The AI Impact: Transforming Tech Roles and the Future of Work | The Pair Program Ep54

Welcome to The Pair Program from hatchpad, the podcast that gives you

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a front row seat to candid conversations

with tech leaders from the startup world.

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I'm your host, Tim Winkler,

the creator of hatchpad.

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And I'm your other host, Mike Gruen.

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Join us each episode as we bring

together two guests to dissect topics

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at the intersection of technology,

startups, and career growth.

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Hello, everyone, and welcome

back to The Pair Program.

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Uh, your host, Tim Winkler,

alongside my cohost, Mike Gruen.

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Mike, um, did you know that today, I

think I've told you this before my wife's

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really, really into the national holiday

days or the calendar days of each day.

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So the first Wednesday of November

is national stress awareness day.

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Um, you never really

experienced stress, right?

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No, um, me neither.

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So in honor of, of stress awareness

day, what's, what's one thing that

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kind of continues to kind of stress you

out and, and how do you cope with it?

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Oh,

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Mike Gruen: you're, you're asking me to

how to, so I can do the coping one much

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easier, which is, uh, I do meditate.

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Um, and I find even just five minutes

or 10 minutes, uh, is enough to sort of.

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Bring me back to like ridiculous mode.

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Um, what's one thing that's

continues to stress me out.

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Um,

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that's a good one.

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Cause there's so many to spitball.

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I mean, like, I mean, like right now it's

definitely a stage of life type stuff.

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Cause my, my oldest is 18.

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He's in high school.

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We're talking about college.

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So when we start thinking about like

his future and what jobs are going

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to be available and what the world

is going to look like in the economy,

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and like, then my brain, I get down

that hamster wheel and just start,

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It's going into some sort of downward

spiral of this is a terrible place.

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Why did I bring it into a world like this?

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But anyway, um,

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you asked, man, you asked.

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Anyway, so yeah, uh, so that

kind of stresses me out.

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And then I, and then I think about it.

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I think about all the things

I, you know, meditate and then

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think about all the things I'm

grateful for and stuff like that.

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So, uh, yeah, but yeah, but yeah, that

type of stuff definitely stressed me out.

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What about you?

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Tim Winkler: Yeah, I, I'd say, uh,

just the running of a small business

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is super stressful, especially, you

know, when we've had these up and down

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kind of years over the last, you know,

since the pandemic, it's been a super

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stressful stretch of four years, um,

uh, paired with managing a, you know,

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a little toddler, uh, has been, you

know, its own kind of It comes with its

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own challenges, but, you know, I think

that's a little bit of a vague answer.

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I'm going to go with it anyways, uh,

but the, um, the way that I combat

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that is just, I have to like it.

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Well, I say, uh, the healthier outlet

here was going to say, you have to

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go to the gym, have to go exercise.

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Um, and if I don't, you know,

it, it certainly weighs on me.

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I can feel it, um, compound.

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So.

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Anyways, exercise and, uh, yeah,

I'm running a small business.

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So there you go.

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And it was, I, I usually start things

for, for, for, I guess I usually start

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things a little bit more lighthearted

and funny and, and job subject.

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Um, I, I, I know there's a lot of stress

in the world these days, so I just

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figured bring some awareness to it and,

you know, recommend coping mechanisms.

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So.

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I'm glad to hear, uh, Mike, uh, Mike,

isn't just going to the bottle as well.

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So thank you, Mike.

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Uh, all right, let's, let's fill

the listeners in on what today's

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episode is all about today.

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We're going to be diving into a topic

that's reshaping the landscape of tech.

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And that would be the evolution of tech

talent and the age of generative AI.

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Uh, joining us, we've got a

couple of fantastic guests that

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have great insights into the

intersection of tech talent and AI.

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Um, we have Diana, uh, Dan, I just

already blew it at, uh, Gail house.

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Diana: That's okay.

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Gail house.

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Oh, you didn't, oh, I got

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Tim Winkler: it.

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Awesome.

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All right.

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Diana Gail house.

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Uh, Diana serves as the director

of economy at this special

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competitive studies project.

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She has nearly 20 years in tech and

talent policy, uh, instrumental in

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positioning the U S as a leader in

AI driven economies, uh, her work on

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AI workforce strategies for the DOD.

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And research on talent pipelines, uh, will

no doubt make for some very insightful

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additions for today's discussion.

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So thank you for joining us, Diana.

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Um, and alongside Diana,

we have Angela cough.

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Angela serves as the special advisor

at the DOD's chief digital and

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artificial intelligence office.

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Uh, she oversees digital talent

management, uh, has experienced

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deploying AI capabilities and

enhancing data literacy across the DOD.

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Uh, Angela offers us real world

insights on adapting workforces for

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an AI driven future, which I'm excited

to expand on and our chat today.

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So Diana and Angela, thank

you both for joining us today.

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Angela: Thank you.

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We're excited to be here.

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Tim Winkler: Awesome.

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All right.

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Now, before we dive in, we do

like to kick things off with a

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fun segment called pair me up.

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Uh, here's what we'll all go

around the room and spit ball.

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A complimentary pairing of our choice.

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Mike, you lead us off.

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What do you got for us?

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Uh, I'm going

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Mike Gruen: with a poker

face and video calls.

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Uh, so the last couple of weeks

has been a lot of, uh, conference

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calls and leadership calls.

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It's the end of the year, so it's

budgeting and lots of things.

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And, uh, my, uh, some of my, uh,

counterparts like to slack and message

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me funny things and do the same.

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So being able to maintain a poker face

while, uh, also having fun on the video,

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uh, because it is a serious topic, uh,

but at the same time still having fun.

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Um, and trying to do it.

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So containing that sort of poker

face while on the video call,

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that would be my, my pairing.

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Can we see your poker face?

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No, because now I'm smiling

and I can't, I can't.

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I thought you could just turn it

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Tim Winkler: on.

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You can't just turn it on.

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Mike Gruen: I mean, I can.

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Okay, there we go.

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There

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Tim Winkler: we go.

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Mike Gruen: I'm serious.

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You're

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Diana: not listening.

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You have to be.

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Oh.

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Yeah, no.

