November 3, 2025 • 30 mins
The episode providing an overview of AI-resistant careers for high school graduates, arguing that technological advancements have made hands-on, physically demanding, and emotionally intelligent jobs increasingly valuable. It highlights thirteen specific occupations—ranging from Elevator Mechanics and Hearing Aid Specialists to Plumbers and Chefs—that require only a high school diploma, boast median annual salaries over $50,000, and face low automation risk through 2032. The episode explains that these roles are "AI-proof" because they rely on real-world judgment, unpredictable problem-solving, and human empathy that machines cannot replicate. The episode further advocates for non-degree paths, such as paid apprenticeships and vocational training, as a debt-free way to achieve financial stability and capitalize on surging demand driven by infrastructure and an aging population.
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Episode Transcript
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Speaker 1 (00:00):
Welcome back to the deep dive. Today. We are waiting
directly into well, maybe the biggest worry for a lot
of people right now, the AI anxiety paradox.
Speaker 2 (00:09):
It's definitely more than just a little worry for some.
It's almost a panic, isn't it, Especially if you're figuring
out your.
Speaker 1 (00:15):
Career path totally. You see headlines everywhere saying AI is
coming for report writing, data analysis, customer service, even coding.
Speaker 2 (00:24):
Yeah, and if you're, say a high school student looking
out at that, it can make planning feel well, almost pointless.
That feeling of vulnerability is real.
Speaker 1 (00:33):
Exactly, And that's the feeling we really want to tackle
head on today because we're going to flip that narrative
completely right.
Speaker 2 (00:39):
We're not here to just list the jobs AI might
take over.
Speaker 1 (00:42):
No, we're here to shine a light on some hopes, really,
the solid, well paying careers that AI well, it just
can't touch. We've done a really thorough deep dive into
this comprehensive twenty twenty five analysis from Resume Now. They
did a great job pulling together data from the US
Bureau of Labor Statistics.
Speaker 2 (01:00):
Focus specifically was on a group that often gets missed
in the whole college or nothing debate.
Speaker 1 (01:06):
Yeah, we looked for career paths perfect for high school
grads who want good money, but crucially without the mountain
of college.
Speaker 2 (01:13):
Debt, which meant setting some really strict rules for this analysis.
The mission was to pull out only those careers that
met for very specific criteria non negotiables, really for being
both AI proof and financially smart.
Speaker 1 (01:28):
Okay, let's lay those out. The four AI proof must have.
Every single job we talk about today hit these marks.
Speaker 2 (01:36):
Okay. Number one requires only a high school diploma or
you know, an equivalent.
Speaker 1 (01:40):
Got it, No bachelor's degree.
Speaker 2 (01:42):
Needed, exactly. Two. It has a median annual salary over
fifty thousand dollars.
Speaker 1 (01:46):
That's key, fifty k minimum medium, okay.
Speaker 2 (01:49):
Three it shows faster than average job growth projected out
to twenty thirty two, so it's in demand.
Speaker 1 (01:54):
Good sign for stability.
Speaker 2 (01:55):
And the last one and four this is critical. It
carries a load to moderate risk of being allivated by
AI if a job failed. Even one of those didn't
make our list.
Speaker 1 (02:04):
What really jumps out when you apply those filters is
how much it pushes back against the constant pressure to
go to college, right, especially with costs soaring.
Speaker 2 (02:13):
Absolutely, we're talking about potentially sixty thousand dollars or more
just for tuition these days, before living costs.
Speaker 1 (02:19):
So we're contrasting that debt spiral with these well, really
viable alternatives. The way into these jobs isn't lectures.
Speaker 2 (02:26):
No, it's things like intensive apprenticeships, specific professional certifications or
really solid paid on the job training.
Speaker 1 (02:34):
And that's the financial hook you mentioned. It's huge, it is.
Speaker 2 (02:37):
Think about it. The usual college route that's four years
minimum of paying out money, taking on debt, and earning
basically nothing. Now compare that to the skilled trades. A
typical path is maybe a four year paid apprenticeship.
Speaker 1 (02:52):
Paid being the key word there.
Speaker 2 (02:53):
Absolutely key. You're learning a skill that's guaranteed to be
in demand, and at the same time you're earning maybe
forty to fifty even sixty thousand dollars a year while
you train.
Speaker 1 (03:03):
That's that's a massive financial swing. You're not just avoiding
sixty grand in debt.
Speaker 2 (03:07):
You could be sixty grand up in savings or starting
to build equity. It's immediate financial freedom. It means graduates
in these fields can start building capital, maybe buying a
home years maybe even a decade earlier than peers with
degrees and debt.
Speaker 1 (03:21):
Okay, so that sets the stage. Financially it makes sense,
But the core question remains. If AI is so smart,
you know, beating chess champions, writing legal documents, why can't
it do these jerbs right?
Speaker 2 (03:35):
And that brings us to the resilience factor, the human advantage.
Why these rules are different.
Speaker 1 (03:40):
We have to look at where algorithms hit their limits
versus well, the messiness of the real world.
Speaker 2 (03:45):
Yeah. AI is fantastic at certain things, repetitive tasks, high
volume stuff, analyzing huge data sets tasks.
Speaker 1 (03:52):
And controlled environments, clean data in, predictable output out. That's
where it shines exactly.
Speaker 2 (03:57):
But the second things get chaotic, unpredictable, or need really
fine motor skills under pressure, that's where AI starts to struggle.
Speaker 1 (04:05):
It's like hitting real world friction. Isn't it a door
that sticks, a part that's broken in a weird, non
standard way, or yeah, dealing with an upset customer. The
digital solution just breaks down.
Speaker 2 (04:15):
And those messy situations demand things that are uniquely human.
That's why these jobs are AI proof. We kind of
identify three big categories. Okay, what's the First, firstus pure
physical dexterity and precision, the kind of thing that needs proprioception,
that subconscious sense of where your body is and just
(04:37):
that physical feel for things.
Speaker 1 (04:38):
Right, like knowing how tight to turn a wrench by feel,
not just by calculated tor exactly.
Speaker 2 (04:44):
Second is emotional intelligence EQ. Absolutely vital for handling human crises,
delicate negotiations, dealing with someone's grief, for fear.
Speaker 1 (04:52):
AI can mimic conversation, yeah, but it can't offer genuine
comfort or read a room, can it not?
Speaker 2 (04:57):
Really not in a way that feels authentic. The third,
and this is huge, is real time judgment and quick
decisions in new, high stake situations.
Speaker 1 (05:05):
So when something totally unexpected.
Speaker 2 (05:07):
Happens precisely, AI can predict based on past data. But
when a wildfire suddenly shifts direction because the wind changes,
or a machine fails in a way no one's ever
seen before, you need human judgment to improvise a safe,
effective solution right then and there.
Speaker 1 (05:19):
And there's also just adapting to the environment itself. Right
The physical space.
Speaker 2 (05:24):
Oh, definitely, the source material really highlights this. Things like
having to crawl into a tiny, dusty attic space that
needs a specific body angle, or dealing with sudden rain
or extreme heat, or figuring out the weird quirks of
an old building's plumbing.
Speaker 1 (05:39):
Stuff that's just too varied, too unpredictable for a standard
robot exactly.
Speaker 2 (05:44):
These are physical logistical hurdles that defy standardized automation.
Speaker 1 (05:48):
Keith Spencer, the expert at resume now, he summed it
up really well. He said, these jobs thrive on problem solving, adaptability,
and real world know how things machines just don't have. Intrinsically.
Speaker 2 (06:02):
AI might optimize a factory's workflow on paper, but it
can't physically crawl under a massive leaking machine in the
middle of the night to figure out why some custom
rig part failed.
Speaker 1 (06:11):
Let's ue some concrete examples from the analysis, because they
really drive this home good idea.
Speaker 2 (06:16):
Think about the physical impossibility for a robot. Can a
machine climb a steep, slippery roof during a heavy rainstorm
to patch a leak?
Speaker 1 (06:24):
No way, it lacks the balance, the grip, the split
second judgment needed to.
Speaker 2 (06:29):
Not fall off or shift gears to eq. Can AI
truly empathize with a guest at a fancy hotel who's
just found out some terrible news back home.
Speaker 1 (06:37):
No, that needs genuine human warmth reassurance, reading subtile cues.
You can't script that comfort.
Speaker 2 (06:43):
And maybe the clearest trades. Example, a pipe bursts inside
a wall in a hundred year old house.
Speaker 1 (06:49):
Oh yeah, nightmare scenario, right.
Speaker 2 (06:51):
An algorithm might suggest the ideal rerouting, but the plumber
on site they have to physically improvise, maybe snake new
pipes through tight, unexpected spaces that aren't on any blueprint.
That's immediate creative physical problem solving.
Speaker 1 (07:05):
And this isn't just about AI not being able to
do the job. It's also about massive real world demand
for these jobs.
