October 20, 2025 • 34 mins
The source provides an extensive examination of Walmart's adoption of Artificial Intelligence (AI) and the subsequent impact on its workforce, particularly on entry-level positions. The episode details how Walmart utilizes AI across various operations, including inventory and supply chain management, customer-facing technology, and workforce scheduling, to enhance efficiency and maintain its competitive edge. Crucially, the document highlights the resulting disappearance of traditional entry-level jobs—such as cashier and stocking roles—which are being replaced by automation and self-service systems, leading to significant concerns about job displacement and economic impact on low-skilled workers. Finally, the source discusses Walmart’s strategies to mitigate these effects through upskilling programs like Live Better U, the creation of new technology-focused roles, and community partnerships to support workforce transition.
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Episode Transcript
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Speaker 1 (00:00):
Welcome to the deep dive. We take these massive global shifts,
try to boil them down and really give you the
essential detailed knowledge.
Speaker 2 (00:08):
You need keep you ahead of the curve exactly.
Speaker 1 (00:11):
And today we're undertaking a pretty complex investigation. We're looking
at the biggest player in global retail, Walmart.
Speaker 2 (00:19):
Yeah that's right. I mean when we talk about tech
transformation reshaping the job market, Walmart isn't just playing along.
They're really setting the pace right, And we're specifically looking
at their well aggressive, almost systemic adoption of artificial intelligence
AI and the impact of that and the profound sort
of dual implications this has, especially for the millions, literally
(00:42):
millions of people who rely on those entry level jobs.
Speaker 1 (00:45):
Okay, so let's try and unpack this. It's a monumental subject. Really,
we're focusing on a company whose scale is it's almost
impossible to really grass isn't it.
Speaker 2 (00:54):
It really are.
Speaker 1 (00:55):
And this isn't just about like streamlining a business process.
This feels more like a structural re engineering of the
whole retail labor market. So our mission today is pretty clear.
We need to analyze exactly how Walmart is using AI
across its vast systems try to quantify the implications which
seem inevitable for those traditional roles cashiers, stalkers, greeters jobs.
(01:19):
We all see, yeah, and critically examine their strategies. And
let's be honest, the challenge is involved in managing this
massive workforce transition.
Speaker 2 (01:28):
And what's fascinating or maybe crucial here is that the
sheer size of their operation almost demands this kind of optimization.
Speaker 1 (01:37):
It's not really optional for them.
Speaker 2 (01:39):
It feels like a competitive necessity at this point. I mean,
we're talking about a network over forty seven hundred stores
just in the US, wow, and a global workforce that's
around two point one million associates.
Speaker 1 (01:49):
It's staggering.
Speaker 2 (01:50):
So when a corporation that big, that's basically an economic
anchor in thousands of communities, when it fundamentally changes its
operating model, it doesn't just cause ripples, No, it's more
like a seismic event for the global job market exactly.
Speaker 1 (02:03):
And you know they're under relentless pressure competitors, especially online
e commerce, yeah, pushing them to cut costs, optimize every
single process, and meet these increasingly impatient digital first customer.
Speaker 2 (02:15):
Expectations speed and convenience.
Speaker 1 (02:17):
Right, So for you listening trying to really get a
handle on the speed of technological change. This deep dive
is absolutely crucial because it focuses right on those roles,
the ones that have historically been the entry point for
millions first step, the first essential step onto the economic ladder.
And the critical question we're wrestling with today is, well,
(02:38):
what happens to the bottom rung when AI automation starts
systematically removing it?
Speaker 2 (02:43):
That really is the core tension, isn't it. This isn't
just a standard you know, business efficiency upgrade. This is
a transformation in how low skill labor, the kind of
labor that builds work ethic, provides that initial stability, how it's.
Speaker 1 (02:57):
Performed, not performed by humans anymore increasingly.
Speaker 2 (03:00):
Not performed by humans in this modern digital economy.
Speaker 1 (03:03):
Okay, so let's move beyond the sort of macro picture
and look at the how how is Walmart actually integrating
this tech across its i mean, sprawling operations. It's clear
AI isn't just an add on tool for them, No,
not at all. It feels like a foundational cornerstone built
into pretty much every system.
Speaker 2 (03:20):
Yeah, from the deepest parts of the supply chain right
up to the customer checking out Okay, their AI journey.
It really seems like a direct response to those demands
for speed and precision you mentioned, especially in a high
volume digital world, and the initial maybe the most significant
gains you see them right away in predictive analysis and logistics.
Speaker 1 (03:41):
Okay, so AI in inventory and supply chain. When we
talk about forecast and demand, I think most people picture
like a basic spreadsheet based on last year's sales.
Speaker 2 (03:51):
Right maybe Christmas sales, predicting next Christmas.
Speaker 1 (03:53):
Yeah, but these machine learning algorithms, they go way beyond
that simple historical data, right.
Speaker 2 (03:58):
Oh. Absolutely, They're constantly in ingesting just massive data sets
in real time. The algorithms analyze historical sales, sure, that's
the baseline.
Speaker 1 (04:06):
But then but then they layer in these complex external
variables things humans could maybe correlate but really slowly manualize.
Speaker 2 (04:14):
What kind of things well, specific local events for instance,
is there a big high school football game happening or
a large community festival that's gonna spike demand for say
bottled water and snacks. Okay, and even more fascinating, they
integrate highly localized weather patterns.
Speaker 1 (04:32):
Okay, give us an example, how does weather change of
stocking order through AI.
Speaker 2 (04:37):
Well, think about the subtlety. If the forecast predicts a
sudden cold snap, maybe with heavy rain, the system immediately
predicts a spike in demand for comfort foods, maybe soup,
hot drinks, perhaps even certain cold remedies makes sense. Conversely,
an unexpected heat wave, yeah, you'll instantly up the forecast
for bottled water, maybe charcoal for grilling, sunscreen.
Speaker 1 (04:59):
And this is store by store exactly.
Speaker 2 (05:01):
Yeah, the algorithm's forecast this demand for specific products at
individual stores, not just regionally, and with pretty incredible accuracy.
The goal always is optimal stock levels, minimizing waste from overstocking,
but also crucially eliminating those dreaded stockouts, because a stockout
just sends a customer straight to a competitor.
Speaker 1 (05:21):
Right. And this efficiency, it doesn't just stay on the
planning sheet, It dictates how the distribution centers, the sort
of nerve centers, actually function.
Speaker 2 (05:29):
That's where you see the heavy metal, the real automation. Yeah,
the shift in warehousing is genuinely staggering. Walmart's invested heavily
in autonomous mobile robots amrs.
Speaker 1 (05:41):
The little moving robots, Yeah.
Speaker 2 (05:43):
Navigating the warehouse floors. They're designed to move goods. Often
in these really high density storage setups, they're frankly difficult
for human teams to navigate quickly and efficiently.
Speaker 1 (05:53):
And we know Walmart has that big partnership with Symbotic.
What are those systems doing that a person can't or
can't do as well?
Speaker 2 (06:01):
Right? The Symbotic deal is key. Their systems don't just
like move boxes from A to B. They're capable of
this high speed three dimensional processing of inventory.
Speaker 1 (06:10):
What does that mean?
Speaker 2 (06:11):
Three dimensional sorting, stacking, and transporting goods deep inside this
automated warehouse structure up down, across way, denser than humans
could manage. And the competitive advantage there is huge speed,
density consistency. These systems can process orders, stage pallets way
faster than any human crew.
Speaker 1 (06:31):
And they don't need brakes.
Speaker 2 (06:33):
Critically, they operate two four, seven, three six five, no brakes,
no shift changes, no training periods, no overtime costs. This
continuous throughput drastically improves logistics metrics, and yeah slashes those
operational costs tied to traditional labor management.
Speaker 1 (06:49):
Okay, so moving from the back end the distribution centers
to the actual store floor, we're seeing AI replace tasks
that used to be essential repetitive entry level. This is
the instore monitoring part exactly.
Speaker 2 (07:02):
Think about AI powered cameras and sensors. Sometimes they're even
integrated into the floor cleaning machines that are already roaming
the aisles, and they're constantly monitoring shelves in real time.