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Well, on the

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Mike Gruen: plus side, I just saw this

awesome, uh, AI, uh, video of a guy who's

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figured out how to just get on the, like,

it's just an AI that's doing him and.

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Acknowledging and whatever, while

he's off playing video games, uh,

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and pretending that he's on the call.

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Gets

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Tim Winkler: sick and sick and mouse

jiggle to a whole nother level.

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That's

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Mike Gruen: ridiculous.

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Diana: I can help you with, by the way,

can transpose your face on a screen.

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That's what I'm saying.

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That's what he has.

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It's a whole

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Tim Winkler: thing of

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Diana: like,

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Tim Winkler: yep.

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That's really what we're going to

talk about here today, how to get

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away with it, how to supplement AI

tools to not have to do anything.

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Uh, I dig it.

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Um, many of times I've had to

put that poker face on many of

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times on these podcast episodes.

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I appreciate you, you calling it out.

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Uh, I'm going to go with a, um, a

top of mind seasonal struggle for

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many folks out there these days.

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And that is the.

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End of daylight savings time and

the age old question of how do

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I change the clock on my stove?

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Uh, because this in my opinion has to be

one of the most annoying Trickle effects

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of dealing with the end of daylight

savings time or just day saving time

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in general Um, but I do think that this

is a pretty common hardship with folks

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across the country, you know, minus your

Arizona, your Hawaii's, uh, but I feel

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like this is something that we all have

to deal with each year, uh, and the, in

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the face of daylight savings time ending.

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And so, um, you know, I feel

everyone's pain with that.

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So that's going to be

my, my pairing for today.

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Has anybody here had to

change that clock yet?

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Diana: I, uh, I empathize with my

small device that I have, which is

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just a clock, but it's also a radio.

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Tim Winkler: And I

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Diana: always have to look up the

instructions because I never remember

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what combination on the remote is

supposed to actually make a change.

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Tim Winkler: That's right.

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Diana: Oh, I can empathize there for sure.

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Tim Winkler: Yeah, sometimes it's those

little analog, you know, scenarios that

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you have to like go and do some googling

on like, how do I, what am I doing?

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Diana: I was actually just going to

also empathize with I recently moved

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and I got into a very heated argument.

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No pun intended with my

microwave about this.

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Very question.

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And I didn't want to, cause

I couldn't figure out how to

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work the new microwave to me.

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And I literally could not, I must've

spent like 15 minutes trying to,

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and I was like, I will not give up.

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I will not surrender.

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I hate quitting.

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Um, and so I gave it a timeout.

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I went away, came back and I

finally figured it out, but

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I know exactly what you mean.

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So that was really funny.

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I feel so

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Mike Gruen: lucky that my, the only

thing that we have to change manually

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at this point is our microwave.

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And you push the clock button

and then it has the instructions.

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Like, do you want to reset the time?

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And it just walks you right through it.

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So, yeah, very spoiled on that.

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I hated my used to have a car.

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Diana: It is Diana for you.

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Yeah.

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And replace it.

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Exactly.

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Tim Winkler: I'm glad I've got some

some empathy points on my pairing.

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I'm going to pass it along

to our guest now, Diana.

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How about a brief intro and your pairing?

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Diana: Sure.

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Well, thank you for the great intro.

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I am an economist by training.

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I care a lot about innovation and growth.

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And when you think about what

drives that it's tech and talent.

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So I've spent many years thinking about

different dimensions of these questions

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started to think about AI as it was

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first coming on the scene as an issue.

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Uh, conversations were

moving from cyber to AI.

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In the workforce policy space,

and so just really been following

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along on, um, how transformative

it's been in such a short time.

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So, uh, that's a little bit about me.

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I come by way of several think

tanks, government and what's next.

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I guess we'll get into that later.

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We're all on a journey.

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My pairing, I think, I, so I went

with something kind of silly, uh, and

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I think Angela will appreciate this.

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I'm going to go with yogurt

and granola because I love it.

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Oh,

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Tim Winkler: that sounds

so good, that's strong.

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Diana: It's like one of the only things

I've ever seen her eat aside from cookies,

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so it's a big part of her daily routine.

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There you go.

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Do you, do

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Mike Gruen: you go with any, any

fresh fruit or is it just the,

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uh, the granola and the yogurt?

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Because I love the granola,

blueberries, yogurt, blueberries.

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Angela: That is the go to for

her that I have approved myself,

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so I appreciate that pairing.

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Mike Gruen: I'm pretty sure I've even

used that pairing because it is so good.

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I support your pairing.

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Tim Winkler: I gotta follow

up on Angela's point there.

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You said, in addition, that's the

only thing you eat aside from cookies.

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Um, what kind of cookies?

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Well,

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Diana: it's definitely not true,

but, uh, but, uh, oh, I love cookies.

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But I do, I do love fruit

and granola and yogurt.

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And I also do love cookie,

chocolate chip cookies.

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Chocolate chip,

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Tim Winkler: classic, yeah.

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Yeah.

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Yeah.

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That's right.

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That's the, that's the, that's

the right answer right there.

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Yeah.

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Yogurt and granola, uh, you know,

with a toddler and, and, uh, and, and

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our family, we are constantly doing

yogurt and granola for breakfast.

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It's a, it's a goat combo.

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Great pairing, Angela, uh,

quick intro and you're pairing.

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Diana: Yeah.

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Um, yeah.

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So, uh, as you well introduced earlier,

and I'm so proud of you for getting

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all of the chief digital and artificial

intelligence office and all of the

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words that go into that title, cause

it can get quite lengthy, um, yeah,

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currently I'm, I'm overseeing the digital

talent management activities, trying

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to enhance and expand the department of

defense access to data and AI talent.

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So it's a very interesting challenge.

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And my.

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Former colleague that I so

desperately miss, Diana.

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I'm so excited that she's here to join me.

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Um, my, you know, it's, it's been an

interesting journey and, and there's

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lots of stuff to come next for the

department and lots of work to do.

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So I'm very excited to be

here to help kind of promote.

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How we can get after it.

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My favorite pairing, I think for right

now, considering we were just talking

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about the wood walls behind me as my

delicious cup of coffee as an entrepreneur

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as well, uh, from my own coffee shop

that I like to pair with my Pacific

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Northwest vibe, um, wood walls as, and,

and, you know, appropriately, uh, vest,

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Tim Winkler: you

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Diana: know, to keep myself nice and cozy.

