December 17, 2025 • 45 mins
Start with a guitar, pass through psychology, and land inside enterprise cloud and AI—our conversation with Andy Sitison maps a creative life that never had to choose between art and engineering. We dig into how narrative data and human-centered design can uncover what dashboards miss, and why empathy is not a “nice to have” but a strategic advantage when you need to make decisions that actually work for people.
Andy breaks down how his team collects long-form stories from employees, customers, and communities, then analyzes them with transformer-based models to score emotions like anxiety, joy, stability, and self-transcendence. That mix of qualitative depth and quantitative clarity surfaces the “personality” of a community and exposes blind spots that surveys routinely miss. We talk through real-world signals—like rising anxiety during a leadership shakeup—and how quotes and thematic maps help leaders act quickly without guessing. You’ll also hear a candid look at the shifting craft of AI work: less model tinkering, more agent design and prompting chops, and a renewed focus on trust, privacy, and consent.
The conversation widens to the big picture: energy use, data centers, global talent, and the social costs of automation. Are we building caretaker systems that augment us, or cold logic that optimizes past human needs? Can intelligence learn compassion, or must we teach it by example? While the future remains open, we land on practical moves anyone can make now—critical thinking, local connection, and using AI to listen before you decide.
If you care about AI ethics, narrative intelligence, employee experience, customer insight, and the future of creative work, this one’s for you. Subscribe, share with a friend who leads teams or research, and leave a quick review to help others find the show. What’s one question you wish your data could answer but never does?
Learn more about SEEQ-APP: https://sharemorestories.com/seeq-app-how-to/
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:00):
Welcome to Mediascape, Insights from
Digital Changemakers, a speakerseries and podcast brought to
you by USC Annenberg's DigitalMedia Management Program.
Join us as we unlock the secretsto success in an increasingly
digital world.
SPEAKER_00 (00:22):
I could not be more thrilled with my next guest,
Andy Citizen, CTO of Share MoreStories.
Andy, just in our littleconversation before we push for
Cord and all of the greatinformation that you provided
me, as well as what I found onthe internet, I'm just really
intrigued by how you've shapedyour career and your narrative
(00:44):
because you've done so manydifferent things.
Yes, you've been at theforefront of tech.
You've been working in AI forlonger than many of us knew that
it existed.
But you also are a nonprofitfounder.
You also are a family man andhave poured a lot into that.
(01:05):
You have so many interestingprojects going on.
And I I really truly believethat this helps you shape the
narrative of ShareMore Storiesand how you work in the world of
tech.
So thank you first and foremostfor being here today.
SPEAKER_02 (01:22):
Hey, I I it was great to get that read back.
I mean, you're right, and uhhadn't thought about it in that
way a lot.
So it's a nice context, a niceframing.
SPEAKER_00 (01:29):
Oh, fantastic.
Well, I'm so glad that you thinkso, because this is truly from
everything you provided me.
You called it a data dump, butit really is a great picture of
who you are as a strategiccreative.
So you talk a little bit abouthow you came to realize that you
were a strategic creative.
SPEAKER_02 (01:48):
Let me do that in a an efficient manner.
And you just hold up a big, youknow, move on store uh a flag or
something if I keep going toolong.
Because, you know, I've been ordoing this for a while, but I
will I'll start at the verybeginning.
I wanted I've always beencreative, though, you know, a
genetically generatemulti-generational creative
family, and and we created inall sorts of ways as a kid, but
(02:09):
I was a musician, I loved youcan see the guitars and banjos
and stuff behind me.
You know, I wanted to be thatguy on stage at one point, and
then just uh realized I was uhdidn't trust that life enough.
So then I went into psychologybecause I thought, well, I like
helping people, so maybe it'spsychology.
And I actually got my undergradin that, but I also realized
(02:31):
that at the time what you weresupposed to do is sit back and
let everybody solve their ownproblems.
I'm like, maybe I'm more of ananalyst because I want to solve
the problem and get moving.
I'm I don't have patience.
So, but I've always beencreative.
And and as I was looking, it wasa struggle for a couple of years
to figure out we opened arestaurant, did that for a few
years, and then I uh my wife gota job at IBM in Rock, and I
(02:54):
said, hmm, this is a nice life.
I don't have to work weekendsand uh, you know, maybe I can do
programming and all this stuff.
So I jumped in and didn't havethe math and actually created a
stutter for about six monthsbecause I started using the
other side of my brain.
I was very right-brained, veryleft-handed uh person, and then
I needed all the left brainparts, and so I fire on both,
(03:17):
which is a kind of a uniquemindset.
But what I found was it itprogramming was creative, so I
dove in and I realized it's justlike writing songs or writing
books or whatever, writing codeand writing and solving
solutioning problems uh wasreally fun and you know gave me
an outlet.
And so I went along that path.
I integrated companies andworked for many in manufacturing
(03:38):
spaces and new drug developmentand nuclear and you know, helped
them stand up systems, and itwas fun.
But I was traveling a lot.
I thought, well, maybe I'll justgo to one of these big high-tech
companies and see what thatmeans.
And uh, so I went to a reallyfun, uh, really high, high grade
uh company, and then I traveledeven more over time because I
(03:58):
eventually took over LatinAmerica and then uh all the
Americas and then all thecountry, whole world for uh a
kind of a business applicationspin.
This was a more IT group and Iwas more business app.
So it was again back to kind ofthe creative deployment of
systems for corporations andmost of the companies we work
with in Fortune 100s back then,and that kind of got us into the
(04:20):
cloud space.
And I was kind of really Iwasn't, I wouldn't say I'm like
the cloud guy, but we weredriving billions of dollars to
the cloud on people that hadrolled out 100 million to a
billion dollar deployment.
So these are very expensive,important things that run
everything we touched, you know,all our financial transactions
and all that.
And so we were helping put thoseon the cloud.
(04:42):
And once that happened, it waskind of a flip, you know, uh
inflection point in the marketwhere these big Fortune 100,
Fortune 500 companies arestarting to see the cloud as the
only way to do it now, and youcan't really keep these schools,
uh, these sets in the house.
And jump ahead.
I took us Matil, and we weregetting acquired, and it was
(05:02):
gonna be very horizontal.
And I'm like, my skill set's notright, so I'm just gonna take a
year off and figure out who Iwanted to be.
And reality is, I had a greatresume that a lot of people
probably wanted, and I didn'twant to be that guy anymore.
So I wanted to get creativeagain.
I would wanted to quit answeringother people's questions and
start just finding my ownquestions.
(05:24):
So I you know, jumped to theend.
You know, I started looking at acouple different kind of
emerging tech spaces.
I'd always been in emerging techand looked at, and it was a data
guy.
I got my master's in datamanagement, which I skipped
over, but I told you I'd try toshrink it.
And uh anyway, I had to uh Istarted looking at data and sort
of AI was showing up, machinelearning was really getting hot.
(05:46):
And this is 2016, 2017, and so Ikind of took four months,
another four months after uhgetting started to kind of teach
myself the math and the code ofAI, and kind of just dove in
from there and ran in a couplefriends and said, Hey, why don't
we work on this startuptogether?
So that is where I'm spending alot of my time.
There's been a couple morestartups in there that I've
(06:08):
helped get going and atdifferent levels and different
roles, but I love it.
You know, I'm probably not asrich as I would have been if I'd
stayed on the plane, but uh I'ma lot happier so and more
creative.
So I'll I'll take a breath anduh but that's kind of the story
in a nutshell.
SPEAKER_00 (06:22):
I I think that's something people some forget
that it we talk about STEM, butwe talk about STEAM because the
arts, that creativity is a largepart of thinking about
mathematical equations,programming, the things that you
talked about.
I'm also a lefty, and so I livemostly in-too.
(06:43):
But I also like you, I majoredin public administration for my
undergrad, but my MBA isspecializing in AI and ML
because there's so much more Iwant to learn about the
analytical side, and that'susing that the data side with
the storytelling is obviouslysomething we do a lot in public
relations, marketing, branding,communications, kind of the side
(07:04):
of the coin that I live on.
And that's something that youalso that's a that's the field
that you play in, is notnecessarily the marketing,
branding, but getting people'sstories, figuring out what the
data is, but also humanizing,right, whether it's employees or
just people in community and howthey come together.
(07:26):
And that's something that Ithink is so vitally important.
And you've also figured out howto use technology to do that
more efficiently, effectively,and getting back to kind of
empathetic AI, which is uhsomething that we've talked
about.
Uh people who've listened to theshow know I I love going to AI
for I go to other AIconferences.
But this year, what I lovedabout it was the conversation
(07:48):
really was about how do we workhand in hand with AI?
It wasn't about AI being thereplacement for everything like
we hear in the media often, orwe we see these big stories and
we think that that's thenarrative with layoffs and
things.
But often if you do a littlemore research, it's not, right?
And so really learning how towork hand in hand with this
(08:09):
great tool that we have.
And yeah, we can get to thequestion of are we the Jetsons
or are we the Terminator laterin the conversation?
But that's really not wherewe're at when it comes to the
day-to-day.
SPEAKER_02 (08:22):
Yeah, I would love to get back to that because I
I'm I'm positive on it, but I'mcritical.
And but to your point about youknow, human versus digital, and
that was uh something that camefrom my sabbatical was I
realized that I felt like weweren't putting the humans in
front.
And the technology shouldn't bea competitive edge against
humans, it should be theopposite, it should be an
(08:45):
enabler for humans, and we willalways have tech.
We will we've had techs, youknow, we've had tech paper and
stone wheels, but since the1780s, 1790s, this whole
industrial revolution's been ona big roll that hasn't really
stopped.
And, you know, if you look atthat, you know, it's it's not a
debolbing, it's evolving.
(09:05):
And you know, AI is absolutely amassive step in the next
direction on that.
So humans can't adopt tech orfight against tech, they have to
find agency with tech.
And so I think it's a reallyabout establishing our overlay
layer that is the human in themix, and so we have to we have
(09:25):
to put ourselves in thoseingredients, and we are frankly,
we're not very good at it,right?
We have allowed society to youknow define what the human role
is as it rolls out, and I thinkthat's one of those things we
have to fix.
It's a continent contemporaryproblem, like it's never been.
So, to your point, back tohumans, and uh before I go down
a whole rabbit hole, I loverabbit holes, so but you know,
(09:50):
back to humans.
What we do uh in short foreverybody is if you you know in
the moment, like let's talk fora second about like has there
ever been something challengingyou've tried to learn or that
you didn't have a digital answerto, like cooking something or
you know, playing guitar, or youknow, right now I'm trying to
fix three carburetors that siton top of each other in an
(10:11):
outboard motor on a boat, right?