Speaker 2 (07:11):
Absolutely, the Blist projections are clear. Demand is surging, and
it's driven by really big fundamental needs. First, huge infrastructure
requirements across the country.
Speaker 1 (07:20):
Roads, bridges, the power grid, everything needs work.
Speaker 2 (07:24):
Second, the demographics. The aging population needs more specialized, high
touch care. That drives demand for roles like hearing aid specialists.
We'll talk about it. And the third big one the
massive shift to renewable energy, the green boom. This isn't
just digital code. It requires thousands of people physically installing
solar panels, wearing up EV chargers, climbing wind turbines.
Speaker 1 (07:46):
All demanding skilled hands on human labor, protected from automation
and critically needed.
Speaker 2 (07:51):
Okay, so now let's get into the really exciting part,
the numbers, the proof that this path is financially sound.
Let's hit that elite tier, the careers that blow past
our fifty dollar k minimum and really peak, all without
needing that four year degree.
Speaker 1 (08:05):
All right, leading the charge the highest paying non degree
median salary on the entire list elevator and escalator installers
and repairers.
Speaker 2 (08:13):
Yeah, the median is stunning. Ninety nine thousand, seven hundred
and twenty dollars.
Speaker 1 (08:16):
Wow, basically six figures medium. Many experienced folks are clearing
that easily.
Speaker 2 (08:21):
And you know they earn it. Think about the responsibility
these people maintain literal vertical lifelines in modern buildings, skyscraper's, hospitals, airports.
Speaker 1 (08:29):
When an elevator goes down in a fifty story hospital,
that's not just an inconvenience, it's potentially critical.
Speaker 2 (08:34):
Exactly, a single glitch can hold everything it demands, incredibly
fast diagnostics under immense pressure.
Speaker 1 (08:41):
And the work environment itself screams AI proof. It's not
standardized at all, not even close.
Speaker 2 (08:48):
It needs absolute physical precision, often in really awkward, noisy,
cramped spaces. Imagine crawling through a dusty ventilation duct or
hauling one hundred pound motor up a narrow shaft, dealing with.
Speaker 1 (09:00):
The building swinging slightly in the wind on a high floor.
Stuff or robot can't easily handle right.
Speaker 2 (09:05):
An algorithm can run simulations, but it can't replace that
human ability to troubleshoot. In three D AI monitors, data
is sure, but it can't feel a slightly loose cable
or hear that weird vibration from a bearing that tells
an experienced tech troubles coming.
Speaker 1 (09:20):
It's complex stuff hydraulics, gears, sensors, heavy loads, all interacting,
and the.
Speaker 2 (09:25):
Path in It's the gold standard for earning while learning
high school diploma. The mandatory tough four year paid apprenticeship, usually.
Speaker 1 (09:32):
Through unions right like the IUEC.
Speaker 2 (09:34):
Almost always the International Union of Elevator Constructors is the
main one, and that means you start earning decent money
right away. Starting pay during training is often forty to
fifty even sixty thousand dollars, so.
Speaker 1 (09:46):
You finish the apprenticeship with zero debt and potentially significant savings.
Speaker 2 (09:50):
Exactly, and the job outlook is solid too. Six percent
growth projected faster than average, driven by more tall buildings
going up, plus all the work needed to update older
elevators for safety and accessibility. That means about twenty five
hundred openings every year.
Speaker 1 (10:04):
The case did he mentioned Jake twenty eight years old.
He really puts a face on it.
Speaker 2 (10:08):
Yeah, bypass college debt entirely went straight into the apprenticeship
cleared one hundred and twenty thousand dollars last year, largely
thanks to emergency overtime calls.
Speaker 1 (10:16):
And his quote about AI perfect.
Speaker 2 (10:18):
It was great. AI. Let it try fixing a jam
door at two am when the power is half out
and its ten degrees below zero.
Speaker 1 (10:24):
Sums up the real world, high stress, often lousy conditions perfectly, okay.
Elite job number two A big shift here, huge.
Speaker 2 (10:32):
Shift from heavy mechanics to high touch, empathetic care hearing
aid specialists. Median salary here eighty nine thousand four to forty.
Speaker 1 (10:40):
Dollars almost ninety dollars. And this job is such an
interesting mix, isn't it?
Speaker 2 (10:45):
It really is. It combines incredible manual finesse fine tuning.
These tiny complex digital devices with precise technical testing like
audiograms and molding custom fits.
Speaker 1 (10:56):
But the crucial part is the human element, the counseling.
Speaker 2 (10:59):
Absolutely, that's the core AI proof element. They're guiding patients
through what can be a really sensitive emotional adjustment, accepting
hearing loss, getting used to a device. It's a big
psychological step for many people.
Speaker 1 (11:12):
AI can analyze the hearing test data the audiogram perfectly
better than a human maybe.
Speaker 2 (11:18):
Sure the raw data analysis, but it cannot provide that
personalized care. It can't comfort an elderly person who's maybe
feeling isolated or frustrated by their hearing loss.
Speaker 1 (11:28):
Or make those tiny real time adjustments based on subtle
feedback right like tweaking the sound profile mid conversation because
the person's face shows they're still struggling in that specific
noisy room exactly.
Speaker 2 (11:38):
That level of nuance, contextual judgment, and empathy that's purely
human right now.
Speaker 1 (11:42):
And the entry path here is much quicker than the
elevator apprenticeship, much.
Speaker 2 (11:46):
Quicker high school diploma. Then you need state licensure that
typically involves about six to twelve months of specialized training
plus really important on the job mentoring. Often through clinics
or the manufacturers.
Speaker 1 (11:57):
Themselves, so potentially earning close to ninety dollars within a
year or two of graduating high school.
Speaker 2 (12:03):
It's definitely possible, and the outlook is very strong. Eleven
percent projected.
Speaker 1 (12:07):
Growth driven by demographics, again almost entirely.
Speaker 2 (12:11):
The aging population baby boomers and Gen X means a
massive growing need for hearing solutions. The demand is just
going to keep increasing.
Speaker 1 (12:19):
Okay. Final one in the elite tier. This one sounds
intense fire prevention and protection specialists.
Speaker 2 (12:25):
It is intense. Salaries vary a lot here depending on
focus urban inspections versus wildland fires, but often seventy thousand
dollars up to ninety thousand dollars or more.
Speaker 1 (12:34):
The source is called it boots on ground heroism. What
does the job actually involve?
Speaker 2 (12:38):
It's a mix high stakes assessment of wildfire risks out
in the field, investigating fires after they happen to figure
out the cause, and crucial community education to prevent future fires.
Speaker 1 (12:48):
And why is this AI proof? Seems like AI could
model fire spread pretty well.
Speaker 2 (12:52):
It can model, yes, based on wind, humidity, terrain data,
but models break down when reality it's chaotic.
Speaker 1 (13:00):
So the human element is needed for the unexpected.
Speaker 2 (13:03):
Exactly, someone has to physically hike that rugged terrain, see
the specific condition of the vegetation, notice that the wind
just shifted unexpectedly, and make immediate life or death calls
about where to cut a fire line or which homes
need evacuating right now.
Speaker 1 (13:17):
And managing the chaos on the ground too. People resources.
Speaker 2 (13:21):
Absolutely, you can't automate that kind of real time, high
stressed command and control, especially when lives are at stake.
It also requires deep technical knowledge, building codes, sprinkler systems,
hazardous materials applied under extreme pressure, and.
Speaker 1 (13:34):
The outlook you said, explosive growth.
Speaker 2 (13:36):
Sadly, yes, the analysis links it directly to climate change. Longer,
more intense wildfire seasons, more severe weather events. The need
for experts in prevention, response, and investigation is growing rapidly
and isn't going away.
Speaker 1 (13:50):
Okay, that's a powerful look at the top tier. Let's
move into what the analysis called the middle tier. Still
great salaries well over sixty k's, but maybe more foundational rules.
Speaker 2 (13:59):
Exactly of the jobs that really rely on grit technical
know how, and sometimes they crucial eq. Factor two. Starting
with the absolute backbone of industry, industrial machinery mechanics. Median
salary here sixty two thousand, seven hundred and eighty dollars,
And these folks are essential. They keep the massive machinery
(14:20):
in factories and plants running, the assembly line robots, the
huge conveyor systems, giant presses, complex packaging lines you said earlier.
Speaker 1 (14:27):
When one of these lines goes down, it costs companies
a fortune.
Speaker 2 (14:30):
Thousands, sometimes tens of thousands an hour. So these mechanics
need to be jacks of all trades. They're welding, fixing, hydraulics,
predicting when a part might fail, and crucially reprogramming the PLCs.
Speaker 1 (14:42):
PLCs programmable logic controllers the brains of the.
Speaker 2 (14:45):
Machines exactly, and troubleshooting them is a key reason this
is AI proof. A failure isn't always a physical broken part.
