created a digital twin that I use so
that you can kind of engage with how I,
how I think, or how I talk or whatever.
It could be good for me.
It could be good for someone else.
I hope it could be good for someone else.
Yeah, I don't know.
So anyway, sorry to be long-winded
there.
No, no, it's very good.
I mean, it gives more context.
So the one key thing that I think
the audience should definitely take
away from this conversation, don't
put a better word that you would get
excited about than prompt engineering.
Yes.
Just call it language.
Uh, language training, whatever,
just come up with a simple,
that's what it really is.
It's about teaching you how to
start writing better questions and
really start thinking about the way
that you can unlock that potential
of that other side of the coin.
Right.
So, um, the one point, I'm not sure how
much time we have, but the one point that
I wanted to, uh, just talk, because what
happened, I don't know how this happened.
I'm not really sure, but
I'm, I'm excited about it.
We're engaging with a couple law firms
and I would've never thought, uh, that
we'd be able to move the needle with them.
But they're actually switched on after
we do like a primer on, because what
we did, um, in the spirit of, keep
it simple, don't be condescending.
Mm-hmm.
Uh, make sure you explain
things in plain English.
Yeah.
And.
Focus on showing that this is not a
monster, actually, it's just a machine
that has been trained by humans.
And, um, you can use it and
exploit it to your, to a, a benefit
whether, whether you're a homemaker.
'cause you're talking about recipes,
whether you're an athlete, you want
training plans or you're a CEO or a board
member, whatever it is, you can, you
know, you can exploit that information.
Right.
And for, for your game.
And then, and then we start talking about.
The things that really matter because
I said, let's just, let's just address
the, the elephant that's in the room.
This is not gonna take away your job.
And you said that spot on.
Lawyers that know how to use
AI will take over your job.
There you go.
Right.
So people, and it's not just for
lawyers, for any, for anybody.
Yes, but I'm, I'm, I'm actually
passionate about this 'cause I wanna
talk about it so that they realize
that one of the most classic or most.
Risk averse, most compliant, right?
Most likely, right?
You think, okay, these guys will
never, ever, you know, adopt this.
And they had that.
Um, I, I mean, for client confidential
confidentiality, I mentioned the
names, but I walked in to the room
with a, with a set of, I don't know,
many attorneys, like, you know, 12
attorneys or more, and they're at the
boxing gloves ready to go, you know?
And then after about an
hour, they're like, wow.
I'm a whole lot less concerned now
person probably if he hears this,
he knows, you know, I'm quo him.
I'm a whole lot less, and
this is a managing partner.
I'm a whole lot less concerned now
than when we started and I actually
think that this, we gotta jump on this.
And a lot of the That's great.
A lot of the skeptics from the
lawyers did a complete 180.
By the end of it, they're
like, now we, now I get it.
We gotta get going, man.
We gotta figure out the new billing model.
Mm-hmm.
We gotta change staffing,
we gotta do this.
We should introduce it
to all the attorneys and.
Which is all valid, but at
least we were able to, if we
can win over lawyers, we can't.
We should be able to win over anybody.
Hey man,
look, you just took a group of
attorneys and I've been in, in sessions.
When I say to folks, when I, when I
have an audience, I was like, Hey,
who are the attorneys in the room?
Right?
And I'd ask, I say, what do you do
in organizations where your own only
governance model is from the legal
community that says, don't, right.
But to your point, whether it's, uh,
I'm gonna give this the most, uh, I'm
gonna assume positive intent Yeah.
To your audience that you had.
Right.
Which was, Hey, we want to
figure out how we can create
greater business opportunity.
Right.
On one side of, I'm certain there were
people in that room, there were also like.
Oh shit, if I don't do this
right now, I won't have a job.
That's right.
Right.
So you have on one side, and that's
one of the things where I think it's
this very like a polarizing moment
for folks and that that's why we call
the podcast AI voice or victim, right?
You are making a choice and
those folks in that room.
They could, they, they
chose to be a voice, right?
They could have very well say, you know
what, I'm not gonna do anything, and
they will very quickly become a victim.
And so it was my sincere hope that
if you can take that parallel to your
point, if you can take a bunch of, uh,
lawyers from anxious to at least curious.
