AI Has Been Here All Along: Fadi Hindi LIVE at CED 2025

AI isn’t new—it’s been here for a while. So why are so many businesses still stuck on the sidelines?

Recorded LIVE at CED Venture Connect 2025 in Raleigh, NC, this episode features Fadi Hindi—AI transformation advisor, professor, and digital twin pioneer. Fadi has been working in AI since the late ’80s, and he joins hosts Erica Rooney and Greg Boone (aka AISerious™) to break down what’s holding companies and individuals back from fully embracing artificial intelligence.

This episode covers:

• Why AI has been “the next big thing” for over 30 years
• The 4 key insights every leader needs to understand about AI right now
• How prompt engineering is the new power skill (yes, even for non-techies)

👉 Don’t forget to subscribe, leave a review, and share this episode with someone navigating the AI revolution.

Subscribe to AI: Voice or Victim for more conversations that move you from AI anxious to AI curious. Hosted by Erica Rooney and Greg Boone aka AISerious™, we're helping people and organizations embrace AI ethically, strategically, and with humanity at the center.

Follow us and join the movement to shape the future — before it shapes us.

🔗 Follow us and dive deeper:

On the web: https://voiceorvictim.com/

Greg Boone on LinkedIn: https://www.linkedin.com/in/gregboone

Erica Rooney on LinkedIn : https://www.linkedin.com/in/ericarooney/

© 2025 Walk West Production

Episode Transcript

(00:07):
If you really break it down, it'sabout language and knowing where,
knowing how to become an expertat using language to expand your
horizons and the quality of your life.
How much money you make, howgood your relationships are.
Et cetera.
Now, if you take this, we're talkingabout it conceptually in an abstract,
if you really reflect on it, so you'vegot a synthetic brain that's been

(00:30):
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 determinewhether you thrive or get left behind.

(00:53):
Welcome to ai, voice or victim.
I'm Greg Boone, marketingexecutive and AI series.
And I'm Eric Rooney, author, speaker,and gender equality advocate.
And I'm AI curious and we are hereto 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 realworld use cases and give you AI driven

(01:16):
strategies that you can apply immediatelyready to stay ahead of the curve.
Let's jump in.
All right, y'all.
We are here at Venture Connectin Raleigh, North Carolina.
This is such an exciting time, and we aresitting here with Fadi Hindi, who is an
AI and digital transformation advisor.

(01:38):
He is a professor, a senior executor.
Founder and y'all, he is reallyinto 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 makethe 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.

(02:00):
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 littlelogo and throw it on there.
That's right.
But Fadi, tell us a bit aboutyourself and what's got you so excited
about AI transformation these days.
Sure.
Um, so I've, um, I was raised herein North Carolina, um, since I
about, I was about five, I guess.
Um, and we, maybe six or seven,whatever the, the number is.

(02:22):
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 crazethat came about since I guess 2023.
Um, I think it starteda little bit earlier.
We could talk about itif you're interested.

(02:44):
When I graduated, I actuallyjoined the consulting, uh, house.
It was, um, Anderson Consulting back then.
Which is, uh, now Accenture.
Yep.
So I remained technical, uh, throughoutthe, you know, 12 or plus, you know,
years that I worked domestically beforeI got hired to start doing global roles.
I. And, uh, I always joke, joke aboutthis because I continued technical

(03:07):
for about 15, 16 years, give or take.
And then my friends alwaysgimme a hard time about this.
I think you and I talked about it.
They call me the Darth Vaderof 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, theyjust keep busting on me because,
uh, they say I'm a turn code.
So half of my career was business.

(03:30):
It, it was interestingthat transition because.
Uh, I was very passionateabout engineering and software
and hardware and everything.
I could care less about p andls and business and Strat.
It's like just a waste oftime because I'm an engineer.
And then I went over to the otherside and it's like, oh my God, this
is like, okay, now it makes sense.
We gotta worry about p and l. We gottaworry about profitability and I'm not

(03:51):
gonna listen to all the engineers.
I just wanna play with tools.
That's not gonna work.
Um, but the 31 years have beenpredominantly digital transf.
It's all transformation.
So.
Um, doing, looking at thetransformation of the organization,
whether it's technical, whether it'sbusiness, whether it's strategic.
And as I got older and more like gotmore senior roles, it was more about,

(04:14):
alright, how do we build a strategy thatmakes sense from a business standpoint?
But then I had the advantage of.
Been like being technical, so wecould actually look at what could
be done within this particulartimeframe, this particular budget.
But it was a lot of automation, alot of digital, and I would say a lot
of AI from 2010, probably onwards.

(04:35):
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 wasfour 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 seemto be just terrified of it.

(04:55):
Right.
And Greg and I kind of have thisscale 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 residingin the anxious area and they think
it's this new thing that's taking over.
So to hear that it's been aroundsince 1989 should be a bit
reassuring, don't you think?
It's been
around since 1950.
There we go.
Even better.

(05:16):
Right?
So, um, I always end up talking aboutthis when I talk to executives or board
members because they need that context toreally come to grips with what's going on.
Um, and I just say, look, thisis 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?

(05:36):
That is a long time.
Right?
So if you, if you think about it thatway, and, and the, the, the grandfather or
the founder of AI was Alan Touring, and Ialways 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 mathematicianfrom the uk, uh, English mathematician,

(05:58):
and he worked on the codebreaker.
It was the first machine that actuallydid, 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 ableto tell if this is a machine or a human.