Sometimes it's a subtle glitch in the custom ladder logic
the software running that specific.
Speaker 1 (14:56):
Machine, and only a human who understands the machine's physical
operation can spot that logic error.
Speaker 2 (15:02):
Often, yes, you need to understand how the code translates
to physical movement and the diagnostics. The source material called
it greasy, hazardous chaos.
Speaker 1 (15:11):
Sounds about right for a factory floor.
Speaker 2 (15:13):
Yeah, machines vibrating, maybe leaking fluids, maybe modified weirdly over
the years. AI is great at monitoring clean data streams,
but it can't physically put a wrench on a seized
bearing in one hundred and twenty degree.
Speaker 1 (15:26):
Plant needs that human intuition, that feel built from experience totally, and.
Speaker 2 (15:30):
The outlook reflects the need. Strong thirteen percent growth projected
plus a massive fifty two thousand openings expected each year,
mainly due to retirements in manufacturing growth. They're desperate for
skilled people, okay.
Speaker 1 (15:42):
Next up, a job really riding the green wave electricians,
medium salary six and ninety dollars.
Speaker 2 (15:49):
Yeah, and this job is changing so much. It's way
beyond just wiring a light switch now right.
Speaker 1 (15:53):
They're doing complex smart home setups, installing those high voltage
ev charging stations.
Speaker 2 (15:58):
Ensuring grid stability during outages. Plus the fundamental, really skilled
physical work of bending metal conduit pipe to exact specifications
on site.
Speaker 1 (16:05):
And the aiproof angle. Here is spatial.
Speaker 2 (16:07):
Reasoning, absolutely pure, complex spatial reasoning in three dimensions. Think
about crawling through a cramped, boiling hot attic trying to
interpret a complex blueprint and then finding say a structural
beam exactly where you plan to run wires.
Speaker 1 (16:23):
You have to improvise constantly.
Speaker 2 (16:25):
Constantly or worse, encountering old unmarked wiring. No robot or
drone can replicate that real time three D problem solving
in an unpredictable, unmapped space, and the stakes are always
high fire risk, electrocution risk.
Speaker 1 (16:40):
And there was a tip about specialization boosting earnings big time.
Speaker 2 (16:43):
Get certified in solar panel installation, battery storage systems, or
industrial automation controls, and you can definitely push your earnings
towards EIGHTYK even one hundred dollar k pretty quickly.
Speaker 1 (16:53):
And the growth reflects that demand.
Speaker 2 (16:55):
For sure, eight percent projected growth and a huge eighty
four thousand openings annually. A lot of that is directly
fueled by green energy projects and infrastructure.
Speaker 1 (17:03):
Upgrades following closely and pay another essential trade plumbers, pipe
fitters and steamfitters median sixty and fifty dollars, And.
Speaker 2 (17:12):
That quote really nails it. Burst pipes don't wait for algorithms.
Speaker 1 (17:15):
It's the emergency unpredictable nature, isn't it.
Speaker 2 (17:18):
Absolutely yes, they install complex systems and new buildings, but
a huge part of the job is dealing with sudden disasters,
major clogs, burst pipes in old buildings, retrofitting systems for
water efficiency, and.
Speaker 1 (17:31):
The AI barrier is again the physical environment.
Speaker 2 (17:34):
Yeah, navigating nasty crawl spaces, diagnosing hidden leaks often just
by sound or feel. You need human senses and dexterity
for that, and adapting to the weird quirks of old
corroded plumbing that never matches the original plans.
Speaker 1 (17:48):
Entry pep here is the apprenticeship model again.
Speaker 2 (17:50):
Typically yes, a four to five year paid apprenticeship and again,
getting into a union program like through the United Association
UA is often the best route for paid training and
access to good job.
Speaker 1 (18:00):
Growth is four percent, but the opening's number is huge
because of retirements.
Speaker 2 (18:04):
Massive turnover forty eight thousand openings projected each year. It's
another field with a huge talent vacuum, guaranteeing job security
for new entrants.
Speaker 1 (18:12):
Okay, let's pivot now away from the tools and pipes
towards jobs where that emotional intelligence filter is the key
AI barrier.
Speaker 2 (18:19):
Right roles needing complex human interaction, often under stress. First
up lodging managers median salary sixty nine and ten dollars.
Speaker 1 (18:29):
This might seem more administrative, but the AI proofing is
about crisis management exactly.
Speaker 2 (18:34):
It's a high wire act overseeing the entire hotel operation,
managing staff, handling VIP issues. Sure, AI chadbots can handle
bookings or answer basic questions, but they.
Speaker 1 (18:44):
Can't handle a really angry guest whose reservation got messed up.
Speaker 2 (18:48):
No way. That needs a human manager face to face
to smooth things over. Show empathy, maybe offer a creative
solution and turn that negative experience into loyalty. That takes
serious EQ and judgment. And the out pretty good seven
percent growth fueled by the travel rebound and the trend
towards more boutique hotels that really emphasize personalized guest experiences.
Speaker 1 (19:08):
Next a job, people often misunderstand flight attendance median sixty eight,
three hundred and seventy dollars.
Speaker 2 (19:15):
Yeah, people think servers in the sky, but that's not
the core function at all.
Speaker 1 (19:17):
They're first responders essentially.
Speaker 2 (19:19):
Absolutely train first responders at thirty thousand feet. They handle
medical emergencies, CPR, defibrillators, even delivering babies. Sometimes they manage
panic during turbulence in force. Safety rules deal with security issues.
Speaker 1 (19:33):
It's about managing group psychology in a crisis in a
confined space.
Speaker 2 (19:37):
Precisely, when two hundred people are scared in a metal tube,
you need train humans to stay calm, take charge, provide reassurance,
and handle any medical needs. Robots can't do that. Human
to human intervention.
Speaker 1 (19:49):
And demand is high here too, very high.
Speaker 2 (19:51):
Eleven percent growth projected directly tied to the boom in
air travel post pandemic.
Speaker 1 (19:56):
Okay, last one in this middle EQ focused group chef
and head cooks median fifty nine thousand, four hundred and
forty dollars.
Speaker 2 (20:04):
This one is protected by creativity and intense time pressure.
Speaker 1 (20:07):
Leading a kitchen team is notoriously.
Speaker 2 (20:09):
Stressful, incredibly stressful. It demands creativity on the fly, split
second timing during a rush, and that really refined palid judgment.
AI can suggest recipes or optimize inventories, sure, but it can't.
Speaker 1 (20:20):
Taste the soup and decide it needs a pinch more salt,
or improvise a new dish when a key ingredient runs
out mid service exactly.
Speaker 2 (20:26):
Or plate a dish beautifully for visual appeal, which is
huge now with social media. That requires human artistry plus
managing the team, motivating people, resolving conflicts, instantly, maintaining quality
when everything's going crazy. That's human leadership.
Speaker 1 (20:42):
And solid growth here too.
Speaker 2 (20:43):
Yep, nine percent growth, especially in higher end restaurants, food tourism,
and the gore mate delivery market.
Speaker 1 (20:49):
Wow. Okay, that covers the elite and middle tiers. Now
what about those hidden gems, the really specialized trades that
maybe don't get as much attention.
Speaker 2 (20:58):
Yeah, these often require very specific, sometimes risky skills that
command good salaries. Let's touch on a few. First strangler
fitters focus purely on fire suppression systems. Salary potential often
seventy two thousand dollars plus, especially in union jobs. They install, inspect,
maintain these complex life saving.
Speaker 1 (21:17):
Systems, and AI proof.
Speaker 2 (21:18):
Because the environments are often compromised active construction sites, post
disaster areas, and the systems require precise hands on installation
and pressure testing, the stakes are simply too high for
automation failure.
Speaker 1 (21:29):
Makes sense. What else?
Speaker 2 (21:30):
First line supervisors of mechanics. This is a leadership role,
often paying around seventy two thousand dollars.
Speaker 1 (21:36):
Medium, So moving up from being a mechanic.
Speaker 2 (21:39):
Exactly, it blends deep technical knowledge with management skills, organizing work,
motivating teams, resolving issues. AI can schedule, but it can't
lead people effectively under pressure.
Speaker 1 (21:52):
Another interesting one wind turbine technicians median sixty two thousand dollars.
Speaker 2 (21:57):
Yeah, this one's AI proof because of the extreme environment.
Speaker 1 (22:00):
Climbing those huge towers.
Speaker 2 (22:01):
Scaling three hundred foot towers, working at heights in potentially
high winds and bad weather, doing complex mechanical and electrical
repairs in a confined space.
Speaker 1 (22:11):
Drones can inspect, maybe, but they can't do the heavy
lifting or complex repairs up there reliably, especially in bad weather. Not.
Speaker 2 (22:17):
Yes, certainly, not with the required reliability. It takes a
highly trained human with specialized safety gear.
Speaker 1 (22:22):
And one more.
