Episode 193

full
Published on:

2nd Apr 2026

Half of New Grads Underemployed – Is the American Playbook Dead? (Full)

The old American playbook is breaking. Jerremy Alexander Newsome and Dave Conley interview Spencer Conley, a Big Four consulting manager, about why the traditional path of degree, hard work and stability no longer delivers. Half of new grads are underemployed while AI rapidly reprices desk jobs. Spencer chose a small liberal arts college to graduate nearly debt-free, then moved from presidential campaign and DOD contractor roles into consulting for variety and higher pay. Most peers carry heavy student debt that limits options. They cover AI deployments in documents, coding and customer experience, efficiency versus headcount questions, blockchain comparison, future workforce shifts, and advice on learning AI, staying flexible, investing and building people skills.

Timestamps:

  • (00:00) Spencer Interview II – Jerremy Alexander Newsome and Dave Conley with Spencer Conley on the old playbook
  • (01:27) Spencer's Unconventional Path – small liberal arts college and graduating nearly debt free
  • (04:36) Career vs. Being Inside the System – presidential campaign and DOD contracting experience
  • (07:48) Why Consulting? – move into AI, sustainability and economic development
  • (09:43) Skills vs. Degrees – what actually matters now
  • (11:40) The College Debt Trap – how student debt limits life choices for most peers
  • (17:22) AI and the Headcount Question – efficiency pitches and restructuring
  • (23:26) Blockchain vs. AI – Dave's take
  • (29:06) The Unemployment Fear – job replacement versus value creation
  • (29:46) How Younger Workers Are Different – rejecting butts in seats and using AI tools
  • (34:44) 25 Years From Now – forecasting AI's workforce impact
  • (38:51) US vs. Other Countries – workforce planning gaps
  • (40:52) One Thing Every Millennial Needs to Hear – learn AI and stay flexible
  • (42:33) Lightning Round – rapid insights
  • (48:37) The Biggest Lie the Laptop Class Tells Itself – desk job myths
  • (50:30) Closing & Takeaways – investing, avoiding debt and building connections

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Transcript
Jerremy:

Every generation in America inherited the exact same playbook.

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Go to school, get the credentials, do

the work, and the system will hold up.

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Its end of the deal.

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Millennials may be the last generation.

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That playbook actually caught.

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Now half of new grads are underemployed.

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AI is repricing desk jobs

faster than anyone predicted.

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And the trades everyone's selling.

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AI is repricing desk jobs

faster than anyone predicted.

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First year pay starts closer to

35,000 rather than six figures,

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and there's a lot of people who

cannot afford their cost of living.

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My name is Jerremy Alexander Newsom.

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Alex: My cohost is Dave Conley, and

this is Solving America's Problems.

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We have a super cool guest today related.

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Spencer Conley is a manager

and a consulting firm.

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Running leading work with artificial

intelligence, sustainability,

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and economic development.

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He did everything.

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The old system asked, guy, got the degree,

did the campaign trail, did the corporate

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climb the ladder, and it delivered?

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He recently married.

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He's in his middle of the career and

sitting inside one of the world's

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biggest consulting firms watching the

rules change right now in real time.

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So I'm really excited to just

dive into how you are seeing the

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landscape change in the workforce.

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Spencer, my guy.

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Thanks for being here,

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Jerremy: dude.

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great to be here.

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Thank you for having me.

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Yeah, man, it's gonna be so cool.

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So dude, you did everything right that

the, old American Playbook said to do.

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Grew up in a good school district,

got the four year degree, worked your

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way into professional class employment

by the conventional scorecard.

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It worked.

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Great.

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So the question I guess would be, did you

get the deal that you were promised or do

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you feel like you got something different?

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Yeah.

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You know, I think that's a,

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that's a really interesting

and really good question.

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I think I did get the deal that

I was promised, but I think

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I went through it in a very.

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Non-conventional way that helped

me kind of get to where I am now.

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whereas I'm seeing

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of my peers kind of follow the

same formulaic approach and

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maybe not get to the same place.

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so I'd say overall, yes,

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but there's some minute differences

that I think I took in my

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personal and professional life.

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and even

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the college I chose that, that really

helped kinda lay that foundation.

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Well, I mean, right outta college.

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Oh, go ahead Dave.

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yeah.

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So like, what, what do you think

those were like, or what, what,

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what do you think sets you apart?

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Like, I think some people could look at

this and be like, Hey, it was just luck.

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And I, you know, like, I saw you coming

through this and it wasn't that like,

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there was a lot of work and there was a

lot of diligence and like, do you think

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your path was more driven or, you know,

could somebody repeat what you did or,

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Dave: or are your friends

getting a raw deal?

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Like what's, what's your vibe on that?

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Spencer Conley: Yeah.

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Another good question.

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Honestly, I think,

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I think it was a couple of key

decisions starting with like,

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honestly, the college I went to.

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I was, you know, grew up in

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Northern Virginia, the Fairfax

area, everybody was going to

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these large state schools.

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There's a big push to go

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to U-V-A-J-M-U, Virginia Tech

because they're great institutions

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and they're publicly available

right to, to the students

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in Virginia.

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I,

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I was fully going to fall into the

trap of like, let's go to a large

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school, have no idea what I want to do.

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of get caught in those generalist

credits and like probably bounce

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around for five or six years

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' at a large institution.

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But instead, I went to a really

small liberal arts school where I

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got a really good financial deal

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sports, that allowed me to get

through college pretty much debt

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free, with, with a more focused degree

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really I don't use right now,

but did help me think critically

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and connected me with a really

good professional network.

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Jerremy: I like it.

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Yeah.

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Well,

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I

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mean, maybe a little bit of a

deeper potential question as well.

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right?

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outta college, right?

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If I remember this correctly, you worked.

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A presidential campaign,

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and then you had a professional

role as a government contractor.

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Now here's what's crazy.

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So you did some of the pentagon's

analytical work without

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actually being in the Pentagon.

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However, you didn't have any

civil service protections, no

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federal pension, no institutional

commitment running in your direction,

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and now you are in one of the

big four, which is awesome,

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but still that external brain

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still

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one layer removed.

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When did you realize or feel, or maybe

even notice that having the career

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and

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being inside the system

were two different things?

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Spencer Conley: Having the career

versus being inside the system.

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You know, I think, honestly I got

connected with, you know, the, the

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presidential campaign was kind of a fluke.

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I actually applied to an internship,

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thought that internship was in DC

and turns out it was in New York.

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So, kind of had to go

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Jerremy: Whoops.

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Spencer Conley: Yeah.

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it was a, it was a big whoops moment.

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I, I actually.

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Like was couch surfing on Craigslist,

which is, you know, a story in itself.

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But, really I jumped in to, it was

DOD consulting and honestly, the

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re the way I got in there was truly

just growing up in the DC area.

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I had some connections through

friends that were working in.

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That world and got connected with a small,

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a small business actually that

was doing consulting work for

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the Marine Corps at the time.

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They're like, Hey, you

should talk to these people.

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Interviewed and eventually got the job

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it, it, was really cool insight into

how government works into how private

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industry works with government.

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But truly at the end of the day,

one of the reasons I left that role

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and pivoted into, you know,

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big Four consulting was, I

mean, government employees were

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falling asleep at their desks.

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It was, I was

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years old and

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we were waking up, you know,

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Joe Schmo for a meeting that

he was actively presenting on.

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And I was like, this, this can't be

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life.

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Even though the impact that we were

driving on Paper wa was real, right?

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It didn't, it didn't really

feel real at the time.

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Dave: So, what I'm hearing

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is the folks that were in the

system, at least in your experience,

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I, I had a slightly different

government experience, but.

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Like, even though you were outside

the system and didn't have all

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those benefits, you didn't have

like the government pension, you

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didn't have the, the, you know, the

stuff that, you, the protections.

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I mean, you are a contractor, Right.

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And you were, you were driving

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really big things that it sounds like

the government employees were just

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like, you know, asleep at the wheel.

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So you had all the responsibilities,

none of the protections and.

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So how did that work for you?

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I mean, did you, that even occur

to you at the beginning or was, I

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mean, are you looking back on this

now being like, oh, that is a scam.

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Like, like how,

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how do you, how do you feel about that,

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Spencer Conley: It's, it's actually,

I've thought about this a lot.

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I, I,

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me didn't, didn't even

think about it, did not care

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about the pension or things

like that because, you know,

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my understanding at the time was

like, yeah, you can become a federal

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employee or government employee

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work for 20 or 30 years.

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You know, you'll get paid,

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you know, at the end

of your 20 year career.

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What I would get paid five years from

now is, you know, 22-year-old me.

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only benefit you have is job

stability, which I think is going

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away in the federal space to a degree.

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and

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you have job stability in a pension.

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But at the end of the day,

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if you're, you know,

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making two to three x what

your clients are making

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after 30 years in their career,

but being 10 years in your career,

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the pension really isn't a,

isn't a factor in, you know,

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22-year-old me's brain, right?

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Jerremy: Yep.

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Yeah.

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Thinking that far ahead

sometimes too, right?

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I mean,

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a lot of people love the pension.

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Love the 4 0 1 Ks.

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How'd you decide on

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consulting?

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Meaning like, how'd you choose that as a

word or a phrase, or like a career path?

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What stood out to you about it?

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Spencer Conley: Yeah, so I mean,

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again, I kind of fell into it,

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it stood out to me because

I came out of college again.

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A generalist.

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I, I got a political science degree,

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tried politics, but it was,

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it really wasn't for me

at the end of the day.

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But the, the draw to consulting, the more

I did the research and talked to folks

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in consulting was, you know, you're,

you're solving different problems

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every day from a different angle

depending on who you're working with.

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So it's not like, you know,

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working in as a GS employee and

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Working in a finance shop for a large

agency, you're gonna be doing that

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type of work for the rest of your life

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if you stick in that.

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But consulting

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has afforded me

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the opportunity to work on

so many different problems,

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expertise in so many different fields

that I would never have had exposure to.

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And I used it as really like

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Admittedly a high paying way

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to experience almost every industry,

which, which I've been able to do and

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was actually the reason I moved from

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a

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smaller kind of mom and shop consulting

group to, to a larger big four firm

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because you can kind of create

your own destiny and try,

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every different industry,

which, which I've done.

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Jerremy: Yeah,

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which I love.

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there's a lot of,

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I

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mean, when we say like

a generalist degree,

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I like to remind people there's

that very popular phrase, a jack

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of all trades, master of none.

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But

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oftentimes better than a master of one.

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Dave: Mm.

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Jerremy: Like that's the end.