So instead of relying on a stocker or a manager
to manually walk every single aisle multiple times a.
Speaker 1 (07:17):
Day, which is slow, expensive and prone.
Speaker 2 (07:19):
To human error, Yeah, the AI system instantly detects issues.
Speaker 1 (07:23):
What kind of issues specifically besides empty shells, well, stock.
Speaker 2 (07:26):
Levels are the big one, obviously flagging empty shelves, but
the systems also monitor crucial details like is the price
tag correct, is it even there? And planogram compliance. A
planogram is that super detailed diagram saying exactly where each
product should sit on the shelf.
Speaker 1 (07:44):
Ah right, making sure things are in the right spot exactly.
Speaker 2 (07:49):
If a product is misplaced or the tag is wrong,
the AI alerts the human staff instantly. This massively reduces
the need for that manual, dedicated shelf auditing that entry
life workers used to do.
Speaker 1 (08:01):
And then there's the digital side. If you're interacting with
Walmart online AI is probably involved.
Speaker 2 (08:05):
Oh, absolutely, those AI driven recommendation engines, they're constantly learning
from your browsing, your.
Speaker 1 (08:10):
Purchase history, you try and predict what I want next.
Speaker 2 (08:13):
Exactly, personalizing product suggestions of the website or the app,
which demonstrably boosts sales margins.
Speaker 1 (08:19):
Okay.
Speaker 2 (08:19):
And furthermore, if you interact with customer service online, increasingly
you're dealing with pretty sophisticated chatbots powered by natural language
processing NLP.
Speaker 1 (08:28):
How good are they now? Is it still just basic
where's my order?
Speaker 2 (08:32):
They're getting far more advanced. They can handle complex multi
step inquiries now, tracking an order, yeah, but also initiating
a complicated return or resolving a minor billing issue.
Speaker 1 (08:43):
Is that used to need a human?
Speaker 2 (08:44):
Absolutely? These functions used to require literally rooms full of
trained human customer service reps. Now the AI handles the
bulk of the volume. It only escalates the really unique
or emotionally charge cases to human agents, So.
Speaker 1 (09:02):
It fundamentally restructures that whole job category completely. And finally,
they've even pushed AI into what used to be a
key managerial task workforce optimization scheduling.
Speaker 2 (09:13):
Yeah, and this is a critical displacement because it doesn't
just impact the frontline worker, it hits the mid level
manager who often oversaw those workers.
Speaker 1 (09:21):
How does that work?
Speaker 2 (09:22):
Complex scheduling algorithms now analyze store traffic patterns minute by minute.
Sometimes they cross reference that with employee availability, specific sales
forecast for that day or week, and then they generate
optimized shifts. These systems can dynamically adjust staffing levels way
faster and more efficiently than any human manager could manually
juggle schedules.
Speaker 1 (09:42):
So that reduces the need for managers to coordinate staffing,
make those on the fly shift adjustments, and that role
that often provided a vital sort of entry level supervisory opportunity,
didn't it for people looking to move up from stocking
or casharing.
Speaker 2 (09:57):
Precisely that was a step up. Now the machine is
increasingly determining labor allocation. It removes a key administrative layer,
and yet crucially, it removes an entire pathway for internal
career advancement into that first level of management.
Speaker 1 (10:11):
Wow. Okay, so the evidence seems overwhelming. AI brings undeniable
operational benefits. Faster stocking, better inventory, uptimized.
Speaker 2 (10:18):
Schedule deficiency gains are real.
Speaker 1 (10:20):
But the necessary and I guess critical consequence is this
significant structural reduction in those roles, the ones that have
historically been the entry point for millions, especially workers maybe
with limited experience or formal education.
Speaker 2 (10:34):
This is the core trade off, isn't it The one
we have to analyze really carefully. Efficiency gains in repetitive
tasks often correlate directly with job displacement.
Speaker 1 (10:43):
And for the everyday customer, the most visible sign of
this has got to be the cashier's right. Absolutely, Walmart's
aggressive expansion of self checkout systems. You walk into a
store now and see rows and rows of these kiosks.
Speaker 2 (10:56):
It's pervasive, and the tech just keeps getting better fast.
These aren't just passive barcode scanners anymore. No, These self
checkout systems use AI combined with sensor technology to detect errors,
prevent loss, flag suspicious activity. How like what well, using
weight sensors in the bagging area, for example, to make
(11:16):
sure the item scanned matches the weight of the item
you actually bagged the old banana trek kind of yeah,
or image recognition to make sure the type of produce
you keed in matches with the camera sees.
Speaker 1 (11:27):
So the AI is actively taking over the supervision and
loss prevention role the human cashier used to provide.
Speaker 2 (11:34):
Exactly the human element is reduced to minimal intervention, often
just one person supervising you know, a dozen.
Speaker 1 (11:41):
Machines, and the industry numbers reflect this.
Speaker 2 (11:43):
Yeah, the metrics show a dramatic shift. Industry estimates suggests
that this massive adoption of self checkout has cut the
need for traditional cashiers by up to thirty percent in
some retail.
Speaker 1 (11:55):
Chain thirty percent.
Speaker 2 (11:57):
And Walmart is definitely a leader in pushing this trend.
While they still keep some staffed lanes for customer preference
or larger orders, the overall long term demand for those
traditional cashier jobs has fundamentally plummeted.
Speaker 1 (12:12):
Okay, moving beyond the front of the store, the work
of stalking shells, keeping track of inventory another foundational entry
level role that's being automated too rapidly.
Speaker 2 (12:22):
We've seen them try different approaches over the years.
Speaker 1 (12:24):
I remember reading about the Boston Nova robots, those tall
ones that rolled up and down the aisles scanning shell.
Speaker 2 (12:29):
Yeah, the pilot program that got a lot of.
Speaker 1 (12:31):
Press, but that specific partnership ended, didn't it.
Speaker 2 (12:34):
It did, But that's a crucial nuance here. The failure
of one specific robot model like bosson Nova doesn't mean
the overall goal of automating shelf scanning failed.
Speaker 1 (12:45):
Ah, they just changed the method exactly.
Speaker 2 (12:47):
The method changed, Walmart continues to deploy other AI powered tools,
maybe stationary sensors on the shelves, smaller purpose built robots,
even drones in some.
Speaker 1 (12:57):
Tests doing the same job, continuously.
Speaker 2 (12:59):
Scanning shells to I'm sure correct stocking pricing compliance, planegram adherence.
These systems do the work that once required teams of
associates walking the floors.
Speaker 1 (13:09):
So instead of a big team of overnight stalkers manually
counting and arranging.
Speaker 2 (13:13):
It's uncontinuously, often by automated systems, reporting back to a
smaller team of humans who act on the data. Okay,
and this is even more prouns Back in the distribution centers,
those automated sorting systems, guided by AI, they handle packing
and sorting pallets far faster than human speed allows, which
again significantly reduces the need for manual warehouse associates doing
(13:34):
those repetitive, high volume tasks.
Speaker 1 (13:36):
Now, we can't forget the more symbolic roles, right, The
Walmart greeter was such an icon well fixture.
Speaker 2 (13:42):
Yeah, providing that human welcome, But even that role has
been subject to well scaling back and tech replacement.
Speaker 1 (13:50):
I think a lot of listeners remember that friendly face
at the door. What's replacing that human interaction?
Speaker 2 (13:56):
In many stores, the greater role has been restructured or yes,
scaled back significantly. The actual functions they are performed are
being absorbed by technology, like what most visibly in areas
like loss prevention, you might have AI powered facial recognition
systems monitoring entrances, flagging known shoplifters maybe okay, or automated
customer assistants kiosks right at the front, handling basic directional questions,
(14:20):
maybe membership checks for Sam's club.
Speaker 1 (14:22):
So even roles that seemed focused on soft skills aren't immune.
If there's a repeatable task involved.
Speaker 2 (14:29):
That shift acknowledge is that Yeah, if a core function
can be automated, even in a customer facing role, it's
likely on the table for restructuring.
Speaker 1 (14:37):
Okay, let's talk scale again, because when you look at
the total number of people involved two point one million globally,
the potential displacement moves from just a corporate efficiency thing
to a real societal issue. We need to try and
put some numbers.