And you think there's enoughYouTubes out there, but there's
not because there's you know,there's metrics, there's data
points, there's data, you know,diagrams.
I know what parts to order, butwhat's the essence of it, right?
Like what's that magic, thewisdom associated with it?
And that's what's happening inmarketing right now for a lot of
(10:32):
companies and organizations,like the you know, CMOs, you
start to see a chief experienceofficer showing up.
And it's because we need toconnect with humans.
We are all feeling it at areally deep level, and it's you
know, we're doing great.
MPS is great, net promoterscores.
Why not?
You know, we've got metrics, wemature the models, we got all
sorts of data, and we learned alot from it.
(10:55):
And I'm not negating any of thatvalue, but there's still a level
of understanding, especially ona critical initiative or if
you're trying to reach apopulation that frankly isn't
getting a voice to you.
That's what we do in in thiswork, and that is to collect
stories from that community,whether it be, you know, a
(11:16):
minority group or geographicallydispersed group or just somebody
that likes uh, you know, a kindof sports shoe, you know, we
want to look talk to them aboutthat.
Sometimes doing something morethan a survey can not only make
them feel more connected andmore trusting of the source of
that and be able to connect andprovide you data, but to tell
that in three or four pages innot a review necessarily, but to
(11:39):
get to the essence of whatmatters in that space.
Let's say if it's sports, why doyou do sports?
You know, why you know are youlosing weight?
Are you trying to stay healthy?
Do you want to live another 20years?
What get to the essence of whythey do what they do and then
ask them to tell us about it.
And surveys are great at gettinganswers to the questions you
know you want to get answered.
(12:00):
But what we do is help youunderstand the questions you
should be asking and giving avoice to that person, letting
them tell you what they need asa community.
And we do that by collectingthose stories through a kind of
web app that kind of gives usweb scale.
But then we use AI in a fewdifferent ways to analyze those
stories.
And one thing we do that's kindof unique is emotive scores,
(12:22):
everything from like activitylevel to you know anxiety to joy
to self-transcendence, and youknow, we have the 55 scores that
sit up on the cloud and justcrunch on these stories.
And what comes back is apersonality.
You know, we don't have apersonality of the people and
the story that's the personthat's telling the story, but we
have a personality of thatcommunity to some degree.
We start to see, hey, they'rereally high, highly anxious, or
(12:44):
they're not, they got a lot ofjoy, they got a lot of hate,
they got a little or anger, theydon't have we don't we have
sadness and anger, not hate, butyou know, all these types of
scores.
And so we start to see howthey're feeling and why they're
feeling that way.
And then you always have thestories themselves with quotes,
and and you know, you can go inonce you you can use GPT to like
analyze something and then say,okay, give me three quotes that
(13:06):
represent that in the stories.
And here GPT comes with youknow, three profound statements
that you're like, oh wow, youknow, I have a new in minutes.
We can take a customer to have awhole new perspective on
something they've worked on for10 years, and and it's almost
like they just birthed the newbaby, like they'll be like, Oh
my god.
And you know, they're they'realways surprised, shocked, and
(13:27):
happy about it.
So it's really fun.
I'll take a breath.
SPEAKER_00 (13:30):
Yeah, I think this is really important.
One of the classes that I took afew semesters ago from my MBA
was creativity and innovation.
And it was talking about how dowe get people in the workplace
to form community to think aboutthings and to leave space for
people to come forward withtheir ideas, with their
thoughts, with their constructs.
(13:51):
That's in essence what you'redoing.
When you we think about surveys,they're often closed, right?
You have scale one to 10, yes,no, maybe.
Maybe you have a little room toleave answers, but it's not
getting the real story like youare.
It's not pulling out the thingsthat people might not be able to
(14:13):
see by that kind of data becauseyou're getting into qualitative
and you're you're really gettingsomebody's thoughts, and they
may not even realize whatthey're providing.
So I think that's justbrilliant.
And I'm interested and intriguedon how you came up with the
solution in the first place.
SPEAKER_02 (14:34):
Yes, I think you you did a good job of hitting on
some key pieces there.
And you know, we you know, I hada friend that was this, you
know, that my partner was astoryteller.
And uh I we we did TEDxtogether.
This red circle behind my headis actually part of TEDx stage
where, and you know, I've got mymachine learning skill set
together and he was doing thisstory collection thing.
(14:56):
And we said, well, you know,it'd be kind of fun to anal
long-form text to uh analyze NLPthe the stories themselves, and
we started looking around atwhat was available, and there
were some, you know, I spentabout I don't know, first eight
months or so looking at kind ofcasalog for 100-150 years of
psychology.
Like, what did people study?
How did they label that?
(15:17):
How did they supervise?
How did they categorize it, ifyou will, and so forth, and then
where what data sets existed outthere?
And and Watson was doing some ofthis stuff, and you know, some
of the earlier tools werestarting to provide some of that
base scoring for these things,and so we started collecting
against that.
We started doing work and didsome web scraping, and you know,
certain you had to get certainsize, had to be we we don't do
(15:39):
any fiction, so it's not fictionstories, but fact story.
So, yeah, that matters if you'regoing to score things, you want
to get supervised labels on thesame type of content.
So, you know, it was it was astruggle to get going, and what
saved us was the LLMs and notGPT, not uh generative AI, but
the large language models, whichare called transformers, because
(16:02):
what happens if if those don'tknow is that these are very
large models trained on semanticrelationships between words,
right?
So you're putting billions ofrecords in there, and then you
have the ability at the very endof this thing.
If you picture a kind of a bigcollection of nodes and webs,
you go to that very end, you cutit off, or you add a couple
layers, and on that layer, youteach it about your stuff, the
(16:24):
domain.
Like, I want to teach you justjoy.
I want you to find evidence ofjoy in this story.
I want you to find evidence ofsadness in this story for us.
That's a different model everytime, and then we categorize
that in what I call the ventimodel.
It's five points, very low.
It's a likridge scale, low,medium, high, very high.
And so surprisingly, it allworked.
(16:46):
You know, we were and what wedid was we did a lot of human
testing on that, and we actuallycompared to other systems like
Watson and we had Burt going andElmo, all the different ones,
and testing that.
And actually, uh, we got a fairamount of inaccuracies at first
or not anomalies between thesystems, and frankly, found Bert
(17:07):
just was crushing everybodyelse.
It was we read the story and welike, oh, this is that's the way
I would rate it, you know.
So we, you know, we did thatwork and sample size, and that
was you know, that was eightyears ago at this point.
So we've been doing this for awhile, but that's
categorization, which I don'tyeah, I'd say 80, 90 percent of
people don't even realize youcan use AI for things other than
(17:27):
generative, but has nothing todo with generative.
Somebody might claim me wrongthere, but you know, no, no, no.
SPEAKER_00 (17:32):
I got when I'm hearing, because I'm wondering
what organi like how big does anorganization have to be?
How much data do you need toformulate these stories and
these insights?
Because one of the things reways that I use AI is that I was
getting very frustrated at mostof my guests are amazing.
But every once in a while, I'dget a guest pitch that seemed
(17:54):
really good on paper from a PRagency, maybe, and then I would
get them on air because I don'tlike to do pre-calls.
I want it to be like our freshconversation.
Yeah.
Right.
So I've done my research, but Iwas finding that even with doing
some research and getting allthis great information, that
maybe the guest really wasn'tprepared.
(18:15):
Maybe they weren't the levelthat their person pitching said
they were.
And so I ended up with someepisodes I couldn't use.
Just you know, and so I createda tech stack that's basically
like, here's my podcast.
Here are the four main criteriathat I'm looking for in a guest.
You know, it's subject matterexpertise, audience fit.
(18:37):
Does it feel like I'm havingcoffee or a really great
in-depth conversation with afriend that people can listen
on?
You know, and then I the fourthone almost escapes me when I
talk about it.
But uh, but I asked for rankingsfrom one to five based on deep
research, from the LLMs andjustification for each score.
So and then it, yeah, are they afit?
(18:58):
Yes, book them now, strongmaybe, low maybe, or no, not
ready for your podcast,justification, all four points,
but then also overalljustification, red flags and
topics that they were an experton that would also resonate with
my audience.
And so I think this is a smallexample of what you're talking
about, is there's so much more.
And I think really generative isfine.
(19:22):
And yes, I teach digital media.
So we talk about AI generatedcontent versus professionally
produced, user-generated, but Ireally feel like research and
this side is where we can getthe most out of our use of
artificial intelligence.
SPEAKER_02 (19:38):
All right, I'm gonna take that and hook on it in a
way that you don't know to askme if you're gonna like where
I'm going with it because I'vebeen thinking about this lately,
and what happens in as creativesand as we go through our
careers, you know, think yourjob changes, right?
It could be you always heard themanager, okay.
I was the player, now I'm themanager, right?
And some people would hate thatmove and everything.
(19:58):
But for me, you know, gettinginto AI, um, and like I said,
I've been eight, eight, nineyears in it at this point.
It's changing.
And with generative AI comingout, it's actually not as fun
because and why?
Because I was dealing with, youknow, like I was looking at cost
chain, I was building models, Iwas dealing with you know,
stochastic gradient descent, youknow, which is this global cost
(20:21):
process that you go through toget good accuracy and you know,
playing with the epsilon modelsand all this stuff, and you
know, machine learningalgorithms and kind of testing
things, playing with the data,feature analysis, principal
component analysis.
And that's the last one I'll doto you, audience.
And that was fun.
But now General comes out andwe're starting now.
We're building custom agents andI'm feeding in instructions.
(20:43):
I'm essentially an APIprogrammer.
Like, I I'm just sending codesand I'm not I'm not doing
anything, I'm not reallycreating like I used to.
Now I'm just staging an agent,and we've I think we're ahead of
a lot of people there becausewe've really locked down the
agents to just work with ourstories, which is pretty cool,
and it is very effective.
It's and like our business sideof the business is just like
(21:05):
blown away by what we're gettingdone there.
But as a programmer creator, I'ma little more bored than I was,
right?
And that's too bad because youknow, and it's gonna happen that
way.
And like as every every job, youknow, people say, Oh, we're
gonna lose all our jobs.
You might not lose all yourjobs, but what you do is gonna
transform and hopefully like itmore than you do what you do
now.
(21:25):
I don't know, you know, it'sjust just a it's just kind of a
moment in time, right?
SPEAKER_00 (21:29):
Oh, very much so, very much so.