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Yeah, that's the end of that phrase.

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So sometimes having all the skills,

doing all the things, knowing all the

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people, having all the connections

and experiencing something,

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you really develop an underlying

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confidence

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or courage to

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go try other things, which

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is very interesting because.

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At

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least I've heard this

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statistic thrown around pretty

often that 85% of employers

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now say they prioritize

skills over degrees.

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I

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think it was a Harvard study

who looked at actual hiring data

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and found fewer than one in 700 hires

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were actually affected by companies

dropping degree requirements.

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And when a hiring manager is

looking at a stack of resumes,

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you know what?

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Actually happens to the one without

a bachelor's degree kind of thing.

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Right.

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So

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do you, do you happen to notice or

feel or even see that that might

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be shifting at all in the, in the

landscape of hiring or consulting firms?

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Spencer Conley: Oh, I mean, absolutely.

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not in the sense of consulting firms.

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I mean, we, we operate.

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A lot of consulting firms operate

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with specific requirements,

especially when selling to,

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to clients.

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Right.

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And a lot of clients, I still think,

you know, in the federal space,

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state and local space or commercial

space, are still operating with,

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you know, they need to grade

resumes on paper and we hire to

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put our best foot forward.

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And oftentimes that includes

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a degree,

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a higher degree than just a, you

know, a general bachelor's degree.

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Or in very specific cases, if

it's like, you know, say we're

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working on space in satellites, you know,

if for some reason a person has gone in,

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a

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degree but has 20, 30 years of industry

experience, that is a case where we would

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kind of shift to, to

hiring that individual.

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I would say

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generally though, with some of

my peers and some of my friends,

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they don't have degrees

and they're getting,

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you know, very specific skills.

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They're learning,

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AI skills or, or music in ai, for example.

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And they're getting degrees out

in high powered, high paying sales

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positions for tech companies.

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where

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previously

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weren't at all,

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you know, they were working in service

industries or things like that.

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Dave: Wow, So,

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Talk about that, just digging

into that a little bit more.

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So like

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there's kind of a structural failure

of getting $180,000 degree and

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being up to your eyeballs and debt.

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You know, like what,

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when you're in this like skip

college, learn a trade crowd,

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what's, what's absolutely right

about that and what's the.

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You know, like I went to a, you

know, an I went to Oberlin and you

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know, like it worked out for me.

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Like, what, what, what do people

need to know about that balance?

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Now

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I

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Spencer Conley: So,

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you know, I was just just

talking to my wife about this.

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'cause we, we talk about this often.

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It's,

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I would say for the, the generalist that

isn't very clear going into college or,

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uh.

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doesn't have a clear passion.

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I think this is really hard for

an 18-year-old to think through,

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but if you're going in and you're

just gonna take some general courses,

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a lot of my friends, I would say

the vast majority of my friends,

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and were, you know, 31, 32,

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go in with an idea of what they

wanted to do or a specific passion.

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They went to a large school set.

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They got buried in gen ed courses.

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They flipped around to different

degrees and ended up spending

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five or six years in an institution

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getting up to their eyeballs in debt

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and not using their degree.

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Right.

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I think Oberlin was helpful.

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It was a liberal arts universe or

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liberal arts college,

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but it did think you, it, it taught

you how to think critically about

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problems regardless of the subject.

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You know, in any course that I took,

whether it was a throwaway physics class.

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music course or one of my core political

science classes, it really gave me that

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skill, which I've been able to use to

become kind of a jack of all trades

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in consulting, which is very helpful.

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But we have family members that

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went to, went to college,

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on scholarships,

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even sports scholarships,

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and just did not,

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didn't fit into the rigor of school, and

then ended up going to a machining trade

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school or to different trade schools,

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and that really worked for them.

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And now they're, they're.

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More career directed and making more

money than most of my peers that went to,

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you know,

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institutions.

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Jerremy: Yeah.

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Well, I mean, how many of your

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circle of peers would you say, 'cause

I heard you mention earlier that you

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were able to get outta college without

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debt.

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What's a percentage of your

peers that you say are still

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years and years later in college debt.

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Spencer Conley: 90%,

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Jerremy: Wow.

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Spencer Conley: And some in

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Dave: Wow.

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Spencer Conley: some in crippling, like

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figure debt still.

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Jerremy: dude.

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Spencer Conley: Yeah.

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It's, it's brutal.

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You know, and you know, a lot of them,

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even a couple from Oberlin, they,

they're like forced to go back to

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the areas where they grew up, right?

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Because they have to live at home

for a while and then that's where

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their job and career kind of expands.

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But they're usually in small,

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small, rural communities, so

they, they can't get out of them.

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They're almost like trapped

in these, these areas, right?

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Because of that debt.

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Dave: Yeah.

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sorry.

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I'm sweating a little bit on that one.

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You know, it's like, holy crap.

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I mean, that's, that also comes up.

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And like in the research

on this, which is like it,

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first step seems to be the

most critical one, right?

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if you, if you don't get off

the mark, like right away,

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no no matter what, if you're not off

that mark, then it cascades not just, you

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know, today and tomorrow and this decade,

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but then, you know, like you end up

making weirder choices as you go along.

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Like you delay having kids,

you delay getting married, you,

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you know, like, like it, it

really leads to a different.

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Outcome in life.

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And I, and one of the, one of

the themes of this series is,

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know, like we were sold a bit of a,

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a, a dream of like, if you just

do this, you're gonna be fine.

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You know, like, and I think you

are among that last generation.

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I was a part of that,

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

do you think that that was ever actually

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true or do you think that that was just

something that society just sold us?

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Spencer Conley: I, you know, I don't know.

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I think, I think it might've been

true to a degree, I mean, back

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when houses were 70 5K, right?

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but also it was easier to start a

business and, and grow a business

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without having to expand to

like, you know, a global market.

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You could do it regionally.

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Like, I mean,

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my grandfather on my mom's side started

a small business without a degree.

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but, you know, through his military

service and it, it cascaded into something

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really successful and, and great.

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Now, I, it just doesn't,

it just doesn't happen.

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You know, it, it's, it's really hard

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to come up with like a business idea.

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If you don't go to college, that

can cascade if you don't target

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like a really global audience

or at least national audience.

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And

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in college, it's like if you go to

college, unless you get a full scholarship

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or, something similar to that,

you're, you're gonna be struggling

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with, with a lot of debt.

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And

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with of my friends

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that are in that kind of debt, I,

I would say they haven't delayed

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marriage necessarily, but all

of those other big milestones.

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They have to put on hold or

really, really think about

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when they're gonna do that because they

have to manage, you know, basically

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a mortgage through student loans.

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Dave: Oof

407

:

Jerremy: Yeah.

408

:

Dave: that.

409

:

that.

410

:

is tough.

411

:

Jerremy: Well, it's, it's really,

really easy for me to get stuck in this.

412

:

Like, Hey, let's talk about student debt

413

:

and is it worth it?

414

:

College, you know, we've, we've

had that conversation before, but

415

:

to just slightly pivot so that

I don't fall dramatically into

416

:

my.

417

:

Heroic rabbit hole of college.

418

:

in your work, Spencer,

when you're consulting and

419

:

connecting companies with ai,

420

:

when a client deploys those

tools into their workforce,

421

:

Alex: what happens to the headcount

422

:

Jerremy: that

423

:

nobody is saying out loud or

maybe everyone's aware of and

424

:

still isn't saying it out loud?

425

:

Spencer Conley: Yeah, you know,

426

:

the context of of my job and also

just talking with my peers, you know,

427

:

I would say

428

:

off the books there, there's definitely

a consideration of headcount, like,

429

:

how can we reduce hours or maybe,

430

:

streamline our temp workforce

or maybe get rid of It

431

:

it

432

:

definitely happens.

433

:

I mean, when you're thinking about

implementing ai, you're thinking

434

:

about people, process and technology

and you know, part of people

435

:

is how many people do you have?

436

:

Jerremy: What are they doing?

437

:

Spencer Conley: can they be

repurposed to either train the AI

438

:

or, or move to a different, more,

439

:

call it

440

:

more impactful area of work where they can

441

:

take,

442

:

I

443

:

Dave: is, that is the

biggest consultant world.

444

:

Jerremy: Yeah.

445

:

Spencer Conley: Right.

446

:

Dave: I can tell you're a consultant.

447

:

It's like,

448

:

oh, you know, we'll, we'll see

if we can restructure this.

449

:

Spencer Conley: Well, I mean,

450

:

that's just it.

451

:

It's like you sell this ai right?

452

:

You know, you implement an ai and it's,

453

:

it's, the value pitch is often, hey, you

know, this will allow your staff to do,

454

:

to focus on more important things.

455

:

But like oftentimes what's

getting automated is

456

:

like that staff person's entire job.

457

:

So like, what else do they have to

458

:

Jerremy: Yeah.

459

:

Spencer Conley: then that's

when headcount comes in, right?

460

:

Jerremy: Yeah.

461

:

Spencer Conley: But

462

:

I would say at large research

firms and a, a couple other of

463

:

industries where I have friends working,

they have actually lost their jobs to ai,

464

:

Dave: Wow.

465

:

Spencer Conley: go through restructuring.

466

:

I'm putting that in air

quotes, but you can't see

467

:

Jerremy: Yeah.

468

:

Yeah, yeah, yeah.

469

:

Spencer, are you able to tell us what

470

:

some of these, either specific

names of the tools are or

471

:

like

472

:

what sector

473

:

they're in?

474

:

I mean, 'cause you say ai, like it could

be software, it could be coding, it

475

:

could be customer service, it could be,

476

:

how much details can you give us on that?

477

:

Spencer Conley: you know,

some, some broad details.

478

:

I would say it's, it's a full,

full suite of, of services.

479

:

Again, you know, this is

480

:

consulting lingo.

481

:

say it's,

482

:

There is a customer experience

aspect, so it's how do you

483

:

leverage AI to make, you know,

484

:

literally

485

:

walking into a business instead of

486

:

talking to a person or a bunch

of people and waiting in line.

487

:

It's an automated kiosk with literally

an AI screen, you know, kind of like a,

488

:

any sci-fi movie that?

489

:

Jerremy: Yeah.

490

:

Or Japan Just got back from there, dude.

491

:

Oh my gosh.

492

:

Yeah.

493

:

Tons of, tons of them.

494

:

Like the, the,

495

:

essentially

496

:

it was an ATM.

497

:

I would do all the work and then I would

498

:

go to my table and then

someone would bring my food.

499

:

Dave: Huh.

500

:

Spencer Conley: Yeah, well like that for,

for every industry, not just service,

501

:

Jerremy: Mm.