Speaker 2 (22:22):
Machinery maintenance workers median around fifty eight thousand, six hundred
thirty dollars. These folks focus more on the routine preventative
maintenance side.
Speaker 1 (22:30):
How's that different from industrial mechanics.
Speaker 2 (22:32):
It often involves more frequent, smaller scale hands on fixes
and adjustments, replacing a warn belt, fixing a sensor bracket,
basic lubrication, minor alignment tweaks. While tasks might seem simple,
the sheer variety and the need for human judgment on
normal wear versus impending failure keeps it largely out of
automation's reach for now.
Speaker 1 (22:53):
Okay, so we've seen the jobs. Now let's zoom out.
Why are these trades specifically so valuable right now in
twenty twenty five? What's the big economic picture?
Speaker 2 (23:03):
It really boils down to a few massive structural forces
that are driving up demand and wages.
Speaker 1 (23:08):
First big one the skills gap.
Speaker 2 (23:11):
Huge, The numbers are kind of scary, actually. Projection suggests
two point four million manufacturing jobs could go unfilled by
twenty thirty simply because there aren't enough people with the
right trade skills.
Speaker 1 (23:20):
That lack of supply gives huge leverage to those with
the skills right and drives it pay absolutely.
Speaker 2 (23:25):
Second force the talent vacuum from retirements. We mentioned it
for plumbers and electricians.
Speaker 1 (23:29):
Forty eight thousand openings a year for plumbers, eighty four
thousand for electricians exactly.
Speaker 2 (23:34):
These aren't temporary blips. It's a predictable, massive wave of retirements,
creating sustained demand and career security for decades.
Speaker 1 (23:42):
And the third driver government spending.
Speaker 2 (23:45):
Yeah, the big infrastructure bills. Billions are flowing into upgrading roads,
bridges the grid and especially green energy projects that guarantees
a pipeline of work for electricians, mechanics, pipe fitters, you
name it, for years to come. It creates a buffer
against ecomic downturns to.
Speaker 1 (24:01):
It's also worth mentioning the shift in who is doing
these jobs, isn't it?
Speaker 2 (24:05):
Definitely the old stereotypes are breaking down. More women and
minorities are entering the trades, which is fantastic. Many unions
and associations now have specific programs, scholarships, and support systems
to encourage diversity. The opportunities are opening up.
Speaker 1 (24:19):
So all this leads back to the bottom line, the
financial payoff. These aren't just getting by.
Speaker 2 (24:24):
Jobs, not at all. The analysis showed the top ten
percent in many of these fields easily clear one hundred
thousand dollars a year, especially with overtime.
Speaker 1 (24:32):
And if you go the entrepreneurial route, the.
Speaker 2 (24:34):
Ceiling goes way up. A successful self employed master plumber
or specialized roofer running their own small business, they can
potentially clear two hundred thousand dollars, maybe even three hundred
thousand dollars a year based purely on their skill and reputation.
Speaker 1 (24:49):
Let's bring back that story of Maria, the one who
left retail for plumbing.
Speaker 2 (24:53):
Great example, left a low wage job, did the apprenticeship,
bought her first house at twenty.
Speaker 1 (24:58):
Five, and her quote was so so simple but powerful,
no debt, just tools and hustle.
Speaker 2 (25:03):
Think about the freedom in that statement at twenty five.
Compare that to a typical college grad, maybe facing decades
of student loan payments limiting their choices.
Speaker 1 (25:13):
Maria was building equity immediately. That head start in building
capital might be the single biggest advantage.
Speaker 2 (25:18):
I think it is trading skill and hard work directly
for high wages and financial freedom.
Speaker 1 (25:23):
Okay, this is all incredibly compelling for listeners out there thinking, Okay,
maybe this is for me. I like working with my hands,
I like problem solving. What are the practical steps? How
do you actually launch one of these careers?
Speaker 2 (25:34):
Right? Let's get practical. First step absolutely crucial. Assess your fit.
Speaker 1 (25:38):
Honestly, it's not for everyone. Right can be physically demanding
odd hours.
Speaker 2 (25:42):
Exactly, you need to be realistic. The best way job
shadowing seriously, contact a local union, hall, IDW for electrical,
UA for plumbing, et cetera, or even just local contractors.
Many are happy to let you spend a day on
a site observing, see if you actually like the environment,
the pace, the work.
Speaker 1 (25:59):
It's good advice. See it before you commit.
Speaker 2 (26:02):
Then training, train smart, not expensive. Look into community college
programs first. Many have excellent technical programs, often subsidized by
things like federal Perkins funding specifically for a career in
tact much cheaper than university.
Speaker 1 (26:17):
But the gold standard is still the paid apprenticeship.
Speaker 2 (26:20):
Absolutely prioritize that if you can. These are official Department
of Labor registered programs classroom learning plus thousands of hours
of paid on the job training. The deal Well website
do wel dot gov is the place to start. Search
registered apprenticeship programs by trade and your location.
Speaker 1 (26:36):
Okay, find an apprenticeship. What else?
Speaker 2 (26:38):
Certify up? Get those extra certifications They translate directly to
more money. OSHA ten or OSHA thirty for safety is
often required anyway, but specialize ones like EPA certification for
handling refrigerants if you're going into HVAC.
Speaker 1 (26:53):
The analysis mentioned bonuses for CERTs, Yeah.
Speaker 2 (26:55):
Five thousand dollars. Sign on bonuses or immediate raises just
for having certain high demands. Sort of cations are pretty common.
They approve your skill level.
Speaker 1 (27:02):
Networking seems important.
Speaker 2 (27:03):
Too, essential in the traits. It's a community. Join the
relevant professional chapters ibw UA, Associated Builders and Contractors ABC.
They offer training connections, job boards, sometimes access to the
best union contracts. Your reputation and who you know matters, and.
Speaker 1 (27:21):
A modern tip the side hustle.
Speaker 2 (27:23):
Yeah, it's a great way to build practical skills while
you're training officially. Use platforms like task grab it for
smaller non licensed jobs basic repairs, furniture assembly, maybe installing
simple fixtures.
Speaker 1 (27:34):
It's confidence tool skills gets you customer.
Speaker 2 (27:36):
Reviews exactly real world practice that looks good on a
resume later.
Speaker 1 (27:39):
So going through all this, it really comes back to
that core idea, the irreplaceable human element in these jobs.
Speaker 2 (27:47):
Definitely, in these chaotic, physical or highly empathetic roles, the
human advantage is clear, and.
Speaker 1 (27:54):
The future isn't really humans versus machines, is it. It's
more collaboration.
Speaker 2 (27:59):
That's the key takeaway. It's a division of labor.
Speaker 1 (28:01):
And that electricians quote we mentioned earlier perfectly sums up
that synthesis, doesn't it.
Speaker 2 (28:05):
It really does. AI designs the circuit, I make it
not explode.
Speaker 1 (28:10):
That's it, right, there, the algorithm does the abstract design.
The human handles the messy, high stakes reality.
Speaker 2 (28:16):
That's the division of labor for the twenty first century.
I think.
Speaker 1 (28:18):
Okay, So, wrapping this all up, the main takeaway from
this whole deep dive seems pretty clear. If you're looking
for a stability, security, maybe even a six figure income
and guaranteed work in twenty twenty.
Speaker 2 (28:28):
Five, path doesn't necessarily run through a university lecture hall
and piles of debt.
Speaker 1 (28:33):
No stability, high earnings, pride in actually billeting and maintaining
the world around us. That path might just require a
hard hat or maybe a specialized tool like a hearing
aid programmer, or yeah, a chef's.
Speaker 2 (28:45):
Knife, and that door. It's wide open and it's debt free.
Speaker 1 (28:49):
Before we sign off, though, we wanted to leave you
with one last thought, something to chew on. That kind
of broadens the picture.
Speaker 2 (28:55):
Right, We focus today entirely on these jobs where the
human element that hands on skill is paramount right now
the stuff AI can't do.
Speaker 1 (29:04):
But it's also true that AI is creating jobs too.
The World Economic Forum predicts AI will actually create a
net increase of seventy eight million jobs. Globally by twenty thirty.
Speaker 2 (29:14):
Yeah, despite all the automation anxiety, the net prediction is.
Speaker 1 (29:17):
Positive and many of those new jobs they'll likely be
in different areas oversight management, training, the AI interpreting its
outputs roles that are quite different from the hands on
work we detail today.
Speaker 2 (29:29):
Think about it. The skilled electrician who's wiring ev stations
today Tomorrow, they might be the person best equipped to
manage the AI system that predicts maintenance needs for the
entire solar grid because they understand the physical reality behind
the data.
Speaker 1 (29:44):
So the real challenge maybe for the next generation isn't
just picking a manual skill to avoid AI displacement, right, It.
Speaker 2 (29:50):
Might be understanding how to bridge that gap. How to
transition from being the expert doing the manual work to
being the expert overseeing the automated systems that assist with
that work, combining that hands on knowledge with oversight skill.