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Because it was 39 degrees this morning

and the daylight savings is giving me

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one more hour of delicious sunlight

to help myself throughout the day.

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Tim Winkler: Nice.

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So I need to clarify,

is it your coffee shop?

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You have a coffee shop out there?

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Diana: Yes.

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Tim Winkler: Want to give it a plug?

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What's it called?

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Diana: Um, it is Hotwire Coffeehouse.

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It was established before Hotwire,

the online, um, uh, place.

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So it's, it's a local coffee

shop, a small little place

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that's in a historical building.

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Actually only a few blocks from

my house, but it's been serving

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this community for over 20 years.

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It, it really is sort of like

a weird little community hub

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that we stay connected with

the neighborhood that I'm in.

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So it's been, yeah.

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Yeah.

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Uh, very interesting.

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We've owned it from the

original founder since wow.

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Tim Winkler: That's really cool.

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I love that.

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And I love coffee and you know, you're,

you're seeing right now for those that

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are looking at watching this on you on

the YouTube, it's, uh, really taking

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me into that Northwest cabin vibe.

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So I, I, I applaud you for, for

bringing all of those pieces together.

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Um, awesome.

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Well, yeah, we're excited

to have both you all on.

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Um, I know that Angela, when we first

had our, Discovery call, you know,

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we, we really had a chance to, you

were the one that really kind of took

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me down this journey of, I think this

is what we really should be talking

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about here, uh, on a larger scale.

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And, um, I think it's a, an

appropriate conversation to have.

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It's one that is, uh, impacting,

you know, for the most part,

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everyone, uh, in some fashion, uh,

and it's certainly highlighted in

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a lot of the tech roles that we.

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Talk to folks about on a daily basis.

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And so I'm excited to peel it back a

little bit and, um, uh, yeah, expose,

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shed a little bit of light on, you know,

from, from a couple of experts here

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on where we think things are going.

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So, um, I'm gonna go ahead and transition

us into the heart of the discussion now.

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And, um, again, a quick recap,

we're going to be talking about

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tech talent in the age of AI.

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Uh, and so again, many of our listeners

have probably experienced in some

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Way shape or form, uh, AI has had

some level of impact on their role.

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And as it continues to redefine roles and

skills, you know, we're going to start

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to see industries rethinking, not just

who they hire, but how they hire as well.

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Uh, and so in this episode, we'll

explore how AI is broadening, what it

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means to be, you know, kind of tech

talent beyond traditional STEM roles.

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And discuss the critical shifts

that, uh, Are going to be needed

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in education to prepare the

workforce for this AI driven future.

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So I want to start by talking

about how AI is reshaping what

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it means to be tech talent.

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Uh, Diana, from your perspective,

how do you see tech roles evolving

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as AI continues to play a bigger

role in the industry at large?

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Diana: I love this question.

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Uh, so I actually think we're already

seeing early signs of these roles

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evolving and what it means to be

a tech worker and you're seeing

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it in a couple of different ways.

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Um, and and I think you'll continue

to see as this technology deploys at

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scale, given the rapid advancements that

are already happening, like, even if.

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You stopped and froze all of the

advancement that's happened over

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the last 2 or 3 years today.

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You'll still see, uh, the effect

on the economy and on the workforce

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over the coming few years.

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So I think you're already starting

to see some signs and and that is.

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The following, right?

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I see it as you've got

different tiers of talent.

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You've got this exquisite talent.

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That's still very high in demand.

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That's not going away anytime soon.

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Right?

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You've got your PhD computer

science researchers, your PhD

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machine learning engineers.

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Um, you know, your PhD scientists of

many different flavors and that, that

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is still a core group of innovators

that are doing a critical R and D.

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Right.

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So we absolutely still need

to cultivate that talent.

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Then you've got this gigantic layer of

practitioner talent, and that's where

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you've got your Debbie's, your software

engineers, your data scientists, a lot

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of that practitioner talent, and, um,

This is where our, like, taxonomies and

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how we are able to, our lexicon about

how we're able to talk about this talent

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starts to get super squishy and outdated.

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Um, and it makes it hard to accommodate

this type of discussion, but I really

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see this talent is it's not just like

one hard technical skill anymore.

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It's really like a I plus like

your ability to leverage a I into

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whatever it is that you're doing.

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Uh, starts to become

really, really key here.

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And you're already seeing shifts in

demand for people with certain types

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of technical skill sets or certain

combinations of technical skill sets.

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Right?

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So, I think that's really a key thing.

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And then the other piece that

people always forget about when

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it comes to technical talent is

the skilled technical workforce.

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And that's also seeing, uh, um.

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A strong, uh, rise in demand, I think,

as we start to think about careers

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in biotech and cyber and advanced

manufacturing, um, and we've cultivated

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some of that with our own policies.

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Right?

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So, but there's this cadre of

talent that you maybe don't need

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that traditional for your degree.

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So you think about other pathways

you think about skills based.

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But they are part of

the technical workforce.

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Um, and so the definition starts

to kind of expand and morph.

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And then you think about how all of

us, just like we all use computers.

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We're on a, you know, a podcast right now.

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How we're going to be operating and

interacting with AI enabled capabilities.

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And how much, how much knowledge

we need to have about what these

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capabilities are and how they work.

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Is to some degree, we're all going to

have to have some level of sophistication.

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Tim Winkler: Yeah.

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I love the, uh, I love how

you just broke that down into

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a couple of different tiers.

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Cause, um, I think that's super helpful

when folks are trying to figure out,

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you know, where do I kind of fit in,

in, in this kind of transition space.

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Um, no doubt still, I agree

wholeheartedly with a lot of

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the, you know, the PhD folks.

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It's the, that next level that you

described that I think is where we get a

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lot of head scratching coming into play,

um, You know, um, front end development

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is a good example or design, right?

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We, we see a lot of these types of

individuals have a little bit of,

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um, uh, you know, I don't want to say

fear, but you know, a little bit of,

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a little bit of uncertainty, right.