And I appreciate what you'resaying.
I think that is a reallyinteresting way to look at it.
We're saying that, I mean, ashumans, we want to be creative.
This is taking away for somepeople, it's opening up their
creativity because maybe theydon't have the skill set.
But for many people, I know formy students, there's a lot of
frustration around not is AIgoing to take our jobs, but
(21:53):
well, what's creative now?
If they've been working inproduction, if they've worked
with professionally produceduser-generated content, and
they've been able to be thecreator, be the strategist.
And now you have to reframethat, as you mentioned, and
think about well, how do westrategize?
But also we we know that thereis an element of generative AI
(22:14):
or AI somewhere in all thatcontent, whether it's we produce
the content and then, you know,I mean, AI is going to help
create show notes for thisepisode.
It's going to create some reels.
My team will go back in andcorrect if if we don't like what
the con, you know, what whatslices AI decided to take from
the episode, but there's stillan element of it which, I mean,
(22:38):
is a blessing because it helpsthings happen faster, but it
also takes out the humanness oflet's pick all of this for
ourselves because we know what'sgoing to resonate with other
humans.
Instead of letting AI decidealgorithmically, this is what we
think will resonate.
SPEAKER_02 (22:54):
Yeah.
I mean, you know, I I look at itlike um I'm an analogy guy.
So I'm gonna go to my one of myfavorites, and that's boats,
right?
I talk about boats all the time.
And I work on stuff, and likeI'll tell you, we need to be
floating docks and not dockspiling to the floor or the
river, right?
And because yes, a lot ofcreate, you know, there's a lot
(23:14):
of image generation and so much,not only is it just the ability
to degenerate it, it's theability for people that don't
have a creative skill set togenerate it and then make it
something less than you knowappetizing to us on the screen,
you know, and constantly insocial, we're getting hit,
you'll get a real story andit'll get a fake image on it,
(23:35):
and it's just like oh, it'ssoulless for me.
Like, I don't like any of thatstuff.
However, and that's part of ourreality.
But I also, you know, I wrote abook and published it last year,
and I I don't know, it's alittle folk tale for my
grandkids.
It's a fun little story aboutour place at the water, and uh
and it's uh takes place in 16th,17th century kind of stuff, and
um, it's fun, but I couldn'tever have gotten that out 10
(23:59):
years ago.
I mean, I I was able to put itforward, I'm I wrote 99% of it,
but I tone checked and some, youknow, cleaned it up.
Took me back a couple of stepsin in just I looked at what it
would be like to be Dutch andspeak in that period of time,
and I went, nah, it's too far,you know.
You so it was like an advisor tome to tune some of it, but more
(24:19):
importantly, I was able togenerate the images, and I I
worked a lot on that.
I mean, I had to type out pagesfor each image exactly what I
wanted to look like, so thatthey matched a little bit, and
that allowed me to publish withthat.
But you know, I couldn't affordthe$500 a draft on the image,
the illustration to do the book.
It wasn't that big a book.
(24:40):
It's you know, I'm I'm Iprobably sold like 60 at most at
this point, and I'd probablysell 100 before I'm done.
You know, it's not that, but socreativity is still here, it's a
different vein, it might look alittle different.
So I just I would say go withthe flow, find your path, and
don't forget about humans in theprocess, right?
SPEAKER_00 (24:57):
Yeah, absolutely.
Getting back to seek who is itgood for versus not?
Going back to that question ofif somebody has a smaller
business, can they use it?
If somebody is doing a project,could they use it?
Could they use it for a use casewhere I mean, I'm not getting
(25:18):
stories from people necessarily,but I'm searching for stories.
So I'd love to hear a little bitmore about that.
And how have you used it or howhave your clients used it so
far?
SPEAKER_02 (25:29):
Yeah, absolutely.
I would say our most of ourcustomers are medium-sized
businesses.
We've had some small customersand we've had some large
customers.
We have I came into business in2017 and I said, you know,
James, my partner, I was like,you know, we're gonna do we're
we're a we're a project servicescompany and a tech company.
We can't be both, like itdilutes to do both, right?
(25:50):
Like we can't fund a prodeveloping team and a
professional services team andand make it.
And guess what?
We're still doing it, right?
Like, and we both depend on, weboth plan to cut that string
eventually, but we're projectproject, we've had a unique
project skill.
You talked earlier about trustand authenticity.
Like, you know, I used to dothat anonymous survey and work.
(26:10):
I'm like, nah, I don't believethat's anonymous.
I'm gonna give you what you wantto hear.
I never really answered itpositively.
I might throw one or twocritiques in, right?
But trusting matters, like soestablishing one of the things
we have to do, and we do thatthrough our people primarily,
and in our culture and oursystem our decisions we've made
in the app, but you know, we weestablish that trust, and people
(26:32):
go, okay, this is a place tothat I can be authentic.
For example, if you're in ourapp, we prove that you're a
person at our recruiting level,but our researchers and our
customers see you as a username,and that can be anonymous, it
can be whatever, and they don'thave access to contact you
unless we set something upbeforehand that does all that.
So we do give you that abilityto really share your opinion and
(26:54):
not get locked into anything,even if you're employees in a
company.
We, you know, there's a certainsize that it gets a little more
challenging.
But you know, if let's say yougot you know five or six places
and they're spread out a littlebit and we don't ask the wrong
the wrong demographic.
We we spend a lot of time makingsure we don't ask the wrong
demographic and surveyquestions.
Anyway, that trust allows you toshare.
(27:14):
People will be authentic if theytrust the process, and so that
is a big piece of our stories,is that people two things.
One is they they feel they cando it, and secondly, it's a
shocking number of people thatwant a voice that don't have a
voice.
I mean, imagine if you you're abig uh consumer brand and you
(27:35):
actually get to share yourmessage to them and they're
actually going to listen and dosomething about it.
I mean, how many of us wouldn'twant to affect some brands out
there, right?
And not that all those are thethat that type of project, but
you know, people want a voice.
Sometimes we we talk tomiddle-aged women, and that was
and part of that story came outwas a lot about long-term care
(27:55):
of their parents.
And it was a play we hear fromour our participants a lot this
idea of this has been therapyfor me.
It's been it's one of the winsin this process is people
sharing that story feel greatabout having done that and
knowing someone's listening tothem.
And so there's a littlepsychology in that.
And and so all that kind ofcomes together to establish
(28:17):
something unique.
It's a little bit of magic saucethat we planned a little, we we
got lucky on some of it.
But you know, we showed upempathetically and treated uh
the content with with respect,and and it's proven to be really
valuable doing that.
SPEAKER_00 (28:31):
I'm just thinking about large organizations that
have had missteps, the Pepsi'sof the world, the targets of the
world, when it comes to makingdecisions for shareholders or
because of political sentiment,but facing different forms of
backlash because they maybe theyhad the wrong advertising
campaign, right?
(28:51):
Or they took certain words offtheir website.
And thinking about if they hadsurveyed employees in this way
and been able to get realinformation about how do the
employees feel about this?
The people on the front line,the people who have these jobs,
sometimes because they justthat's the job they can get is
(29:13):
working at a target, right?
Not even people who arenecessarily super high up.
But so taking a little differentstep, right?
And and thinking about all ofthe people on the floor and what
they're hearing, but also howthey're feeling about all of the
changes happening in a company,what kind of different decisions
could be made that could beperhaps for the good of not just
(29:34):
their employees, but all oftheir customers by actually
taking the time to listen thatintently.
SPEAKER_02 (29:41):
That's it.
And we hear about listening,right?
And it's what are you listeningto that we're changing?
And you've highlighted two.
One one was like we did a lot ofnew hire work, and we we've
hired, we talked to the newhires and we talked to the
hiring managers and theexecutives.
And it's interesting to hear thecommonalities across those.
Those new hires hearing theirexecutive stories, and some of
(30:02):
those guys would stand up andshare their story.
You know, it changes it changesthat crew.
That crew goes, Wow, I have aconnection to this business more
than I thought I did.
What I loved was we were doingkind of recurring every six,
eight months, we would do a fewmore stories, you know, go back.
We kind of trickle collect uhwith a group and that were in
healthcare, and they were thephone service of her healthcare
(30:23):
group.
Yeah.
Yeah.
I mean, these these ladies, theywere mostly ladies, so I can say
they were ladies, but theseladies were just amazing.
Like they just had hearts ofstone.
I mean, they were so strong.
That's our wrong term there.
Anyway, but we saw, like, ineverything looked normal, but
(30:44):
then all of a sudden we just sawa lot of anxiety going up, and
there was this need forstructure.
That was one of the things wemeasured and stability, and need
for stability.
And it was just a sign of worryand concern that was showing up
in all these scores.
And we're like, heck, what'sgoing on, man?
Like, this is weird.
You got something pointing.
Oh, we just changed out ourexecutive team, you know, three
months ago, and now we'rerestructuring everything.
(31:05):
And we're like, it, well, yourpeople are showing it, right?
Like, and these people do astressful job, right?
Like, it's a tough job, butthey're changing their responses
in the content they're giving usbecause what I we expect is the
business.
So we can't give you everything.
We're it's a very kind of it's abit of a dull tool, right?
(31:26):
It because we're not asking adirect question.
But what's cool is from this,and GPT has really stepped this
up, is you can put it out thereand kind of rub the crystal ball
and start to see messages.
And one thing I love with GPT,you know, we're doing our
stories and what's the themes?
What are you, what, what's theimprovement you suggest?
So blah blah blah.
And it drops a report quickly.
(31:48):
At the end of that, I'll I'll goback and say, okay, uh, of what
are uh for the the stories thatare here, of all that I've asked
about, what didn't I ask aboutthat the stories seem to tell
you matters?
And it will come back with fouror five things, and they're
always profound, or at least acouple of well, you'll be like,
Oh, wow, I didn't even think toask that.
And that's the brilliance of AI,finding the complex patterns
(32:12):
that humans can't find.
And so tie this back,creativity.
Like your creativity using theprompting is huge, right?
Like you could never get toogood at uh you know, tuning and
prompting and creating how youwhat orthogonal questions you
ask it, because that's that'swhere the amazing stuff comes
(32:36):
from.
SPEAKER_00 (32:37):
Yeah.
It's such an important note.
Sometimes I'll realize that Ididn't prompt prickly, and I use
Claude a lot.
And so Claude will go a littledeeper and spend a little more
time doing research than Ianticipated.
Because I'm like, dang it, Ileft out that one important
sentence.
SPEAKER_02 (32:56):
So well, my my partner's an English major, and
I'm uh uh a tech guy from NorthCarolina.