Speaker 2 (14:50):
On this, We really do, and just for global context,
remember the World Economic Forum estimated I think back in
twenty twenty that automation and AI could display something like
eighty five million jobs globally by twenty twenty five. Wow.
And retail, because it has so many repetitive tasks, is
consistently named as one of the hardest hit sectors.
Speaker 1 (15:08):
So let's bring that down to Walmart specifically using their
operational scale. This is the kind of calculation that helps
understand the sheer magnitude.
Speaker 2 (15:15):
Okay, consider the logistics over forty seven hundred US stores. Now,
let's say each of those stores, through implementing advanced self
checkout and some automated stocking tech, Let's say they reduce
their total entry level workforce cashiers in basic stockers by
just ten positions, which feels like.
Speaker 1 (15:32):
A pretty conservative estimate.
Speaker 2 (15:33):
Honestly, it likely is very conservative given the size of
these stores. But even just ten positions per store, that
translates immediately to forty seven thousand fewer jobs nationwide.
Speaker 1 (15:45):
Forty seven thousand jobs gone.
Speaker 2 (15:48):
That figure just highlights the terrifying cumulative impact of seemingly
small tech changes When they're replicated across a massive organization
like Walmart. These aren't jobs that just shift somewhere else.
They effectively vapor from the low scale economy.
Speaker 1 (16:01):
Okay, this brings us squarely to the so what factor.
We can't just talk about operational efficiency without digging deep
into the profound implications for the workers, for their communities,
for broader economic stability, Moving beyond just the cost metrics,
absolutely the human costs. What's the primary economic impact on
these workers?
Speaker 2 (16:20):
Well, the most mediate and obvious is the loss of
vital income these entry level jobs. Yeah, they're often low paying,
but they are foundational. They're the first step for young
workers entering the force. They're often the primary income for
single earner households. They can be crucial supplemental income for retirees,
or the re entry point for someone trying to get
back into the workforce after say raising kids.
Speaker 1 (16:41):
And we have some wage data. The median hourly wage
for a Walmart cashier or stalker cited around fourteen to
sixteen dollars an hour based on twenty twenty five data.
Speaker 2 (16:52):
That's about right. And while that's often above the legal
minimum wage, it still puts workers on a very tight
Financial Wire.
Speaker 1 (16:59):
Yeah, sixteen dollars an hour doesn't leave much.
Speaker 2 (17:01):
Room, Absolutely not. When workers are earning that, they usually
have little to know financial buffer. They're living paycheck to paycheck, right,
And this is especially true for workers in more rural areas,
where Walmart might be the single largest, most accessible employer around.
Speaker 1 (17:16):
The only option for many pretty much.
Speaker 2 (17:18):
So when these roles disappear, those workers don't just face unemployment,
they face intense competition for a shrinking pool of similar
low skill jobs and other sectors if those even exist.
Speaker 1 (17:29):
Locally, and the impact ripples outwards.
Speaker 2 (17:32):
Oh yeah, the economic stability of entire small towns that
really rely on the Walmart job structure that could be
destabilized almost overnight by these kinds of automation pushes.
Speaker 1 (17:43):
Now, Walmart, like a lot of big companies facing this,
emphasizes its upskilling programs as the solution, the antidote to displacement.
We'll get into the details, but the reality seems to
be that the opportunity gap is still immense. Upskilling, even
when is offered for free, is universally accessible? Is it? No?
Speaker 2 (18:02):
And this is the sort of cruel catch twenty two
of automation displacement The main challenge often isn't even the
cost of the training itself. Walmart often covers.
Speaker 1 (18:11):
That, So what is it? Then?
Speaker 2 (18:12):
It's the logistical and financial pressure of actually pursuing the training.
We have to remember the demographic holding many of these
entry level jobs. You might have single parents, workers juggling
two jobs just to make ends meet, people relying on
often unreliable public transport.
Speaker 1 (18:26):
So even if the training is online and free, what
stops them.
Speaker 2 (18:30):
Time and resources and really time. If you're working a
variable physically demanding forty hour week maybe more, and you
have to arrange and pay for.
Speaker 1 (18:38):
Childcare, finding extra hours is tough.
Speaker 2 (18:40):
Finding the necessary fifteen maybe twenty hours per week that
are often required to seriously complete a complex online course
in say supply chain analytics or software diagnostics, it's often
just impossible. Yeah, The immediate financial calculus of I need
to work this shift to pay rent today almost always
(19:01):
outweighs the long term career planning for maybe I can
get a better job next year. The very job that
provided the income to live now prevents the worker from
acquiring the skills to escape the economic tier of that job.
Speaker 1 (19:14):
And even if they somehow overcome those logistical hurdles the
type of skills needed for the new roles data analysts,
AI technicians. They're fundamentally different, aren't they.
Speaker 2 (19:23):
Precisely, these higher skill positions require specific technical expertise, often
strong foundational math skills, maybe formal certifications or even degrees
that's often far beyond what many entry level workers currently possess,
especially if they only have a high school diploma or less.
You can't just easily jump from decades of stocking shelves
to programming or maintaining complex robotics without a serious foundational
(19:46):
learning commitment. It's a huge skills mismatch.
Speaker 1 (19:49):
So it creates this massive structural barrier, even if the
training course itself is technically free exactly.
Speaker 2 (19:56):
The company might create the opportunity on paper for a
new type of job, but the displaced worker may be
structurally unable to actually step into it and.
Speaker 1 (20:06):
Zooming out even further, This affects the whole fabric of communities,
doesn't it, Especially in small town America where Walmart is
often dominant.
Speaker 2 (20:12):
It really does. In towns where Walmart is the main
economic engine, this kind of automation can lead directly to
higher local unemployment rates and consequently reduced consumer spending across
the board in that.
Speaker 1 (20:24):
Town, kicking off a negative feedback loop.
Speaker 2 (20:26):
Exactly, fewer jobs means less local spending, which hurts the
small businesses on main Street, which in turn can lead
to further economic decline in the town.
Speaker 1 (20:35):
And we should also pause and just acknowledge the social
role these jobs play beyond the paycheck.
Speaker 2 (20:40):
That's often overlooked in the pure efficiency calculations.
Speaker 1 (20:43):
Yet they provide community interaction, a structure to the day,
maybe a sense of purpose.
Speaker 2 (20:48):
Absolutely, retail jobs often serve as important social hubs. They
provide daily interaction and connection, not just for the employees,
but for the customers too, especially maybe older or more
isolated people. Losing these jobs means losing those daily, often
quite meaningful interactions, which can contribute to social isolation, weaking
community cohesion. The impact is cultural and social, not just
(21:11):
dollars and cents.
Speaker 1 (21:13):
Okay, So given this scale of human impact we're just
laid out, how is Walmart responding? How are they trying
to mitigate this? They must recognize the ethical the logistical
challenge here.
Speaker 2 (21:22):
They definitely do, and they have instituted concrete, multifaceted strategies
to try and address the human impact. The focus is
heavily on internal workforce transition.
Speaker 1 (21:32):
Okay, let's talk about the Livebetter You Program LBU. That's
their flagship initiative for this right launch back in twenty eighteen.
Speaker 2 (21:39):
It is, and it's a critical investment on their part.
LBU offers employees access to fully funded education and training,
and that means covering the cost of tuition.
Speaker 1 (21:47):
Books, fees, fully funded, fully.
Speaker 2 (21:49):
Funded often from day one of employment, which removes that
primary cost barrier to getting higher education or certifications.
Speaker 1 (21:57):
And what specific fields are they pushing employees use towards
to prepare them for this more automated reality.
Speaker 2 (22:04):
Well, they offer online degrees and certifications specifically tailored towards
high demand fields that actually support the automated future of
the company itself, like what technology, obviously advanced logistics and
supply chain management, data analysis, business management, even some healthcare
fields which are generally in high demand. The goal is
(22:25):
clearly to funnel existing talent, people who already know Walmart
into roles that the robots can't.
Speaker 1 (22:31):
Do, and they're reporting some success.
Speaker 2 (22:33):
They are. By twenty twenty five, Walmart's reporting significant uptake
over one hundred thousand employees have participated, and they claim
many of those have successfully transitioned into those higher paying,
more tech oriented roles within the company.