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Of, of, you know, where, where

is my skillset going to be in

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demand in five, 10 years from now,

if these tools are starting to

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have the ability to do X, Y, Z.

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Um, and so, you know, the way that

you describe that, I think is, you

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know, Is important to note, um,

the AI plus, uh, we ran an episode

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about this previously about, you

know, uh, generalist for specialist.

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And I think this is where you're starting

to see some of these specialists and

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verticals really becoming so key if it's

health care, if it's finance, what, what

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have you, if it's in defense, um, but

combining these, these, uh, skill sets

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and, and to, you know, one overarching

kind of skill set is, uh, is something

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that I think you were kind of alluding

to, which I think is really intriguing.

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Angela, I want to, uh, pass it to you

because you're obviously doing a lot of

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work within Department of Defense and,

um, you know, you're transforming on the

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ground, you know, firsthand some of this.

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What are, what are you

seeing on this topic?

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Diana: I think some of it, I like the idea

of referring to it as AI plus, because

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in part, the analogy that I like to use

is we're sort of, this is sort of the

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next advent of when we introduced the PC.

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Right.

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It's another tool in folks toolkit

and it will become something that

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we have to adapt to use and and

will become part of our daily lives.

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I think I refer to them as soft skills,

but really there's this idea of increasing

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people's awareness from a product

perspective of what they're trying to do.

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You know, Diana mentioned, you've

got these hyper specialists that can

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sort of do this R& D development,

sort of see how the frontier is

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going to expand with respect to AI.

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Then we have the people who have

to be able to implement, use, and

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support, maintain, and test that AI.

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And then we have folks that

are going to be hyper focused

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on certain technical pathways.

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And we're seeing this trend happen

across education too, where we've

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got concentrated areas for machine

learning, concentrated areas for

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other thoughts and practices.

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And But there's also this piece about,

you know, uh, AI and the ability to be

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able to develop models that truly reflect,

um, human behavior is going to require

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human behavioral sciences, people who

understand how people think, how people

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work, how people do things day to day,

um, and then be able to expand upon

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that into what would be Traditionally,

not necessarily be a technical role,

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but it's like product development.

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Do you understand the scope of the

problem that you're trying to solve for?

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And then how do you translate

that to an effective outcome?

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Um, you know, I was asked at a

previous sort of conference that I

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attended what skills these college

folks should be looking to have.

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And I was like, figure out how to

deconstruct problems that you're

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given and then how you can actually

put that into consumable products.

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Um, sort of executable tasks, but then

also be able to identify what parts

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of that could potentially benefit

from some level of process automation

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and how to differentiate that from A.

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I.

447

Enablement because there

is there is a conflation.

448

I think that's occurred across

the landscape right now, which

449

is Sprinkle a little AI on it,

and it'll solve your problem.

450

But there are a lot of moving

parts that have to go into actually

451

making that accomplishable.

452

And so, it's important for us to

have folks who can actually think and

453

deconstruct what they're trying to

accomplish, and then understand how to

454

adapt that to some of the tools that are

available right now, because it's just

455

part of our toolkit is what we're going

to expand into, and then be able to adapt

456

that to their day to day work environment,

kind of regardless of what they

457

actually do or have their skill set in.

458

And I agree with Diana and what we've been

talking about a lot in side conversations,

459

you know, across this landscape, which

is the traditional four year degree in

460

computer science may become less relevant.

461

It doesn't mean that it is not

relevant, but it may, but there are

462

certain barriers I think to access and

contribution in this space that will

463

be sort of like equitably um, leveled.

464

For some folks that would otherwise

be able to contribute their thought

465

and diversity of thought to a

space of technical application.

466

So I, I as an individual can

actually share my thoughts

467

about how to design a model.

468

I may not know how to technically

do it, but I can at least be part

469

of the process of getting it done.

470

And that's where I think we have an

opportunity to really leverage more

471

diversity of thought and experiences

and application and perspective.

472

In a way that wasn't quite, I

think, as appreciated and it goes

473

beyond traditional stem scope.

474

Mike Gruen: I think one of the things you

sort of talked about, because so before

475

AI like really took off, like this would

be 10 years ago, whatever I was working

476

in a company, we're doing, um, inferential

statistics and describing human behavior.

477

We're looking for inside

risk inside threat.

478

One of the biggest model, like

one of the biggest things we could

479

do is identify someone who was

thinking about leaving their job.

480

Right.

481

So flight risk and we had, right.

482

And we sort of talked about, and we sort

of getting back to that, like decomposing

483

and understanding people's behavior.

484

And then we tested all these,

what was interesting at that time

485

was we built these models and

then they were fairly effective.

486

Like we ran it on data sets.

487

We could show every time we talked to

anybody, they were always like, well, how

488

do you know you didn't just get lucky?

489

How do you know that

this is really working?

490

And I think that's one of the things

that being able to like explain what's

491

going on and not just trusting that

the AI, like, Oh, that's the answer.

492

There is something and being

and having that experience to

493

be able to recognize, I think.

494

When, you know, maybe it isn't really

describing human behavior, and there's

495

some bias in the data set and being

able to question it and stuff like that.

496

I think that's maybe that's a little

bit what you're talking about.

497

And I love the like, um, the plus.

498

I mean, there we spent a lot of time.

499

We had data scientists come in.

500

They didn't know how to

do software engineering.

501

We spent a lot of time teaching

them how to do software engineering,

502

um, so that they could build

this stuff themselves again.

503

It's that sort of bringing

these These things together.

504

I like a lot of what both of

you were saying about that.

505

It's like, you have to bring these

multiple disciplines together

506

in order to sort of get it.

507

To really work and function.

508

Um, I think that's an important part and

I appreciate you guys bringing it up.

509

Diana: Can I just, I want to also

emphasize here what you're just,

510

what you're saying without saying is

the importance of technical teams.

511

Tim Winkler: Yes.

512

Diana: Yeah.

513

It's really not just one part.

514

We can't ask everything, everybody.

515

It's like saying I, you know, you need

a partner who's everything to everyone.

516

I mean, that's just not right.

517

You, you can't have, not everyone can be

a unicorn who does everything all right.

518

So it's.

519

It's also about the team and making sure

that you're thinking about technical teams

520

and how you're deploying those teams.