Speaker 1 (30:04):
So the question for you to ponder is which side
of that future equation excites you more the hands on
problem solving like fixing that pipe using pure human intuition,
or managing the complex systems. Maybe the predictive model that
alerted you to the leak in the first place, something
to think about.
Speaker 2 (30:20):
Definitely something to think about. We'll see you next time
on the Deep Dive
Welcome back to the deep dive. Today. We are waiting
directly into well, maybe the biggest worry for a lot
of people right now, the AI anxiety paradox.
Speaker 2 (00:09):
It's definitely more than just a little worry for some.
It's almost a panic, isn't it, Especially if you're figuring
out your.
Speaker 1 (00:15):
Career path totally. You see headlines everywhere saying AI is
coming for report writing, data analysis, customer service, even coding.
Speaker 2 (00:24):
Yeah, and if you're, say a high school student looking
out at that, it can make planning feel well, almost pointless.
That feeling of vulnerability is real.
Speaker 1 (00:33):
Exactly, And that's the feeling we really want to tackle
head on today because we're going to flip that narrative
completely right.
Speaker 2 (00:39):
We're not here to just list the jobs AI might
take over.
Speaker 1 (00:42):
No, we're here to shine a light on some hopes, really,
the solid, well paying careers that AI well, it just
can't touch. We've done a really thorough deep dive into
this comprehensive twenty twenty five analysis from Resume Now. They
did a great job pulling together data from the US
Bureau of Labor Statistics.
Speaker 2 (01:00):
Focus specifically was on a group that often gets missed
in the whole college or nothing debate.
Speaker 1 (01:06):
Yeah, we looked for career paths perfect for high school
grads who want good money, but crucially without the mountain
of college.
Speaker 2 (01:13):
Debt, which meant setting some really strict rules for this analysis.
The mission was to pull out only those careers that
met for very specific criteria non negotiables, really for being
both AI proof and financially smart.
Speaker 1 (01:28):
Okay, let's lay those out. The four AI proof must have.
Every single job we talk about today hit these marks.
Speaker 2 (01:36):
Okay. Number one requires only a high school diploma or
you know, an equivalent.
Speaker 1 (01:40):
Got it, No bachelor's degree.
Speaker 2 (01:42):
Needed, exactly. Two. It has a median annual salary over
fifty thousand dollars.
Speaker 1 (01:46):
That's key, fifty k minimum medium, okay.
Speaker 2 (01:49):
Three it shows faster than average job growth projected out
to twenty thirty two, so it's in demand.
Speaker 1 (01:54):
Good sign for stability.
Speaker 2 (01:55):
And the last one and four this is critical. It
carries a load to moderate risk of being allivated by
AI if a job failed. Even one of those didn't
make our list.
Speaker 1 (02:04):
What really jumps out when you apply those filters is
how much it pushes back against the constant pressure to
go to college, right, especially with costs soaring.
Speaker 2 (02:13):
Absolutely, we're talking about potentially sixty thousand dollars or more
just for tuition these days, before living costs.
Speaker 1 (02:19):
So we're contrasting that debt spiral with these well, really
viable alternatives. The way into these jobs isn't lectures.
Speaker 2 (02:26):
No, it's things like intensive apprenticeships, specific professional certifications or
really solid paid on the job training.
Speaker 1 (02:34):
And that's the financial hook you mentioned. It's huge, it is.
Speaker 2 (02:37):
Think about it. The usual college route that's four years
minimum of paying out money, taking on debt, and earning
basically nothing. Now compare that to the skilled trades. A
typical path is maybe a four year paid apprenticeship.
Speaker 1 (02:52):
Paid being the key word there.
Speaker 2 (02:53):
Absolutely key. You're learning a skill that's guaranteed to be
in demand, and at the same time you're earning maybe
forty to fifty even sixty thousand dollars a year while
you train.
Speaker 1 (03:03):
That's that's a massive financial swing. You're not just avoiding
sixty grand in debt.
Speaker 2 (03:07):
You could be sixty grand up in savings or starting
to build equity. It's immediate financial freedom. It means graduates
in these fields can start building capital, maybe buying a
home years maybe even a decade earlier than peers with
degrees and debt.
Speaker 1 (03:21):
Okay, so that sets the stage. Financially it makes sense,
But the core question remains. If AI is so smart,
you know, beating chess champions, writing legal documents, why can't
it do these jerbs right?
Speaker 2 (03:35):
And that brings us to the resilience factor, the human advantage.
Why these rules are different.
Speaker 1 (03:40):
We have to look at where algorithms hit their limits
versus well, the messiness of the real world.
Speaker 2 (03:45):
Yeah. AI is fantastic at certain things, repetitive tasks, high
volume stuff, analyzing huge data sets tasks.
Speaker 1 (03:52):
And controlled environments, clean data in, predictable output out. That's
where it shines exactly.
Speaker 2 (03:57):
But the second things get chaotic, unpredictable, or need really
fine motor skills under pressure, that's where AI starts to struggle.
Speaker 1 (04:05):
It's like hitting real world friction. Isn't it a door
that sticks, a part that's broken in a weird, non
standard way, or yeah, dealing with an upset customer. The
digital solution just breaks down.
Speaker 2 (04:15):
And those messy situations demand things that are uniquely human.
That's why these jobs are AI proof. We kind of
identify three big categories. Okay, what's the First, firstus pure
physical dexterity and precision, the kind of thing that needs proprioception,
that subconscious sense of where your body is and just
(04:37):
that physical feel for things.
Speaker 1 (04:38):
Right, like knowing how tight to turn a wrench by feel,
not just by calculated tor exactly.
Speaker 2 (04:44):
Second is emotional intelligence EQ. Absolutely vital for handling human crises,
delicate negotiations, dealing with someone's grief, for fear.
Speaker 1 (04:52):
AI can mimic conversation, yeah, but it can't offer genuine
comfort or read a room, can it not?
Speaker 2 (04:57):
Really not in a way that feels authentic. The third,
and this is huge, is real time judgment and quick
decisions in new, high stake situations.
Speaker 1 (05:05):
So when something totally unexpected.
Speaker 2 (05:07):
Happens precisely, AI can predict based on past data. But
when a wildfire suddenly shifts direction because the wind changes,
or a machine fails in a way no one's ever
seen before, you need human judgment to improvise a safe,
effective solution right then and there.
Speaker 1 (05:19):
And there's also just adapting to the environment itself. Right
The physical space.
Speaker 2 (05:24):
Oh, definitely, the source material really highlights this. Things like
having to crawl into a tiny, dusty attic space that
needs a specific body angle, or dealing with sudden rain
or extreme heat, or figuring out the weird quirks of
an old building's plumbing.
Speaker 1 (05:39):
Stuff that's just too varied, too unpredictable for a standard
robot exactly.
Speaker 2 (05:44):
These are physical logistical hurdles that defy standardized automation.
Speaker 1 (05:48):
Keith Spencer, the expert at resume now, he summed it
up really well. He said, these jobs thrive on problem solving, adaptability,
and real world know how things machines just don't have. Intrinsically.
Speaker 2 (06:02):
AI might optimize a factory's workflow on paper, but it
can't physically crawl under a massive leaking machine in the
middle of the night to figure out why some custom
rig part failed.
Speaker 1 (06:11):
Let's ue some concrete examples from the analysis, because they
really drive this home good idea.
Speaker 2 (06:16):
Think about the physical impossibility for a robot. Can a
machine climb a steep, slippery roof during a heavy rainstorm
to patch a leak?
Speaker 1 (06:24):
No way, it lacks the balance, the grip, the split
second judgment needed to.
Speaker 2 (06:29):
Not fall off or shift gears to eq. Can AI
truly empathize with a guest at a fancy hotel who's
just found out some terrible news back home.
Speaker 1 (06:37):
No, that needs genuine human warmth reassurance, reading subtile cues.
You can't script that comfort.
Speaker 2 (06:43):
And maybe the clearest trades. Example, a pipe bursts inside
a wall in a hundred year old house.
Speaker 1 (06:49):
Oh yeah, nightmare scenario, right.
Speaker 2 (06:51):
An algorithm might suggest the ideal rerouting, but the plumber
on site they have to physically improvise, maybe snake new
pipes through tight, unexpected spaces that aren't on any blueprint.
That's immediate creative physical problem solving.
Speaker 1 (07:05):
And this isn't just about AI not being able to
do the job. It's also about massive real world demand
for these jobs.
Speaker 2 (07:11):
Absolutely, the Blist projections are clear. Demand is surging, and
it's driven by really big fundamental needs. First, huge infrastructure
requirements across the country.
Speaker 1 (07:20):
Roads, bridges, the power grid, everything needs work.
Speaker 2 (07:24):
Second, the demographics. The aging population needs more specialized, high
touch care. That drives demand for roles like hearing aid specialists.