(06:21):
Yeah.
And we're blown pastthat by now, of course.
Right.
You can't, you can't really tell anymore.
Um, but then it progressed fromthat into an age of neural networks
and fuzzy logic which disappeared.
It might have been embedded in someof the things that we're seeing
now, but that's a lot of the thingsthat we were working on back then.
And, um, you know, God bless him, myprofessor, his name was, uh, Dr. John

(06:44):
Sutton at NC State, uh, hardcore man.
Just like he's awesome guy, but it'sjust hardcore AI even back then.
Um, and, um.
That we were always, we wouldget excited when we can get a
hundred images of something, right?
'cause you can actually trainthe network, do some prediction.
Is this a flower?
Is it not a flower?
Right.
It's like, just to kind of crack ajoke, you guys have seen Silicon Valley?

(07:05):
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 basicback then, or, you know, but
then it evolved from there.
So.
Then you started getting, theysupervised learning and machine
learning and unsupervised learning.
We can talk about those if, if you want.
Uh, but the real, uh, interestingmoment was when DeepMind, which, uh,

(07:30):
is a, a British company, um, that builtthe 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 momentbecause that's when it was like the
Sputnik moment for us in America.

(07:52):
Right, right.
And, um, from that point onwards, it'sjust been an acceleration, an exponential
acceleration of, uh, the technology.
'cause Google acquired DeepMindand they invested in the AlphaGo
project to, to kind of create.
Um, and we can talk about it Again, Idon't want to spend too much time on,

(08:12):
on, uh, one particular thing, but ifyou're interested, we can drill into it.
That was a turning point, I think, forai, because they saw moves they have never
seen before, and the, the, the go championin China was flabbergasted because
there's like, I don't know, the numberof permutations it's like equals the
number of atoms in the universe or someridiculous, you know, number like that.

(08:33):
And they saw unconventional movesthat 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 startedusing that because it was so successful.
Google started using thatfor language translation.

(08:54):
So when you go to translate thatgoogle.com, it's actually a lot of the,
the, um, the algorithms form DeepMind.
Um, and it was the birthof large language models.
If you really, that that'swhen it happened, right?
And then it was a convergenceof things, the advancement of
compute, the availability of data.
You got all the data you couldever dream of, um, and then

(09:15):
advancements in the algorithms itself.
All of that convergence just kind ofgave birth to open AI and the chat bots.
And um, and it's actually interestingyou have time, go back and look at
G GT one and compare to GT four.
Mm-hmm.
It's like a massivenine, massive difference.
I don't know the exact numbers.
Something like a hundred thousandwebsites or something, or 10,000.

(09:36):
That was GT one and now GPT-4 andBeyond 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, Ibelieve my theory right now is that we
are, I'm gonna say this, I'm gonna stop.
Sure.
You have can ask whateverquestions you want.

(09:56):
We are, you know, about exponential.
Mm-hmm.
Technologies, right?
So I think we're past thenear of the curve now.
I really do.
I think we're gonna hitsingularity in a couple
years.
Yeah.
I mean there's a lot of folks thatare talking about that, so we, we
appreciate the story arc and thebackstory as I, we talk about this a
lot and you, you referenced it, AI'sbeen around for a very long time.
Yeah, right.

(10:16):
There's two moments, and I would sayin the last five or six years that
have really advanced the adoptionat the corporate level of ai.
I would say first is the pandemic.
Yep.
When, uh, everyone was saying, Hey, it'sgonna take two years to get a vaccine out.
And that's when like, uh, thepharmaceutical company started whispering.
It was like, Hey, we kind ofgot this secret weapon if we are
actually allowed to use it now.

(10:37):
Yeah.
That we can come up withthings a lot faster.
Right.
To your point, whether it'schest moves or go moves, right.
There's opportunities there.
The second was the, uh, thelaunch 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 isthis is finally the moment where they

(10:59):
start to see wide scale adoption.
Right?
You know, the other GPT acronym isfor general purpose technology, right?
Once folks started to define it asgeneral purpose technology and everybody
had it in their hands, now, that's whenyou saw all the folks come out and say,
all right, there's enough air cover.
Not only can we use gen AI and thenwe can use some of the, the ML and
some of the deep learning, some ofthe other things that we've been

(11:21):
working on for the last 20 years.
Right, right.
Because now we have the air cover.
Now it's sociallyacceptable enough, right?
Yeah.
Everyone's not adopting it, butpeople recognize that it's there.
Yeah.
Right.
So those things I think haveadvanced it in the last few years.
To your point though, but it'sbeen around for a very long time.
This is your AI curious girl here.
Are there any stats on like the percentageof adoption, like a, I don't know, not

(11:44):
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 theconsulting work that I do, and
this is very like, specificallytargeted over the past six months.
Actually, my post thismorning was about this.

(12:05):
I'm disappointed with the levelof adoption and the level of
the fud, the fear, uncertainty,and doubt that we see from, I.
Executives, you know, from business ownersand it's, um, it's just unfounded because
I think of the fear mongering that goesaround around ai, it, you know, it's
news and bad news, you know, brings on,brings home the cash kind of a thing.

(12:28):
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 youexpect it to be like everybody's doing it.
Actually, it's quite the opposite.
I mean, if you had a pie thisbig, 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

(12:50):
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 notdoing 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 thingsthat are really happening.
Uh, but to, to Greg's point,if you actually look back, um,

(13:15):
the machine learning aspect ofit, 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 algorithmsthat can identify patterns into two
separate, uh, branches, supervisedlearning or unsupervised learning.
And I, I was, I've studied, uh, the courseof Dr. Andrew, uh, in from Stanford for

(13:39):
like six or eight months as I was doingan, an AI consultancy, uh, uh, overseas.
It was very powerful.
Right.
And that is more, that has more ofa hold today than generative ai.
It's just, I'm talkingabout 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.