521

Um, uh, you know, whether it's within a

business unit or more enterprise wide.

522

Mike Gruen: Absolutely.

523

And I also think the whole notion of the

CS degree, which I think is funny at this

524

point, because most of what I learned

in computer science when I took computer

525

science was all about like machine

architect, like computer architecture and

526

stuff that like, I think in the course

of my career has come up maybe, you know,

527

thrice where I've been like, Oh, I really

am glad I understand how paging and memory

528

works because by rewriting this loop in

this way, I've like, You know, actually,

529

uh, solve the problem because it was a

caching issue inside the chip, you know,

530

like some crazy like Edge case, but these

days I think that doesn't happen anymore.

531

And I think a lot of what traditional,

like, I think most of what people

532

are coming out of now is basically

just software engineering,

533

which is really just a vocation.

534

And I can, you know, if you're pretty

technical, it's, I don't think you

535

necessarily need a four to four year

degree to learn how to write software.

536

Um,

537

Diana: Well, I think you're, you're

walking around the edge too of another

538

thing that we're seeing, which is

there's a lot of people who are very

539

passionate about analyzing information.

540

It didn't necessarily start out there.

541

So like, for example, not to age myself

too much, I ran into somebody who was

542

perhaps in their early thirties that I

used to babysit when they were a baby.

543

And I found out from talking to them

that they actually got, I think they

544

mentioned a history degree in college

because it was something that they were

545

particularly passionate about, et cetera.

546

But they also were just naturally

very good at math and then happened

547

across the pathway of data science,

went and did a concentrated effort

548

to become very skilled in that space.

549

And now they work full

time as a data scientist.

550

And as they put it, they are far more

effective at their job because they

551

both have the passion for the work

that they're doing, the aptitude to

552

do it well, but they also didn't come

from sort of like a, a limited scope

553

background in just technology to apply it.

554

And I think that's where we

can really see some shifts in.

555

How we're finding some of the folks who

are very effective in this space, they

556

may not have started out there, but the

advent of the opportunity ahead of them

557

is what's starting to shift their focus

and the passion of getting nerdy about

558

the numbers is really what's driving their

interest, which is a little bit different

559

than I think what we've seen, um, today,

560

Mike Gruen: right, but I think that

parallels a lot of what I saw happen

561

with software right back in the Bye.

562

Bye.

563

When I started my career in the

nineties, like it was very, the

564

like writing software was harder.

565

It was more esoteric, whatever.

566

And as we've built more and more tools

and made it more accessible, we've

567

seen a lot of creatives, like people,

like the best front end engineers I've

568

worked with, like they went to art

school, they didn't get a CS degree.

569

And like, so opening those doors

for these people to be able to

570

build, like take their idea.

571

And have that in software without

having to work through like a series of

572

engineers and business analysts and the

rest of it and describing it all, but

573

like they actually can bring it to life.

574

And I think we're seeing the same thing.

575

It's just in data.

576

And I think.

577

This is that sort of enablement where

we just, you know, we build the tools

578

and then it opens the doors for more

people to be able to do more things.

579

Um, so hopefully it's not

too scary for folks, um, that

580

they're not losing their job.

581

It's creating more

opportunities to do more.

582

Tim Winkler: I kind of want to, uh,

pull on the thread, Angela, of, uh,

583

the, the term soft skills that you

referenced, because I, I want to kind

584

of dissect a little bit more of like,

what are these skills that we think are

585

going to become so essential in this?

586

Yeah, next era of, you know, A.

587

I.

588

Um, and, you know, for example, right?

589

I think for me personally, I think

kids, students, you know, coming out

590

of school, you know, it's not about

memorization and regurgitating because.

591

You've got this PhD in your pocket, right?

592

With chat, GPT, AI.

593

Um, so what is it that's going to

become essential and, you know,

594

learning how to interpret, I think

is, is becoming a real key skillset.

595

What I'd love to just hear your thoughts

on that, because, you know, where, where

596

do you see, you know, from an education

perspective, Well, where teachers are

597

going to be placing a lot of emphasis

with students, um, down the line.

598

Diana: So from my perspective, and

I'd love to hear Diana's, uh, input on

599

this too, because she's studying this

also, and she and I have had lots of

600

philosophical conversations about this.

601

I would say that What I would, what

I've observed and what I think is going

602

to become particularly important, and

I also think similar to Mike's comment

603

about like how the evolution of like

computer science has changed over time.

604

Nobody thinks about the computer that

they have in their hands every single

605

day because it just kind of works.

606

And I do think that that has allowed

us to um, allow our critical thinking

607

skills to fall off a little bit because

we just take things for granted.

608

We don't like, I've had this

conversation just recently.

609

I'm like, how many teenagers

out there do you think actually

610

know how a toilet flushes?

611

Because they don't ever really think about

having to do anything with it and they're

612

going to call somebody to come and fix it.

613

So when we're thinking about, um,

the, what I'm referring to as soft

614

skills, and I don't know if that's

technically the right term for it,

615

but I'm just thinking about what are

the skills that are not taught to you,

616

that are not those rote memorization,

algorithmic thought processes.

617

That allow you to be able to adapt what

you are trying to do and then think

618

critically about how to solve for or fix

or come up with solutions for that thing.

619

Um, and our ability to revisit really

developing individual critical thinking

620

skills and problem solving skills and kind

of going back to reinvigorating curiosity.

621

And, and, um, valuing the

ability to deconstruct and

622

then understand and then apply.

623

That's very abstract, but I do think

that it is something that when we

624

weren't so in our devices was something

that we were kind of forced to do

625

a little bit more in the olden days

of doing stuff, like you had to go

626

figure out how to entertain yourself.

627

You couldn't just simply

stare at a phone and.

628

Regularly flip through Instagram,

for example, which is pushing to

629

Mike Gruen: you, right?

630

I mean, I think that's part of

like, there's this, I agree.

631

And like the, the, how do I do this?

632

And just being able to look

it up so quickly and always

633

being able to find the answer.

634

So you don't even have to

like really spend a lot of

635

time trying to figure it out.

636

You just, why would I waste my time

doing that when I can just Google it?

637

Um,

638

Diana: right.

639

Mike Gruen: Right.