502

:

Spencer Conley: it's, it's, you know,

multi-cloud and distributed cloud.

503

:

So connecting different

cloud instances where.

504

:

You know, a client might have three

different versions of a cloud, from any

505

:

software provider, you know, pick one

506

:

the s and p 500.

507

:

But it's connecting all of those

508

:

to be able to inject ai, connect every

509

:

single piece of data in an organization,

510

:

and make decisions and,

and pull it all together.

511

:

But it's

512

:

Jerremy: Yeah.

513

:

Spencer Conley: for coding, right?

514

:

Like you give it a

515

:

Jerremy: Yeah.

516

:

Spencer Conley: and instead of a

developer doing it, it'll give you a

517

:

90% solution in like, you know,

518

:

Jerremy: Seconds, minutes.

519

:

Yeah.

520

:

Dave: Is,

521

:

there like one,

522

:

so like I think of AI like

this, you can either do.

523

:

you know, you can keep the same number

of people and you can do a lot more work,

524

:

or you can completely replace a function.

525

:

Like, I, I called my dentist the

other day and I just talked to an

526

:

AI and, and scheduled everything.

527

:

And actually it worked

really, really well.

528

:

You know, like I was, I was shocked.

529

:

like, like, you know, actually removing

a, a, a job and then it's like, okay,

530

:

we are not gonna do more.

531

:

We're going to,

532

:

you know, like we're going to actually

re, you know, quote unquote restructure

533

:

and, and, do and, and,

and shrink head count.

534

:

So when, when the different

implementations come in, do you see

535

:

like one thing that, that companies

are looking to do, or is it just like,

536

:

it could be anything and, and they see

where it lands you, you know what I mean?

537

:

Is it,

538

:

is it, adding more value,

like growing the pie?

539

:

Is it, shrinking the,

the, the, the top line?

540

:

or is it growing the bottom line?

541

:

I.

542

:

Spencer Conley: Yeah,

543

:

Jerremy: Hmm.

544

:

Spencer Conley: that's really good

545

:

question.

546

:

I would say it's, it's

a little bit of both.

547

:

I would say

548

:

trend that, that I'm seeing is.

549

:

I think a lot of clients

are interested in ai,

550

:

they're still a little bit hesitant.

551

:

So we,

552

:

we get in there and, and what we typically

pitch, or what I like to pitch is like,

553

:

you know, quick wins first, right?

554

:

So It's, thing, it's

555

:

not adding new value.

556

:

It's, it's really like, you

know, document automation.

557

:

So if like you have paper

processes, which still happen a

558

:

lot, or very manual, like PDFs,

559

:

it's reading those, putting those into

a system that you can layer AI and, and.

560

:

Literally route all of that

paperwork and analyze it for

561

:

accuracy and compliance, immediately.

562

:

Whereas that would take multiple people.

563

:

That's usually kind of

like the foot in the door.

564

:

you start talking about, you know,

what, what the art of the possible is.

565

:

That's where you start to get

to like the value drivers.

566

:

It's like, Hey, we would really like to do

567

:

really cool initiative that

is very exploratory for us.

568

:

You know, us being the client.

569

:

let's start thinking about that

now that we have some breathing

570

:

room with this document processing,

saving us a bunch of time.

571

:

So, honestly, like clients are

trying to catch up right now, and

572

:

once they've caught up with AI,

they start thinking bigger picture

573

:

Jerremy: Yeah.

574

:

Dave: internally yourself

are, are like, are you,

575

:

you

576

:

know, are,

577

:

are,

578

:

you taking your own medicine?

579

:

Spencer Conley: all the time.

580

:

It is

581

:

Dave: Yeah.

582

:

Spencer Conley: my job

583

:

so much.

584

:

I, I can't even

585

:

save me so much time.

586

:

Jerremy: Hmm,

587

:

man, that's so cool.

588

:

that is, is cool and scary and terrifying.

589

:

Dave, I'm gonna ask you a question.

590

:

Give me your,

591

:

the same level of,

592

:

deep dive that you gave me

on blockchain and crypto

593

:

on ai.

594

:

Like

595

:

what is the, so everyone in the world was

like, blockchain, blockchain, blockchain.

596

:

Dave: Yeah, it was

597

:

Jerremy: And Dave Conley

was like blockchain.

598

:

Fucking sucks.

599

:

It's awful.

600

:

It's the worst.

601

:

What does it actually do?

602

:

Does it do anything that

anyone wants it to do?

603

:

And it's not gonna work

the way anyone wants it to.

604

:

And you're one of the very few

people that's like poo-pooing crypto.

605

:

w which, which, I mean, you're,

you're not wrong really on like

606

:

Dave: hand, I also spent a year

looking at it because, you know,

607

:

Jerremy: Yeah.

608

:

Dave: crypto was sort of born,

609

:

you know, as I was,

610

:

as I was in a very

611

:

Jerremy: As you were born.

612

:

Dave: back in my day,

613

:

back in my day.

614

:

So I was a very senior technology level.

615

:

And so blockchain was really kind

of coming out, you know, Bitcoin was

616

:

there and we were, we were looking at

it in a bunch of different ways and

617

:

you know, like the one thing that

we, we liked was the aspect of,

618

:

you know, the permanent record.

619

:

And

620

:

it didn't replace anything.

621

:

It was, it was, it was a fancy

spreadsheet in the, in the cloud, right.

622

:

And so, like all of the

systems that we already had

623

:

already did what it did,

and it didn't add any value.

624

:

So nobody was gonna do

it because it was slow.

625

:

You know, it's like you could be

doing it with Oracle databases and

626

:

it was like, nobody's gonna replace

Oracle databases with, you know,

627

:

your, your, your, your Bitcoins,

628

:

Jerremy: Mm-hmm.

629

:

Dave: and.

630

:

You know, like we were looking at

it as, as also as adoption, right?

631

:

So like,

632

:

you know, when a technology is

important, when it's adopted,

633

:

so, you know, like

634

:

nobody had, like, we all had cell

phones for years before the iPhone

635

:

came out, and then the iPhone comes

out and like everybody has one within

636

:

a year and it just changed everything.

637

:

You know, like we're still trying to

figure out what's going on with blockchain

638

:

and it's been 15 years and it's like,

eh, you know, like if, if it's not there

639

:

now, it's not gonna magically get there.

640

:

Jerremy: Yeah,

641

:

Dave: with ai,

642

:

Jerremy: I.

643

:

Dave: am, I.

644

:

am on the way other side of this.

645

:

It is, this is a technology that

we don't, we don't understand.

646

:

Like the people who are in it, you

know, like that are in edge cases,

647

:

they don't understand it.

648

:

It is a technology.

649

:

We are growing.

650

:

we

651

:

Jerremy: Mm-hmm.

652

:

Dave: it.

653

:

It is,

654

:

it also is also a prob

655

:

probabilistic system.

656

:

Meaning it like, it, it's, it's

constantly guessing and it's always

657

:

bringing you sort of new things to do.

658

:

It is, it is not deterministic.

659

:

So it, it's, you know, like if you

tell it to do one thing and do it

660

:

this way, you know, like it might come

up with a different way of doing it.

661

:

so it's.

662

:

It is

663

:

a chaos agent that we simply do

not know where it's going to do.

664

:

Because

665

:

when the internet came around

and the technology really hit,

666

:

you know, like, it was very clear

that this was going to disrupt

667

:

music, disrupt, disrupt magazines,

di disrupt, you know, media.

668

:

And then it was like, okay, yeah,

Netflix was going to be a thing.

669

:

You know, like, and, and then it

was, then it was, you know, content

670

:

and then it was social media, like

it was so clear what the path was

671

:

for ai.

672

:

Man, it is, it is, the unknown

is probably the scariest part of

673

:

this, and the weirdest part is that

we are inserting this technology.

674

:

Treat it as a, an, as an

entity, as a, as a child

675

:

with superhuman skills.

676

:

And we are wiring it in as the central

nervous system to our most critical

677

:

mission critical, systems on the planet.

678

:

Like

679

:

AI is all over the government.

680

:

And do you really want something

that you don't understand?

681

:

Being all over the place inside of your

government like that is terrifying to me.

682

:

And not just in like a.

683

:

you know, like, like tinfoil hat way.

684

:

It's, it's that.

685

:

You, you understand?

686

:

Like a Windows operating system and

be like, okay, that's, that's Bill

687

:

Gates, and that's broken, right?

688

:

Like, we get it.

689

:

And then these, these systems get

very complex and, and also we get it.

690

:

is, we have it, it is unbounded,

uncontrollable, and it keeps on

691

:

going and it changes every day.

692

:

We are, we are not wired for that.

693

:

We do not know the, consequences of it.

694

:

And when you don't know the

consequences of something, then

695

:

you are not, you are not

in any control of it.

696

:

And then, then you start like layering in,

697

:

oh, we're, you know, we're gonna

start putting things that like,

698

:

are life and death to people.

699

:

On top of that,

700

:

nah, this is, there are more

moral and ethical questions that

701

:

go into this than anything else.

702

:

And, and, and nobody is saying,

Hey, time out here, because

703

:

if we do

704

:

means that somebody else

is going to do it first.

705

:

And

706

:

Jerremy: Hmm.

707

:

Dave: like the bomb is already gone off.

708

:

The genie is way out of the bottle.

709

:

And so like, if we don't keep feeding

the beast, we're equally screwed.

710

:

So like I, it is.

711

:

It is a Faustian bargain man, And

712

:

we we're gonna have a whole

series on ai and it is,

713

:

Jerremy: Yeah.

714

:

Well it.

715

:

Dave: we can only react to it.

716

:

we,

717

:

because

718

:

we don't have any choice, we're

gonna have to react to it.

719

:

Alright, that's my, that

720

:

there

721

:

Jerremy: It is a great take.

722

:

No, it's a great, it's

a, it's a great take.

723

:

It's a it, because I think

724

:

there, there's a, there's

a few people like,

725

:

alright guys, and

726

:

one

727

:

of them act, I mean, one of them

is Elon, where he is like, hold on,

728

:

hold on.

729

:

Don't,

730

:

don't

731

:

you think we're going a little fast.

732

:

So do you think we're going a

little fast down this rabbit hole?

733

:

what's your take on that, Spencer?

734

:

Like, just kind of to your

point, like what Dave just said,

735

:

where AI is going because

I mean, my, my general fear

736

:

also, and I'll lead you with

a very long question, probably

737

:

the general, the general fear is AI

becomes so good and it's so helpful

738

:

and it's so usable and it happens so

quickly that in three years we have

739

:

55% unemployment in the country.