We'll talk about it. And the third big one the
massive shift to renewable energy, the green boom. This isn't
just digital code. It requires thousands of people physically installing
solar panels, wearing up EV chargers, climbing wind turbines.
Speaker 1 (07:46):
All demanding skilled hands on human labor, protected from automation
and critically needed.
Speaker 2 (07:51):
Okay, so now let's get into the really exciting part,
the numbers, the proof that this path is financially sound.
Let's hit that elite tier, the careers that blow past
our fifty dollar k minimum and really peak, all without
needing that four year degree.
Speaker 1 (08:05):
All right, leading the charge the highest paying non degree
median salary on the entire list elevator and escalator installers
and repairers.
Speaker 2 (08:13):
Yeah, the median is stunning. Ninety nine thousand, seven hundred
and twenty dollars.
Speaker 1 (08:16):
Wow, basically six figures medium. Many experienced folks are clearing
that easily.
Speaker 2 (08:21):
And you know they earn it. Think about the responsibility
these people maintain literal vertical lifelines in modern buildings, skyscraper's, hospitals, airports.
Speaker 1 (08:29):
When an elevator goes down in a fifty story hospital,
that's not just an inconvenience, it's potentially critical.
Speaker 2 (08:34):
Exactly, a single glitch can hold everything it demands, incredibly
fast diagnostics under immense pressure.
Speaker 1 (08:41):
And the work environment itself screams AI proof. It's not
standardized at all, not even close.
Speaker 2 (08:48):
It needs absolute physical precision, often in really awkward, noisy,
cramped spaces. Imagine crawling through a dusty ventilation duct or
hauling one hundred pound motor up a narrow shaft, dealing with.
Speaker 1 (09:00):
The building swinging slightly in the wind on a high floor.
Stuff or robot can't easily handle right.
Speaker 2 (09:05):
An algorithm can run simulations, but it can't replace that
human ability to troubleshoot. In three D AI monitors, data
is sure, but it can't feel a slightly loose cable
or hear that weird vibration from a bearing that tells
an experienced tech troubles coming.
Speaker 1 (09:20):
It's complex stuff hydraulics, gears, sensors, heavy loads, all interacting,
and the.
Speaker 2 (09:25):
Path in It's the gold standard for earning while learning
high school diploma. The mandatory tough four year paid apprenticeship, usually.
Speaker 1 (09:32):
Through unions right like the IUEC.
Speaker 2 (09:34):
Almost always the International Union of Elevator Constructors is the
main one, and that means you start earning decent money
right away. Starting pay during training is often forty to
fifty even sixty thousand dollars, so.
Speaker 1 (09:46):
You finish the apprenticeship with zero debt and potentially significant savings.
Speaker 2 (09:50):
Exactly, and the job outlook is solid too. Six percent
growth projected faster than average, driven by more tall buildings
going up, plus all the work needed to update older
elevators for safety and accessibility. That means about twenty five
hundred openings every year.
Speaker 1 (10:04):
The case did he mentioned Jake twenty eight years old.
He really puts a face on it.
Speaker 2 (10:08):
Yeah, bypass college debt entirely went straight into the apprenticeship
cleared one hundred and twenty thousand dollars last year, largely
thanks to emergency overtime calls.
Speaker 1 (10:16):
And his quote about AI perfect.
Speaker 2 (10:18):
It was great. AI. Let it try fixing a jam
door at two am when the power is half out
and its ten degrees below zero.
Speaker 1 (10:24):
Sums up the real world, high stress, often lousy conditions perfectly, okay.
Elite job number two A big shift here, huge.
Speaker 2 (10:32):
Shift from heavy mechanics to high touch, empathetic care hearing
aid specialists. Median salary here eighty nine thousand four to forty.
Speaker 1 (10:40):
Dollars almost ninety dollars. And this job is such an
interesting mix, isn't it?
Speaker 2 (10:45):
It really is. It combines incredible manual finesse fine tuning.
These tiny complex digital devices with precise technical testing like
audiograms and molding custom fits.
Speaker 1 (10:56):
But the crucial part is the human element, the counseling.
Speaker 2 (10:59):
Absolutely, that's the core AI proof element. They're guiding patients
through what can be a really sensitive emotional adjustment, accepting
hearing loss, getting used to a device. It's a big
psychological step for many people.
Speaker 1 (11:12):
AI can analyze the hearing test data the audiogram perfectly
better than a human maybe.
Speaker 2 (11:18):
Sure the raw data analysis, but it cannot provide that
personalized care. It can't comfort an elderly person who's maybe
feeling isolated or frustrated by their hearing loss.
Speaker 1 (11:28):
Or make those tiny real time adjustments based on subtle
feedback right like tweaking the sound profile mid conversation because
the person's face shows they're still struggling in that specific
noisy room exactly.
Speaker 2 (11:38):
That level of nuance, contextual judgment, and empathy that's purely
human right now.
Speaker 1 (11:42):
And the entry path here is much quicker than the
elevator apprenticeship, much.
Speaker 2 (11:46):
Quicker high school diploma. Then you need state licensure that
typically involves about six to twelve months of specialized training
plus really important on the job mentoring. Often through clinics
or the manufacturers.
Speaker 1 (11:57):
Themselves, so potentially earning close to ninety dollars within a
year or two of graduating high school.
Speaker 2 (12:03):
It's definitely possible, and the outlook is very strong. Eleven
percent projected.
Speaker 1 (12:07):
Growth driven by demographics, again almost entirely.
Speaker 2 (12:11):
The aging population baby boomers and Gen X means a
massive growing need for hearing solutions. The demand is just
going to keep increasing.
Speaker 1 (12:19):
Okay. Final one in the elite tier. This one sounds
intense fire prevention and protection specialists.
Speaker 2 (12:25):
It is intense. Salaries vary a lot here depending on
focus urban inspections versus wildland fires, but often seventy thousand
dollars up to ninety thousand dollars or more.
Speaker 1 (12:34):
The source is called it boots on ground heroism. What
does the job actually involve?
Speaker 2 (12:38):
It's a mix high stakes assessment of wildfire risks out
in the field, investigating fires after they happen to figure
out the cause, and crucial community education to prevent future fires.
Speaker 1 (12:48):
And why is this AI proof? Seems like AI could
model fire spread pretty well.
Speaker 2 (12:52):
It can model, yes, based on wind, humidity, terrain data,
but models break down when reality it's chaotic.
Speaker 1 (13:00):
So the human element is needed for the unexpected.
Speaker 2 (13:03):
Exactly, someone has to physically hike that rugged terrain, see
the specific condition of the vegetation, notice that the wind
just shifted unexpectedly, and make immediate life or death calls
about where to cut a fire line or which homes
need evacuating right now.
Speaker 1 (13:17):
And managing the chaos on the ground too. People resources.
Speaker 2 (13:21):
Absolutely, you can't automate that kind of real time, high
stressed command and control, especially when lives are at stake.
It also requires deep technical knowledge, building codes, sprinkler systems,
hazardous materials applied under extreme pressure, and.
Speaker 1 (13:34):
The outlook you said, explosive growth.
Speaker 2 (13:36):
Sadly, yes, the analysis links it directly to climate change. Longer,
more intense wildfire seasons, more severe weather events. The need
for experts in prevention, response, and investigation is growing rapidly
and isn't going away.
Speaker 1 (13:50):
Okay, that's a powerful look at the top tier. Let's
move into what the analysis called the middle tier. Still
great salaries well over sixty k's, but maybe more foundational rules.
Speaker 2 (13:59):
Exactly of the jobs that really rely on grit technical
know how, and sometimes they crucial eq. Factor two. Starting
with the absolute backbone of industry, industrial machinery mechanics. Median
salary here sixty two thousand, seven hundred and eighty dollars,
And these folks are essential. They keep the massive machinery
(14:20):
in factories and plants running, the assembly line robots, the
huge conveyor systems, giant presses, complex packaging lines you said earlier.
Speaker 1 (14:27):
When one of these lines goes down, it costs companies
a fortune.
Speaker 2 (14:30):
Thousands, sometimes tens of thousands an hour. So these mechanics
need to be jacks of all trades. They're welding, fixing, hydraulics,
predicting when a part might fail, and crucially reprogramming the PLCs.
Speaker 1 (14:42):
PLCs programmable logic controllers the brains of the.
Speaker 2 (14:45):
Machines exactly, and troubleshooting them is a key reason this
is AI proof. A failure isn't always a physical broken part.
Sometimes it's a subtle glitch in the custom ladder logic
the software running that specific.
Speaker 1 (14:56):
Machine, and only a human who understands the machine's physical
operation can spot that logic error.
Speaker 2 (15:02):
Often, yes, you need to understand how the code translates
to physical movement and the diagnostics. The source material called
it greasy, hazardous chaos.
Speaker 1 (15:11):
Sounds about right for a factory floor.