640

And I

641

Diana: feel like part of really what we

can do to really maximize the adoption

642

of some level of AI enablement is to

really get people to start thinking about,

643

um, how can this help me, but also how

does it help me to actually move faster,

644

do things better, be more efficient.

645

And if you're not even thinking about it

as an art of the possible, Then you're not

646

even going to look for those solutions.

647

And that's where I think people are

kind of on the edge of thinking about

648

how does this really, I'm not going to,

well, maybe I will be outing myself.

649

Like, for example, I noticed that

for my own business, there were

650

these incredibly philosophical

responses to people's reviews.

651

And I was like, where did those come from?

652

Turns out we decided to go with using

generative AI to come up with responses

653

to reviews that could not be argued with.

654

Right.

655

And that was something that helped us

to be able to respond in a way that, um,

656

allowed us to be able to, uh, Have our

customers feel heard, but at the same

657

time made it so that they couldn't argue.

658

And I was like, wow, that's genius.

659

Because not only does it take the burden

off of my husband for having to write

660

the review, because he would get super

emotional about sometimes like if somebody

661

didn't like their coffee or whatever.

662

Um, and now he's just putting in

a prompt that says, Uh, here's

663

the problem that was presented.

664

Please come up with a philosophical

way of responding to that problem.

665

And that becomes the review

response, which is hilarious, but

666

also incredibly effective because

I read them and I'm like, that's an

667

interesting way of thinking about

that particular piece of feedback.

668

But again, it's a creative way of

solving for what is something that

669

we have to manage every day, but

coming up with a way to make it

670

better because the outcome is better.

671

Um, but also adapting something that

also helps us to save time and focus

672

our efforts on other activities.

673

Tim Winkler: Yeah, Diana, uh, Angela

kind of referenced that you're,

674

you know, you're spending a lot

of time in this area specifically.

675

Um, I'd love to hear your, your

thoughts on, on that subject.

676

Diana: Well, this is sort of

the age old question, right?

677

It's not really a new question.

678

Everyone's always asking what, you

know, what should I study in school?

679

What are the future?

680

I started my career doing,

uh, employment projections.

681

Like this is always for the Bureau

of Labor Statistics, super wonky,

682

but this is always something

that people care about of.

683

Like where's the future job demand and

where are the skills, uh, gaps going

684

to be, and, and, you know, education

always lags a few years behind.

685

So now we've caught up with demand

for data scientists and software

686

developers, but, you know, so you have to.

687

Now, continue to move.

688

Look, I think there are some things

that never go out of style and

689

Angela touched on some of them.

690

Critical thinking will not

go out of style anytime soon.

691

Social and emotional skills, being

able to communicate, uh, effectively

692

will never go out of style.

693

And something that I

call plan for competence.

694

It's a term in the literature.

695

That's really about how you can

design a plan and follow through

696

and execute on that with competence.

697

So, you know, being able to think

through and you talk kind of

698

back to the conversation earlier.

699

What am I trying to achieve?

700

What is the best way

I'm going to get there?

701

How am I, what are the

steps that I need to do?

702

Things that only, that are

right, that are uniquely human.

703

For us to use AI as a tool to help us

achieve certain things, but don't waste

704

your time on summarizing the memo.

705

Waste your time or use your

time to advance an agenda, to

706

advance a conversation, to ask

the right question, to know which

707

questions you should ask, right?

708

Like even doing data analytics.

709

Um, you know, I managed a team of

brilliant researchers in a previous

710

role, and they, it was, how do you

know what the right question is to ask?

711

How do you design a research project?

712

And when I have a data set, that's great.

713

What am I trying to achieve?

714

Like, what do I want to actually get at?

715

What's how should I construct

these at the the analytics and

716

and in a way that creates results.

717

results and actionable

recommendations, right?

718

So you, you need to be able to,

um, it's a combination of skills,

719

I think, and that's where the

soft skills come in because it's.

720

It's part art and part science, right?

721

Part being able to communicate, part

being able to think critically, part

722

being able to, um, work on a team

and, you know, part being able to

723

think through what it is that we need.

724

And then how to get there and then

following through and achieving it.

725

And, you know, and those

are not easy things, right?

726

A lot of people, it's like you

started out at life is stressful.

727

We're stressed out.

728

Um, and so it's really

easy to just say, screw it.

729

I'm gonna, you know, just

keep my time on the microwave.

730

An hour ahead because I can't

deal with it in a few months.

731

It'll be

732

Mike Gruen: right again.

733

Diana: You know, but, but these are

like in the workplace, these skills

734

are, are really, really invaluable.

735

I think being, and the final thing is

really being able to adapt, flex, stretch.

736

Like what, what do we actually need?

737

Like I wear so many hats

and I've worn so many hats.

738

Like Angela will tell you at CDAO, I

really wore the hat of an action officer.

739

I was a researcher, but that's

not what they needed at that time.

740

So that's that.

741

So no, take off that hat and

put on the hat that you need to

742

have on and be able to adapt.

743

Mike Gruen: I think the mentioned

something that, um, the being able

744

to like question things like the, I

think a lot of people just are ready

745

to sort of follow and want to be.

746

Told what to take it for what it is and

not take that step back and be like,

747

yeah, we could solve that, but you know,

or we could do that, but if we actually

748

did this other thing, it solves in a

completely different, more effective,

749

like, and it does take that, like

stepping up, stepping out of the moment

750

and having that sort of bigger picture,

critical thinking, like, viewpoint

751

of like, does this even make sense?

752

Or is there a better way

to go about doing this?

753

And how do we do that?

754

And I think that is, um, I mean, I

manage a lot of people, um, and it

755

seems to be a diminishing skill.

756

Um, it's something I interview for.

757

Diana: Well, and that's, and

that's the thing is it seems

758

to be a diminishing skill.

759

So you asked earlier, what should

educators in schools be focusing on?

760

And I don't know what the magic

mix is from an educational

761

perspective of how you re.

762

Invest and re educate people to start

having that level of critical thought.

763

Um, you know, it's like, it's almost

like I want to give my kid a hammer,

764

a nail and, you know, a box of

screws and And tell them, now figure

765

out how to make a table, right?