Speaker 1 (22:45):
Okay, one hundred thousand employees participating is definitely an impressive number,
but when you contrast that with the two point one
million global workforce.
Speaker 2 (22:52):
Right, it shows the immense scale of the effort that's
still required, doesn't it. It shows commitment, absolutely, but it
also highlights the deep resume of workers who still need
to make that pivot, or maybe can't make that pivot.
It's a marathon, not a sprint, and the ultimate success
of LBU is still contingent on workers being able to
overcome those logistical and foundational learning barriers we talked about earlier.
Speaker 3 (23:16):
Right the time, the childcare, the base skills exactly. Okay,
besides LBU, what about creating new types of jobs within
the store itself. This is where the hybrid needs emerge.
Speaker 2 (23:26):
Right, Yeah, exactly, As the AI systems take over the
simple repetitive stuff, new roles have to be created that
need human oversight maintenance diagnostics for those very systems.
Speaker 1 (23:36):
So what's the ideal scenario they're aiming for?
Speaker 2 (23:38):
Well, in an ideal world, that displays, cashier or stoker
gets retrained to become something like a digital team lead
or a field support technician.
Speaker 1 (23:47):
Doing what exactly.
Speaker 2 (23:48):
These new roles require employees to, say, maintain and troubleshoot
the increasingly complex robots on the floor, or monitor the
live feedback coming from the AI driven inventory systems, or
maybe analyze the operational data generated from customer interactions and
sales flows to spot issues or opportunities.
Speaker 1 (24:08):
So instead of being completely replaced, the employee's role is
sort of augmented, elevated. But there are fewer of these roles.
Speaker 2 (24:14):
They are definitely fewer in quantity, yes, but they are
qualitatively better jobs. How So, these positions typically offer higher
wages significantly greater job security because the skills are more
specialized and they require a skill set that's harder to
automate away quickly. The focus seems to be on retaining
and repurposing existing employees who already understand the company culture,
(24:36):
the core retail processes.
Speaker 1 (24:38):
And shifting their expertise.
Speaker 2 (24:39):
Shifting their expertise away from muscle and manual repetition towards
technology management and problem solving.
Speaker 1 (24:45):
Okay, but it's not just an internal pivot is it?
Walmart must recognize that not every displaced worker can or
will transition into a tech role within Walmart. What's their
strategy for external support?
Speaker 2 (24:57):
Yeah, that external strategy is vital, especially for community stability
in those hard hit areas. Walmart has partnered with community colleges,
local governments, various nonprofits you do what to provide broader
job placement services and training programs. This acknowledges that some
workers might simply need or want to find work in
entirely different industries altogether.
Speaker 1 (25:18):
What kind of industries are they targeting outside of retail tech?
Speaker 2 (25:22):
The focus tends to be on sectors where labor demand
remains reliably high, often outside traditional retail, things like specialized manufacturing,
maybe local skilled trades like plumbing or electrical work, or
critically healthcare roles.
Speaker 1 (25:36):
Okay.
Speaker 2 (25:37):
By facilitating this kind of external transition, Walmart aims to
at least partially mitigate the negative economic shockways hitting those
small towns and local economies that depend so heavily on
their traditional employment structure.
Speaker 1 (25:48):
Okay, let's try and connect the dots. Now, shift the
focus from just Walmart specifically to the retail industry as
a whole. And the broader societal maybe policy implications of
this whole trend.
Speaker 2 (26:00):
Right, because we have to understand Walmart's actions, as big
as they are, they're not happening in a vacuum. No,
They're part of this massive, systemic and probably irreversible trend
right across global retail. They're investing billions, partly because they
have to to remain competitive in a landscape increasingly dominated
by speed, logistics, and data.
Speaker 1 (26:21):
And we see competitors really setting the benchmarks here. Amazon
is the obvious example, right with their cashierless ghost stores.
They're incredibly automated fulfillment centers. They set an extremely high bar.
Speaker 2 (26:32):
For efficiency, and that competitive pressure is inescapable. It pushes
everyone else target Kroger regional chains to have vest similarly
in AI and automation, even if they maybe lack Walmart's
vast resources to do it at the same scale.
Speaker 1 (26:44):
And the consequence for workers is the consequence seems pretty stark.
Speaker 2 (26:48):
The retail sector is likely facing a permanent structural reduction
in those traditional entry level job opportunities, full stop.
Speaker 1 (26:55):
Wow.
Speaker 2 (26:56):
This requires either a truly massive upscaling effort across the
board or completely new career shifts for millions of workers.
It confirms this isn't just some temporary economic dip. It's
a fundamental shade in the nature of labor demand in
one of the world's biggest employment sectors.
Speaker 1 (27:14):
And when job displacement hits on this kind of scale,
affecting the core economic ladder for so many, it inevitably
sparks intense political debate. Doesn't it calls for policy intervention
to protect workers and communities.
Speaker 2 (27:27):
Absolutely, that's right. The source material we looked at outline
several key intervention ideas that are being discussed. On one end,
you have ideas for strengthening social safety nets like what
like say, government funded retraining programs that are independent of
any single employer, offering more flexibility, or the much bigger,
more systemic idea of universal basic income UBI.
Speaker 1 (27:48):
Okay UBI, that's providing a baseline income level regardless of
employment status. What's the argument for UBI in this specific
context of automation.
Speaker 2 (27:56):
Well, proponents argue that if routine work is simply vanishing
due to automation, if the jobs just aren't there anymore,
UBI provides the necessary economic stability. It gives people the
breathing room to pursue long term retraining, maybe care for
family members, or even start small businesses. It ensures that
technological progress, which creates immense wealth, doesn't simultaneously lead to
(28:19):
mass poverty.
Speaker 1 (28:20):
So it's seen as a kind of moral or necessary
safety net in this new era.
Speaker 2 (28:24):
That's the argument, Yeah, in an era of potentially unprecedented
productivity gains driven by machines.
Speaker 1 (28:30):
And then there's the really controversial idea, the robot tax.
Speaker 2 (28:34):
Yeah, this is one of the most hotly debated proposals
out there. The basic idea is that tax companies that
automate heavily, maybe based on the number or value of
the robots or AI systems they deploy.
Speaker 1 (28:44):
What's the logic behind that.
Speaker 2 (28:46):
The logic is, well, since robots displace human workers who
used to pay income tax and payroll taxes, the robots
or the companies using them extensively, should somehow be taxed
to replace that lost public revenue, and that revenue would
be used for rev a new would then, in theory,
be used to fund social support programs like enhanced uninformant
benefits or those retraining initiatives for the workers who are
(29:08):
displaced by the automation itself.
Speaker 1 (29:10):
But the pushback against a robot tax is pretty intense
too write What are the main arguments against it?
Speaker 2 (29:16):
The primary argument against it is that it would stifle innovation.
Critics say taxing technology essentially amounts to taxing productivity gains,
which would make companies, say in the US, less competitive
against companies and countries that don't have such a tax. Plus,
there are huge logistical challenges. How do you even define
a robot for tax purposes. Do sophisticated scheduling software count
(29:39):
Is it only physical machines? Tricky very And if you
tax automation too heavily, companies might just shift their automated
manufacturing or logistics operations over these to avoid the tax,
which kind of defeats the whole purpose of generating domestic funding.
It really highlights this deep tension between wanting to foster
capital efficiency and innovation and ensuring social equid and worker support.
Speaker 1 (30:01):
So ultimately, the big question for these modern mega corporations
like Walmart is how do you balance that relentless pursuit
of efficiency, which, as you said, is almost necessary to
survive competitively, with the responsibility you arguably owe to your
long term employees and the communities that host your operations.
Speaker 2 (30:18):
That balancing act, it really demands an ethical framework, and
that framework has to be rooted in transparency and genuinely
robust support, meaning meaning companies need to be upfront with
workers about exactly what job changes are coming and ideally
when giving people actual time to plan and access programs
like LBU and stressing the need for robust support not
(30:41):
just technically free tuition, but maybe things like stipends to
cover living cost while training, or guaranteed schedule flexibility to
allow for training. That's paramount.