(14:01):
They have dedicated programs thatare looking at future ventures.
So they are gonna, they areinvesting in this right now.
How much of this is getting adopted?
I'm not talking about bankMedicare, I'm just talking in
general about bigger enterprises.
How much of that.
Research getting integratedback into the enterprise.
I would say it's very low.
It's just my take on it.
Then there's a, a group of companiesthat have been using this machine

(14:21):
learning stuff for a long time becauseit's predicting engine failures,
like, you know, very, very expensiveengine failures like airplane engines,
rolls Royce or ge, whatever it is.
And being able to do unsupervisedlearning to actually detect, use
thousands of parameters to be able to.
Hone in on one or two engines that arehaving a problem out of this production.

(14:44):
You know, it's extremely valuableand 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'sbeen the case all the way through
to, I would say, I mean probablythe until GPT one came out.
Yep.
But the mass adoptionwas around GPT-3 or 3.5,

(15:07):
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 usingGoogle Maps in ways.
Yeah.
If you're gonna cut it off,then cut off all the things that

(15:28):
you're getting to benefit from.
Right.
You just aren't recognizingthat 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 videoson, 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 ofthe videos, part of the coursework
that I, I do, and talking about.

(15:48):
Agen AI and this and that, right?
But we can go, that's a differenttopic for a different day.
I think, you know, when we talk morebroadly about AI adoption, I say this
all the time, folks, is my sincerehope that more people start to wake
up and understand how they can usethis to, to get productivity gains.
How they can use this to, you know, have,uh, basically it's a PhD in your pocket.

(16:10):
Right.
So, and people ask me all the timeabout, you know, I'm getting all these
certificates and things, this coursework.
It's like, yeah, becauseI'm very inquisitive.
I want to, it's the first time inmy history where I can learn so many
different things at a level that thatdoesn't take me months and years.
To learn.
Right.
And so even if you're just using it toget smarter about whatever it is, it could

(16:32):
be just getting smarter about the world.
Yeah.
Right.
And so for me, the adoption farexceeds the, the technical folks and
then some of the marketing folks.
I was just at a conference in Vegaslast week and one of the things
I kept saying to folks, I'm like.
So you guys keep talking about AIadoption, but you're talking to a
crowd that already believes in ai.
That's right.
I said, but this is the first technologyin our lifetime that affects every

(16:55):
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 containedto this is only gonna impact it.
So you're only concerned about oneor two potential saboteurs, right?
Or this is only gonna impact themarketing organization, right.
This impacts everyone.
And so it's not surprising to methat when I look at organizations

(17:19):
and companies that are saying, Hey, Ihave these great business use cases,
but I can't get the team to adopt it.
It's like you haven't addressedthe 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 intothis business case that you have.
Yeah.
When you haven't solved for themindividually in their careers.
Right.
And so that's where I'm kind ofmy head is at, and that's why.

(17:39):
Recently I, I got a c uh, certificate inai, uh, specialization in AI for business,
uh, from from University of Penn becauseit was focused on non-data scientists.
It was focused on people management.
It was focused on functions thataren't highly technical by design.
That's why I took that, that course.
So I was trying to understand howwe get more people to adopt this.

(17:59):
Well, and that tees it up perfectly.
I was at a AI networking event and Iheard the best quote ever, and it's
like, AI will not take your job, butpeople who know AI will take your job.
And so I've been using that whenI've just been working with the
women that I coach and it reallykind of clicks for them there.
It's like, okay, it's not this robotthat's, that's taking over what I do.

(18:20):
It's not removing the human.
But with all that being said, whatare the different roles that you
are seeing being impacted by ai?
You know, um, you saidsomething that is spot on.
This is a general exponentialtechnology, so there nothing is safe.

(18:40):
I mean, I can't think ofa, including consultants.
I can't think of any single jobthat will not be distracted.
Absolutely.
Consultants.
Oh yeah.
Actually consultants or lawyersprobably 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 theappetite to say, if we don't do

(19:03):
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 realizedthat they've got to fundamentally change.
'cause somebody, you know, switched on.
Others are just, you know, theyjust, they've just resisted because
of the, you know, think about costfactor, um, the privacy, security.

(19:24):
There's just so manythings that come into play.
But there's, if you guys are interestedin some insights, I'd love to share some.
Please do.
You know, so if I look at the span of the31 years and then the early, um, AI work.
And by the way, we're embeddingAI algorithms in robots.
We, I was the, uh, uh, I was the captainand a team leader for a period of two,

(19:47):
two to three years for the, for therobots that NC State were building
for the Mars Mission Research Lab.
We worked on a ed robot and we workedon a rover and it was, the mission of it
was sponsored by GM and Motorola and likea lot of money that was getting pulled
into research and it was looking at.
Building robots this again, backin like nine, like 1990 timeframe.

(20:10):
Um, they, we were looking at the bestway to be able, there wasn't, um,
enough satellite and real time feedback.
And so there's a lot of work aroundembedding intelligence using sonars to
be able to map, uh, for the robot toactually figure out from sonar readings.
This is before lidar, that sonarreadings from all the sensors around.

(20:33):
It's got a, it's got a map of acertain typographical area of Mars,
like a spot, and it will use, itwill do the navigation, but motor
errors in a long run over a twohour period is gonna throw it off.
It could be, you know, miles offfrom where it originally started.
So use sonar sensors to get dataand compare the projected, uh,

(20:58):
where it currently thinks it'sat versus what it's reading from.
That sonar sensors and it would do themath and say, okay, I'm actually off
by five degrees and it will adjust.
So yeah, it was fairly intelligentfor 1990 if you really think about it.
And there were other robots.
We had a bi bed, like a, a robot thatuses two legs to climb stairs and go,
there's just a lot of embedded systemsand it was super cool way back then.