766

That really is what we're asking people

to do, oftentimes, is here, here are

767

some tools, now figure out how to

make this happen with those tools.

768

And if you're not curious enough to

fill in the gaps in between, and if

769

you need too much hand holding along

the way, You're not a, you're not

770

an effective part of that mechanism,

and so I don't know right now at this

771

exact moment what the list of skills

are or processes that we would have to

772

then reinstitute into our educational

ecosystem to make that critical thought.

773

become more, more prevalent,

I think, in the development of

774

folks going into the job space.

775

But I do feel like that is one of those

where if you're not critical in the

776

way that you think about the problems

and the things that are given to you

777

to solve, you also are going to risk

not also being a critical consumer

778

of what AI is generating for you.

779

And that is just as important To not

just take what's being regurgitated

780

from a, you know, statistical, logic

based, um, you know, generative tool

781

like ChatGPT or something like that.

782

If you're also not going to be a critical

consumer of that, you're also going to

783

risk just accepting what's given to you.

784

And then translating that out as if it's

fact, because there's both the, how are

785

you going to use it for your intent and

purpose, but also are you not just going

786

to accept it and also validate what

you, the tool you are using or that is

787

aiding you in a responsible way so that

you can then implement what it's helping

788

you to do and feel confident about it.

789

And Diana, you referred to,

what did you refer to it as?

790

Planful.

791

Oh, planful competence.

792

Planful competence.

793

I think.

794

It's going to take me a moment to

make sure that I put that into my

795

brain and hold on to it as a phrase.

796

But that idea is the same thing I was,

I was referring to when I was talking

797

about this critical deconstruction.

798

And so that planful competence aspect

is just as important for our utilization

799

of the outputs of AI, uh, products.

800

And also what we're going to put

into it to then generate that output.

801

There's that constant cycle of feedback

and then retesting validation, um, that

802

I think is very important for us to own.

803

As consumers of that technology,

804

okay, with failure to, by the way, in

that, like, this is a, it's a process

805

and we don't learn if we don't fail.

806

Mike Gruen: Well, I would actually,

and somebody asked me, I get this all

807

the time when I do reference checks for

people or whatever, like somebody, Oh,

808

how to talk to Travis time when this

person failed, whatever, in my opinion.

809

Failure is not learning from a mistake.

810

So mistakes are mistakes.

811

They're not failures.

812

To me, a failure is when you

actually just accept like you haven't

813

learned anything from that mistake.

814

So, um, I'm always sensitive to the

whole concept of failure, right?

815

Don't be afraid to fail.

816

I'd like a psychologically

safe environment.

817

There is no such thing.

818

It's just you made a mistake.

819

We'll learn from it.

820

Let's move on.

821

Tim Winkler: Something I wanted to

just kind of, um, uh, ask Diana, you,

822

cause you know, when we, we talked

about the AI plus, um, and I, I dropped

823

one vertical like healthcare, but what

verticals do you think are, are kind of

824

ripe for upskilling workers and AI plus?

825

Diana: Oh, everything, you know,

no, really, uh, I think, right.

826

We've got.

827

What we, I guess, when you consider

what you're talking about a vertical

828

as an industry sector, it would be

like, education and health care.

829

And those are the 2 big nuts, by the way,

that are notoriously low productivity

830

sectors that have 0 incentive.

831

Um, to move, uh, for many reasons, for

many, many, many reasons, uh, and we

832

could talk about some of the education

stuff, but I mean, when you look at

833

what's growing job wise, it's state,

it's education and healthcare right now.

834

That's really what's driving a job

growth and that's worth noting.

835

Um, so.

836

I think that you've got a lot of

different, um, opportunities for AI plus,

837

I call it AI plus X and, uh, it's not my,

uh, generic term, by the way, like that's

838

something that other countries have also

latched onto in their education system.

839

So we're actually a little bit

behind, uh, our education systems a

840

Mike Gruen: little behind.

841

Diana: Hello, you know what?

842

That's not fair.

843

And so in some in some places for

some students and you talked about

844

we were talking about the classroom.

845

It's but also the students learn

differently and we don't have a

846

model that's set up for student

success for all student success.

847

Anyway, um, So, you know, I think,

uh, that you've got, you know, the AI

848

plus finance, the AI plus healthcare,

the, um, AI, literally you could

849

do like, I could do AI plus retail.

850

Like you could literally go down

the entire industry taxonomy and

851

say like, AI is going to transform.

852

Some faster than others, some places

faster than others, and some places

853

more disruptive than others, like the

information sector, the publishing,

854

the media, the news sector, right?

855

Like, that is, that is taking a hit more

quickly than in some of these other areas.

856

And you'll see AI come out with the,

you know, the, I think the scope here

857

is Gen AI, but you've got other AI,

like autonomous vehicles that are,

858

that are also going to continue to

disrupt the transportation sector.

859

So I think that.

860

Yeah.

861

The list doesn't really end in terms

of where you're going to be able to

862

apply AI and as a practitioner, uh,

where you'll be particularly valuable

863

if you've got, um, some subject matter

expertise, some domain experience, and

864

then also being able to understand how

to leverage these tools in that domain.

865

Tim Winkler: All right.

866

Um, obviously just scratching the surface

of the conversation here, uh, seems like

867

a, uh, an episode prime for a followup

sequel at some point, so we'll, we'll

868

We'll have to table it just because

we, we of course have to get the five

869

seconds scramble segment in, or this

is not a complete podcast episode.

870

So I'm going to wrap it on that note and

transition us into this final segment,

871

five seconds, scramble, quick, rapid Q and

a, uh, try to keep it under five seconds.

872

Otherwise we will air

horn you off the stage.

873

Um, some business, some fun,

uh, personal, not too personal.

874

Uh, Mike, why don't you lead us off with

Diana, uh, and then I'll get to Angela.

875

Yep.

876

Mike Gruen: Sounds great.

877

And I apologize ahead of time because

I thought of most of these questions

878

ahead of time, and we spent a lot of

time talking about them, but hopefully

879

we'll be able to get a little concise.

880

All right.

881

Uh, Diana, uh, describe

the culture at S, uh, CSP.

882

Diana: Innovative.

883

Mike Gruen: Nice.

884

Uh, any types of roles or people

that you're looking to hire there?