Speaker 1 (30:50):
Looking ahead, then what does the store or maybe the
retail operation of the future look like.
Speaker 2 (30:55):
In this context, it seems inevitably like it's going to
be a hybrid model. The future of work at Walmart
and probably across most of retail will likely combine streamlined
human labor with advanced complex automation.
Speaker 1 (31:07):
Humans and machines working together.
Speaker 2 (31:09):
Yeah, human workers will likely be augmented by the tech,
focusing more on tasks that require creativity, empathy, complex problem solving,
managing the overall customer experience, things machines aren't good at.
Speaker 1 (31:22):
While the machines handle.
Speaker 2 (31:23):
While the machines handle, the vast majority of the time
previously spent on purely repetitive logistics, stocking, data entry, basic transactions.
This requires real agility and serious long term structural planning
from everyone involved, the company, the workers themselves, and the
policy makers who set the rules of the economic environment.
Speaker 1 (31:43):
Okay, so let's try to synthesize this whole deep dive.
We've seen that AI adoption isn't just a choice for
a company the size of Walmart. It feels like an
operational necessity. It's driving efficiency games that were frankly unimaginable
just a decade or two ago.
Speaker 2 (31:57):
The benefits to the operation are.
Speaker 1 (31:59):
Undeniable, but that necessity stands in stark unavoidable contrast to
the huge structural challenge it poses to the traditional entry
level labor market, the market that has historically been the
economic backbone for so many individuals and communities.
Speaker 2 (32:12):
And the core conflict really remains this. The new types
of roles being created, the robot maintenance tech, the data analyst,
the supply chain specialist. They definitely offer higher wages better
job security, which is undeniably a positive outcome for those
who successfully make that transition. But the critical societal task,
(32:34):
the big IF, is ensuring that these new better roles
are genuinely and equitably accessible to the diverse workforce that
currently holds those entry level positions, especially those who might
lack advanced foundational education or the immediate financial means and
time to pursue extensive retraining.
Speaker 1 (32:53):
So the path forward needs more than just massive corporate
investment like LBU, doesn't it It seems to require a much
deeper policy conversation about vital responsibility in the face of
such rapid technological displacement.
Speaker 2 (33:03):
Absolutely, because you know that traditional entry level job it
was always far more than just a paycheck. It was
a first rung on the economic latter. It provided that
initial income, sure, but it also established a work history,
It taught essential professional skills like just showing up on
time teamwork, and it offered at least the possibility of
upward mobility within the company or the economy.
Speaker 1 (33:26):
And as AI accelerates the systematic elimination of these kinds
of roles.
Speaker 2 (33:30):
We have to urgently ask where does the next generation
find that first step or where do people re entering
the workforce after a break, Where do they now gain
their initial experience, their initial income, and maybe most importantly,
that foundational opportunity for advancement.
Speaker 1 (33:46):
This loss of the first rung, the very entry point
to sustained economic activity. That's perhaps the most critical long
term implication we've uncovered in this deep dive. It feels
like the foundation of workforce opportunity is fundamentally shifting under
our feet.
Speaker 2 (34:00):
Definitely something to keep thinking about.
Welcome to the deep dive. We take these massive global shifts,
try to boil them down and really give you the
essential detailed knowledge.
Speaker 2 (00:08):
You need keep you ahead of the curve exactly.
Speaker 1 (00:11):
And today we're undertaking a pretty complex investigation. We're looking
at the biggest player in global retail, Walmart.
Speaker 2 (00:19):
Yeah that's right. I mean when we talk about tech
transformation reshaping the job market, Walmart isn't just playing along.
They're really setting the pace right, And we're specifically looking
at their well aggressive, almost systemic adoption of artificial intelligence
AI and the impact of that and the profound sort
of dual implications this has, especially for the millions, literally
(00:42):
millions of people who rely on those entry level jobs.
Speaker 1 (00:45):
Okay, so let's try and unpack this. It's a monumental subject. Really,
we're focusing on a company whose scale is it's almost
impossible to really grass isn't it.
Speaker 2 (00:54):
It really are.
Speaker 1 (00:55):
And this isn't just about like streamlining a business process.
This feels more like a structural re engineering of the
whole retail labor market. So our mission today is pretty clear.
We need to analyze exactly how Walmart is using AI
across its vast systems try to quantify the implications which
seem inevitable for those traditional roles cashiers, stalkers, greeters jobs.
(01:19):
We all see, yeah, and critically examine their strategies. And
let's be honest, the challenge is involved in managing this
massive workforce transition.
Speaker 2 (01:28):
And what's fascinating or maybe crucial here is that the
sheer size of their operation almost demands this kind of optimization.
Speaker 1 (01:37):
It's not really optional for them.
Speaker 2 (01:39):
It feels like a competitive necessity at this point. I mean,
we're talking about a network over forty seven hundred stores
just in the US, wow, and a global workforce that's
around two point one million associates.
Speaker 1 (01:49):
It's staggering.
Speaker 2 (01:50):
So when a corporation that big, that's basically an economic
anchor in thousands of communities, when it fundamentally changes its
operating model, it doesn't just cause ripples, No, it's more
like a seismic event for the global job market exactly.
Speaker 1 (02:03):
And you know they're under relentless pressure competitors, especially online
e commerce, yeah, pushing them to cut costs, optimize every
single process, and meet these increasingly impatient digital first customer.
Speaker 2 (02:15):
Expectations speed and convenience.
Speaker 1 (02:17):
Right, So for you listening trying to really get a
handle on the speed of technological change. This deep dive
is absolutely crucial because it focuses right on those roles,
the ones that have historically been the entry point for
millions first step, the first essential step onto the economic ladder.
And the critical question we're wrestling with today is, well,
(02:38):
what happens to the bottom rung when AI automation starts
systematically removing it?
Speaker 2 (02:43):
That really is the core tension, isn't it. This isn't
just a standard you know, business efficiency upgrade. This is
a transformation in how low skill labor, the kind of
labor that builds work ethic, provides that initial stability, how it's.
Speaker 1 (02:57):
Performed, not performed by humans anymore increasingly.
Speaker 2 (03:00):
Not performed by humans in this modern digital economy.
Speaker 1 (03:03):
Okay, so let's move beyond the sort of macro picture
and look at the how how is Walmart actually integrating
this tech across its i mean, sprawling operations. It's clear
AI isn't just an add on tool for them, No,
not at all. It feels like a foundational cornerstone built
into pretty much every system.
Speaker 2 (03:20):
Yeah, from the deepest parts of the supply chain right
up to the customer checking out Okay, their AI journey.
It really seems like a direct response to those demands
for speed and precision you mentioned, especially in a high
volume digital world, and the initial maybe the most significant
gains you see them right away in predictive analysis and logistics.
Speaker 1 (03:41):
Okay, so AI in inventory and supply chain. When we
talk about forecast and demand, I think most people picture
like a basic spreadsheet based on last year's sales.
Speaker 2 (03:51):
Right maybe Christmas sales, predicting next Christmas.
Speaker 1 (03:53):
Yeah, but these machine learning algorithms, they go way beyond
that simple historical data, right.
Speaker 2 (03:58):
Oh. Absolutely, They're constantly in ingesting just massive data sets
in real time. The algorithms analyze historical sales, sure, that's
the baseline.
Speaker 1 (04:06):
But then but then they layer in these complex external
variables things humans could maybe correlate but really slowly manualize.
Speaker 2 (04:14):
What kind of things well, specific local events for instance,
is there a big high school football game happening or
a large community festival that's gonna spike demand for say
bottled water and snacks. Okay, and even more fascinating, they
integrate highly localized weather patterns.
Speaker 1 (04:32):
Okay, give us an example, how does weather change of
stocking order through AI.
Speaker 2 (04:37):
Well, think about the subtlety. If the forecast predicts a
sudden cold snap, maybe with heavy rain, the system immediately
predicts a spike in demand for comfort foods, maybe soup,
hot drinks, perhaps even certain cold remedies makes sense. Conversely,
an unexpected heat wave, yeah, you'll instantly up the forecast
for bottled water, maybe charcoal for grilling, sunscreen.