(21:21):
So if you look at the progression ofthat 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, youhave yet linear algebra.
I'm not throwing these to.
Make it sound like I'm actuallythrowing these out to say this
is the insight that was a barrierto entry for the average user.

(21:46):
1998, I worked on a roboticsproject at North Carolina a
and t that shout out to Dr. Yu.
There you go.
I, I was chosen to work with, uh, hergrad students and basically my role
was to write the code to keep basicallythis robot in this contained area.
Right.
So in my mind, very cool.
I was, I was making this LandRover type thing for Mars Rover,
whatever you wanna call it, right?

(22:07):
But in reality, I think what I wasworking on was the Roomba of 1998.
I was like, oh, this is how peopletook that kind of information
and what they did with it.
But anyway, I understand.
I had to take the linear algebraand all the math courses in
computer science and then,
but keep going.
And then the algorithms you getout of there are things like, uh,
linear and logistics regressions.
Gradient descent and blah, blah, blah.

(22:28):
And you gotta do matrices andmultiplication and it's just too much man.
Right?
You, it's just too muchfor 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.

(22:48):
Now, insight two, generativeai, 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 theexplosion is that they gave a window
to AI similar when they gave usthe browser back in 1992, right.

(23:11):
The internet was this, youknow, 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 upmodems and all that stuff.
The same thing happened with chat GPTBy giving you a moment into the real
potential of AI and the generative AI istotally different than machine learning.

(23:32):
But you know, again, we can, wecan talk about that if you're
interested, but that, that is thereason why this thing just took off.
You know, because now the average user canaccess 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 iswhat we have to watch out for.
Well, let's stay on Insighttwo for just a second.

(23:54):
Yeah, go ahead.
The other piece that I would addto that is the NLP part of it.
The natural language processing.
Yeah.
The fact that you can talkin common language Yeah.
To the machine, and itcan understand you right.
Now, one thing before we get intoInsight three, I just wanna make sure
it's, I, I make this point becauseI talk about this a lot, is a lot

(24:14):
of people are struggling because.
People talk about gen AIin a very abstract way.
Yeah.
Right.
And so I always use the oldanalogy, the the Henry Ford.
Right?
If you would ask people what theywanted, they would've said faster horses.
And the reason that is, is they had noimagination to understand what a car is.
Right.
And so part of what we have to doa better job is understanding that

(24:37):
we have made this natural leap tounderstand, but you're basically
talking to people, not about going fromhorses to cars, but going from horses
directly to space shuttles, right?
And then people are surprisedthat people aren't adopting it.
It's like, I have no cluewhat you're talking about.
Context, window, naturallanguage processing.
Could you just show me?
And so as we walk around and weshow people on our phones or on our

(24:58):
desktops, then people are like, oh,that's what we're talking about.
'cause I really thought wewere talking about robot.
Yeah, great.
On
Tuesday, I was atcheerleading practice, right?
And all the moms are hanging out in theroom 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 Iwould say about half the room.

(25:22):
Were women professionals who were saying,oh, I use it to do this in my business,
or I'm a therapist and it makes mynote takings process so much faster.
And then the other half wasjust 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 allwith their allergies on what
days and their shopping lists.

(25:42):
And they were like, oh mygosh, this is kind of cool.
And then another one wasdoing it for a job negotiation
and how to write the email.
So it was professional, butalso, you know, felt like her and
they were just like loving it.
But it is, it's takingit from this very weird.
Abstract to, wow, this ishow I can use it in my life.
Right?
And so we just have to get outthere and show people more.

(26:04):
Yeah.
So that's one of the things we wanna do.
But I cut you off.
I just wanted to makesure 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 computervision is integrated with speech, text,
and text to speech and image generation.
That's Agent Agentic AI already, right?

(26:25):
People don't talk about it.
They, they're trying to like positionthe next big thing and everyone
is racing to coin the next term.
But agent AI is alreadyhappening 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, textgenerating images, generating speech.

(26:46):
And just doing all thoseconversions, which is basically an
automated workflow for AI is low.
It's any type of
deep research is going off andbasically 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 imagegeneration is having its moment.
Yeah.
Right, right now in thelast couple of weeks mm-hmm.

(27:06):
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 ifwe have a little bit of time at the
end, but there we are moving from justthe 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.

(27:27):
Like you're not gonna be able totell that you're talking to a human.
That's right or not.
And I actually think this is a greattime to introduce you to our game
that we love to play on this podcast.
Ooh, great.
And this is really helps peopleunderstand how they can use
AI in their everyday life.
But you have to pull out your phoneand you have to go to chat, GPT
or Gemini, whichever you're using.
And you have to tell me whatis the last thing you asked?

(27:48):
We called this last chatt.
Yes.
Your last chat.
Look and don't go over there tryingto 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 progressivelyworse if it gets to me, so,
oh look, y'all.

(28:09):
Okay.
My husband is, um, stealing my chatbecause it's literally my last chat is
Junior pm it cybersecurity interview.
So that ain't me.
That is my husband, DanRooney stealing my chat login.
I'm calling him out my last chat.
But, but he looks like he, he'strying to help himself through
an interview and what to do.
Do I think he needs
to interview somebody today?