This T I call myself TLDR.
I should have a t-shirt with it,but uh everything needs to be
short and acronymed.
And and when it comes toprompting, man, I I just it's so
stressful, it wears me out.
He's he'll sit there and justyou know, he'll spend he'll
treat it like it's his socialmedia, like he loves it, like
(33:17):
he'll stay in it and it he'sbrilliant at prompting.
Yeah, so it's definitely a skillset.
SPEAKER_00 (33:22):
Yeah, for sure.
So one or two things that you'rereally big on are making sure
that we stay human-centered.
Humans matter, the earthmatters.
We know that these are twoissues that can sometimes butt
heads when we talk, have theseAI conversations when we're
thinking about energy usage withdata centers.
Even when we're seeing thingslike AWS go down and affect not
(33:45):
only, you know, our ability touse different LLMs, but the
ability even for, I mean, ithappened during final
assessments.
So my students couldn't gettheir final assessments turned
in.
You know, it affects so manydifferent things.
And then just thinking about oh,energy usage for Indiana now
that uh that big data center istwo times uses as much energy as
(34:09):
I think two Atlantas.
And so we have all these issuesthat we need to bring into the
conversation and make sure thatwe're using our all of our
resources really smartly andpaying attention to these things
that matter.
So I'd love to hear a little bitmore about your thoughts on
this.
SPEAKER_02 (34:26):
Well, I I kind of want to go on a journey and hang
with about five smart people fordays to to solve this because it
and we wouldn't solve it.
It's it is the moment of ourtime, and it's probably I've
lived through a lot of these uhuh evolutionary changes in
technology.
(34:46):
We're gonna change the world,and you know, they did a little.
They did, you know, and youknow, I didn't do the paper
cards at the beginning.
Luckily, I missed that piece,but you know, I've been around
for a little bit of it.
But I think this one is is uhprofound.
I really do.
It is I don't think it'sexistential, but we should be
thinking that way.
And our planet, you know, thisisn't new.
(35:06):
We've been destroying, we'vebeen burning our planet at 10,
50x, its ability to can createitself, probably 100x.
And what you know, in 1870, westart burning oil and we blew
through 50 in first 50 years orwhatever, right?
Like it we can't live like thatfor 500 years, we just can't.
It won't happen.
And the planet matters becausethe planet's what we live on,
(35:29):
and our society matters becausethat's who we are, and then the
what how we live is after that,right?
So, you know, I think all that'sthat's not just let's hug each
other.
That's you want to live, youwant your grandkids to live, you
got to figure out what to dohere.
And I I mentioned earlier aboutagency, I really think human
agency is a really hot topicthat we all need to be not only
(35:52):
talking about, but using ourfeet.
You know, I joined a group herecalled Befriend that just meets
with people all over the cityand other states and stuff, and
then we just walk and talk, andthey're all different classes
and social economic backgrounds,and it's a place, a safe place
to come together and just meetpeople and build relationships.
So we need to be like that, morehumans connecting the humans
(36:15):
physically, you know,handshakes, walk and talk and
all that.
But we also need to actuallyproactively organize society.
I mean, like, we can't letcapitalism define us.
You know, it's done a we've hada really good run for 50 or 60
years on capitalism, but at somepoint, it the scales of these
things change.
(36:35):
You listen to Eric Schmidt talkabout AI, and he's like, you
know, in six, 10 years, it'sgonna be smarter than the
collective human intelligence,right?
That's that's San Franciscotalking.
I don't know that I believe allthat is gonna happen the way
they see it.
But to your point, that's apretty amazing, it has a really
big impact on humans and how wework.
And you know, you know, are wethe Jetsons?
(36:57):
Do we not need to work anymore?
That I I think that's a viablething.
If we blow society, why workthis much anymore if we
technology can do it right?
At the same time, it can alsocreate more wars, create more
ways to kill us, and all thatgood stuff.
So we need to be holding thehands of the controls of this.
Uh, and then back to your pointabout resiliency and just
realizing that we're burningagain against all these very
(37:20):
limited resources that most ofus don't realize there's a
global war going on for accessto these resources in a in an
ugly way.
Like it's just it's profit whenyou know, power wins profit on
all this stuff.
And it's a war that's you know,look at space right now.
I don't know where that goes,but we were doing a lot to take
(37:42):
over all of space, and I thinkprimarily to find minerals,
right?
And so, you know, and I I Idon't follow conspiracy
theories, and I don't want toget off on that, but those are
all real things right now, atleast in instance in there's
instances of all that.
And so I went really broad onthat, but I think we all need to
understand what that means, andwe need to find a common
(38:04):
dialogue, we need to find acommon emotional connection and
perspective to what is to be agood human in this next
generation, or we're gonna letit define itself to us.
And you know, I I'm not lovingthat second option.
SPEAKER_00 (38:21):
Yeah, no, I I'm in complete agreement with you.
And I think what's what's reallyinteresting is even the the
people who created, I guess, AIas we know it today, right?
Jeffrey Hinton talks about AI isgoing to be, we need to make
sure AI is empathetic, that it'sbasically thinks of it as our
mother and a caretaker to us, sothat when it is super, you know,
(38:45):
when it's reached a pinnacle,that it still sees us as worthy
of being taken care of anddoesn't just take us over and
you know, all the things, allthe the worst case scenarios.
Then we have the godmother,Feife Lee, who's talking about
actually AI is going to allow usto live in different dimensions.
And she doesn't really meandifferent planes, right?
(39:07):
She means you're driving theexample she used at AI for in
her keynote was you're a mom,you're also a surgeon, you get
in your self-driving car, whichis really a robot taking you to
work so that you can perform asurgery.
On the way there, you rememberthat you need to order certain
groceries so you're able to, youknow, flip to that screen on
(39:28):
your glasses and go into thegrocery store, put your order
in, and they'll be home by thetime you get home that evening.
You go do the surgery, roboteyes help you do a more precise
surgery.
You're done.
You get back in the car, yourealize that you need to order
outfits for your daughter'sschool dance, you go into a
virtual store.
And so she was talking about itlike that and making us more
(39:51):
efficient in those ways whilealso still being able to be
human, have families, live ourlives, feel fulfilled at work.
And so those are interesting, Ifeel very two very different
ways of looking at exactly whatyou're talking about.
And then we think aboutdeveloping countries.
I was just recently in Vietnamand Thailand, and we had the
(40:13):
opportunity to go visit anAI-based business in Vietnam.
And they have 4,000 softwareengineers working at this
business and they work inhealthcare and different things
for companies all over theworld, even things like crops,
being able to tell if there arecertain bugs.
You know, it they have all thesedifferent solutions that are
really innovative andinteresting, but they realized
(40:36):
that they needed to train alltheir software engineers to
prepare for this next stage.
So they have spent the last twoyears training 4,000 engineers,
getting AI certifications.
I mean, like it's it's reallyamazing.
SPEAKER_02 (40:50):
Yeah.
SPEAKER_00 (40:51):
And there are so many markets like that that we
don't even realize in the UnitedStates because we're so US
centered.
There are so many other marketsthat are being set up to compete
with us that are new tocapitalism and trying to get
there.
But also we have to think aboutif we go into a country like
that, set up a data center, howis that going to harm a country
(41:14):
that's mostly agriculturalstill?
SPEAKER_02 (41:16):
Well, I don't know if we deal with this question,
but I'm going to leave you witha question.
Well, maybe we have to come backfor it.
And that is if this is our grandtechnology, if technology
becomes the intelligence at thelevel we're talking, is empathy
and compassion a function ofintelligence?
Is it correlated or causal, orare they devoid of connection,
(41:39):
right?
And do we have to can we justassume something that's super
intelligent will be empathetic?
There's signs that say thatcould be true, but nature
nurture, right?
If we're nature and nurture,what do we have to do for our AI
future cohorts to make sure thatthey're not, you know, Arnold
Schwarzenegger?
SPEAKER_00 (42:00):
That is actually the perfect question to end this
conversation on.
And we will be having futureconversations.
I hope that you'll uh do me thehonor of coming back on the show
at a future date.
I think there's there's a lotmore to unpack here.
SPEAKER_02 (42:15):
So much.
I really enjoyed it.
It's great meeting you andhaving the conversation.
And uh I look forward towatching all that you put forth
in uh coming years.
SPEAKER_00 (42:24):
Oh, and likewise, uh two final things, a final
thought, a piece of advice forsomebody who's looking at what
do I do next to prepare myselffor the next stage of my career
or to make that pivot, knowingthat I need to lean into
technology.
SPEAKER_02 (42:43):
Okay, yeah, I'm gonna say two things.
One is critical thinking at yourlevel, whatever, if that's
crossword seducos or if that'sgoing back to school, make sure
your brain cognates and uh anddoes it in a thought-healthy
way.
Secondly, connect locally,especially if you're getting out
of college.
Find that thing.
Your job doesn't matter as muchas you think it does.
(43:06):
Do not give it 80 hours a week.
Find somebody to help them learnhow to read, help them find
food, help them just meet, havefun together, whatever, teach
people how to kayak.
I don't care what it is, connectlocally.
I didn't do that for 20 plusyears, and I'm trying to redeem
myself now.
And your career is never goingto change to make you do it.
But those that do do itthroughout their career, advance
(43:28):
more, matter more, impact theworld a little more.
So, you know, make a localconnection and use your mind.
SPEAKER_00 (43:36):
Fantastic.
And then do you have, and itmight be hard to choose, but do
you have a favorite quote,Montraverse, poem, family motto?
SPEAKER_02 (43:46):
There's this French guy's name.
I can't think of his name, andI'm gonna use this.
Something and I am gonnaparaphrase it.
It says some essentiallysomething is finished, not when
you can you can't add any moreto it, but when you can't take
anything more away from it.
And it's a great quote.
It's better.
It I should know my own quotesbetter than that, but that's a
(44:08):
and and I should know the guy'sname.
I'll have to record that andthen mail it to you on my curve.
SPEAKER_00 (44:14):
That is such a creative way to look at things,
too, because that's often right,the artist's work is never
finished.
You just have to know when tostop.
SPEAKER_02 (44:22):
Yes, yes, I hear it.
SPEAKER_00 (44:24):
Slightly different, but similar vein of thought.
SPEAKER_02 (44:27):
Insert any Mark Twain quote right here, too.
I'm pretty big fan of that guy,too.
SPEAKER_00 (44:31):
Yeah.
Amazing.
Andy, if it's okay with you,I'll put your LinkedIn attached
to your name in the show notes,as well as, of course, sharemore
stories.com so people can findout more about what you're up
to, how they could possibly workwith you.