740

:

No one needs jobs anymore.

741

:

And that's the fear that a lot

of people don't actually have.

742

:

So if that does happen, what do you

see occurring in workforce dispenser?

743

:

Spencer Conley: Yeah.

744

:

You know, that's,

745

:

I, I almost go back and

forth on it every day.

746

:

It's,

747

:

Jerremy: Yep.

748

:

Wow, dude.

749

:

What

750

:

bro?

751

:

Dave: So yet.

752

:

Jerremy: they're waiting tables, dude.

753

:

I, I got, I

754

:

would, I mean, I shouldn't say it's AI

waiting tables, but I, I went to three

755

:

restaurants where I got served by a robot.

756

:

Dude,

757

:

a robot brought my food to me.

758

:

I picked it up off the tray.

759

:

But

760

:

I mean,

761

:

other

762

:

than beer, it brought me

everything other than beer.

763

:

which I'm sure is probably some

law or something, but man, it's.

764

:

Dave: like the,

765

:

that's the.

766

:

Jerremy: Robots not 21.

767

:

Dave: back up a sec.

768

:

So, you know, like you're, you know, like

you're, you're, you're coming into like

769

:

the top of your, your, your mid career,

770

:

and you're.

771

:

Presumably like hiring younger, like the,

the young analysts that are coming in,

772

:

how are they different?

773

:

You know, like did they, did,

did they do different paths?

774

:

Did they, did they come to

you guys in different ways?

775

:

Are they, you know, like, are they

afraid or excited about different things?

776

:

Like how are they different

than where you were at that age?

777

:

Spencer Conley: Yeah, there

778

:

honestly very different,

in, in a good way.

779

:

So they,

780

:

think

781

:

I was probably one of the last waves of,

of people entering the workforce that was

782

:

kind of bound to that traditional contract

that we talked about at the intro.

783

:

I,

784

:

was always wearing a suit.

785

:

I was always trying to present, very

polished, coming in, literally just

786

:

sitting at my desk until 5:00 PM

even though my clients were falling

787

:

asleep just because I had to be there.

788

:

You know, I called it

a but in a seat, right?

789

:

I was very grounded in

that responsibility.

790

:

I think now people coming into

the workforce, you know, they,

791

:

they do get the technical skills.

792

:

They do still go to

college for the most part.

793

:

But they don't really put up with

that shit, to be honest with you.

794

:

You're not, they're, they're not

just gonna sit at a desk until

795

:

five if they have nothing to do.

796

:

like, where can I drive value?

797

:

They have all of these AI tools,

798

:

They're

799

:

all so connected to the internet.

800

:

They're gonna be the most efficient

they can, they're very open to

801

:

raising their hands to help with

additional work when they're done.

802

:

Dave: Ooh.

803

:

Spencer Conley: also like, Hey,

I'm taking PTO, and I don't have

804

:

anything to do and it's 3:00 PM

on a Thursday, I'm gonna leave.

805

:

And obviously they, they work

with us to clear it as junior

806

:

practitioners, but like,

807

:

they're very upfront about that, which,

like 21-year-old me would've been like,

808

:

mean, I'll go hide in the bathroom for

two hours, try to take a nap instead

809

:

of, you know, asking my boss to leave.

810

:

And there's nothing to do.

811

:

Jerremy: Hmm.

812

:

Dave: Hmm.

813

:

Jerremy: That's a good

814

:

point,

815

:

Spencer Conley: man.

816

:

So.

817

:

Jerremy: Yeah,

818

:

it's been really

819

:

Spencer Conley: good.

820

:

Jerremy: I,

821

:

think, I think the

822

:

the adding of the value.

823

:

Is

824

:

the

825

:

part that

826

:

jumped out to me the most because

827

:

that's what

828

:

we're gonna have to do

829

:

as,

830

:

as,

831

:

a nation going forward.

832

:

I mean, the, the workforce, the work

833

:

work is

834

:

gonna be changing so dramatically over

the next five years, so, so quickly

835

:

that

836

:

every person listening

837

:

has

838

:

to imagine that not only is is it going

to shift, it is already shifting and

839

:

is it is gonna happen even faster.

840

:

we have to come up with that question

on a daily, weekly basis, right?

841

:

How can I add more value to this

organization, to this company

842

:

to keep a form of employment?

843

:

you do work for yourself

844

:

or by

845

:

yourself or own your own business,

846

:

it's gonna even be even more imperative,

to continue to add that value.

847

:

What

848

:

do you see,

849

:

in the

850

:

workforce right now that

are the most insulated?

851

:

From

852

:

AI and AI tools, sweeping

and chains, in industry,

853

:

as

854

:

you know, faster as some of the

other ones are being slower.

855

:

Spencer Conley: so, so

more insulated from AI

856

:

in

857

:

terms of like, okay.

858

:

that's a really good question.

859

:

I mean,

860

:

Quite frankly, everything,

everything I've, I've seen from a

861

:

professional, like most, most of

the industries are being impactful.

862

:

I might, for my own thought exercise,

start from most impacted to least, I mean,

863

:

most impactful is I think, you know, the

the healthcare and the, the professional

864

:

research, like, like a Gartner for

example, is getting heavily impacted.

865

:

They used to hire, you know, NPAs and,

and folks like that to do very detailed

866

:

technical research and reporting.

867

:

And now it's all shifting to like AI for

some of these larger research companies.

868

:

obviously technical coding, even the

big tech companies, they're, they're

869

:

shifting to ai, where whereas I, would

say sales we're, we're seeing an influx

870

:

in, some, some sales positions coming.

871

:

So people being technically savvy,

maybe even former developers.

872

:

With a technical solution that might

not have previously had ai, but

873

:

now has an AI layer on top of it.

874

:

They're hiring those folks

to be their salespeople.

875

:

Like, Hey, I worked in, know, AutoCAD

or something like that as an engineer.

876

:

Now AI is doing a lot of

that work on top of cad.

877

:

but let's pull you in as a

salesperson to help sell that work.

878

:

I think is is kind of,

we're seeing a shift there.

879

:

but really the things that that

aren't getting impacted, I would say

880

:

sustainability isn't getting impacted a

lot of the sectors, or sorry, a lot of

881

:

the accelerators around sustainability,

like how we monitor climate impact, do.

882

:

geospatial analytics, all of that stuff.

883

:

you know, water, water analytics for

water quality are getting bolstered

884

:

by AI but the scientists and the

people behind it going out into the

885

:

field are not being impacted as much.

886

:

they're really just

getting a better tool belt.

887

:

Right.

888

:

those are the things that

stick out to me right now, but

889

:

it's, it's a great question.

890

:

Jerremy: I love it.

891

:

Let's

892

:

daydream for a second,

893

:

Spencer, using your

894

:

wide

895

:

array of skills and industries

that you worked with and

896

:

worked for and worked under.

897

:

What does 25 years from now look like,

898

:

I

899

:

mean, I get it.

900

:

I get it.

901

:

It's gonna be

902

:

giant

903

:

hypothetical all the way into the future.

904

:

It's probably too far even imagine,

905

:

but let's just do it for a bit.

906

:

Where do you see America

as a country going

907

:

as

908

:

it

909

:

relates to the, workforce

and to all the people using

910

:

adopting,

911

:

shifting into AI's already fully here.

912

:

What's the country look like?

913

:

Spencer Conley: Yeah.

914

:

You know, actually I read an article that

got me thinking actually this morning.

915

:

I think it's, it's gonna it's gonna

change a lot, you know, working in

916

:

corporate America and, And consulting.

917

:

I think my job's gonna change a lot, to

be honest with you it's gonna be more

918

:

people focused, you know, shaking hands,

going to conferences, things like that

919

:

Whereas where a lot of my work is now,

slide development, data analytics is all

920

:

gonna be done by my junior resource ai.

921

:

Right.

922

:

but I do think there's gonna be a

big shift in the younger generation,

923

:

the younger workforce going into

trade schools, going into, you know,

924

:

machining or specific engineering

trades, things where, you know, you

925

:

can drive out a van to go do something.

926

:

I think that is where a lot of folks are,

are thinking about pivoting to because

927

:

it feels secure, it's a specific skill.

928

:

I don't think, you know, we're gonna

have 25 years from now, know, an AI

929

:

engineer come in and fix, you know,

lighting in your house, which, which

930

:

is, it's a little bit morbid because

that's, you know, not necessarily

931

:

where you might want the, the brightest

minds to go of the younger generation.

932

:

But I think that's where a lot of you

know, highly financially motivated and

933

:

highly self-driven people from our younger

generation are gonna pivot towards.

934

:

where does the thought leadership go?

935

:

you know, outside of those spaces,

hopefully not to ai, but I don't really

936

:

see like, a necessarily like clear

path for that to happen unless you

937

:

get into academia or PhD in science

or, you know, healthcare space.

938

:

Jerremy: Yeah.

939

:

Which you're gonna,

940

:

I love how you said, you

know, most impacted to least

941

:

impacted, I mean, healthcare,

942

:

health

943

:

space, doctors, hospitals, right.

944

:

Imaging

945

:

vascular

946

:

becomes so much more efficient.

947

:

and.

948

:

Help,

949

:

help hopefully decrease a

lot of the costs as well.

950

:

'cause healthcare in the US is

951

:

too expensive.

952

:

so

953

:

there's gonna be a lot of

fun, a lot of fun changes, and

954

:

a lot of ones that are scary.

955

:

What

956

:

about you, Mr.

957

:

Dave Conley?

958

:

If you had to daydream,

959

:

Yeah.

960

:

Three, five years from now look like?

961

:

DC.

962

:

Dave: Oh man.

963

:

I think it's, it's,

964

:

we, we, can't predict

more than than five and.

965

:

For, for me, I, I, you know, I think like

we are at the inflection point right now,

966

:

and, and it's, it's, it's no kidding.

967

:

you know, like we're in a, a world of,

of sort of a, a lot of, I mean, there's

968

:

a lot of change and it's a lot of, a

lot of, a lot of shifting, you know,

969

:

shifting stuff under our feet, you know,

970

:

and I know that I, I feel like that the

younger folks are, are concerned about

971

:

like what their opportunities might be.

972

:

I don't know.

973

:

You tell me, Spence,

974

:

Like,

975

:

you know, like

976

:

what's the, you know, like

what's the long term of things?

977

:

and I think older workers are,

are scared of being boxed out.

978

:

You know, it's like you don't

wanna lose your job in your,

979

:

you know, forties and fifties.