(28:30):
Right.
So that's good.
Okay.
But mine was how many people fitin the Fillmore in Charlotte?
And that is because I had to take my10-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

(28:51):
ops of Ops at, uh, at my firm.
I said, please update this agreementby adding an, sorry, this was the
Chad GPT, and then I sent the link tomy, uh, the managing partner of ops.
Update this agreement.
It's an NDA by adding an emailfield to the signature block.
Also, add a signatureblock for the client.
Beautiful and updated the NDA.
Beautiful.

(29:12):
So we're gonna get into that in a second.
Okay.
About the impact on the legal community.
Alright, so let's, let's getto, well, lemme get my chat.
Hold on.
Hold on.
Hold.
Could be good.
Alright, so mine was, weknow it's gonna be good.
Knowing him, it's not gonna be good.
It is not gonna be bad, butit's be very intentional.
I can, can we agree on that?
At least I a hundred percent.
Alright.
Fair.

(29:33):
You know, in preparation for, fortoday, one of the things I wanted to
find out was I said, Hey, give me threeexamples 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.

(29:54):
And they gave you a lot moredetail as to how one would do it,
even including the tools, right?
So you could use Jasper or copyai, or chatt pt Claude, whatever
the second one was automateemployee onboarding content, right?
Again, trying to make sure peopleunderstand how they could actually
use this to be a lot more productive.
Um, and then the last one wassummarize employee feedback

(30:15):
and engagement data, right?
A lot of companies, especiallyif you're a smaller organization,
we're at Venture Connect right now.
There's a lot of founders.
Whether it's legal things that theyneed to, you know, get prepared and
then just have an attorney review, or ifthere's HR related things, or there are
tasks, right, they can be a lot better.
Right.
One of the other things that I'mlooking at that's not in here is I

(30:36):
did something similar for CMOs, right?
How could a founder get the access to ACMO 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 differentways, 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 gonnaget back to insight three.

(30:56):
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 andforth 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 machinelearning side of it and the complexity
of it and why people don't do it.

(31:17):
Right.
Even though it's giving youthe biggest value, right?
Right.
And then we talked about what was Insighttwo is about the, um, the explosion.
It was the context,
the text.
Yeah.
And the, the whole thing about theexplosion 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 wantabout open ai, good or bad.

(31:37):
I'm not here to judge, but itwas a stroke of genius to give
it, give us an internet browser.
Give us the Netscape for ai.
Yep.
Right.
And they've been ableto maintain dominance.
Good for them.
Right?
So far, because Netscape actuallywasn't able to keep it up for,
just pause there for a second.
For those of you that don'tknow what Netscape is, oh God.

(31:57):
Right?
You're talking about two folks that camethrough 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 thefirst like introduction to.
The browser environment, I guess, or,or an experience for, for regular users.

(32:19):
Um, so now for Insight three, I'msaying is that now that these things
have converged and, uh, chat, GPT,like LLMs are getting trained on code.
Yeah.
And you, we, we werejust talking about this.
Yeah.
So what's gonna happen is thatwe're gonna start demolishing that
barrier to the productive machinelearning stuff, and the two are

(32:41):
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 thegradient descent, and K means, and
it'll do all that stuff for you by.
You knowing how to prompt itproperly and hence brings insight.

(33:02):
Number four.
If you guys are not doing aprompt engineering, you gotta
start doing prompt engineering.
And to do some, um, I guess some promotionfor 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'smore like a thousand percent.
A hundred percent, right?

(33:23):
Yeah.
It's all about prompt engineering.
Whether you take that course or youdon't take that course, you invest
on your own, you go get a Coursera.
My advice to you is startworking in prompt engineering.
That's gonna be the singularmost important skill.
Um, I think in the coming
two to five years, you know, so, so tothat point right in, there's two points.
One, um, I'm on Coursera a lot.

(33:44):
Awesome.
I'm looking, looking for likesome points or something.
I don't know what I get asyou 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 bitback in time, meaning like two years ago,
because what I'm trying to figure out ishow right or wrong people have been Yeah.

(34:07):
Over that timeframe.
And there was two pointsfrom two or three years ago.
That are so fundamentallyoff, in my opinion.
And there was a Harvard business,uh, review Harvard Business.
I don't know what exactly it was somethingrelated to Harvard, but there was someone
a couple years ago that said that promptengineering was gonna, was not the
future and it was gonna just go away.

(34:30):
How wrong could that have been?
That's right.
Right.
And I think, again, itgoes 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 whereI thought was so wrong.
Thing two was there was also a lot oftalk back then that the only people
that were gonna see 10 X productivitygains were gonna be less experienced.

(34:54):
Individuals.
I mean, there were peoplewriting articles about that.
It's like, yes, this is great if you'rea junior employee and this and that.
And I was like, how wrongcould you have been?
That's right.
Because what they didn't understand iswhat someone that was seasoned could
actually do, could imagine, could createwhen they had all of this time back.
Right.
You know, we talk, uh, jokingly, Ericand I about, you know, uh, pessimists

(35:17):
get to be right, optimists get tobe 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 tounderstand always talk about
flipping that time equation.
Right.
And so if you're a good promptengineer and you can tell the machine
exactly what you're looking for,like one of the greatest hacks I was

(35:39):
listening to recently, someone said,and I thought this was brilliant.
Not a lot of people use the, uh, youknow, 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 makethings more concise when we type.