Speaker 1 (04:59):
And this is store by store exactly.
Speaker 2 (05:01):
Yeah, the algorithm's forecast this demand for specific products at
individual stores, not just regionally, and with pretty incredible accuracy.
The goal always is optimal stock levels, minimizing waste from overstocking,
but also crucially eliminating those dreaded stockouts, because a stockout
just sends a customer straight to a competitor.
Speaker 1 (05:21):
Right. And this efficiency, it doesn't just stay on the
planning sheet, It dictates how the distribution centers, the sort
of nerve centers, actually function.
Speaker 2 (05:29):
That's where you see the heavy metal, the real automation. Yeah,
the shift in warehousing is genuinely staggering. Walmart's invested heavily
in autonomous mobile robots amrs.
Speaker 1 (05:41):
The little moving robots, Yeah.
Speaker 2 (05:43):
Navigating the warehouse floors. They're designed to move goods. Often
in these really high density storage setups, they're frankly difficult
for human teams to navigate quickly and efficiently.
Speaker 1 (05:53):
And we know Walmart has that big partnership with Symbotic.
What are those systems doing that a person can't or
can't do as well?
Speaker 2 (06:01):
Right? The Symbotic deal is key. Their systems don't just
like move boxes from A to B. They're capable of
this high speed three dimensional processing of inventory.
Speaker 1 (06:10):
What does that mean?
Speaker 2 (06:11):
Three dimensional sorting, stacking, and transporting goods deep inside this
automated warehouse structure up down, across way, denser than humans
could manage. And the competitive advantage there is huge speed,
density consistency. These systems can process orders, stage pallets way
faster than any human crew.
Speaker 1 (06:31):
And they don't need brakes.
Speaker 2 (06:33):
Critically, they operate two four, seven, three six five, no brakes,
no shift changes, no training periods, no overtime costs. This
continuous throughput drastically improves logistics metrics, and yeah slashes those
operational costs tied to traditional labor management.
Speaker 1 (06:49):
Okay, so moving from the back end the distribution centers
to the actual store floor, we're seeing AI replace tasks
that used to be essential repetitive entry level. This is
the instore monitoring part exactly.
Speaker 2 (07:02):
Think about AI powered cameras and sensors. Sometimes they're even
integrated into the floor cleaning machines that are already roaming
the aisles, and they're constantly monitoring shelves in real time.
So instead of relying on a stocker or a manager
to manually walk every single aisle multiple times a.
Speaker 1 (07:17):
Day, which is slow, expensive and prone.
Speaker 2 (07:19):
To human error, Yeah, the AI system instantly detects issues.
Speaker 1 (07:23):
What kind of issues specifically besides empty shells, well, stock.
Speaker 2 (07:26):
Levels are the big one, obviously flagging empty shelves, but
the systems also monitor crucial details like is the price
tag correct, is it even there? And planogram compliance. A
planogram is that super detailed diagram saying exactly where each
product should sit on the shelf.
Speaker 1 (07:44):
Ah right, making sure things are in the right spot exactly.
Speaker 2 (07:49):
If a product is misplaced or the tag is wrong,
the AI alerts the human staff instantly. This massively reduces
the need for that manual, dedicated shelf auditing that entry
life workers used to do.
Speaker 1 (08:01):
And then there's the digital side. If you're interacting with
Walmart online AI is probably involved.
Speaker 2 (08:05):
Oh, absolutely, those AI driven recommendation engines, they're constantly learning
from your browsing, your.
Speaker 1 (08:10):
Purchase history, you try and predict what I want next.
Speaker 2 (08:13):
Exactly, personalizing product suggestions of the website or the app,
which demonstrably boosts sales margins.
Speaker 1 (08:19):
Okay.
Speaker 2 (08:19):
And furthermore, if you interact with customer service online, increasingly
you're dealing with pretty sophisticated chatbots powered by natural language
processing NLP.
Speaker 1 (08:28):
How good are they now? Is it still just basic
where's my order?
Speaker 2 (08:32):
They're getting far more advanced. They can handle complex multi
step inquiries now, tracking an order, yeah, but also initiating
a complicated return or resolving a minor billing issue.
Speaker 1 (08:43):
Is that used to need a human?
Speaker 2 (08:44):
Absolutely? These functions used to require literally rooms full of
trained human customer service reps. Now the AI handles the
bulk of the volume. It only escalates the really unique
or emotionally charge cases to human agents, So.
Speaker 1 (09:02):
It fundamentally restructures that whole job category completely. And finally,
they've even pushed AI into what used to be a
key managerial task workforce optimization scheduling.
Speaker 2 (09:13):
Yeah, and this is a critical displacement because it doesn't
just impact the frontline worker, it hits the mid level
manager who often oversaw those workers.
Speaker 1 (09:21):
How does that work?
Speaker 2 (09:22):
Complex scheduling algorithms now analyze store traffic patterns minute by minute.
Sometimes they cross reference that with employee availability, specific sales
forecast for that day or week, and then they generate
optimized shifts. These systems can dynamically adjust staffing levels way
faster and more efficiently than any human manager could manually
juggle schedules.
Speaker 1 (09:42):
So that reduces the need for managers to coordinate staffing,
make those on the fly shift adjustments, and that role
that often provided a vital sort of entry level supervisory opportunity,
didn't it for people looking to move up from stocking
or casharing.
Speaker 2 (09:57):
Precisely that was a step up. Now the machine is
increasingly determining labor allocation. It removes a key administrative layer,
and yet crucially, it removes an entire pathway for internal
career advancement into that first level of management.
Speaker 1 (10:11):
Wow. Okay, so the evidence seems overwhelming. AI brings undeniable
operational benefits. Faster stocking, better inventory, uptimized.
Speaker 2 (10:18):
Schedule deficiency gains are real.
Speaker 1 (10:20):
But the necessary and I guess critical consequence is this
significant structural reduction in those roles, the ones that have
historically been the entry point for millions, especially workers maybe
with limited experience or formal education.
Speaker 2 (10:34):
This is the core trade off, isn't it The one
we have to analyze really carefully. Efficiency gains in repetitive
tasks often correlate directly with job displacement.
Speaker 1 (10:43):
And for the everyday customer, the most visible sign of
this has got to be the cashier's right. Absolutely, Walmart's
aggressive expansion of self checkout systems. You walk into a
store now and see rows and rows of these kiosks.
Speaker 2 (10:56):
It's pervasive, and the tech just keeps getting better fast.
These aren't just passive barcode scanners anymore. No, These self
checkout systems use AI combined with sensor technology to detect errors,
prevent loss, flag suspicious activity. How like what well, using
weight sensors in the bagging area, for example, to make
(11:16):
sure the item scanned matches the weight of the item
you actually bagged the old banana trek kind of yeah,
or image recognition to make sure the type of produce
you keed in matches with the camera sees.
Speaker 1 (11:27):
So the AI is actively taking over the supervision and
loss prevention role the human cashier used to provide.
Speaker 2 (11:34):
Exactly the human element is reduced to minimal intervention, often
just one person supervising you know, a dozen.
Speaker 1 (11:41):
Machines, and the industry numbers reflect this.
Speaker 2 (11:43):
Yeah, the metrics show a dramatic shift. Industry estimates suggests
that this massive adoption of self checkout has cut the
need for traditional cashiers by up to thirty percent in
some retail.
Speaker 1 (11:55):
Chain thirty percent.
Speaker 2 (11:57):
And Walmart is definitely a leader in pushing this trend.
While they still keep some staffed lanes for customer preference
or larger orders, the overall long term demand for those
traditional cashier jobs has fundamentally plummeted.
Speaker 1 (12:12):
Okay, moving beyond the front of the store, the work
of stalking shells, keeping track of inventory another foundational entry
level role that's being automated too rapidly.
Speaker 2 (12:22):
We've seen them try different approaches over the years.
Speaker 1 (12:24):
I remember reading about the Boston Nova robots, those tall
ones that rolled up and down the aisles scanning shell.
Speaker 2 (12:29):
Yeah, the pilot program that got a lot of.
Speaker 1 (12:31):
Press, but that specific partnership ended, didn't it.