980

:

so, you know, if there's an economic

downturn, it might happen anyways, but is

981

:

there gonna be a job on the other side?

982

:

you know, like what?

983

:

I mean, what I think about

Spence is like other,

984

:

other countries

985

:

like Denmark and Germany

and Singapore and Japan,

986

:

like they have this real

987

:

connection between

988

:

industry, so enterprise industry

and academia and government.

989

:

They all come together to be like, okay,

this is, these are the kind of things that

990

:

we need to teach and be, and, and shape,

991

:

you know, the entire workforce and.

992

:

Like they're, they're

really deliberate about it.

993

:

And in America it's like we

put everything on the worker.

994

:

I mean, you've seen how, you

know, companies make these

995

:

decisions from the inside.

996

:

Is, is it,

997

:

do we need to change something

in the United States and model

998

:

what other countries do or do

they need to be more like us?

999

:

Spencer Conley: Yeah, I I, I think

maybe a a healthy combination of both.

:

00:39:03,195 --> 00:39:07,065

I think, you know, the, the coming

together of higher education

:

00:39:07,065 --> 00:39:08,625

and government and industry

:

00:39:08,985 --> 00:39:09,555

on like, where do we

:

00:39:09,555 --> 00:39:10,305

really wanna drive

:

00:39:10,305 --> 00:39:11,865

this, given this like, incredible

:

00:39:11,865 --> 00:39:13,695

technology moving forward as a

:

00:39:13,695 --> 00:39:14,505

society is like

:

00:39:14,505 --> 00:39:18,865

a big picture, moment that, that

we just don't have right now.

:

00:39:18,925 --> 00:39:21,235

and I think it's leading

to, you know, a lot of

:

00:39:21,235 --> 00:39:23,275

competing interests and, you know, in.

:

00:39:23,860 --> 00:39:25,570

Right now in the economy,

it's, you know, how do

:

00:39:25,570 --> 00:39:26,800

we cut costs and how do we do

:

00:39:26,800 --> 00:39:28,330

things a little bit faster?

:

00:39:28,330 --> 00:39:31,870

So without that like unified vision and

how we're, we're teaching the younger

:

00:39:31,870 --> 00:39:35,470

generation on how to, how to execute

this vision, it's just gonna lead to

:

00:39:35,470 --> 00:39:39,520

more cutthroat mentality, which is

gonna lead to, you know, decreased,

:

00:39:39,730 --> 00:39:41,590

potentially decreased opportunities

:

00:39:41,680 --> 00:39:41,770

Jerremy: mm-hmm.

:

00:39:42,160 --> 00:39:43,150

Spencer Conley: In the workforce.

:

00:39:43,150 --> 00:39:43,660

But.

:

00:39:44,660 --> 00:39:45,110

Yeah.

:

00:39:45,110 --> 00:39:49,490

I mean that's, it's a really,

it's a really problem.

:

00:39:49,490 --> 00:39:52,040

I mean, now we're seeing

where, where I'm seeing a

:

00:39:52,040 --> 00:39:54,290

lot of folks like the

:

00:39:54,290 --> 00:39:55,310

college entering the

:

00:39:55,310 --> 00:39:57,290

workforce, come into

:

00:39:57,770 --> 00:40:01,220

provide value and, and leverage

AI is like, they, it, it's almost

:

00:40:01,220 --> 00:40:03,860

like, it's like a competition,

you know, it's like a shark tank.

:

00:40:03,860 --> 00:40:05,120

They just start playing around with AI

:

00:40:05,330 --> 00:40:06,860

come up with something

that they think is a

:

00:40:06,860 --> 00:40:08,990

a take market opportunity that they,

:

00:40:08,990 --> 00:40:10,940

can do better than

somebody else or that isn't

:

00:40:10,940 --> 00:40:11,510

being done.

:

00:40:12,510 --> 00:40:17,130

and come up with this cool coding

idea and try to use AI to, to scale

:

00:40:17,130 --> 00:40:23,580

it But, you know, it's either taking

another company's job or white space

:

00:40:23,580 --> 00:40:28,410

opportunities for a, a net new idea are

becoming fewer and far between, I think.

:

00:40:29,310 --> 00:40:30,150

Jerremy: Yeah.

:

00:40:30,210 --> 00:40:30,420

Yep.

:

00:40:30,420 --> 00:40:31,800

You, you really

:

00:40:32,800 --> 00:40:33,130

hinted

:

00:40:33,130 --> 00:40:33,760

towards this.

:

00:40:33,760 --> 00:40:35,640

I'll ask you more directly, before we

:

00:40:35,640 --> 00:40:36,210

go,

:

00:40:36,570 --> 00:40:36,870

before I

:

00:40:36,870 --> 00:40:37,050

go

:

00:40:37,050 --> 00:40:38,550

through some other quick questions.

:

00:40:38,820 --> 00:40:39,150

You

:

00:40:39,150 --> 00:40:41,880

are, you know, you and I

both, but we're really in that

:

00:40:42,880 --> 00:40:44,290

Cohort, let's call it,

:

00:40:44,590 --> 00:40:44,980

of

:

00:40:45,010 --> 00:40:45,790

what the series

:

00:40:45,790 --> 00:40:47,560

is about and, and who is really gonna

:

00:40:47,560 --> 00:40:48,310

be impacted

:

00:40:48,760 --> 00:40:49,030

knowing

:

00:40:49,030 --> 00:40:50,110

everything you know,

:

00:40:51,110 --> 00:40:51,260

the

:

00:40:51,260 --> 00:40:52,370

data, the clients.

:

00:40:52,370 --> 00:40:53,450

If you could grab

:

00:40:53,900 --> 00:40:54,230

every

:

00:40:54,230 --> 00:40:58,970

American your age by the shoulders

and tell them one thing about

:

00:40:58,970 --> 00:41:03,020

their working future that the

system is not preparing them for.

:

00:41:04,100 --> 00:41:04,610

Spencer, what

:

00:41:04,610 --> 00:41:05,030

are you saying

:

00:41:05,390 --> 00:41:05,510

to

:

00:41:05,510 --> 00:41:05,690

them?

:

00:41:06,050 --> 00:41:08,150

Spencer Conley: Oh,

that's a great question.

:

00:41:08,150 --> 00:41:11,810

I had a couple of conversations with, with

friends and peers recently about this.

:

00:41:11,810 --> 00:41:11,900

I

:

00:41:11,900 --> 00:41:17,540

would, I would shake them and

say, learn about ai, learn how to

:

00:41:17,540 --> 00:41:21,140

use it, learn what it can do, and

what some of the drawbacks are.

:

00:41:22,160 --> 00:41:26,060

also don't put all of

your eggs in that basket.

:

00:41:26,060 --> 00:41:27,620

So understand what it can do.

:

00:41:27,620 --> 00:41:30,470

Have all the information to

think critically about it, and

:

00:41:30,470 --> 00:41:33,260

don't just latch onto one single

idea or one single use case.

:

00:41:33,260 --> 00:41:34,190

'cause I think that just

:

00:41:34,610 --> 00:41:38,060

laser focuses people on like an idea.

:

00:41:38,060 --> 00:41:40,310

They think, oh, okay, well

now I have this great idea.

:

00:41:41,120 --> 00:41:45,390

I can use AI for this specific use case,

and come up with a company about it.

:

00:41:45,390 --> 00:41:46,560

But like, that's,

:

00:41:46,740 --> 00:41:48,840

I just think that's a little too laser

:

00:41:48,840 --> 00:41:49,530

focused.

:

00:41:50,160 --> 00:41:51,000

think big picture.

:

00:41:51,000 --> 00:41:55,440

Also, you know, think about what you truly

enjoy doing personally and even outside

:

00:41:55,440 --> 00:41:58,650

of work and see if there's, there's a

way that you can grow a career in that,

:

00:41:58,650 --> 00:42:00,480

that might not even be technology based.

:

00:42:00,480 --> 00:42:04,410

I think there's a, a little wake

up moment that I'm seeing a lot of

:

00:42:04,410 --> 00:42:07,110

my peers that have been working for

about 10 years kind of hit and they're

:

00:42:07,110 --> 00:42:09,750

like, I don't really like doing

what I've been doing for 10 years.

:

00:42:09,750 --> 00:42:12,210

I really like skiing.

:

00:42:12,210 --> 00:42:15,270

I'm gonna go out and, you know,

maybe work at Vail or Breckenridge

:

00:42:15,270 --> 00:42:16,110

or something like that.

:

00:42:16,110 --> 00:42:16,410

Right.

:

00:42:16,410 --> 00:42:16,890

So like.

:

00:42:17,685 --> 00:42:20,835

I think our generation was previously

shamed for thinking things like

:

00:42:20,835 --> 00:42:23,115

that, but I would keep an open mind.

:

00:42:24,105 --> 00:42:24,795

Jerremy: Yeah,

:

00:42:24,800 --> 00:42:25,170

totally.

:

00:42:26,170 --> 00:42:26,440

Totally,

:

00:42:26,440 --> 00:42:26,860

totally.

:

00:42:27,860 --> 00:42:27,920

All

:

00:42:27,920 --> 00:42:29,450

right, let's do a quick lightning round.

:

00:42:29,780 --> 00:42:29,900

You

:

00:42:29,900 --> 00:42:32,180

get an answer as long

or as fast as you want.

:

00:42:32,990 --> 00:42:33,170

Spencer Conley: Cool.

:

00:42:33,620 --> 00:42:34,040

Jerremy: Alright.

:

00:42:35,040 --> 00:42:36,000

Learn to code.

:

00:42:36,720 --> 00:42:38,700

s still good career advice in:

:

00:42:39,700 --> 00:42:41,390

Spencer Conley: Y this is a,

:

00:42:41,815 --> 00:42:42,385

I've thought about this.

:

00:42:42,385 --> 00:42:44,185

So I, I had to learn code on the fly.

:

00:42:44,185 --> 00:42:48,365

I would say yes because it helps you think

through problems, in a very unique way,

:

00:42:48,615 --> 00:42:50,355

that will help you even outside of coding.

:

00:42:50,595 --> 00:42:53,655

But no, if you think it's gonna

actually just get you a high paying job,

:

00:42:54,655 --> 00:42:54,685

Jerremy: I

:

00:42:54,685 --> 00:42:55,075

like it.

:

00:42:56,065 --> 00:42:56,575

Finish

:

00:42:56,575 --> 00:42:57,505

this sentence.

:

00:42:57,565 --> 00:43:01,105

The old American deal broke because of.

:

00:43:02,105 --> 00:43:03,395

Spencer Conley: the old American deal.