(36:01):
Right when we're in a text.
So her hack was just talk to it becauseyou're gonna give it a lot more context
and be free flowing than you would if youactually just typed, and it's just easier.
So people are talkingto, 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 haveto be great at figuring out exactly what,
just say the things that are on your mind.
Yeah.
And then over time you'llstart to understand, now there

(36:22):
are patterns, there's Yeah.
Flip interactions.
There's all these other things that youcan 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, soall this pattern stuff made sense to me.
Yeah.
Templates and all of that.
Right.
And I think you can be very muchdumbed down, but at the end of the
day, if you could just talk to it,you'll find that you're gonna give

(36:45):
it a lot more context, you know?
Yeah.
So, um, something that we talk aboutin the class, because the beginning
of the class is a bit more, um, ofan expanded primer to what I just
covered at the start of the podcast.
Uh, to put things in context and dispelmyths and shine, like turn on the lights.
Yeah.
Uh, in plain English so peopledon't feel dumb and they

(37:06):
actually say, oh, now I get it.
This is like electricity beingintroduced to our life and I
need to make, I live in the dark.
I want electricity because youcan't live without it these days.
Right.
It's the same, I think it's the sameanalogy, the same paradigm shift.
Right.
So having said that.
Everything that we just talked about.
There are two things for theselarge language models is the term

(37:29):
for them, which is, I coveredit from AlphaGo into et cetera.
That became the foundation for Chad,GPT and Claude and all these guys.
Um, my analogy for it, 'cause Ialways 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.

(37:50):
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 forgenerative pre-trained Transformer.
Transformer.
It actually means nothing to correct tosomeone that's trying to figure this out.
You know, you kind, you kind of lookat these things and you, you kind of
say, okay, so in order for you, solemme put it in plain English, your

(38:11):
ability to 10 x, or I would say even.
20, 30, 40 x your productivity andcapability is based on two things.
The, the, the, the, the,I'm gonna keep it simple.
The bot that you're using isgonna be, uh, super awesome.
Supernatural.
If it's been trained on a large data set,such as the internet or whatever, and

(38:34):
it's been, there's been reinforcement.
So that's, I call it the, the, theone half, the one phase of the coin.
It's actually getting, it'sactually 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 totalk to you and hopefully it makes sense.

(38:56):
Um, it's the same withbuilding that synthetic brain.
It's as if you've got a fadi that youtrained over 53 years or whatever.
And you codified all of thatinto that synthetic brain.
So that's one half.
If you can do that, you've createdlike a digital twin for Fadi.
Yep.
And by the way, we, we have a, wehave a digital Fadi in the firm.

(39:17):
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 coinis going to be about how can you.
How can you quiz it?
Right.
And, uh, I'm gonna borrowfrom Tony Robbins here.

(39:39):
Um, and I know he got it from hundredsor thousands of people he met and
hundreds of books that he read.
But he said something that was just spoton, that actually was for me and my wife.
It changed our life because, uh, wewere both working at the same startup.
I, I was the founder and she wasa part of the team, et cetera.
Um, the, it's, by theway, it's very profound.

(40:02):
I mean, just forget AI and everything.
It's just very profound.
If you reflect on this littlenugget, 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 yourquestions will directly is a direct line
and influence to the quality of your life.
So if you think about that, the argumentis that if you ask better questions.

(40:26):
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 askingyourself better questions, all of a sudden
new resources will be unlocked, uh, bad.
Um, thinking will be edited outby simply changing the language.
So what I'm getting to is that if youreally break it down, it's about language.

(40:51):
And knowing where, knowing how to becomean expert at using language to expand your
horizons and the quality of your life.
How much money you make, howgood your relationships are.
Et cetera.
Now you take this, we're talking aboutit conceptually in an abstract, if you
really reflect on it by then, I thinkthe book was, uh, awaken the Giant.

(41:11):
I know it's an older book, likean 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, morebalanced, whatever it is that the
ques, the better questions for your.
For your, your, your journey, uh, isgoing to be, we take this concept again.

(41:38):
You buy into that, take that concept, andyou apply to the second half of that coin.
So you've got a syntheticbrain 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 ofit if, or updating that NDA for
the firm instead of it taking.

(41:59):
Four hours of my time,it took five minutes.
That's invaluable, man.
I saw a post just this morningon LinkedIn, um, and I think the
guy's name is Greg Eisenberg.
I'll have to go back and look, buthe posted about, I don't know how
real the story is or not, but thepoint still remain, which was he
said, now I can do all of theseNDAs and all these legal documents.

(42:21):
That's correct.
Uh, I can provide now myattorneys the first draft.
And now what?
That's right.
Would've taken them 10 hoursor 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 gonnabe death by a thousand cuts.
'cause he's saying a lot ofthese folks aren't recognizing
what's happening in real time.
They're gonna slowly but surely startto see their billable hours in the

(42:42):
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 somethingI already drafted by a billion.
Yeah.
Uh, by, by a million attorneysacross the entire internet.
Have all agreed in my, my,you know, context window.

(43:03):
Right.
And so I think that you'regonna see that same situation.
You know, we work in an agency model.
I say the same thing.
It was like the hourly rateis, 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 thisidea that the junior folks would
do these mundane tasks, that wouldtake, you know, many hours, right?

(43:26):
One 'cause there's others concern, right.
And I'll make a point here, whichone of the concerns is that if you
have these things that can give youall of this administrative type work,
and that's what we traditionallyhave had people less experienced in
companies doing, then how do they gettrained up to do the senior thing?
And what my argument would be is.
Do you really think that havingthe intern, you know, go get coffee

(43:49):
and move paper from one desk, thatthe other one was training them on
the, the nature of the business?
How about you think about it differentlyand say all of that busy work that you
gave that individual, now that time isback for you and for that individual
and now you could actually spend timementoring them on what actually they
need to do to learn for their job.
Right.
You don't have to Mr. Miyagi and tellthem, you know, by picking up that piece
of paper, you're painting the fence.