Right now, then we should be
able to take this across a lot
of other industries, professions.
I know the three of us have talked
about this offline, but the ability
to go in and talk to HR professionals
mm-hmm.
Sales and marketing, um, operations
folks that are not technical in
nature, but this will absolutely
be a game changer for them, or it
would be a career killer for them.
Yeah.
Right.
But at the end, I, I just
fundamentally believe it's a choice.
Right.
And so in a lot, and by way there's
one myth I wanna dispel also.
Alright.
Um, I was a, I was a voice of concern
when we were doing Chad GPT, and
I still am to a, a certain extent.
Yeah.
Because for a reg, heavily regulated
industry, we gotta look like, uh,
law, the things, when we did the, the
deep dive and researched, um, what's
happening in law, uh, with ai, we
really, we spent, I can't remember, 16
or 20 person hours doing the research.
To really identify what's the holdup here?
You know, why, why can't attorneys
get over the, uh, get over the
hump on this particular thing?
And, um, we really got down to
the things we were able to discuss
in a non-threatening way, safe
environment to say, bring your,
bring your heavy questions, man.
Bring the questions that you right.
Don't want to ask, ask 'em.
And, you know, hit below the bell.
Go for it.
Let's, let's duke it out.
Right?
And you, you make it in plain English.
You show 'em the, you show 'em
what they're concerned about.
You're concerned about your
chargeability, you're concerned
about, um, uh, hallucination,
you're concerned about this.
You're concerned about privacy.
You're concerned about, um, user
consent if you're gonna be training this
bot on some of your customer's data.
Alright, well, let's spend
time working on that.
And the thing that I'm gonna talk
about when I started this, uh.
You know, this, this, this, uh,
explanation, um, Chad, GPT, even
Chad GPT, have an option where
they don't train on your data.
Yeah.
It's in their T's and C's.
So if you get that membership lo
level, it takes away all these issues.
Yeah.
And my argument with many people that
say, oh no, but you know, they're
gonna copy my data, and I'm like.
My friend, they already got your data.
If you're using all the
cloud providers already.
That's one of the arguments.
One of the professors already,
you already got your data,
your data's already out there.
Dr. Jus wife from, uh, Vanderbilt,
one of the, uh, specializations I got.
He talks about that right in
the cybersecurity session.
He's like, if your concern is
that your data might get copied
or take, he's like, you're already
using all the cloud providers.
They already have it.
That's right.
Yeah.
Like,
so that is not actually
a concern and it's not a leap of faith.
It's a, it's a leap of.
Understanding fact, those providers,
Microsoft AWS, Google, all the
hyperscalers, et cetera, all of those guys
are compliant with federal requirements.
Mm-hmm.
Yes.
Right.
So there's no concern.
You don't have any concern as an
attorney today to be hosting on
Azure, like you're using Office 365.
Guess what?
That's all God knows.
Actually, your data is all over
the world because it's getting
replicated to every single country
that they have a data center in.
But don't worry about it because.
It should you get sued, Microsoft are
gonna have to step up to the plate.
The Forrester uh, uh, guy speaking at the
Adobe Summit Conference last week said he
had a, he had a quote on the screen that
just said you had me at, in indemnify.
Right?
It's once those organizations said that
we were gonna indemnify you, then people
fired was like, alright, now I get it.
Right.
And if you're using.
You know, any of these enterprise
versions, whether it's Google, Gemini,
which is attached to everything
in your Google Workspace, right?
So there's an added bonus there.
Yes.
Or Microsoft absolutely
copilot all these, right.
You're in the best shape
that you're gonna be.
That's right.
Right.
And now the other side of it, uh, Dr.
Michael Jabor was speaking
a couple months ago.
He said, he said, I hope
there's no CISOs in the room.
You know, any security folks.
He said, because they did a study
at Microsoft and 78% of the folks
bringing AI to work, whether
you said Sue or not, right?
Yeah.
Yep, that's right.
So it's irrelevant.
So you better that you have a governance
model and that you're focused on that.
But, um, that's a, a, a very,
uh, compelling argument.
Right.
You know, for, you know.