:

00:43:03,395 --> 00:43:05,735

Broke because of.

:

00:43:06,230 --> 00:43:09,830

this isn't the whole problem, but

probably, you know, just in the sense

:

00:43:09,830 --> 00:43:13,250

that you need to get a four year degree

and that four year degree can cost you

:

00:43:13,730 --> 00:43:18,710

$160,000, but that's your barrier to

entry to get the professional workforce,

:

00:43:18,950 --> 00:43:19,430

Jerremy: bro.

:

00:43:19,520 --> 00:43:20,270

Yes,

:

00:43:21,170 --> 00:43:21,710

yes.

:

00:43:21,770 --> 00:43:21,920

And

:

00:43:21,920 --> 00:43:22,250

also

:

00:43:22,250 --> 00:43:23,000

Spencer Conley: buying a house.

:

00:43:24,000 --> 00:43:25,800

Jerremy: Yeah, the, Old

American deal broke because

:

00:43:26,160 --> 00:43:26,370

it's

:

00:43:26,370 --> 00:43:28,620

built on an educational system that's

:

00:43:28,950 --> 00:43:33,360

been in place since the late 18

hundreds, and the entire country

:

00:43:33,360 --> 00:43:35,040

has changed so dramatically and so

:

00:43:35,460 --> 00:43:36,900

hugely, and that hasn't

:

00:43:37,680 --> 00:43:38,010

all.

:

00:43:38,465 --> 00:43:38,885

That's

:

00:43:38,885 --> 00:43:39,085

why,

:

00:43:39,625 --> 00:43:39,835

Dave: Well,

:

00:43:39,835 --> 00:43:40,315

then it was,

:

00:43:40,320 --> 00:43:40,650

Jerremy: right?

:

00:43:41,095 --> 00:43:42,745

Dave: well, and then it

was financed by debt.

:

00:43:42,780 --> 00:43:43,645

So, so it was like

:

00:43:43,645 --> 00:43:46,915

you, you take, take a system and then you

pour an enormous amount of money on top

:

00:43:46,915 --> 00:43:48,625

of it and like, well, what do you expect?

:

00:43:49,080 --> 00:43:49,380

Jerremy: Yeah.

:

00:43:50,380 --> 00:43:53,440

Spencer Conley: But it's also,

and I, you know, I keep coming

:

00:43:53,440 --> 00:43:55,690

back to my wife 'cause she, she

thinks about this stuff a lot too.

:

00:43:55,690 --> 00:43:59,560

But it's also, I think we were

all told you need to buy a

:

00:43:59,560 --> 00:44:00,640

house to become successful.

:

00:44:00,640 --> 00:44:02,650

Like, you gotta get married,

you gotta buy a house.

:

00:44:02,650 --> 00:44:02,980

Right.

:

00:44:03,980 --> 00:44:08,570

All of the jobs are in cities, cities,

houses are so expensive, outrageously

:

00:44:08,570 --> 00:44:13,490

expensive, so you gotta get the high

paying job, but the house is worth way

:

00:44:13,490 --> 00:44:15,230

or priced way more than it's worth.

:

00:44:15,230 --> 00:44:17,030

You feel like you need

to stretch to get that.

:

00:44:17,930 --> 00:44:19,700

but there isn't like a thought

of like, move out of the

:

00:44:19,700 --> 00:44:21,440

city, leverage remote work.

:

00:44:21,830 --> 00:44:25,610

and you can be very successful and save a

lot of money if, if you rent consistently.

:

00:44:25,610 --> 00:44:29,270

I think that's, impacting a lot of

my generation, at least right now.

:

00:44:29,870 --> 00:44:29,960

Jerremy: Mm-hmm.

:

00:44:30,960 --> 00:44:31,980

Yes.

:

00:44:33,000 --> 00:44:33,510

Yes.

:

00:44:33,540 --> 00:44:33,660

Is

:

00:44:33,660 --> 00:44:36,600

there a book that you read that

changed your mindset on that or gave

:

00:44:36,600 --> 00:44:38,430

you that shift or awakening Spencer?

:

00:44:39,430 --> 00:44:43,150

Spencer Conley: No, it was

really just peer peer learning.

:

00:44:43,260 --> 00:44:46,650

we bought a house, so like I

didn't fall into that, but we were

:

00:44:47,070 --> 00:44:51,420

really on the precipice of do we

just continue to rent and live in

:

00:44:51,420 --> 00:44:52,770

where we wanna live and save money?

:

00:44:53,250 --> 00:44:54,840

But we, we did end up buying a house.

:

00:44:54,840 --> 00:44:56,310

No, there, there wasn't a piece of

:

00:44:56,310 --> 00:44:57,900

literature that, that really mm-hmm.

:

00:44:58,140 --> 00:44:58,560

Influenced

:

00:44:58,560 --> 00:44:58,800

us.

:

00:44:58,805 --> 00:44:58,855

Mm-hmm.

:

00:44:58,935 --> 00:45:01,350

It was seeing friends to buy

:

00:45:01,350 --> 00:45:04,950

homes and then not be able to do

anything that they really enjoy and

:

00:45:04,950 --> 00:45:08,970

then continue to just work really

hard to be able to pay for said house.

:

00:45:09,390 --> 00:45:09,780

Jerremy: Yeah.

:

00:45:10,110 --> 00:45:10,440

Yeah.

:

00:45:11,440 --> 00:45:11,500

I

:

00:45:11,500 --> 00:45:12,250

mean, I see it too.

:

00:45:12,250 --> 00:45:13,120

And again, this, this,

:

00:45:13,170 --> 00:45:15,840

I wouldn't say the series is dedicated

to that directly, but there's,

:

00:45:15,870 --> 00:45:17,040

there's definitely a unique shift.

:

00:45:17,040 --> 00:45:19,620

And I mentioned to Dave in

a previous episode, I think

:

00:45:19,620 --> 00:45:20,880

that we should change the.

:

00:45:21,360 --> 00:45:21,900

American

:

00:45:21,900 --> 00:45:25,830

Dream from owning a home to owning a

thousand shares of the s and p 500,

:

00:45:26,620 --> 00:45:28,000

or the spy, right?

:

00:45:28,390 --> 00:45:28,870

Spencer Conley: I love it.

:

00:45:28,875 --> 00:45:29,095

Yeah.

:

00:45:29,290 --> 00:45:29,560

Jerremy: Yeah.

:

00:45:29,560 --> 00:45:29,680

It's

:

00:45:29,680 --> 00:45:33,730

like make that your shift because

one is a debt fueled instrument that

:

00:45:34,600 --> 00:45:36,310

essentially you pay into.

:

00:45:36,370 --> 00:45:41,290

And with mortgages and with interest

rates as they are approximately presently,

:

00:45:42,070 --> 00:45:42,370

your

:

00:45:42,370 --> 00:45:44,890

house needs to double in value.

:

00:45:45,685 --> 00:45:46,045

Over

:

00:45:46,045 --> 00:45:50,545

30 years to then when you sell it,

you actually end up making any return.

:

00:45:51,295 --> 00:45:52,735

granted, you can do a lot faster,

:

00:45:53,545 --> 00:45:54,055

obviously,

:

00:45:54,055 --> 00:45:54,385

but

:

00:45:54,715 --> 00:45:54,925

if

:

00:45:54,925 --> 00:45:57,985

you are buying and living in

it and you're going, again, the

:

00:45:57,985 --> 00:46:00,025

traditional American deal route,

:

00:46:00,415 --> 00:46:02,665

get married, get four

year degree, buy a house,

:

00:46:02,935 --> 00:46:03,175

work

:

00:46:03,175 --> 00:46:05,095

20 years, live in that

house, pay the house off,

:

00:46:06,095 --> 00:46:06,425

Dude,

:

00:46:06,455 --> 00:46:06,875

like

:

00:46:07,175 --> 00:46:07,325

you

:

00:46:07,325 --> 00:46:08,405

haven't actually made any money.

:

00:46:08,405 --> 00:46:08,465

Yeah,

:

00:46:09,425 --> 00:46:09,725

right.

:

00:46:10,565 --> 00:46:13,535

You know, both of your money is

going to debt and it's not pro

:

00:46:13,565 --> 00:46:16,625

producing any income, which makes

it, by definition a liability.

:

00:46:17,495 --> 00:46:17,585

Spencer Conley: Right?

:

00:46:18,425 --> 00:46:18,665

Jerremy: Yeah,

:

00:46:18,725 --> 00:46:19,265

Spencer Conley: absolutely.

:

00:46:19,265 --> 00:46:20,495

That, that's actually something, you

:

00:46:20,495 --> 00:46:21,035

know, I'd build

:

00:46:21,035 --> 00:46:23,945

on and say like actually Dave,

you know, being my, my uncle,

:

00:46:23,945 --> 00:46:24,395

really?

:

00:46:25,010 --> 00:46:26,570

Helped out with is, I think early in

:

00:46:26,570 --> 00:46:30,680

my career, I'd sat, he sat me down and I

asked like, what should I do financially?

:

00:46:30,680 --> 00:46:33,080

And a lot of it was

invest in the s and p 500.

:

00:46:33,710 --> 00:46:37,250

And I did that, without thinking about,

like, I wasn't even thinking about buying

:

00:46:37,250 --> 00:46:41,750

a house, but that foundation over 10 years

before I bought a house, not using that

:

00:46:41,750 --> 00:46:45,320

money for a house, did lay the foundation

for me to be a little bit more flexible

:

00:46:45,320 --> 00:46:49,130

down the road And is probably a better

financial investment than the house.

:

00:46:49,130 --> 00:46:51,170

I'm taking this podcast from.

:

00:46:52,170 --> 00:46:53,430

Jerremy: Yeah, no, I feel you, man.

:

00:46:53,915 --> 00:46:57,275

Dave: for your wedding, you almost

got 50 shares of, of, of Tesla.

:

00:46:57,275 --> 00:47:00,635

But I, I, I couldn't figure out how

to get the, get the share thing so.

:

00:47:01,635 --> 00:47:02,605

Jerremy: Well, I, love it.

:

00:47:02,725 --> 00:47:03,640

I love, I love that.

:

00:47:03,690 --> 00:47:04,440

I love that awareness.

:

00:47:04,440 --> 00:47:06,090

And, and I will also say, you know,

:

00:47:06,360 --> 00:47:07,050

because Grant

:

00:47:07,050 --> 00:47:07,710

Cardone

:

00:47:07,980 --> 00:47:08,430

posts

:

00:47:08,430 --> 00:47:10,770

on Twitter five times a

day, and I follow Grant,

:

00:47:11,190 --> 00:47:12,990

and I agree with some

of the things he says.