(44:10):
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 yourfence and, and sand the floor.
Right.
And so I think that that's the partwhere, you know, as whether it's in
your, I don't know if it's gonna be rightfor your course, but I also a hundred
percent agree with you is, you know.
Brings us back to prompt engineeringand what you're talking about, right?

(44:31):
The level of specificityand detail and context.
You get it?
There's gonna be a direct correlation andreflection in the output that you get.
That's right.
So people in this constant motion, in theearly days of, you know, chatt, PT and
others, and always use this example andthey say, well, which number's bigger?
9.11 or 9.9.
Right?
And for us, out of context, for theaverage person, they'll say 9.9 is bigger.

(44:54):
Right.
But in earlier days chat, GBTwould say 9.11 and people say, see?
Got you.
But what I didn't realize isit was trained on software
release cycles and from thatcontext, that is a bigger number.
Yeah.
But if you didn't give it enoughinformation, I think what they call
that, uh, LAN ese, uh, paradox.
Right.
Us as humans, we know a lotmore than we can ever tell.

(45:15):
Yeah.
Right.
So if you ask someone how to ride abike or how do they use their phone?
There's a million steps that theydon't, that are very implicit to us.
That's right over with,with with our lives.
Right?
So we don't say everysingle thing that we do.
Right.
And so to your point, you trainup that one side of the coin, I.
To know everything that we do.
That's right.
Right.
And then you use theother side of the coin.

(45:36):
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 sothat you can kind of engage with how I,

(45:58):
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 thinkthe audience should definitely take
away from this conversation, don'tput a better word that you would get
excited about than prompt engineering.

(46:19):
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 tostart writing better questions and
really start thinking about the waythat you can unlock that potential
of that other side of the coin.
Right.
So, um, the one point, I'm not sure howmuch time we have, but the one point that
I wanted to, uh, just talk, because whathappened, I don't know how this happened.

(46:41):
I'm not really sure, butI'm, I'm excited about it.
We're engaging with a couple law firmsand I would've never thought, uh, that
we'd be able to move the needle with them.
But they're actually switched on afterwe do like a primer on, because what
we did, um, in the spirit of, keepit simple, don't be condescending.
Mm-hmm.
Uh, make sure you explainthings in plain English.

(47:03):
Yeah.
And.
Focus on showing that this is not amonster, actually, it's just a machine
that has been trained by humans.
And, um, you can use it andexploit 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 boardmember, whatever it is, you can, you

(47:27):
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 becauseI 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 useAI will take over your job.
There you go.
Right.

(47:47):
So people, and it's not just forlawyers, for any, for anybody.
Yes, but I'm, I'm, I'm actuallypassionate about this 'cause I wanna
talk about it so that they realizethat one of the most classic or most.
Risk averse, most compliant, right?
Most likely, right?
You think, okay, these guys willnever, ever, you know, adopt this.
And they had that.
Um, I, I mean, for client confidentialconfidentiality, I mentioned the

(48:10):
names, but I walked in to the roomwith a, with a set of, I don't know,
many attorneys, like, you know, 12attorneys or more, and they're at the
boxing gloves ready to go, you know?
And then after about anhour, they're like, wow.
I'm a whole lot less concerned nowperson probably if he hears this,
he knows, you know, I'm quo him.

(48:30):
I'm a whole lot less, andthis is a managing partner.
I'm a whole lot less concerned nowthan 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 thelawyers did a complete 180.
By the end of it, they'relike, 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.

(48:51):
We should introduce itto all the attorneys and.
Which is all valid, but atleast 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 ofattorneys and I've been in, in sessions.
When I say to folks, when I, when Ihave an audience, I was like, Hey,
who are the attorneys in the room?
Right?
And I'd ask, I say, what do you doin organizations where your own only

(49:11):
governance model is from the legalcommunity 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 tofigure out how we can create
greater business opportunity.
Right.
On one side of, I'm certain there werepeople in that room, there were also like.

(49:34):
Oh shit, if I don't do thisright now, I won't have a job.
That's right.
Right.
So you have on one side, and that'sone of the things where I think it's
this very like a polarizing momentfor folks and that that's why we call
the podcast AI voice or victim, right?
You are making a choice andthose folks in that room.
They could, they, theychose to be a voice, right?
They could have very well say, you knowwhat, I'm not gonna do anything, and

(49:55):
they will very quickly become a victim.
And so it was my sincere hope thatif 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 beable to take this across a lot
of other industries, professions.
I know the three of us have talkedabout this offline, but the ability
to go in and talk to HR professionals

(50:15):
mm-hmm.
Sales and marketing, um, operationsfolks that are not technical in
nature, but this will absolutelybe a game changer for them, or it
would be a career killer for them.
Yeah.
Right.
But at the end, I, I justfundamentally 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 concernwhen we were doing Chad GPT, and

(50:38):
I still am to a, a certain extent.
Yeah.
Because for a reg, heavily regulatedindustry, we gotta look like, uh,
law, the things, when we did the, thedeep dive and researched, um, what's
happening in law, uh, with ai, wereally, we spent, I can't remember, 16
or 20 person hours doing the research.

(50:59):
To really identify what's the holdup here?
You know, why, why can't attorneysget over the, uh, get over the
hump on this particular thing?
And, um, we really got down tothe things we were able to discuss
in a non-threatening way, safeenvironment to say, bring your,
bring your heavy questions, man.