Sounds great.
Great.
Thank you so much for joining metoday.
This has been such a pleasure.
(44:52):
I'm excited to learn more fromyou.
And this is only the beginning.
SPEAKER_02 (44:56):
Great.
Thank you.
SPEAKER_00 (44:58):
Yeah.
Thank you to everybody who'swatching this episode or
listening to it on your favoriteplatform.
Please leave us a rating review.
Check out sharemorestories.com.
I think you'll be as intriguedas I am.
And I hope that you learnedsomething today and that it made
you feel a little more confidentin building your future.
SPEAKER_01 (45:16):
To learn more about the Master of Science and
Digital Media Managementprogram, visit us on the web at
dmm.usc.edu.
Welcome to Mediascape, Insights from
Digital Changemakers, a speakerseries and podcast brought to
you by USC Annenberg's DigitalMedia Management Program.
Join us as we unlock the secretsto success in an increasingly
digital world.
SPEAKER_00 (00:22):
I could not be more thrilled with my next guest,
Andy Citizen, CTO of Share MoreStories.
Andy, just in our littleconversation before we push for
Cord and all of the greatinformation that you provided
me, as well as what I found onthe internet, I'm just really
intrigued by how you've shapedyour career and your narrative
(00:44):
because you've done so manydifferent things.
Yes, you've been at theforefront of tech.
You've been working in AI forlonger than many of us knew that
it existed.
But you also are a nonprofitfounder.
You also are a family man andhave poured a lot into that.
(01:05):
You have so many interestingprojects going on.
And I I really truly believethat this helps you shape the
narrative of ShareMore Storiesand how you work in the world of
tech.
So thank you first and foremostfor being here today.
SPEAKER_02 (01:22):
Hey, I I it was great to get that read back.
I mean, you're right, and uhhadn't thought about it in that
way a lot.
So it's a nice context, a niceframing.
SPEAKER_00 (01:29):
Oh, fantastic.
Well, I'm so glad that you thinkso, because this is truly from
everything you provided me.
You called it a data dump, butit really is a great picture of
who you are as a strategiccreative.
So you talk a little bit abouthow you came to realize that you
were a strategic creative.
SPEAKER_02 (01:48):
Let me do that in a an efficient manner.
And you just hold up a big, youknow, move on store uh a flag or
something if I keep going toolong.
Because, you know, I've been ordoing this for a while, but I
will I'll start at the verybeginning.
I wanted I've always beencreative, though, you know, a
genetically generatemulti-generational creative
family, and and we created inall sorts of ways as a kid, but
(02:09):
I was a musician, I loved youcan see the guitars and banjos
and stuff behind me.
You know, I wanted to be thatguy on stage at one point, and
then just uh realized I was uhdidn't trust that life enough.
So then I went into psychologybecause I thought, well, I like
helping people, so maybe it'spsychology.
And I actually got my undergradin that, but I also realized
(02:31):
that at the time what you weresupposed to do is sit back and
let everybody solve their ownproblems.
I'm like, maybe I'm more of ananalyst because I want to solve
the problem and get moving.
I'm I don't have patience.
So, but I've always beencreative.
And and as I was looking, it wasa struggle for a couple of years
to figure out we opened arestaurant, did that for a few
years, and then I uh my wife gota job at IBM in Rock, and I
(02:54):
said, hmm, this is a nice life.
I don't have to work weekendsand uh, you know, maybe I can do
programming and all this stuff.
So I jumped in and didn't havethe math and actually created a
stutter for about six monthsbecause I started using the
other side of my brain.
I was very right-brained, veryleft-handed uh person, and then
I needed all the left brainparts, and so I fire on both,
(03:17):
which is a kind of a uniquemindset.
But what I found was it itprogramming was creative, so I
dove in and I realized it's justlike writing songs or writing
books or whatever, writing codeand writing and solving
solutioning problems uh wasreally fun and you know gave me
an outlet.
And so I went along that path.
I integrated companies andworked for many in manufacturing
(03:38):
spaces and new drug developmentand nuclear and you know, helped
them stand up systems, and itwas fun.
But I was traveling a lot.
I thought, well, maybe I'll justgo to one of these big high-tech
companies and see what thatmeans.
And uh, so I went to a reallyfun, uh, really high, high grade
uh company, and then I traveledeven more over time because I
(03:58):
eventually took over LatinAmerica and then uh all the
Americas and then all thecountry, whole world for uh a
kind of a business applicationspin.
This was a more IT group and Iwas more business app.
So it was again back to kind ofthe creative deployment of
systems for corporations andmost of the companies we work
with in Fortune 100s back then,and that kind of got us into the
(04:20):
cloud space.
And I was kind of really Iwasn't, I wouldn't say I'm like
the cloud guy, but we weredriving billions of dollars to
the cloud on people that hadrolled out 100 million to a
billion dollar deployment.
So these are very expensive,important things that run
everything we touched, you know,all our financial transactions
and all that.
And so we were helping put thoseon the cloud.
(04:42):
And once that happened, it waskind of a flip, you know, uh
inflection point in the marketwhere these big Fortune 100,
Fortune 500 companies arestarting to see the cloud as the
only way to do it now, and youcan't really keep these schools,
uh, these sets in the house.
And jump ahead.
I took us Matil, and we weregetting acquired, and it was
(05:02):
gonna be very horizontal.
And I'm like, my skill set's notright, so I'm just gonna take a
year off and figure out who Iwanted to be.
And reality is, I had a greatresume that a lot of people
probably wanted, and I didn'twant to be that guy anymore.
So I wanted to get creativeagain.
I would wanted to quit answeringother people's questions and
start just finding my ownquestions.
(05:24):
So I you know, jumped to theend.
You know, I started looking at acouple different kind of
emerging tech spaces.
I'd always been in emerging techand looked at, and it was a data
guy.
I got my master's in datamanagement, which I skipped
over, but I told you I'd try toshrink it.
And uh anyway, I had to uh Istarted looking at data and sort
of AI was showing up, machinelearning was really getting hot.
(05:46):
And this is 2016, 2017, and so Ikind of took four months,
another four months after uhgetting started to kind of teach
myself the math and the code ofAI, and kind of just dove in
from there and ran in a couplefriends and said, Hey, why don't
we work on this startuptogether?
So that is where I'm spending alot of my time.
There's been a couple morestartups in there that I've
(06:08):
helped get going and atdifferent levels and different
roles, but I love it.
You know, I'm probably not asrich as I would have been if I'd
stayed on the plane, but uh I'ma lot happier so and more
creative.
So I'll I'll take a breath anduh but that's kind of the story
in a nutshell.
SPEAKER_00 (06:22):
I I think that's something people some forget
that it we talk about STEM, butwe talk about STEAM because the
arts, that creativity is a largepart of thinking about
mathematical equations,programming, the things that you
talked about.
I'm also a lefty, and so I livemostly in-too.
(06:43):
But I also like you, I majoredin public administration for my
undergrad, but my MBA isspecializing in AI and ML
because there's so much more Iwant to learn about the
analytical side, and that'susing that the data side with
the storytelling is obviouslysomething we do a lot in public
relations, marketing, branding,communications, kind of the side
(07:04):
of the coin that I live on.
And that's something that youalso that's a that's the field
that you play in, is notnecessarily the marketing,
branding, but getting people'sstories, figuring out what the
data is, but also humanizing,right, whether it's employees or
just people in community and howthey come together.
(07:26):
And that's something that Ithink is so vitally important.
And you've also figured out howto use technology to do that
more efficiently, effectively,and getting back to kind of
empathetic AI, which is uhsomething that we've talked
about.
Uh people who've listened to theshow know I I love going to AI
for I go to other AIconferences.
But this year, what I lovedabout it was the conversation
(07:48):
really was about how do we workhand in hand with AI?
It wasn't about AI being thereplacement for everything like
we hear in the media often, orwe we see these big stories and
we think that that's thenarrative with layoffs and
things.
But often if you do a littlemore research, it's not, right?
And so really learning how towork hand in hand with this
(08:09):
great tool that we have.
And yeah, we can get to thequestion of are we the Jetsons
or are we the Terminator laterin the conversation?
But that's really not wherewe're at when it comes to the
day-to-day.
SPEAKER_02 (08:22):
Yeah, I would love to get back to that because I
I'm I'm positive on it, but I'mcritical.
And but to your point about youknow, human versus digital, and
that was uh something that camefrom my sabbatical was I
realized that I felt like weweren't putting the humans in
front.
And the technology shouldn't bea competitive edge against
humans, it should be theopposite, it should be an
(08:45):
enabler for humans, and we willalways have tech.
We will we've had techs, youknow, we've had tech paper and
stone wheels, but since the1780s, 1790s, this whole
industrial revolution's been ona big roll that hasn't really
stopped.
And, you know, if you look atthat, you know, it's it's not a
debolbing, it's evolving.
(09:05):
And you know, AI is absolutely amassive step in the next
direction on that.
So humans can't adopt tech orfight against tech, they have to
find agency with tech.
And so I think it's a reallyabout establishing our overlay
layer that is the human in themix, and so we have to we have
(09:25):
to put ourselves in thoseingredients, and we are frankly,
we're not very good at it,right?
We have allowed society to youknow define what the human role
is as it rolls out, and I thinkthat's one of those things we
have to fix.
It's a continent contemporaryproblem, like it's never been.
So, to your point, back tohumans, and uh before I go down
a whole rabbit hole, I loverabbit holes, so but you know,
(09:50):
back to humans.
What we do uh in short foreverybody is if you you know in
the moment, like let's talk fora second about like has there
ever been something challengingyou've tried to learn or that
you didn't have a digital answerto, like cooking something or
you know, playing guitar, or youknow, right now I'm trying to
fix three carburetors that siton top of each other in an
(10:11):
outboard motor on a boat, right?
And you think there's enoughYouTubes out there, but there's
not because there's you know,there's metrics, there's data
points, there's data, you know,diagrams.
I know what parts to order, butwhat's the essence of it, right?
Like what's that magic, thewisdom associated with it?
And that's what's happening inmarketing right now for a lot of
(10:32):
companies and organizations,like the you know, CMOs, you
start to see a chief experienceofficer showing up.
And it's because we need toconnect with humans.
We are all feeling it at areally deep level, and it's you
know, we're doing great.
MPS is great, net promoterscores.
Why not?
You know, we've got metrics, wemature the models, we got all
sorts of data, and we learned alot from it.