:

00:47:12,990 --> 00:47:14,520

I'm like 50 50 on Grant Cardone

:

00:47:15,520 --> 00:47:19,915

The, the the house thing is always one

that I have kind of agreed with for a

:

00:47:19,915 --> 00:47:21,295

long period of time where it's like, Hey,

:

00:47:21,625 --> 00:47:21,925

use

:

00:47:21,925 --> 00:47:25,075

your money that you want and then

go buy an asset and then rent

:

00:47:25,525 --> 00:47:26,245

liabilities.

:

00:47:26,305 --> 00:47:26,425

right?

:

00:47:26,425 --> 00:47:26,485

Yeah.

:

00:47:26,965 --> 00:47:27,295

So,

:

00:47:27,815 --> 00:47:28,540

and, and.

:

00:47:28,600 --> 00:47:30,670

and similar to what you did.

:

00:47:30,670 --> 00:47:31,570

You, I heard you say it right?

:

00:47:31,570 --> 00:47:34,270

You and your wife were renting, and

then you had your money in the s and

:

00:47:34,270 --> 00:47:37,270

p, and then that grew and you were

able to leverage that through whatever,

:

00:47:37,270 --> 00:47:39,130

down payments or just collateral or

:

00:47:39,400 --> 00:47:39,520

you

:

00:47:39,520 --> 00:47:41,590

know, your assets on your

p and l balance sheet.

:

00:47:41,590 --> 00:47:42,880

Hey, I can't afford to buy this house.

:

00:47:42,880 --> 00:47:43,480

And so you did.

:

00:47:43,570 --> 00:47:45,280

And that is also because.

:

00:47:45,880 --> 00:47:46,120

There

:

00:47:46,120 --> 00:47:46,660

are

:

00:47:46,960 --> 00:47:47,260

other

:

00:47:47,260 --> 00:47:48,730

people in your relationship

:

00:47:49,570 --> 00:47:49,660

' right?

:

00:47:49,895 --> 00:47:53,000

and, shout out to any man listening.

:

00:47:53,000 --> 00:47:55,310

Your woman wants a place of security

:

00:47:55,700 --> 00:47:56,000

and

:

00:47:56,000 --> 00:47:56,240

is

:

00:47:56,270 --> 00:48:02,480

calm for her nervous system to, to

nest, to be to own, to have to settle.

:

00:48:02,840 --> 00:48:06,560

And so there is that component

too that we have to consider where

:

00:48:06,560 --> 00:48:09,090

I think most dudes, if we had it

our way, would just live on a.

:

00:48:10,090 --> 00:48:10,210

A,

:

00:48:10,215 --> 00:48:15,035

a mattress in the middle of nothing and

just work and watch TV from a T, you

:

00:48:15,035 --> 00:48:18,185

know, from rent all day and just travel

and throw all our money into the markets.

:

00:48:18,185 --> 00:48:18,545

Sure.

:

00:48:19,415 --> 00:48:19,565

But

:

00:48:19,565 --> 00:48:21,275

there's another person

that you have to consider.

:

00:48:21,275 --> 00:48:24,365

And so there's also that component that a

lot of people don't bring up is you have

:

00:48:24,365 --> 00:48:27,035

to have that conversation with your lady

as well, which obviously I know you did

:

00:48:27,395 --> 00:48:28,055

and.

:

00:48:28,805 --> 00:48:28,895

I

:

00:48:28,895 --> 00:48:30,575

think it's just a, a beautiful awareness.

:

00:48:30,575 --> 00:48:31,865

Like, hey, communication is

:

00:48:32,195 --> 00:48:32,345

at

:

00:48:32,345 --> 00:48:34,145

the utmost importance

in all relationships.

:

00:48:34,625 --> 00:48:36,995

alright, so speaking of communications,

two more, two more questions.

:

00:48:36,995 --> 00:48:37,055

Yeah.

:

00:48:37,295 --> 00:48:42,785

What's the biggest lie the laptop class

is currently telling themselves about

:

00:48:42,785 --> 00:48:43,145

ai?

:

00:48:44,190 --> 00:48:44,410

the

:

00:48:45,410 --> 00:48:49,880

Spencer Conley: the laptop class is

telling themselves about ai, laptop

:

00:48:49,880 --> 00:48:53,480

class being, you know, I'd say like mid

to late career professionals, is that

:

00:48:54,570 --> 00:48:54,870

Dave: Nah.

:

00:48:54,870 --> 00:48:56,165

Anybody who works on a laptop.

:

00:48:57,260 --> 00:48:57,560

Spencer Conley: Oh.

:

00:48:57,680 --> 00:48:58,670

Is telling themselves.

:

00:48:59,150 --> 00:49:02,860

I would say that Hmm.

:

00:49:03,775 --> 00:49:04,825

That's a really tough light.

:

00:49:04,975 --> 00:49:07,075

This Is lightning, so I

gotta respond quickly.

:

00:49:07,135 --> 00:49:13,025

that they don't need to understand

it They, other people can understand

:

00:49:13,025 --> 00:49:15,035

it and It won't take, take their job.

:

00:49:15,065 --> 00:49:19,275

I think speaking from a point

of ignorance about ai, and

:

00:49:19,275 --> 00:49:22,665

just saying everything's gonna be okay,

I can keep grinding away at my computer,

:

00:49:22,785 --> 00:49:26,505

especially if you're just working remotely

or you're predominantly on a computer,

:

00:49:26,505 --> 00:49:28,005

is a, is a really dangerous place to be.

:

00:49:29,085 --> 00:49:29,355

Jerremy: Yeah.

:

00:49:30,165 --> 00:49:31,305

Spencer Conley: And the

people skills, right?

:

00:49:31,485 --> 00:49:31,725

Jerremy: Yeah.

:

00:49:31,725 --> 00:49:33,285

Well, I mean, it, is, it's a big lie.

:

00:49:33,285 --> 00:49:34,455

It's like, oh, it's gonna be fine.

:

00:49:34,455 --> 00:49:34,965

I'm good.

:

00:49:34,995 --> 00:49:36,045

I'm, I'm gonna be shielded.

:

00:49:36,825 --> 00:49:37,185

dog,

:

00:49:38,185 --> 00:49:38,615

No, no.

:

00:49:38,620 --> 00:49:38,740

I

:

00:49:38,740 --> 00:49:40,285

don't think most people are shielded.

:

00:49:40,565 --> 00:49:42,395

it's gonna be something that's gonna

be, I mean, it's just gonna be a

:

00:49:42,395 --> 00:49:44,195

requirement kinda like the computer,

:

00:49:44,675 --> 00:49:45,185

you know,

:

00:49:45,875 --> 00:49:48,815

you said, I don't want to ever learn the

computer, that's just not gonna be for me.

:

00:49:49,085 --> 00:49:49,265

That's

:

00:49:49,265 --> 00:49:51,545

probably how AI is gonna

be, except obviously faster.

:

00:49:52,295 --> 00:49:52,655

Right?

:

00:49:52,785 --> 00:49:53,085

right.

:

00:49:53,145 --> 00:49:56,745

we all gonna need to, to understand it

and to integrate it into work with it.

:

00:49:56,835 --> 00:49:58,095

That makes, makes perfect sense.

:

00:49:58,095 --> 00:49:58,155

Yeah.

:

00:49:59,155 --> 00:49:59,575

Spencer Conley: And I think.

:

00:50:00,575 --> 00:50:03,905

Also one of the biggest laws

is like, you know, lot of work

:

00:50:03,905 --> 00:50:05,285

is done on a computer, right?

:

00:50:05,315 --> 00:50:09,455

and companies do like, social events, and

there's a lot of professional networking.

:

00:50:09,455 --> 00:50:13,735

And I think a lot of our, our generation

right now is, too busy right now, on

:

00:50:13,735 --> 00:50:17,665

the computer to take their head out of

the computer and actually work with real

:

00:50:17,665 --> 00:50:20,695

people on real things in the real world.

:

00:50:21,775 --> 00:50:25,495

and social skills, you, know, we're seeing

a decline in that, in, in the workforce,

:

00:50:25,495 --> 00:50:26,755

kind of across the board because of that.

:

00:50:27,755 --> 00:50:27,995

Jerremy: Yeah.

:

00:50:28,085 --> 00:50:28,565

I love it.

:

00:50:29,555 --> 00:50:29,945

Well,

:

00:50:30,945 --> 00:50:31,275

thanks

:

00:50:31,275 --> 00:50:31,545

man.

:

00:50:31,605 --> 00:50:32,895

This was a fun conversation.

:

00:50:33,015 --> 00:50:34,125

It was really a great meeting.

:

00:50:34,185 --> 00:50:35,295

You, I am

:

00:50:35,655 --> 00:50:37,935

a huge raving fan of your uncle,

:

00:50:38,935 --> 00:50:39,295

and,

:

00:50:39,295 --> 00:50:39,520

me too.

:

00:50:39,990 --> 00:50:44,675

yeah, super, super happy that you

joined us on the podcast because

:

00:50:44,975 --> 00:50:45,335

again,

:

00:50:45,335 --> 00:50:49,175

our goal is to not only take this

information, but to share it, to learn, to

:

00:50:49,895 --> 00:50:53,045

crosspollinate, co-create, collaborate,

:

00:50:53,465 --> 00:50:54,305

synergize.

:

00:50:55,175 --> 00:50:58,505

All types of information

and just really create and

:

00:50:58,505 --> 00:51:00,695

determine some actual value that

:

00:51:01,055 --> 00:51:01,325

takes

:

00:51:01,325 --> 00:51:02,195

problems,

:

00:51:03,215 --> 00:51:03,455

comes

:

00:51:03,455 --> 00:51:04,655

up with solutions,

:

00:51:05,375 --> 00:51:08,765

begins implementing them faster than

people might imagine possible, because

:

00:51:09,185 --> 00:51:12,485

that's what this podcast is aimed

to do, solve America's problems.

:

00:51:12,515 --> 00:51:16,505

If you are listening to this, thank

you so much for making it to the end.

:

00:51:16,535 --> 00:51:18,605

Please go ahead and share this

episode with your friends,

:

00:51:18,875 --> 00:51:20,705

anyone that you know that works

:

00:51:21,005 --> 00:51:21,245

if they

:

00:51:21,245 --> 00:51:21,935

have a job.

:

00:51:22,935 --> 00:51:23,715

send this to them.

:

00:51:23,715 --> 00:51:25,755

And if they don't have a job, definitely

send this to 'em 'cause they have all

:

00:51:25,755 --> 00:51:26,955

the time in the world to listen to it.