Yeah.
Because you're for an option.
Absolutely.
Attorneys argument was
compelling, you know?
Um, I wish I could have
been a fly on the wall.
Hmm.
Like, and I definitely wanna
talk to you about this a little
bit more, but, um, we're gonna
need to start wrapping up here.
Eric, what we on with this?
Yes,
we do.
We do.
I just wanna say thank you so much.
Of course.
I mean, of course.
This is Venture Connect.
This is an incredible space
to be in, and we're gonna be
doing a lot of workshops on ai.
You wanna talk about that a little bit?
Yeah, I mean, I think that, you
know, I'm trying to bring this,
this, this crew together here.
We got AI Curious, that's very focused
on the hr. She used to be a chief people
officer, now she's out there as executive
coach and keynote speaker, Fadi amazing
transformational architect in this
space, um, and dated himself earlier.
I. About how long he's
been in the space, how long
I got to the age.
I don't really care anymore.
I mean, he basically, what he said was
tenured when, when I was five years old.
I started getting into this
in the eighties is basically
what you said, right?
That's how I'm gonna
paraphrase this, right?
And then myself, I call myself AI
series, but it's really about, from a,
a business perspective, you and I both.
Moved to the dark side years ago, right.
Where we, you know, 'cause
at the end of the day, right,
we're making that it's fun.
It is because make,
it's fun.
It's, it's fun on the dark side.
Y you
know, there was a, uh, the CEO, come
on, Luke, the ceo O the CEO, EO of
Coca-Cola, last week when I was at this
conference, he said something that was,
that was very compelling, which is he
said, uh, stop selling what you make.
And start making what sells.
Yeah.
There you go.
Right?
And so as you move over to the business
side and you start thinking about AI
and how you can use this, I think it'd
be a great opportunity as we look to,
you know, better advance folks and
help them, again, move from anxious to
curious and then maybe serious, right?
But we have to get the AI adoption, we
have to get folks on board that aren't
just the traditional techies or the senior
marketers that have been using ML and, you
know, and their workflow for, for years.
Right.
And so we're gonna be doing
a few workshops, right.
Some of them will be free.
Uh, we would do things, uh, in person
and there are also gonna be some
webinars and things that we'll do.
But it's our goal to try to bring as
much of this to folks and make sure
that people feel a little less angst.
Yeah.
Right.
And a lot more productive
and a lot more curious.
Yes.
We appreciate it.
One question we do have to ask
though, in the next 24 hours, what
would you advise someone to do?
They only have 24 hours.
What would you advise 'em to
do to get started using ai?
I, I would say, look at Udemy.
Look at Coursera.
Just find a course that is free.
I mean, there's a lot of
courses that are free.
You're spoken
like a true professor right
now.
Everyone else says, oh,
just go play with the tools.
But you're like, Hey,
could you just learn about this
first?
So the good thing about, uh, and you know
this about Coursera and Udemy is actually
they're instructing you using the tool.
They're walking you through a
hundred percent examples of how
to, how to use language to get
what you want out of it, right.
So I think that's the most cost effective,
fastest way to actually start doing some
damage in a good way for your career,
your personal life and everything.
That's what I would do.
Amazing.
There's one, one last thing I'd
like to leave the audience with.
Um, there's a book that is by the, the,
actually the founder of DeepMind, Mustafa.
Suleman, I think is his name.
Okay.
You can find it on Amazon
Audible, you name it.
It's called the Coming Wave.
And my post this morning
on LinkedIn was about this.
So the past six months I've seen the
behavior and as a person that's been
on, you know, digital and AI for so
many years, I can see this massive
tsunami that's coming in two years.
Hmm.
Yes.
But it's silent and it's
just like, it's deadly.
It's silent, but it's like just,
it's not an issue of transform,
but it's gonna wipe out so many.
Um, so many jobs and it's gonna
disrupt so many industries.
And this is different than
other technologies like
workflow automation or whatever.
Right.
This one is electricity or the car.
Yep, yep.
Um, you know, so I'd
say go read that book.
Amazing, amazing suggestion.
Thank you.
Yeah,
absolutely.
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on AI, voice or victim.
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