(10:55):
And I'm not negating any of thatvalue, but there's still a level
of understanding, especially ona critical initiative or if
you're trying to reach apopulation that frankly isn't
getting a voice to you.
That's what we do in in thiswork, and that is to collect
stories from that community,whether it be, you know, a
(11:16):
minority group or geographicallydispersed group or just somebody
that likes uh, you know, a kindof sports shoe, you know, we
want to look talk to them aboutthat.
Sometimes doing something morethan a survey can not only make
them feel more connected andmore trusting of the source of
that and be able to connect andprovide you data, but to tell
that in three or four pages innot a review necessarily, but to
(11:39):
get to the essence of whatmatters in that space.
Let's say if it's sports, why doyou do sports?
You know, why you know are youlosing weight?
Are you trying to stay healthy?
Do you want to live another 20years?
What get to the essence of whythey do what they do and then
ask them to tell us about it.
And surveys are great at gettinganswers to the questions you
know you want to get answered.
(12:00):
But what we do is help youunderstand the questions you
should be asking and giving avoice to that person, letting
them tell you what they need asa community.
And we do that by collectingthose stories through a kind of
web app that kind of gives usweb scale.
But then we use AI in a fewdifferent ways to analyze those
stories.
And one thing we do that's kindof unique is emotive scores,
(12:22):
everything from like activitylevel to you know anxiety to joy
to self-transcendence, and youknow, we have the 55 scores that
sit up on the cloud and justcrunch on these stories.
And what comes back is apersonality.
You know, we don't have apersonality of the people and
the story that's the personthat's telling the story, but we
have a personality of thatcommunity to some degree.
We start to see, hey, they'rereally high, highly anxious, or
(12:44):
they're not, they got a lot ofjoy, they got a lot of hate,
they got a little or anger, theydon't have we don't we have
sadness and anger, not hate, butyou know, all these types of
scores.
And so we start to see howthey're feeling and why they're
feeling that way.
And then you always have thestories themselves with quotes,
and and you know, you can go inonce you you can use GPT to like
analyze something and then say,okay, give me three quotes that
(13:06):
represent that in the stories.
And here GPT comes with youknow, three profound statements
that you're like, oh wow, youknow, I have a new in minutes.
We can take a customer to have awhole new perspective on
something they've worked on for10 years, and and it's almost
like they just birthed the newbaby, like they'll be like, Oh
my god.
And you know, they're they'realways surprised, shocked, and
(13:27):
happy about it.
So it's really fun.
I'll take a breath.
SPEAKER_00 (13:30):
Yeah, I think this is really important.
One of the classes that I took afew semesters ago from my MBA
was creativity and innovation.
And it was talking about how dowe get people in the workplace
to form community to think aboutthings and to leave space for
people to come forward withtheir ideas, with their
thoughts, with their constructs.
(13:51):
That's in essence what you'redoing.
When you we think about surveys,they're often closed, right?
You have scale one to 10, yes,no, maybe.
Maybe you have a little room toleave answers, but it's not
getting the real story like youare.
It's not pulling out the thingsthat people might not be able to
(14:13):
see by that kind of data becauseyou're getting into qualitative
and you're you're really gettingsomebody's thoughts, and they
may not even realize whatthey're providing.
So I think that's justbrilliant.
And I'm interested and intriguedon how you came up with the
solution in the first place.
SPEAKER_02 (14:34):
Yes, I think you you did a good job of hitting on
some key pieces there.
And you know, we you know, I hada friend that was this, you
know, that my partner was astoryteller.
And uh I we we did TEDxtogether.
This red circle behind my headis actually part of TEDx stage
where, and you know, I've got mymachine learning skill set
together and he was doing thisstory collection thing.
(14:56):
And we said, well, you know,it'd be kind of fun to anal
long-form text to uh analyze NLPthe the stories themselves, and
we started looking around atwhat was available, and there
were some, you know, I spentabout I don't know, first eight
months or so looking at kind ofcasalog for 100-150 years of
psychology.
Like, what did people study?
How did they label that?
(15:17):
How did they supervise?
How did they categorize it, ifyou will, and so forth, and then
where what data sets existed outthere?
And and Watson was doing some ofthis stuff, and you know, some
of the earlier tools werestarting to provide some of that
base scoring for these things,and so we started collecting
against that.
We started doing work and didsome web scraping, and you know,
certain you had to get certainsize, had to be we we don't do
(15:39):
any fiction, so it's not fictionstories, but fact story.
So, yeah, that matters if you'regoing to score things, you want
to get supervised labels on thesame type of content.
So, you know, it was it was astruggle to get going, and what
saved us was the LLMs and notGPT, not uh generative AI, but
the large language models, whichare called transformers, because
(16:02):
what happens if if those don'tknow is that these are very
large models trained on semanticrelationships between words,
right?
So you're putting billions ofrecords in there, and then you
have the ability at the very endof this thing.
If you picture a kind of a bigcollection of nodes and webs,
you go to that very end, you cutit off, or you add a couple
layers, and on that layer, youteach it about your stuff, the
(16:24):
domain.
Like, I want to teach you justjoy.
I want you to find evidence ofjoy in this story.
I want you to find evidence ofsadness in this story for us.
That's a different model everytime, and then we categorize
that in what I call the ventimodel.
It's five points, very low.
It's a likridge scale, low,medium, high, very high.
And so surprisingly, it allworked.
(16:46):
You know, we were and what wedid was we did a lot of human
testing on that, and we actuallycompared to other systems like
Watson and we had Burt going andElmo, all the different ones,
and testing that.
And actually, uh, we got a fairamount of inaccuracies at first
or not anomalies between thesystems, and frankly, found Bert
(17:07):
just was crushing everybodyelse.
It was we read the story and welike, oh, this is that's the way
I would rate it, you know.
So we, you know, we did thatwork and sample size, and that
was you know, that was eightyears ago at this point.
So we've been doing this for awhile, but that's
categorization, which I don'tyeah, I'd say 80, 90 percent of
people don't even realize youcan use AI for things other than
(17:27):
generative, but has nothing todo with generative.
Somebody might claim me wrongthere, but you know, no, no, no.
SPEAKER_00 (17:32):
I got when I'm hearing, because I'm wondering
what organi like how big does anorganization have to be?
How much data do you need toformulate these stories and
these insights?
Because one of the things reways that I use AI is that I was
getting very frustrated at mostof my guests are amazing.
But every once in a while, I'dget a guest pitch that seemed
(17:54):
really good on paper from a PRagency, maybe, and then I would
get them on air because I don'tlike to do pre-calls.
I want it to be like our freshconversation.
Yeah.
Right.
So I've done my research, but Iwas finding that even with doing
some research and getting allthis great information, that
maybe the guest really wasn'tprepared.
(18:15):
Maybe they weren't the levelthat their person pitching said
they were.
And so I ended up with someepisodes I couldn't use.
Just you know, and so I createda tech stack that's basically
like, here's my podcast.
Here are the four main criteriathat I'm looking for in a guest.
You know, it's subject matterexpertise, audience fit.
(18:37):
Does it feel like I'm havingcoffee or a really great
in-depth conversation with afriend that people can listen
on?
You know, and then I the fourthone almost escapes me when I
talk about it.
But uh, but I asked for rankingsfrom one to five based on deep
research, from the LLMs andjustification for each score.
So and then it, yeah, are they afit?
(18:58):
Yes, book them now, strongmaybe, low maybe, or no, not
ready for your podcast,justification, all four points,
but then also overalljustification, red flags and
topics that they were an experton that would also resonate with
my audience.
And so I think this is a smallexample of what you're talking
about, is there's so much more.
And I think really generative isfine.
(19:22):
And yes, I teach digital media.
So we talk about AI generatedcontent versus professionally
produced, user-generated, but Ireally feel like research and
this side is where we can getthe most out of our use of
artificial intelligence.
SPEAKER_02 (19:38):
All right, I'm gonna take that and hook on it in a
way that you don't know to askme if you're gonna like where
I'm going with it because I'vebeen thinking about this lately,
and what happens in as creativesand as we go through our
careers, you know, think yourjob changes, right?
It could be you always heard themanager, okay.
I was the player, now I'm themanager, right?
And some people would hate thatmove and everything.
(19:58):
But for me, you know, gettinginto AI, um, and like I said,
I've been eight, eight, nineyears in it at this point.
It's changing.
And with generative AI comingout, it's actually not as fun
because and why?
Because I was dealing with, youknow, like I was looking at cost
chain, I was building models, Iwas dealing with you know,
stochastic gradient descent, youknow, which is this global cost
(20:21):
process that you go through toget good accuracy and you know,
playing with the epsilon modelsand all this stuff, and you
know, machine learningalgorithms and kind of testing
things, playing with the data,feature analysis, principal
component analysis.
And that's the last one I'll doto you, audience.
And that was fun.
But now General comes out andwe're starting now.
We're building custom agents andI'm feeding in instructions.
(20:43):
I'm essentially an APIprogrammer.
Like, I I'm just sending codesand I'm not I'm not doing
anything, I'm not reallycreating like I used to.
Now I'm just staging an agent,and we've I think we're ahead of
a lot of people there becausewe've really locked down the
agents to just work with ourstories, which is pretty cool,
and it is very effective.
It's and like our business sideof the business is just like
(21:05):
blown away by what we're gettingdone there.
But as a programmer creator, I'ma little more bored than I was,
right?
And that's too bad because youknow, and it's gonna happen that
way.
And like as every every job, youknow, people say, Oh, we're
gonna lose all our jobs.
You might not lose all yourjobs, but what you do is gonna
transform and hopefully like itmore than you do what you do
now.
(21:25):
I don't know, you know, it'sjust just a it's just kind of a
moment in time, right?
SPEAKER_00 (21:29):
Oh, very much so, very much so.
And I appreciate what you'resaying.
I think that is a reallyinteresting way to look at it.
We're saying that, I mean, ashumans, we want to be creative.
This is taking away for somepeople, it's opening up their
creativity because maybe theydon't have the skill set.
But for many people, I know formy students, there's a lot of
frustration around not is AIgoing to take our jobs, but
(21:53):
well, what's creative now?
If they've been working inproduction, if they've worked
with professionally produceduser-generated content, and
they've been able to be thecreator, be the strategist.
And now you have to reframethat, as you mentioned, and
think about well, how do westrategize?
But also we we know that thereis an element of generative AI
(22:14):
or AI somewhere in all thatcontent, whether it's we produce
the content and then, you know,I mean, AI is going to help
create show notes for thisepisode.
It's going to create some reels.