:

00:51:27,795 --> 00:51:27,975

So

:

00:51:27,975 --> 00:51:30,735

two people that should listen to

this episode, send it to 'em, share

:

00:51:31,425 --> 00:51:35,715

it with your friends, click the five

star review, follow us on Twitter

:

00:51:36,045 --> 00:51:36,315

and

:

00:51:36,315 --> 00:51:39,495

also Instagram Solving

America's Problems Podcast.

:

00:51:39,855 --> 00:51:40,035

Thanks

:

00:51:40,035 --> 00:51:40,755

so much for listening.

:

00:51:40,815 --> 00:51:41,235

You rock.

:

00:51:42,235 --> 00:51:42,685

Dave: All right.

:

00:51:42,685 --> 00:51:43,405

What'd you learn?

:

00:51:43,585 --> 00:51:44,845

What did you learn?

:

00:51:45,845 --> 00:51:47,465

Jerremy: I learned that you

have great people in your life.

:

00:51:48,465 --> 00:51:49,095

Dave: Yes, I do.

:

00:51:49,676 --> 00:51:52,946

Jerremy: I learned that you know

the answer, Saul, you know, right.

:

00:51:52,946 --> 00:51:54,686

Buy, in the s and p.

:

00:51:54,686 --> 00:51:55,616

Don't buy a house yet.

:

00:51:56,145 --> 00:51:56,445

Dave: Hmm.

:

00:51:57,026 --> 00:52:00,026

Jerremy: dude, I still, I, I

definitely learned that the college.

:

00:52:01,061 --> 00:52:01,811

is still broken,

:

00:52:02,415 --> 00:52:02,745

Dave: Yeah.

:

00:52:02,801 --> 00:52:03,851

Jerremy: system is still broken.

:

00:52:04,161 --> 00:52:07,311

I heard Spencer very clearly say, yeah,

man, I just took a different route and

:

00:52:07,311 --> 00:52:10,341

kind of figured it out and did things

a little differently and now I'm not in

:

00:52:10,341 --> 00:52:14,511

debt and I invested properly and me and

my wife can buy a house and a lot of the

:

00:52:14,511 --> 00:52:16,341

problems are gonna affect 95% of this.

:

00:52:17,241 --> 00:52:20,031

The workforce don't affect me

because I had great mentors.

:

00:52:20,091 --> 00:52:20,901

You in my life.

:

00:52:20,901 --> 00:52:24,631

I had, people that I listened to

and I just did a, a few things

:

00:52:24,841 --> 00:52:26,521

differently than most people would.

:

00:52:27,481 --> 00:52:28,621

That's, that's what I heard.

:

00:52:29,536 --> 00:52:30,166

That's what I learned.

:

00:52:30,196 --> 00:52:34,766

I mean, it's, it's repetitive, fortunately

slash unfortunately, because that is

:

00:52:34,766 --> 00:52:39,086

really the problem is kinda like I

said in the podcast, and I've probably

:

00:52:39,086 --> 00:52:45,026

said it 400 times in my life every

other week for the last 10 years, the

:

00:52:45,026 --> 00:52:50,126

education system in America's Broken

College is broken, dude, and we need,

:

00:52:50,186 --> 00:52:51,656

we need desperately to restructure it.

:

00:52:52,611 --> 00:52:56,001

and to change it and to update it, and to

implement it, and to make it faster and

:

00:52:56,001 --> 00:53:06,321

more flexible and more adaptable because

we, American contract, this American dream

:

00:53:07,321 --> 00:53:09,301

was created in the Industrial Revolution,

:

00:53:09,940 --> 00:53:10,140

Dave: Yeah.

:

00:53:10,621 --> 00:53:13,651

Jerremy: and we are entering, I,

I actually had a podcast fun fact,

:

00:53:13,651 --> 00:53:16,111

that no one ever listened to called

the Fourth Industrial Revolution.

:

00:53:16,950 --> 00:53:17,070

Dave: Hmm.

:

00:53:17,341 --> 00:53:20,611

Jerremy: In 20 16, 20 17, talking

about crypto and how crypto is gonna

:

00:53:20,611 --> 00:53:25,241

change the world specifically, and

also all the things are gonna be

:

00:53:25,241 --> 00:53:29,331

coming with crypto Web3 and you know,

the fourth industrial revolution.

:

00:53:29,331 --> 00:53:33,171

So we're here and we're, we're getting

into the AI world, and guess what?

:

00:53:33,171 --> 00:53:34,851

College schools still the same.

:

00:53:34,851 --> 00:53:38,251

It's like, bro, 140 years later, right?

:

00:53:38,461 --> 00:53:38,701

No.

:

00:53:38,701 --> 00:53:40,501

And no one's working in factories anymore.

:

00:53:40,940 --> 00:53:41,240

Dave: Yeah.

:

00:53:41,251 --> 00:53:44,881

Jerremy: No one's doing the

30 year career, the 30 year.

:

00:53:45,881 --> 00:53:49,901

Mortgage the 30 year, like we're doing

things differently and yet that huge

:

00:53:49,991 --> 00:53:53,811

portion of this country is unchanged.

:

00:53:53,961 --> 00:53:55,341

Like that to me is a travesty.

:

00:53:56,341 --> 00:53:56,421

Dave: Hmm.

:

00:53:57,421 --> 00:54:03,186

This, this is what I, I heard because

we, we went all, all around like AI and,

:

00:54:03,191 --> 00:54:05,071

and Spencer's path and the rest of it.

:

00:54:05,461 --> 00:54:09,721

But I think what I, I took most from

it and what I think our, our listeners

:

00:54:09,721 --> 00:54:16,951

can hear from this is that the, the

insulation from the insulation, from,

:

00:54:16,981 --> 00:54:22,321

from, from bad stuff happening and

the lever in order to have success.

:

00:54:22,831 --> 00:54:25,441

The one thing that doesn't change.

:

00:54:26,146 --> 00:54:28,546

Is that it still is about people.

:

00:54:29,446 --> 00:54:33,736

and, and to the side of that is don't

get into crushing debt because that, that

:

00:54:33,736 --> 00:54:35,656

really limits your ability to do anything.

:

00:54:36,016 --> 00:54:38,866

But what I mean by it's

all about people is this.

:

00:54:39,421 --> 00:54:44,461

You know, no matter how much AI is going

to, you know, change your world, there

:

00:54:44,461 --> 00:54:49,321

is always going to be a lot for somebody

who has that people skill, that has that

:

00:54:49,321 --> 00:54:54,301

ability to go and shake hands that has

that ability to connect that you can

:

00:54:54,451 --> 00:55:00,631

create or, or, you can create and be,

be a, you have a, so much more freedom.

:

00:55:01,121 --> 00:55:04,721

so many more opportunities

by the connections that you

:

00:55:04,721 --> 00:55:08,321

make and the connections don't

happen from behind a screen.

:

00:55:08,381 --> 00:55:13,151

They happen with old fashioned networking

and calling people and getting them on

:

00:55:13,151 --> 00:55:16,841

the phone and being truly interested

in who they are and what they do.

:

00:55:16,841 --> 00:55:16,851

And.

:

00:55:17,226 --> 00:55:21,576

That no matter what comes your way,

whether it's ai, whether it's changing

:

00:55:21,576 --> 00:55:24,906

in your job, you know, like it's never

too late to actually learn the skill.

:

00:55:24,906 --> 00:55:28,716

And that is going out and, and

being, just being truly interested

:

00:55:28,716 --> 00:55:29,976

in connecting with other people.

:

00:55:30,066 --> 00:55:34,116

And that is going to be, you know,

like, that's going to be your future.

:

00:55:34,116 --> 00:55:37,716

As far as any kind of workforce

development goes, any kind of work, a

:

00:55:37,716 --> 00:55:41,436

workplace, any, anything, it's, it's

still always gonna be about people.

:

00:55:42,216 --> 00:55:43,026

At least I hope it will.

:

00:55:44,026 --> 00:55:44,296

Jerremy: Yeah.

:

00:55:44,446 --> 00:55:45,046

Yeah, let's hope.

:

00:55:45,166 --> 00:55:45,976

But I agree with you.

:

00:55:46,856 --> 00:55:47,426

Totally agree with you.

:

00:55:47,786 --> 00:55:52,621

I mean, that's gonna be I, I think,

and back to what Spencer said,

:

00:55:53,075 --> 00:55:53,495

Dave: Yeah.

:

00:55:54,495 --> 00:55:56,805

Jerremy: it's like add value,

find a way to add value.

:

00:55:56,864 --> 00:55:57,284

Dave: Mm-hmm.

:

00:55:58,215 --> 00:56:01,305

Jerremy: is probably a skillset set

that will never go away, meaning it's

:

00:56:01,305 --> 00:56:04,995

always gonna be needed and it's just

gonna change how we can add value.

:

00:56:05,625 --> 00:56:06,015

Right?

:

00:56:06,045 --> 00:56:10,395

45 years ago, weren't adding

value through a YouTube channel

:

00:56:11,085 --> 00:56:13,035

doing webinars, and now that's.

:

00:56:14,130 --> 00:56:17,430

all I do to add value, to

add value to the world.

:

00:56:17,430 --> 00:56:21,930

So va, the value component is changing,

will change and will continue to change.

:

00:56:22,380 --> 00:56:24,720

But as long as you have that mindset

of like, Hey, I wanna show up and

:

00:56:24,720 --> 00:56:27,750

figure out a way to make it better,

figure out how this opportunity

:

00:56:27,750 --> 00:56:30,510

changed places didn't go away.

:

00:56:31,200 --> 00:56:32,460

And learn how to adapt to it.

:

00:56:32,460 --> 00:56:40,620

Learn how to pivot, learn how to

create to think and grow rich.

:

00:56:41,620 --> 00:56:41,980

Dave: Boom.

:

00:56:42,980 --> 00:56:44,420

Mike a drop.

:

00:56:44,836 --> 00:56:45,436

Jerremy: Yeah,

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About the Podcast

Solving America's Problems
Solving America’s Problems isn’t just a podcast—it’s a journey. Co-host Jerremy Newsome, a successful entrepreneur and educator, is pursuing his lifelong dream of running for president. Along the way, he and co-host Dave Conley bring together experts, advocates, and everyday Americans to explore the real, actionable solutions our country needs.

With dynamic formats—one-on-one interviews, panel discussions, and more—we cut through the noise of divisive rhetoric to uncover practical ideas that unite instead of divide. If you’re ready to think differently, act boldly, and join a movement for meaningful change, subscribe now.