Speaker 2 (12:34):
It did, But that's a crucial nuance here. The failure
of one specific robot model like bosson Nova doesn't mean
the overall goal of automating shelf scanning failed.
Speaker 1 (12:45):
Ah, they just changed the method exactly.
Speaker 2 (12:47):
The method changed, Walmart continues to deploy other AI powered tools,
maybe stationary sensors on the shelves, smaller purpose built robots,
even drones in some.
Speaker 1 (12:57):
Tests doing the same job, continuously.
Speaker 2 (12:59):
Scanning shells to I'm sure correct stocking pricing compliance, planegram adherence.
These systems do the work that once required teams of
associates walking the floors.
Speaker 1 (13:09):
So instead of a big team of overnight stalkers manually
counting and arranging.
Speaker 2 (13:13):
It's uncontinuously, often by automated systems, reporting back to a
smaller team of humans who act on the data. Okay,
and this is even more prouns Back in the distribution centers,
those automated sorting systems, guided by AI, they handle packing
and sorting pallets far faster than human speed allows, which
again significantly reduces the need for manual warehouse associates doing
(13:34):
those repetitive, high volume tasks.
Speaker 1 (13:36):
Now, we can't forget the more symbolic roles, right, The
Walmart greeter was such an icon well fixture.
Speaker 2 (13:42):
Yeah, providing that human welcome, But even that role has
been subject to well scaling back and tech replacement.
Speaker 1 (13:50):
I think a lot of listeners remember that friendly face
at the door. What's replacing that human interaction?
Speaker 2 (13:56):
In many stores, the greater role has been restructured or yes,
scaled back significantly. The actual functions they are performed are
being absorbed by technology, like what most visibly in areas
like loss prevention, you might have AI powered facial recognition
systems monitoring entrances, flagging known shoplifters maybe okay, or automated
customer assistants kiosks right at the front, handling basic directional questions,
(14:20):
maybe membership checks for Sam's club.
Speaker 1 (14:22):
So even roles that seemed focused on soft skills aren't immune.
If there's a repeatable task involved.
Speaker 2 (14:29):
That shift acknowledge is that Yeah, if a core function
can be automated, even in a customer facing role, it's
likely on the table for restructuring.
Speaker 1 (14:37):
Okay, let's talk scale again, because when you look at
the total number of people involved two point one million globally,
the potential displacement moves from just a corporate efficiency thing
to a real societal issue. We need to try and
put some numbers.
Speaker 2 (14:50):
On this, We really do, and just for global context,
remember the World Economic Forum estimated I think back in
twenty twenty that automation and AI could display something like
eighty five million jobs globally by twenty twenty five. Wow.
And retail, because it has so many repetitive tasks, is
consistently named as one of the hardest hit sectors.
Speaker 1 (15:08):
So let's bring that down to Walmart specifically using their
operational scale. This is the kind of calculation that helps
understand the sheer magnitude.
Speaker 2 (15:15):
Okay, consider the logistics over forty seven hundred US stores. Now,
let's say each of those stores, through implementing advanced self
checkout and some automated stocking tech, Let's say they reduce
their total entry level workforce cashiers in basic stockers by
just ten positions, which feels like.
Speaker 1 (15:32):
A pretty conservative estimate.
Speaker 2 (15:33):
Honestly, it likely is very conservative given the size of
these stores. But even just ten positions per store, that
translates immediately to forty seven thousand fewer jobs nationwide.
Speaker 1 (15:45):
Forty seven thousand jobs gone.
Speaker 2 (15:48):
That figure just highlights the terrifying cumulative impact of seemingly
small tech changes When they're replicated across a massive organization
like Walmart. These aren't jobs that just shift somewhere else.
They effectively vapor from the low scale economy.
Speaker 1 (16:01):
Okay, this brings us squarely to the so what factor.
We can't just talk about operational efficiency without digging deep
into the profound implications for the workers, for their communities,
for broader economic stability, Moving beyond just the cost metrics,
absolutely the human costs. What's the primary economic impact on
these workers?
Speaker 2 (16:20):
Well, the most mediate and obvious is the loss of
vital income these entry level jobs. Yeah, they're often low paying,
but they are foundational. They're the first step for young
workers entering the force. They're often the primary income for
single earner households. They can be crucial supplemental income for retirees,
or the re entry point for someone trying to get
back into the workforce after say raising kids.
Speaker 1 (16:41):
And we have some wage data. The median hourly wage
for a Walmart cashier or stalker cited around fourteen to
sixteen dollars an hour based on twenty twenty five data.
Speaker 2 (16:52):
That's about right. And while that's often above the legal
minimum wage, it still puts workers on a very tight
Financial Wire.
Speaker 1 (16:59):
Yeah, sixteen dollars an hour doesn't leave much.
Speaker 2 (17:01):
Room, Absolutely not. When workers are earning that, they usually
have little to know financial buffer. They're living paycheck to paycheck, right,
And this is especially true for workers in more rural areas,
where Walmart might be the single largest, most accessible employer around.
Speaker 1 (17:16):
The only option for many pretty much.
Speaker 2 (17:18):
So when these roles disappear, those workers don't just face unemployment,
they face intense competition for a shrinking pool of similar
low skill jobs and other sectors if those even exist.
Speaker 1 (17:29):
Locally, and the impact ripples outwards.
Speaker 2 (17:32):
Oh yeah, the economic stability of entire small towns that
really rely on the Walmart job structure that could be
destabilized almost overnight by these kinds of automation pushes.
Speaker 1 (17:43):
Now, Walmart, like a lot of big companies facing this,
emphasizes its upskilling programs as the solution, the antidote to displacement.
We'll get into the details, but the reality seems to
be that the opportunity gap is still immense. Upskilling, even
when is offered for free, is universally accessible? Is it? No?
Speaker 2 (18:02):
And this is the sort of cruel catch twenty two
of automation displacement The main challenge often isn't even the
cost of the training itself. Walmart often covers.
Speaker 1 (18:11):
That, So what is it? Then?
Speaker 2 (18:12):
It's the logistical and financial pressure of actually pursuing the training.
We have to remember the demographic holding many of these
entry level jobs. You might have single parents, workers juggling
two jobs just to make ends meet, people relying on
often unreliable public transport.
Speaker 1 (18:26):
So even if the training is online and free, what
stops them.
Speaker 2 (18:30):
Time and resources and really time. If you're working a
variable physically demanding forty hour week maybe more, and you
have to arrange and pay for.
Speaker 1 (18:38):
Childcare, finding extra hours is tough.
Speaker 2 (18:40):
Finding the necessary fifteen maybe twenty hours per week that
are often required to seriously complete a complex online course
in say supply chain analytics or software diagnostics, it's often
just impossible. Yeah, The immediate financial calculus of I need
to work this shift to pay rent today almost always
(19:01):
outweighs the long term career planning for maybe I can
get a better job next year. The very job that
provided the income to live now prevents the worker from
acquiring the skills to escape the economic tier of that job.
Speaker 1 (19:14):
And even if they somehow overcome those logistical hurdles the
type of skills needed for the new roles data analysts,
AI technicians. They're fundamentally different, aren't they.
Speaker 2 (19:23):
Precisely, these higher skill positions require specific technical expertise, often
strong foundational math skills, maybe formal certifications or even degrees
that's often far beyond what many entry level workers currently possess,
especially if they only have a high school diploma or less.
You can't just easily jump from decades of stocking shelves
to programming or maintaining complex robotics without a serious foundational
(19:46):
learning commitment. It's a huge skills mismatch.
Speaker 1 (19:49):
So it creates this massive structural barrier, even if the
training course itself is technically free exactly.
Speaker 2 (19:56):
The company might create the opportunity on paper for a
new type of job, but the displaced worker may be
structurally unable to actually step into it and.
Speaker 1 (20:06):
Zooming out even further, This affects the whole fabric of communities,
doesn't it, Especially in small town America where Walmart is
often dominant.
Speaker 2 (20:12):
It really does. In towns where Walmart is the main
economic engine, this kind of automation can lead directly to
higher local unemployment rates and consequently reduced consumer spending across
the board in that.
Speaker 1 (20:24):
Town, kicking off a negative feedback loop.