My team will go back in andcorrect if if we don't like what
the con, you know, what whatslices AI decided to take from
the episode, but there's stillan element of it which, I mean,
(22:38):
is a blessing because it helpsthings happen faster, but it
also takes out the humanness oflet's pick all of this for
ourselves because we know what'sgoing to resonate with other
humans.
Instead of letting AI decidealgorithmically, this is what we
think will resonate.
SPEAKER_02 (22:54):
Yeah.
I mean, you know, I I look at itlike um I'm an analogy guy.
So I'm gonna go to my one of myfavorites, and that's boats,
right?
I talk about boats all the time.
And I work on stuff, and likeI'll tell you, we need to be
floating docks and not dockspiling to the floor or the
river, right?
And because yes, a lot ofcreate, you know, there's a lot
(23:14):
of image generation and so much,not only is it just the ability
to degenerate it, it's theability for people that don't
have a creative skill set togenerate it and then make it
something less than you knowappetizing to us on the screen,
you know, and constantly insocial, we're getting hit,
you'll get a real story andit'll get a fake image on it,
(23:35):
and it's just like oh, it'ssoulless for me.
Like, I don't like any of thatstuff.
However, and that's part of ourreality.
But I also, you know, I wrote abook and published it last year,
and I I don't know, it's alittle folk tale for my
grandkids.
It's a fun little story aboutour place at the water, and uh
and it's uh takes place in 16th,17th century kind of stuff, and
um, it's fun, but I couldn'tever have gotten that out 10
(23:59):
years ago.
I mean, I I was able to put itforward, I'm I wrote 99% of it,
but I tone checked and some, youknow, cleaned it up.
Took me back a couple of stepsin in just I looked at what it
would be like to be Dutch andspeak in that period of time,
and I went, nah, it's too far,you know.
You so it was like an advisor tome to tune some of it, but more
(24:19):
importantly, I was able togenerate the images, and I I
worked a lot on that.
I mean, I had to type out pagesfor each image exactly what I
wanted to look like, so thatthey matched a little bit, and
that allowed me to publish withthat.
But you know, I couldn't affordthe$500 a draft on the image,
the illustration to do the book.
It wasn't that big a book.
(24:40):
It's you know, I'm I'm Iprobably sold like 60 at most at
this point, and I'd probablysell 100 before I'm done.
You know, it's not that, but socreativity is still here, it's a
different vein, it might look alittle different.
So I just I would say go withthe flow, find your path, and
don't forget about humans in theprocess, right?
SPEAKER_00 (24:57):
Yeah, absolutely.
Getting back to seek who is itgood for versus not?
Going back to that question ofif somebody has a smaller
business, can they use it?
If somebody is doing a project,could they use it?
Could they use it for a use casewhere I mean, I'm not getting
(25:18):
stories from people necessarily,but I'm searching for stories.
So I'd love to hear a little bitmore about that.
And how have you used it or howhave your clients used it so
far?
SPEAKER_02 (25:29):
Yeah, absolutely.
I would say our most of ourcustomers are medium-sized
businesses.
We've had some small customersand we've had some large
customers.
We have I came into business in2017 and I said, you know,
James, my partner, I was like,you know, we're gonna do we're
we're a we're a project servicescompany and a tech company.
We can't be both, like itdilutes to do both, right?
(25:50):
Like we can't fund a prodeveloping team and a
professional services team andand make it.
And guess what?
We're still doing it, right?
Like, and we both depend on, weboth plan to cut that string
eventually, but we're projectproject, we've had a unique
project skill.
You talked earlier about trustand authenticity.
Like, you know, I used to dothat anonymous survey and work.
(26:10):
I'm like, nah, I don't believethat's anonymous.
I'm gonna give you what you wantto hear.
I never really answered itpositively.
I might throw one or twocritiques in, right?
But trusting matters, like soestablishing one of the things
we have to do, and we do thatthrough our people primarily,
and in our culture and oursystem our decisions we've made
in the app, but you know, we weestablish that trust, and people
(26:32):
go, okay, this is a place tothat I can be authentic.
For example, if you're in ourapp, we prove that you're a
person at our recruiting level,but our researchers and our
customers see you as a username,and that can be anonymous, it
can be whatever, and they don'thave access to contact you
unless we set something upbeforehand that does all that.
So we do give you that abilityto really share your opinion and
(26:54):
not get locked into anything,even if you're employees in a
company.
We, you know, there's a certainsize that it gets a little more
challenging.
But you know, if let's say yougot you know five or six places
and they're spread out a littlebit and we don't ask the wrong
the wrong demographic.
We we spend a lot of time makingsure we don't ask the wrong
demographic and surveyquestions.
Anyway, that trust allows you toshare.
(27:14):
People will be authentic if theytrust the process, and so that
is a big piece of our stories,is that people two things.
One is they they feel they cando it, and secondly, it's a
shocking number of people thatwant a voice that don't have a
voice.
I mean, imagine if you you're abig uh consumer brand and you
(27:35):
actually get to share yourmessage to them and they're
actually going to listen and dosomething about it.
I mean, how many of us wouldn'twant to affect some brands out
there, right?
And not that all those are thethat that type of project, but
you know, people want a voice.
Sometimes we we talk tomiddle-aged women, and that was
and part of that story came outwas a lot about long-term care
(27:55):
of their parents.
And it was a play we hear fromour our participants a lot this
idea of this has been therapyfor me.
It's been it's one of the winsin this process is people
sharing that story feel greatabout having done that and
knowing someone's listening tothem.
And so there's a littlepsychology in that.
And and so all that kind ofcomes together to establish
(28:17):
something unique.
It's a little bit of magic saucethat we planned a little, we we
got lucky on some of it.
But you know, we showed upempathetically and treated uh
the content with with respect,and and it's proven to be really
valuable doing that.
SPEAKER_00 (28:31):
I'm just thinking about large organizations that
have had missteps, the Pepsi'sof the world, the targets of the
world, when it comes to makingdecisions for shareholders or
because of political sentiment,but facing different forms of
backlash because they maybe theyhad the wrong advertising
campaign, right?
(28:51):
Or they took certain words offtheir website.
And thinking about if they hadsurveyed employees in this way
and been able to get realinformation about how do the
employees feel about this?
The people on the front line,the people who have these jobs,
sometimes because they justthat's the job they can get is
(29:13):
working at a target, right?
Not even people who arenecessarily super high up.
But so taking a little differentstep, right?
And and thinking about all ofthe people on the floor and what
they're hearing, but also howthey're feeling about all of the
changes happening in a company,what kind of different decisions
could be made that could beperhaps for the good of not just
(29:34):
their employees, but all oftheir customers by actually
taking the time to listen thatintently.
SPEAKER_02 (29:41):
That's it.
And we hear about listening,right?
And it's what are you listeningto that we're changing?
And you've highlighted two.
One one was like we did a lot ofnew hire work, and we we've
hired, we talked to the newhires and we talked to the
hiring managers and theexecutives.
And it's interesting to hear thecommonalities across those.
Those new hires hearing theirexecutive stories, and some of
(30:02):
those guys would stand up andshare their story.
You know, it changes it changesthat crew.
That crew goes, Wow, I have aconnection to this business more
than I thought I did.
What I loved was we were doingkind of recurring every six,
eight months, we would do a fewmore stories, you know, go back.
We kind of trickle collect uhwith a group and that were in
healthcare, and they were thephone service of her healthcare
(30:23):
group.
Yeah.
Yeah.
I mean, these these ladies, theywere mostly ladies, so I can say
they were ladies, but theseladies were just amazing.
Like they just had hearts ofstone.
I mean, they were so strong.
That's our wrong term there.
Anyway, but we saw, like, ineverything looked normal, but
(30:44):
then all of a sudden we just sawa lot of anxiety going up, and
there was this need forstructure.
That was one of the things wemeasured and stability, and need
for stability.
And it was just a sign of worryand concern that was showing up
in all these scores.
And we're like, heck, what'sgoing on, man?
Like, this is weird.
You got something pointing.
Oh, we just changed out ourexecutive team, you know, three
months ago, and now we'rerestructuring everything.
(31:05):
And we're like, it, well, yourpeople are showing it, right?
Like, and these people do astressful job, right?
Like, it's a tough job, butthey're changing their responses
in the content they're giving usbecause what I we expect is the
business.
So we can't give you everything.
We're it's a very kind of it's abit of a dull tool, right?
(31:26):
It because we're not asking adirect question.
But what's cool is from this,and GPT has really stepped this
up, is you can put it out thereand kind of rub the crystal ball
and start to see messages.
And one thing I love with GPT,you know, we're doing our
stories and what's the themes?
What are you, what, what's theimprovement you suggest?
So blah blah blah.
And it drops a report quickly.
(31:48):
At the end of that, I'll I'll goback and say, okay, uh, of what
are uh for the the stories thatare here, of all that I've asked
about, what didn't I ask aboutthat the stories seem to tell
you matters?
And it will come back with fouror five things, and they're
always profound, or at least acouple of well, you'll be like,
Oh, wow, I didn't even think toask that.
And that's the brilliance of AI,finding the complex patterns
(32:12):
that humans can't find.
And so tie this back,creativity.
Like your creativity using theprompting is huge, right?
Like you could never get toogood at uh you know, tuning and
prompting and creating how youwhat orthogonal questions you
ask it, because that's that'swhere the amazing stuff comes
(32:36):
from.
SPEAKER_00 (32:37):
Yeah.
It's such an important note.
Sometimes I'll realize that Ididn't prompt prickly, and I use
Claude a lot.
And so Claude will go a littledeeper and spend a little more
time doing research than Ianticipated.
Because I'm like, dang it, Ileft out that one important
sentence.
SPEAKER_02 (32:56):
So well, my my partner's an English major, and
I'm uh uh a tech guy from NorthCarolina.
This T I call myself TLDR.
I should have a t-shirt with it,but uh everything needs to be
short and acronymed.
And and when it comes toprompting, man, I I just it's so
stressful, it wears me out.
He's he'll sit there and justyou know, he'll spend he'll
treat it like it's his socialmedia, like he loves it, like
(33:17):
he'll stay in it and it he'sbrilliant at prompting.
Yeah, so it's definitely a skillset.
SPEAKER_00 (33:22):
Yeah, for sure.
So one or two things that you'rereally big on are making sure
that we stay human-centered.
Humans matter, the earthmatters.
We know that these are twoissues that can sometimes butt
heads when we talk, have theseAI conversations when we're
thinking about energy usage withdata centers.