Speaker 2 (15:13):
Yeah, machines vibrating, maybe leaking fluids, maybe modified weirdly over
the years. AI is great at monitoring clean data streams,
but it can't physically put a wrench on a seized
bearing in one hundred and twenty degree.
Speaker 1 (15:26):
Plant needs that human intuition, that feel built from experience totally, and.
Speaker 2 (15:30):
The outlook reflects the need. Strong thirteen percent growth projected
plus a massive fifty two thousand openings expected each year,
mainly due to retirements in manufacturing growth. They're desperate for
skilled people, okay.
Speaker 1 (15:42):
Next up, a job really riding the green wave electricians,
medium salary six and ninety dollars.
Speaker 2 (15:49):
Yeah, and this job is changing so much. It's way
beyond just wiring a light switch now right.
Speaker 1 (15:53):
They're doing complex smart home setups, installing those high voltage
ev charging stations.
Speaker 2 (15:58):
Ensuring grid stability during outages. Plus the fundamental, really skilled
physical work of bending metal conduit pipe to exact specifications
on site.
Speaker 1 (16:05):
And the aiproof angle. Here is spatial.
Speaker 2 (16:07):
Reasoning, absolutely pure, complex spatial reasoning in three dimensions. Think
about crawling through a cramped, boiling hot attic trying to
interpret a complex blueprint and then finding say a structural
beam exactly where you plan to run wires.
Speaker 1 (16:23):
You have to improvise constantly.
Speaker 2 (16:25):
Constantly or worse, encountering old unmarked wiring. No robot or
drone can replicate that real time three D problem solving
in an unpredictable, unmapped space, and the stakes are always
high fire risk, electrocution risk.
Speaker 1 (16:40):
And there was a tip about specialization boosting earnings big time.
Speaker 2 (16:43):
Get certified in solar panel installation, battery storage systems, or
industrial automation controls, and you can definitely push your earnings
towards EIGHTYK even one hundred dollar k pretty quickly.
Speaker 1 (16:53):
And the growth reflects that demand.
Speaker 2 (16:55):
For sure, eight percent projected growth and a huge eighty
four thousand openings annually. A lot of that is directly
fueled by green energy projects and infrastructure.
Speaker 1 (17:03):
Upgrades following closely and pay another essential trade plumbers, pipe
fitters and steamfitters median sixty and fifty dollars, And.
Speaker 2 (17:12):
That quote really nails it. Burst pipes don't wait for algorithms.
Speaker 1 (17:15):
It's the emergency unpredictable nature, isn't it.
Speaker 2 (17:18):
Absolutely yes, they install complex systems and new buildings, but
a huge part of the job is dealing with sudden disasters,
major clogs, burst pipes in old buildings, retrofitting systems for
water efficiency, and.
Speaker 1 (17:31):
The AI barrier is again the physical environment.
Speaker 2 (17:34):
Yeah, navigating nasty crawl spaces, diagnosing hidden leaks often just
by sound or feel. You need human senses and dexterity
for that, and adapting to the weird quirks of old
corroded plumbing that never matches the original plans.
Speaker 1 (17:48):
Entry pep here is the apprenticeship model again.
Speaker 2 (17:50):
Typically yes, a four to five year paid apprenticeship and again,
getting into a union program like through the United Association
UA is often the best route for paid training and
access to good job.
Speaker 1 (18:00):
Growth is four percent, but the opening's number is huge
because of retirements.
Speaker 2 (18:04):
Massive turnover forty eight thousand openings projected each year. It's
another field with a huge talent vacuum, guaranteeing job security
for new entrants.
Speaker 1 (18:12):
Okay, let's pivot now away from the tools and pipes
towards jobs where that emotional intelligence filter is the key
AI barrier.
Speaker 2 (18:19):
Right roles needing complex human interaction, often under stress. First
up lodging managers median salary sixty nine and ten dollars.
Speaker 1 (18:29):
This might seem more administrative, but the AI proofing is
about crisis management exactly.
Speaker 2 (18:34):
It's a high wire act overseeing the entire hotel operation,
managing staff, handling VIP issues. Sure, AI chadbots can handle
bookings or answer basic questions, but they.
Speaker 1 (18:44):
Can't handle a really angry guest whose reservation got messed up.
Speaker 2 (18:48):
No way. That needs a human manager face to face
to smooth things over. Show empathy, maybe offer a creative
solution and turn that negative experience into loyalty. That takes
serious EQ and judgment. And the out pretty good seven
percent growth fueled by the travel rebound and the trend
towards more boutique hotels that really emphasize personalized guest experiences.
Speaker 1 (19:08):
Next a job, people often misunderstand flight attendance median sixty eight,
three hundred and seventy dollars.
Speaker 2 (19:15):
Yeah, people think servers in the sky, but that's not
the core function at all.
Speaker 1 (19:17):
They're first responders essentially.
Speaker 2 (19:19):
Absolutely train first responders at thirty thousand feet. They handle
medical emergencies, CPR, defibrillators, even delivering babies. Sometimes they manage
panic during turbulence in force. Safety rules deal with security issues.
Speaker 1 (19:33):
It's about managing group psychology in a crisis in a
confined space.
Speaker 2 (19:37):
Precisely, when two hundred people are scared in a metal tube,
you need train humans to stay calm, take charge, provide reassurance,
and handle any medical needs. Robots can't do that. Human
to human intervention.
Speaker 1 (19:49):
And demand is high here too, very high.
Speaker 2 (19:51):
Eleven percent growth projected directly tied to the boom in
air travel post pandemic.
Speaker 1 (19:56):
Okay, last one in this middle EQ focused group chef
and head cooks median fifty nine thousand, four hundred and
forty dollars.
Speaker 2 (20:04):
This one is protected by creativity and intense time pressure.
Speaker 1 (20:07):
Leading a kitchen team is notoriously.
Speaker 2 (20:09):
Stressful, incredibly stressful. It demands creativity on the fly, split
second timing during a rush, and that really refined palid judgment.
AI can suggest recipes or optimize inventories, sure, but it can't.
Speaker 1 (20:20):
Taste the soup and decide it needs a pinch more salt,
or improvise a new dish when a key ingredient runs
out mid service exactly.
Speaker 2 (20:26):
Or plate a dish beautifully for visual appeal, which is
huge now with social media. That requires human artistry plus
managing the team, motivating people, resolving conflicts, instantly, maintaining quality
when everything's going crazy. That's human leadership.
Speaker 1 (20:42):
And solid growth here too.
Speaker 2 (20:43):
Yep, nine percent growth, especially in higher end restaurants, food tourism,
and the gore mate delivery market.
Speaker 1 (20:49):
Wow. Okay, that covers the elite and middle tiers. Now
what about those hidden gems, the really specialized trades that
maybe don't get as much attention.
Speaker 2 (20:58):
Yeah, these often require very specific, sometimes risky skills that
command good salaries. Let's touch on a few. First strangler
fitters focus purely on fire suppression systems. Salary potential often
seventy two thousand dollars plus, especially in union jobs. They install, inspect,
maintain these complex life saving.
Speaker 1 (21:17):
Systems, and AI proof.
Speaker 2 (21:18):
Because the environments are often compromised active construction sites, post
disaster areas, and the systems require precise hands on installation
and pressure testing, the stakes are simply too high for
automation failure.
Speaker 1 (21:29):
Makes sense. What else?
Speaker 2 (21:30):
First line supervisors of mechanics. This is a leadership role,
often paying around seventy two thousand dollars.
Speaker 1 (21:36):
Medium, So moving up from being a mechanic.
Speaker 2 (21:39):
Exactly, it blends deep technical knowledge with management skills, organizing work,
motivating teams, resolving issues. AI can schedule, but it can't
lead people effectively under pressure.
Speaker 1 (21:52):
Another interesting one wind turbine technicians median sixty two thousand dollars.
Speaker 2 (21:57):
Yeah, this one's AI proof because of the extreme environment.
Speaker 1 (22:00):
Climbing those huge towers.
Speaker 2 (22:01):
Scaling three hundred foot towers, working at heights in potentially
high winds and bad weather, doing complex mechanical and electrical
repairs in a confined space.
Speaker 1 (22:11):
Drones can inspect, maybe, but they can't do the heavy
lifting or complex repairs up there reliably, especially in bad weather. Not.
Speaker 2 (22:17):
Yes, certainly, not with the required reliability. It takes a
highly trained human with specialized safety gear.
Speaker 1 (22:22):
And one more.
Speaker 2 (22:22):
Machinery maintenance workers median around fifty eight thousand, six hundred
thirty dollars. These folks focus more on the routine preventative
maintenance side.
Speaker 1 (22:30):
How's that different from industrial mechanics.
Speaker 2 (22:32):
It often involves more frequent, smaller scale hands on fixes
and adjustments, replacing a warn belt, fixing a sensor bracket,
basic lubrication, minor alignment tweaks. While tasks might seem simple,
the sheer variety and the need for human judgment on
normal wear versus impending failure keeps it largely out of
automation's reach for now.