(51:21):
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 'emwhat they're concerned about.
You're concerned about yourchargeability, you're concerned
about, um, uh, hallucination,you're concerned about this.
You're concerned about privacy.
You're concerned about, um, userconsent if you're gonna be training this

(51:44):
bot on some of your customer's data.
Alright, well, let's spendtime working on that.
And the thing that I'm gonna talkabout when I started this, uh.
You know, this, this, this, uh,explanation, um, Chad, GPT, even
Chad GPT, have an option wherethey don't train on your data.
Yeah.
It's in their T's and C's.
So if you get that membership lolevel, it takes away all these issues.
Yeah.
And my argument with many people thatsay, oh no, but you know, they're

(52:06):
gonna copy my data, and I'm like.
My friend, they already got your data.
If you're using all thecloud 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 inthe cybersecurity session.
He's like, if your concern isthat your data might get copied
or take, he's like, you're alreadyusing all the cloud providers.

(52:29):
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 guysare compliant with federal requirements.
Mm-hmm.
Yes.
Right.
So there's no concern.
You don't have any concern as anattorney today to be hosting on

(52:51):
Azure, like you're using Office 365.
Guess what?
That's all God knows.
Actually, your data is all overthe world because it's getting
replicated to every single countrythat they have a data center in.
But don't worry about it because.
It should you get sued, Microsoft aregonna have to step up to the plate.
The Forrester uh, uh, guy speaking at theAdobe Summit Conference last week said he
had a, he had a quote on the screen thatjust said you had me at, in indemnify.

(53:17):
Right?
It's once those organizations said thatwe 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 enterpriseversions, whether it's Google, Gemini,
which is attached to everythingin your Google Workspace, right?
So there's an added bonus there.
Yes.
Or Microsoft absolutelycopilot all these, right.
You're in the best shapethat you're gonna be.

(53:37):
That's right.
Right.
And now the other side of it, uh, Dr.
Michael Jabor was speakinga couple months ago.
He said, he said, I hopethere's no CISOs in the room.
You know, any security folks.
He said, because they did a studyat Microsoft and 78% of the folks
bringing AI to work, whetheryou said Sue or not, right?
Yeah.
Yep, that's right.
So it's irrelevant.
So you better that you have a governancemodel and that you're focused on that.

(53:58):
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 wascompelling, you know?
Um, I wish I could havebeen a fly on the wall.
Hmm.
Like, and I definitely wannatalk to you about this a little
bit more, but, um, we're gonnaneed to start wrapping up here.
Eric, what we on with this?
Yes,
we do.

(54:18):
We do.
I just wanna say thank you so much.
Of course.
I mean, of course.
This is Venture Connect.
This is an incredible spaceto 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, youknow, I'm trying to bring this,
this, this crew together here.
We got AI Curious, that's very focusedon the hr. She used to be a chief people
officer, now she's out there as executivecoach and keynote speaker, Fadi amazing

(54:41):
transformational architect in thisspace, um, and dated himself earlier.
I. About how long he'sbeen in the space, how long
I got to the age.
I don't really care anymore.
I mean, he basically, what he said wastenured when, when I was five years old.
I started getting into thisin the eighties is basically
what you said, right?
That's how I'm gonnaparaphrase this, right?
And then myself, I call myself AIseries, but it's really about, from a,
a business perspective, you and I both.

(55:02):
Moved to the dark side years ago, right.
Where we, you know, 'causeat 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, comeon, Luke, the ceo O the CEO, EO of
Coca-Cola, last week when I was at thisconference, he said something that was,
that was very compelling, which is hesaid, uh, stop selling what you make.

(55:23):
And start making what sells.
Yeah.
There you go.
Right?
And so as you move over to the businessside and you start thinking about AI
and how you can use this, I think it'dbe a great opportunity as we look to,
you know, better advance folks andhelp them, again, move from anxious to
curious and then maybe serious, right?
But we have to get the AI adoption, wehave to get folks on board that aren't
just the traditional techies or the seniormarketers that have been using ML and, you

(55:49):
know, and their workflow for, for years.
Right.
And so we're gonna be doinga few workshops, right.
Some of them will be free.
Uh, we would do things, uh, in personand there are also gonna be some
webinars and things that we'll do.
But it's our goal to try to bring asmuch of this to folks and make sure
that people feel a little less angst.
Yeah.
Right.
And a lot more productiveand a lot more curious.

(56:11):
Yes.
We appreciate it.
One question we do have to askthough, in the next 24 hours, what
would you advise someone to do?
They only have 24 hours.
What would you advise 'em todo 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 ofcourses that are free.
You're spoken
like a true professor right
now.

(56:32):
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 knowthis about Coursera and Udemy is actually
they're instructing you using the tool.
They're walking you through ahundred percent examples of how
to, how to use language to getwhat you want out of it, right.
So I think that's the most cost effective,fastest way to actually start doing some

(56:55):
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'dlike 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 AmazonAudible, you name it.

(57:16):
It's called the Coming Wave.
And my post this morningon LinkedIn was about this.
So the past six months I've seen thebehavior and as a person that's been
on, you know, digital and AI for somany years, I can see this massive
tsunami that's coming in two years.
Hmm.
Yes.
But it's silent and it'sjust like, it's deadly.

(57:36):
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 gonnadisrupt so many industries.
And this is different thanother technologies like
workflow automation or whatever.
Right.
This one is electricity or the car.
Yep, yep.
Um, you know, so I'dsay go read that book.

(57:58):
Amazing, amazing suggestion.
Thank you.
Yeah,
absolutely.
Thanks for joining uson AI, voice or victim.
If you want to stay competitivein the AI age, start now.
Take one insight from today's episode andput it into practice in the next 24 hours.

(58:19):
Make sure to follow us, shareyour thoughts, and subscribe
for more actionable AI insights.
See you next time.