Even when we're seeing thingslike AWS go down and affect not
(33:45):
only, you know, our ability touse different LLMs, but the
ability even for, I mean, ithappened during final
assessments.
So my students couldn't gettheir final assessments turned
in.
You know, it affects so manydifferent things.
And then just thinking about oh,energy usage for Indiana now
that uh that big data center istwo times uses as much energy as
(34:09):
I think two Atlantas.
And so we have all these issuesthat we need to bring into the
conversation and make sure thatwe're using our all of our
resources really smartly andpaying attention to these things
that matter.
So I'd love to hear a little bitmore about your thoughts on
this.
SPEAKER_02 (34:26):
Well, I I kind of want to go on a journey and hang
with about five smart people fordays to to solve this because it
and we wouldn't solve it.
It's it is the moment of ourtime, and it's probably I've
lived through a lot of these uhuh evolutionary changes in
technology.
(34:46):
We're gonna change the world,and you know, they did a little.
They did, you know, and youknow, I didn't do the paper
cards at the beginning.
Luckily, I missed that piece,but you know, I've been around
for a little bit of it.
But I think this one is is uhprofound.
I really do.
It is I don't think it'sexistential, but we should be
thinking that way.
And our planet, you know, thisisn't new.
(35:06):
We've been destroying, we'vebeen burning our planet at 10,
50x, its ability to can createitself, probably 100x.
And what you know, in 1870, westart burning oil and we blew
through 50 in first 50 years orwhatever, right?
Like it we can't live like thatfor 500 years, we just can't.
It won't happen.
And the planet matters becausethe planet's what we live on,
(35:29):
and our society matters becausethat's who we are, and then the
what how we live is after that,right?
So, you know, I think all that'sthat's not just let's hug each
other.
That's you want to live, youwant your grandkids to live, you
got to figure out what to dohere.
And I I mentioned earlier aboutagency, I really think human
agency is a really hot topicthat we all need to be not only
(35:52):
talking about, but using ourfeet.
You know, I joined a group herecalled Befriend that just meets
with people all over the cityand other states and stuff, and
then we just walk and talk, andthey're all different classes
and social economic backgrounds,and it's a place, a safe place
to come together and just meetpeople and build relationships.
So we need to be like that, morehumans connecting the humans
(36:15):
physically, you know,handshakes, walk and talk and
all that.
But we also need to actuallyproactively organize society.
I mean, like, we can't letcapitalism define us.
You know, it's done a we've hada really good run for 50 or 60
years on capitalism, but at somepoint, it the scales of these
things change.
(36:35):
You listen to Eric Schmidt talkabout AI, and he's like, you
know, in six, 10 years, it'sgonna be smarter than the
collective human intelligence,right?
That's that's San Franciscotalking.
I don't know that I believe allthat is gonna happen the way
they see it.
But to your point, that's apretty amazing, it has a really
big impact on humans and how wework.
And you know, you know, are wethe Jetsons?
(36:57):
Do we not need to work anymore?
That I I think that's a viablething.
If we blow society, why workthis much anymore if we
technology can do it right?
At the same time, it can alsocreate more wars, create more
ways to kill us, and all thatgood stuff.
So we need to be holding thehands of the controls of this.
Uh, and then back to your pointabout resiliency and just
realizing that we're burningagain against all these very
(37:20):
limited resources that most ofus don't realize there's a
global war going on for accessto these resources in a in an
ugly way.
Like it's just it's profit whenyou know, power wins profit on
all this stuff.
And it's a war that's you know,look at space right now.
I don't know where that goes,but we were doing a lot to take
(37:42):
over all of space, and I thinkprimarily to find minerals,
right?
And so, you know, and I I Idon't follow conspiracy
theories, and I don't want toget off on that, but those are
all real things right now, atleast in instance in there's
instances of all that.
And so I went really broad onthat, but I think we all need to
understand what that means, andwe need to find a common
(38:04):
dialogue, we need to find acommon emotional connection and
perspective to what is to be agood human in this next
generation, or we're gonna letit define itself to us.
And you know, I I'm not lovingthat second option.
SPEAKER_00 (38:21):
Yeah, no, I I'm in complete agreement with you.
And I think what's what's reallyinteresting is even the the
people who created, I guess, AIas we know it today, right?
Jeffrey Hinton talks about AI isgoing to be, we need to make
sure AI is empathetic, that it'sbasically thinks of it as our
mother and a caretaker to us, sothat when it is super, you know,
(38:45):
when it's reached a pinnacle,that it still sees us as worthy
of being taken care of anddoesn't just take us over and
you know, all the things, allthe the worst case scenarios.
Then we have the godmother,Feife Lee, who's talking about
actually AI is going to allow usto live in different dimensions.
And she doesn't really meandifferent planes, right?
(39:07):
She means you're driving theexample she used at AI for in
her keynote was you're a mom,you're also a surgeon, you get
in your self-driving car, whichis really a robot taking you to
work so that you can perform asurgery.
On the way there, you rememberthat you need to order certain
groceries so you're able to, youknow, flip to that screen on
(39:28):
your glasses and go into thegrocery store, put your order
in, and they'll be home by thetime you get home that evening.
You go do the surgery, roboteyes help you do a more precise
surgery.
You're done.
You get back in the car, yourealize that you need to order
outfits for your daughter'sschool dance, you go into a
virtual store.
And so she was talking about itlike that and making us more
(39:51):
efficient in those ways whilealso still being able to be
human, have families, live ourlives, feel fulfilled at work.
And so those are interesting, Ifeel very two very different
ways of looking at exactly whatyou're talking about.
And then we think aboutdeveloping countries.
I was just recently in Vietnamand Thailand, and we had the
(40:13):
opportunity to go visit anAI-based business in Vietnam.
And they have 4,000 softwareengineers working at this
business and they work inhealthcare and different things
for companies all over theworld, even things like crops,
being able to tell if there arecertain bugs.
You know, it they have all thesedifferent solutions that are
really innovative andinteresting, but they realized
(40:36):
that they needed to train alltheir software engineers to
prepare for this next stage.
So they have spent the last twoyears training 4,000 engineers,
getting AI certifications.
I mean, like it's it's reallyamazing.
SPEAKER_02 (40:50):
Yeah.
SPEAKER_00 (40:51):
And there are so many markets like that that we
don't even realize in the UnitedStates because we're so US
centered.
There are so many other marketsthat are being set up to compete
with us that are new tocapitalism and trying to get
there.
But also we have to think aboutif we go into a country like
that, set up a data center, howis that going to harm a country
(41:14):
that's mostly agriculturalstill?
SPEAKER_02 (41:16):
Well, I don't know if we deal with this question,
but I'm going to leave you witha question.
Well, maybe we have to come backfor it.
And that is if this is our grandtechnology, if technology
becomes the intelligence at thelevel we're talking, is empathy
and compassion a function ofintelligence?
Is it correlated or causal, orare they devoid of connection,
(41:39):
right?
And do we have to can we justassume something that's super
intelligent will be empathetic?
There's signs that say thatcould be true, but nature
nurture, right?
If we're nature and nurture,what do we have to do for our AI
future cohorts to make sure thatthey're not, you know, Arnold
Schwarzenegger?
SPEAKER_00 (42:00):
That is actually the perfect question to end this
conversation on.
And we will be having futureconversations.
I hope that you'll uh do me thehonor of coming back on the show
at a future date.
I think there's there's a lotmore to unpack here.
SPEAKER_02 (42:15):
So much.
I really enjoyed it.
It's great meeting you andhaving the conversation.
And uh I look forward towatching all that you put forth
in uh coming years.
SPEAKER_00 (42:24):
Oh, and likewise, uh two final things, a final
thought, a piece of advice forsomebody who's looking at what
do I do next to prepare myselffor the next stage of my career
or to make that pivot, knowingthat I need to lean into
technology.
SPEAKER_02 (42:43):
Okay, yeah, I'm gonna say two things.
One is critical thinking at yourlevel, whatever, if that's
crossword seducos or if that'sgoing back to school, make sure
your brain cognates and uh anddoes it in a thought-healthy
way.
Secondly, connect locally,especially if you're getting out
of college.
Find that thing.
Your job doesn't matter as muchas you think it does.
(43:06):
Do not give it 80 hours a week.
Find somebody to help them learnhow to read, help them find
food, help them just meet, havefun together, whatever, teach
people how to kayak.
I don't care what it is, connectlocally.
I didn't do that for 20 plusyears, and I'm trying to redeem
myself now.
And your career is never goingto change to make you do it.
But those that do do itthroughout their career, advance
(43:28):
more, matter more, impact theworld a little more.
So, you know, make a localconnection and use your mind.
SPEAKER_00 (43:36):
Fantastic.
And then do you have, and itmight be hard to choose, but do
you have a favorite quote,Montraverse, poem, family motto?
SPEAKER_02 (43:46):
There's this French guy's name.
I can't think of his name, andI'm gonna use this.
Something and I am gonnaparaphrase it.
It says some essentiallysomething is finished, not when
you can you can't add any moreto it, but when you can't take
anything more away from it.
And it's a great quote.
It's better.
It I should know my own quotesbetter than that, but that's a
(44:08):
and and I should know the guy'sname.
I'll have to record that andthen mail it to you on my curve.
SPEAKER_00 (44:14):
That is such a creative way to look at things,
too, because that's often right,the artist's work is never
finished.
You just have to know when tostop.
SPEAKER_02 (44:22):
Yes, yes, I hear it.
SPEAKER_00 (44:24):
Slightly different, but similar vein of thought.
SPEAKER_02 (44:27):
Insert any Mark Twain quote right here, too.
I'm pretty big fan of that guy,too.
SPEAKER_00 (44:31):
Yeah.
Amazing.
Andy, if it's okay with you,I'll put your LinkedIn attached
to your name in the show notes,as well as, of course, sharemore
stories.com so people can findout more about what you're up
to, how they could possibly workwith you.
Sounds great.
Great.
Thank you so much for joining metoday.
This has been such a pleasure.
(44:52):
I'm excited to learn more fromyou.
And this is only the beginning.
SPEAKER_02 (44:56):
Great.
Thank you.
SPEAKER_00 (44:58):
Yeah.
Thank you to everybody who'swatching this episode or
listening to it on your favoriteplatform.
Please leave us a rating review.
Check out sharemorestories.com.
I think you'll be as intriguedas I am.
And I hope that you learnedsomething today and that it made
you feel a little more confidentin building your future.
SPEAKER_01 (45:16):
To learn more about the Master of Science and
Digital Media Managementprogram, visit us on the web at
dmm.usc.edu.
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