Speaker 1 (22:53):
Okay, so we've seen the jobs. Now let's zoom out.
Why are these trades specifically so valuable right now in
twenty twenty five? What's the big economic picture?
Speaker 2 (23:03):
It really boils down to a few massive structural forces
that are driving up demand and wages.
Speaker 1 (23:08):
First big one the skills gap.
Speaker 2 (23:11):
Huge, The numbers are kind of scary, actually. Projection suggests
two point four million manufacturing jobs could go unfilled by
twenty thirty simply because there aren't enough people with the
right trade skills.
Speaker 1 (23:20):
That lack of supply gives huge leverage to those with
the skills right and drives it pay absolutely.
Speaker 2 (23:25):
Second force the talent vacuum from retirements. We mentioned it
for plumbers and electricians.
Speaker 1 (23:29):
Forty eight thousand openings a year for plumbers, eighty four
thousand for electricians exactly.
Speaker 2 (23:34):
These aren't temporary blips. It's a predictable, massive wave of retirements,
creating sustained demand and career security for decades.
Speaker 1 (23:42):
And the third driver government spending.
Speaker 2 (23:45):
Yeah, the big infrastructure bills. Billions are flowing into upgrading roads,
bridges the grid and especially green energy projects that guarantees
a pipeline of work for electricians, mechanics, pipe fitters, you
name it, for years to come. It creates a buffer
against ecomic downturns to.
Speaker 1 (24:01):
It's also worth mentioning the shift in who is doing
these jobs, isn't it?
Speaker 2 (24:05):
Definitely the old stereotypes are breaking down. More women and
minorities are entering the trades, which is fantastic. Many unions
and associations now have specific programs, scholarships, and support systems
to encourage diversity. The opportunities are opening up.
Speaker 1 (24:19):
So all this leads back to the bottom line, the
financial payoff. These aren't just getting by.
Speaker 2 (24:24):
Jobs, not at all. The analysis showed the top ten
percent in many of these fields easily clear one hundred
thousand dollars a year, especially with overtime.
Speaker 1 (24:32):
And if you go the entrepreneurial route, the.
Speaker 2 (24:34):
Ceiling goes way up. A successful self employed master plumber
or specialized roofer running their own small business, they can
potentially clear two hundred thousand dollars, maybe even three hundred
thousand dollars a year based purely on their skill and reputation.
Speaker 1 (24:49):
Let's bring back that story of Maria, the one who
left retail for plumbing.
Speaker 2 (24:53):
Great example, left a low wage job, did the apprenticeship,
bought her first house at twenty.
Speaker 1 (24:58):
Five, and her quote was so so simple but powerful,
no debt, just tools and hustle.
Speaker 2 (25:03):
Think about the freedom in that statement at twenty five.
Compare that to a typical college grad, maybe facing decades
of student loan payments limiting their choices.
Speaker 1 (25:13):
Maria was building equity immediately. That head start in building
capital might be the single biggest advantage.
Speaker 2 (25:18):
I think it is trading skill and hard work directly
for high wages and financial freedom.
Speaker 1 (25:23):
Okay, this is all incredibly compelling for listeners out there thinking, Okay,
maybe this is for me. I like working with my hands,
I like problem solving. What are the practical steps? How
do you actually launch one of these careers?
Speaker 2 (25:34):
Right? Let's get practical. First step absolutely crucial. Assess your fit.
Speaker 1 (25:38):
Honestly, it's not for everyone. Right can be physically demanding
odd hours.
Speaker 2 (25:42):
Exactly, you need to be realistic. The best way job
shadowing seriously, contact a local union, hall, IDW for electrical,
UA for plumbing, et cetera, or even just local contractors.
Many are happy to let you spend a day on
a site observing, see if you actually like the environment,
the pace, the work.
Speaker 1 (25:59):
It's good advice. See it before you commit.
Speaker 2 (26:02):
Then training, train smart, not expensive. Look into community college
programs first. Many have excellent technical programs, often subsidized by
things like federal Perkins funding specifically for a career in
tact much cheaper than university.
Speaker 1 (26:17):
But the gold standard is still the paid apprenticeship.
Speaker 2 (26:20):
Absolutely prioritize that if you can. These are official Department
of Labor registered programs classroom learning plus thousands of hours
of paid on the job training. The deal Well website
do wel dot gov is the place to start. Search
registered apprenticeship programs by trade and your location.
Speaker 1 (26:36):
Okay, find an apprenticeship. What else?
Speaker 2 (26:38):
Certify up? Get those extra certifications They translate directly to
more money. OSHA ten or OSHA thirty for safety is
often required anyway, but specialize ones like EPA certification for
handling refrigerants if you're going into HVAC.
Speaker 1 (26:53):
The analysis mentioned bonuses for CERTs, Yeah.
Speaker 2 (26:55):
Five thousand dollars. Sign on bonuses or immediate raises just
for having certain high demands. Sort of cations are pretty common.
They approve your skill level.
Speaker 1 (27:02):
Networking seems important.
Speaker 2 (27:03):
Too, essential in the traits. It's a community. Join the
relevant professional chapters ibw UA, Associated Builders and Contractors ABC.
They offer training connections, job boards, sometimes access to the
best union contracts. Your reputation and who you know matters, and.
Speaker 1 (27:21):
A modern tip the side hustle.
Speaker 2 (27:23):
Yeah, it's a great way to build practical skills while
you're training officially. Use platforms like task grab it for
smaller non licensed jobs basic repairs, furniture assembly, maybe installing
simple fixtures.
Speaker 1 (27:34):
It's confidence tool skills gets you customer.
Speaker 2 (27:36):
Reviews exactly real world practice that looks good on a
resume later.
Speaker 1 (27:39):
So going through all this, it really comes back to
that core idea, the irreplaceable human element in these jobs.
Speaker 2 (27:47):
Definitely, in these chaotic, physical or highly empathetic roles, the
human advantage is clear, and.
Speaker 1 (27:54):
The future isn't really humans versus machines, is it. It's
more collaboration.
Speaker 2 (27:59):
That's the key takeaway. It's a division of labor.
Speaker 1 (28:01):
And that electricians quote we mentioned earlier perfectly sums up
that synthesis, doesn't it.
Speaker 2 (28:05):
It really does. AI designs the circuit, I make it
not explode.
Speaker 1 (28:10):
That's it, right, there, the algorithm does the abstract design.
The human handles the messy, high stakes reality.
Speaker 2 (28:16):
That's the division of labor for the twenty first century.
I think.
Speaker 1 (28:18):
Okay, So, wrapping this all up, the main takeaway from
this whole deep dive seems pretty clear. If you're looking
for a stability, security, maybe even a six figure income
and guaranteed work in twenty twenty.
Speaker 2 (28:28):
Five, path doesn't necessarily run through a university lecture hall
and piles of debt.
Speaker 1 (28:33):
No stability, high earnings, pride in actually billeting and maintaining
the world around us. That path might just require a
hard hat or maybe a specialized tool like a hearing
aid programmer, or yeah, a chef's.
Speaker 2 (28:45):
Knife, and that door. It's wide open and it's debt free.
Speaker 1 (28:49):
Before we sign off, though, we wanted to leave you
with one last thought, something to chew on. That kind
of broadens the picture.
Speaker 2 (28:55):
Right, We focus today entirely on these jobs where the
human element that hands on skill is paramount right now
the stuff AI can't do.
Speaker 1 (29:04):
But it's also true that AI is creating jobs too.
The World Economic Forum predicts AI will actually create a
net increase of seventy eight million jobs. Globally by twenty thirty.
Speaker 2 (29:14):
Yeah, despite all the automation anxiety, the net prediction is.
Speaker 1 (29:17):
Positive and many of those new jobs they'll likely be
in different areas oversight management, training, the AI interpreting its
outputs roles that are quite different from the hands on
work we detail today.
Speaker 2 (29:29):
Think about it. The skilled electrician who's wiring ev stations
today Tomorrow, they might be the person best equipped to
manage the AI system that predicts maintenance needs for the
entire solar grid because they understand the physical reality behind
the data.
Speaker 1 (29:44):
So the real challenge maybe for the next generation isn't
just picking a manual skill to avoid AI displacement, right, It.
Speaker 2 (29:50):
Might be understanding how to bridge that gap. How to
transition from being the expert doing the manual work to
being the expert overseeing the automated systems that assist with
that work, combining that hands on knowledge with oversight skill.
Speaker 1 (30:04):
So the question for you to ponder is which side
of that future equation excites you more the hands on
problem solving like fixing that pipe using pure human intuition,
or managing the complex systems. Maybe the predictive model that
alerted you to the leak in the first place, something
to think about.
Speaker 2 (30:20):
Definitely something to think about. We'll see you next time
on the Deep Dive
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