885

Diana: Yes.

886

Come to our website.

887

Mike Gruen: Uh, what's an important

skill you look for in a new hire?

888

Diana: Planful competence.

889

You can see it on a resume.

890

Mike Gruen: There you go.

891

Oh, yes.

892

Right.

893

Um, have you ever seen it on a resume?

894

Diana: Not that word, but you can

see what they follow through on

895

Mike Gruen: what

896

Diana: they were able to

897

Mike Gruen: execute.

898

Um, what's the best advice

you've ever been given?

899

Diana: Sounds terrible.

900

No one's going to look

out for you, but you.

901

Mike Gruen: That's a good one.

902

I like that.

903

Um, and then, uh, touched on it

a little bit throughout the pod,

904

but, uh, What advice would you

give to my high school student?

905

Diana: Uh, do what gives you

passion, back to Angela's point.

906

Mike Gruen: Nice.

907

Um, what's something you did as

a kid that you still enjoy doing?

908

Diana: Eating cookies.

909

Mike Gruen: What's something you

enjoy doing but are really bad at?

910

Diana: Everything

911

playing the clarinet.

912

I'm not very good,

913

Mike Gruen: but you enjoy it.

914

That's great.

915

Um, all right.

916

My personal favorite.

917

What's your, uh, what's the

largest land animal you think

918

you could take in a street fight?

919

A

920

Diana: fuzzy dog.

921

Mike Gruen: I'm assuming

a small fuzzy dog.

922

Small fuzzy dog.

923

What's a charity or corporate

philanthropy that's near and dear to you?

924

Diana: Uh, St.

925

Jude's Hospital.

926

Mike Gruen: Nice.

927

Um, if you could, uh, live

in any fictional universe,

928

which one would you choose?

929

Diana: I wanna defer.

930

Come back to me on

931

Mike Gruen: that.

932

That's the last one.

933

We'll come back to you.

934

You can get

935

Tim Winkler: some time.

936

That's a deep one.

937

You keep thinking, Diana.

938

We're gonna come back to you.

939

Angela, are you ready?

940

Diana: Yeah, if it's on what

we just heard, let's go.

941

Tim Winkler: There's a

few, few overlaps there.

942

Um, explain why folks from

industry would want to come to

943

Diana: CDAO.

944

Mission based work.

945

You can make a big impact.

946

Tim Winkler: How would you

describe the culture at CDAO?

947

Diana: Ooh, it's complex.

948

Uh, it is diverse and, uh,

you know, we're rearing to go.

949

It's a new PSA.

950

Thank you.

951

Tim Winkler: What kind of technologists

thrives in that environment?

952

Diana: We have a lot of work

to do, so I think the planful

953

confidence would be very important.

954

Mike Gruen: I think we

have an episode title.

955

I think you're right.

956

I think you're right.

957

Tim Winkler: Um,

958

Diana: sociology, academic journal.

959

Tim Winkler: What kind of,

uh, kind of tech roles are

960

you hiring for at the moment?

961

Diana: Ooh, there's a lot.

962

I would encourage folks to go to AI.

963

mil to find out what we're hiring

for exactly, but we've got lots of

964

AI and actual technical positions

that are being hired for, including

965

product management and others.

966

Tim Winkler: What would you say

is the biggest challenge facing

967

968

Diana: Getting in its own way.

969

Tim Winkler: Nice.

970

Uh, describe your morning routine.

971

Diana: I get up and I check my phone for

what my calendar is going to be for the

972

day, and then I head out for a delicious

cup of coffee from my coffee shop.

973

Tim Winkler: What's your

favorite app on your phone?

974

Diana: Oh, favorite app on my phone?

975

Probably, uh, that's a good question.

976

I might get air horned for this one.

977

Uh, we'll have to come back to it.

978

Tim Winkler: Come back to it.

979

Uh, what's a charity or corporate

philanthropy that's near and dear to you?

980

Diana: Um, I'm actually big on, uh,

human rights and anything that has to

981

do with preventing human trafficking.

982

Thank you.

983

Tim Winkler: If you could have

dinner with any celebrity past

984

or present, who would it be with?

985

Diana: Ooh, Maya Angelou.

986

Tim Winkler: What was the first, oh

sorry, what was the worst fashion

987

trend that you've ever followed?

988

Diana: Oh, probably, you know, back

in the day when we had those great

989

leotards that you had to, that you had

to exercise in that all the comedians

990

now have created like really great.

991

Uh, spooks on movement.

992

That stuff is good.

993

Tim Winkler: Yeah.

994

Those are fun.

995

Little commercials to look back on, right?

996

997

Diana: and it's really not a good

look for the majority of human beings.

998

Tim Winkler: Pays it out.

999

Uh, and the last question, what

was your dream job as a kid?

Diana: I actually wanted to

be a, um, uh, heart surgeon.

Whoa.

Whoa.

Tim Winkler: Whoa.

Deep.

That was deep.

That was deep.

Um, all right.

I, I do want to come back to Diana,

the fictional universe, the question.

Diana: Yeah.

Oh, and I wish I got the app question.

Um, so I, I actually, so the first, I'm

going to go with the first thing that

came into my mind and it's a movie called

Defending Your Life and it's from the 90s.

I remember that movie.

I thought that was a

really cool place to be.

Tim Winkler: Oh, I'm going

to have to Google that.

Um, and then Angela, we had to come

back to the question for you too.

Diana: It was the app question.

And I actually, you know what it is?

It's the New York Times games app where

Wordle, other crossword things are.

Tim Winkler: Yeah.

There you go.

And connections.

Yeah.

Diana: One.

It's a

Tim Winkler: part of my

morning routine is connections

Diana: and it's my night routine.

So

Tim Winkler: Diana.

All right.

Look, I know you're itching.

What's your, what's your favorite?

Oh my gosh.

So much.

So many questions now.

Um, all right.

That's, that's, that's, uh, that's a wrap.

I think you guys both nailed it.

Um, thank you for participating and

thank you for joining us on the podcast.

You've been great guests.

Uh, sharing your insights on

this evolution of tech talent

in the age of gen AI, uh, and

thanks for joining us on the pod.

Diana: Thank you, Neural.