Speaker 2 (20:26):
Exactly, fewer jobs means less local spending, which hurts the
small businesses on main Street, which in turn can lead
to further economic decline in the town.
Speaker 1 (20:35):
And we should also pause and just acknowledge the social
role these jobs play beyond the paycheck.
Speaker 2 (20:40):
That's often overlooked in the pure efficiency calculations.
Speaker 1 (20:43):
Yet they provide community interaction, a structure to the day,
maybe a sense of purpose.
Speaker 2 (20:48):
Absolutely, retail jobs often serve as important social hubs. They
provide daily interaction and connection, not just for the employees,
but for the customers too, especially maybe older or more
isolated people. Losing these jobs means losing those daily, often
quite meaningful interactions, which can contribute to social isolation, weaking
community cohesion. The impact is cultural and social, not just
(21:11):
dollars and cents.
Speaker 1 (21:13):
Okay, So given this scale of human impact we're just
laid out, how is Walmart responding? How are they trying
to mitigate this? They must recognize the ethical the logistical
challenge here.
Speaker 2 (21:22):
They definitely do, and they have instituted concrete, multifaceted strategies
to try and address the human impact. The focus is
heavily on internal workforce transition.
Speaker 1 (21:32):
Okay, let's talk about the Livebetter You Program LBU. That's
their flagship initiative for this right launch back in twenty eighteen.
Speaker 2 (21:39):
It is, and it's a critical investment on their part.
LBU offers employees access to fully funded education and training,
and that means covering the cost of tuition.
Speaker 1 (21:47):
Books, fees, fully funded, fully.
Speaker 2 (21:49):
Funded often from day one of employment, which removes that
primary cost barrier to getting higher education or certifications.
Speaker 1 (21:57):
And what specific fields are they pushing employees use towards
to prepare them for this more automated reality.
Speaker 2 (22:04):
Well, they offer online degrees and certifications specifically tailored towards
high demand fields that actually support the automated future of
the company itself, like what technology, obviously advanced logistics and
supply chain management, data analysis, business management, even some healthcare
fields which are generally in high demand. The goal is
(22:25):
clearly to funnel existing talent, people who already know Walmart
into roles that the robots can't.
Speaker 1 (22:31):
Do, and they're reporting some success.
Speaker 2 (22:33):
They are. By twenty twenty five, Walmart's reporting significant uptake
over one hundred thousand employees have participated, and they claim
many of those have successfully transitioned into those higher paying,
more tech oriented roles within the company.
Speaker 1 (22:45):
Okay, one hundred thousand employees participating is definitely an impressive number,
but when you contrast that with the two point one
million global workforce.
Speaker 2 (22:52):
Right, it shows the immense scale of the effort that's
still required, doesn't it. It shows commitment, absolutely, but it
also highlights the deep resume of workers who still need
to make that pivot, or maybe can't make that pivot.
It's a marathon, not a sprint, and the ultimate success
of LBU is still contingent on workers being able to
overcome those logistical and foundational learning barriers we talked about earlier.
Speaker 3 (23:16):
Right the time, the childcare, the base skills exactly. Okay,
besides LBU, what about creating new types of jobs within
the store itself. This is where the hybrid needs emerge.
Speaker 2 (23:26):
Right, Yeah, exactly, As the AI systems take over the
simple repetitive stuff, new roles have to be created that
need human oversight maintenance diagnostics for those very systems.
Speaker 1 (23:36):
So what's the ideal scenario they're aiming for?
Speaker 2 (23:38):
Well, in an ideal world, that displays, cashier or stoker
gets retrained to become something like a digital team lead
or a field support technician.
Speaker 1 (23:47):
Doing what exactly.
Speaker 2 (23:48):
These new roles require employees to, say, maintain and troubleshoot
the increasingly complex robots on the floor, or monitor the
live feedback coming from the AI driven inventory systems, or
maybe analyze the operational data generated from customer interactions and
sales flows to spot issues or opportunities.
Speaker 1 (24:08):
So instead of being completely replaced, the employee's role is
sort of augmented, elevated. But there are fewer of these roles.
Speaker 2 (24:14):
They are definitely fewer in quantity, yes, but they are
qualitatively better jobs. How So, these positions typically offer higher
wages significantly greater job security because the skills are more
specialized and they require a skill set that's harder to
automate away quickly. The focus seems to be on retaining
and repurposing existing employees who already understand the company culture,
(24:36):
the core retail processes.
Speaker 1 (24:38):
And shifting their expertise.
Speaker 2 (24:39):
Shifting their expertise away from muscle and manual repetition towards
technology management and problem solving.
Speaker 1 (24:45):
Okay, but it's not just an internal pivot is it?
Walmart must recognize that not every displaced worker can or
will transition into a tech role within Walmart. What's their
strategy for external support?
Speaker 2 (24:57):
Yeah, that external strategy is vital, especially for community stability
in those hard hit areas. Walmart has partnered with community colleges,
local governments, various nonprofits you do what to provide broader
job placement services and training programs. This acknowledges that some
workers might simply need or want to find work in
entirely different industries altogether.
Speaker 1 (25:18):
What kind of industries are they targeting outside of retail tech?
Speaker 2 (25:22):
The focus tends to be on sectors where labor demand
remains reliably high, often outside traditional retail, things like specialized manufacturing,
maybe local skilled trades like plumbing or electrical work, or
critically healthcare roles.
Speaker 1 (25:36):
Okay.
Speaker 2 (25:37):
By facilitating this kind of external transition, Walmart aims to
at least partially mitigate the negative economic shockways hitting those
small towns and local economies that depend so heavily on
their traditional employment structure.
Speaker 1 (25:48):
Okay, let's try and connect the dots. Now, shift the
focus from just Walmart specifically to the retail industry as
a whole. And the broader societal maybe policy implications of
this whole trend.
Speaker 2 (26:00):
Right, because we have to understand Walmart's actions, as big
as they are, they're not happening in a vacuum. No,
They're part of this massive, systemic and probably irreversible trend
right across global retail. They're investing billions, partly because they
have to to remain competitive in a landscape increasingly dominated
by speed, logistics, and data.
Speaker 1 (26:21):
And we see competitors really setting the benchmarks here. Amazon
is the obvious example, right with their cashierless ghost stores.
They're incredibly automated fulfillment centers. They set an extremely high bar.
Speaker 2 (26:32):
For efficiency, and that competitive pressure is inescapable. It pushes
everyone else target Kroger regional chains to have vest similarly
in AI and automation, even if they maybe lack Walmart's
vast resources to do it at the same scale.
Speaker 1 (26:44):
And the consequence for workers is the consequence seems pretty stark.
Speaker 2 (26:48):
The retail sector is likely facing a permanent structural reduction
in those traditional entry level job opportunities, full stop.
Speaker 1 (26:55):
Wow.
Speaker 2 (26:56):
This requires either a truly massive upscaling effort across the
board or completely new career shifts for millions of workers.
It confirms this isn't just some temporary economic dip. It's
a fundamental shade in the nature of labor demand in
one of the world's biggest employment sectors.
Speaker 1 (27:14):
And when job displacement hits on this kind of scale,
affecting the core economic ladder for so many, it inevitably
sparks intense political debate. Doesn't it calls for policy intervention
to protect workers and communities.
Speaker 2 (27:27):
Absolutely, that's right. The source material we looked at outline
several key intervention ideas that are being discussed. On one end,
you have ideas for strengthening social safety nets like what
like say, government funded retraining programs that are independent of
any single employer, offering more flexibility, or the much bigger,
more systemic idea of universal basic income UBI.
Speaker 1 (27:48):
Okay UBI, that's providing a baseline income level regardless of
employment status. What's the argument for UBI in this specific
context of automation.
Speaker 2 (27:56):
Well, proponents argue that if routine work is simply vanishing
due to automation, if the jobs just aren't there anymore,
UBI provides the necessary economic stability. It gives people the
breathing room to pursue long term retraining, maybe care for
family members, or even start small businesses. It ensures that
technological progress, which creates immense wealth, doesn't simultaneously lead to
(28:19):
mass poverty.
Speaker 1 (28:20):
So it's seen as a kind of moral or necessary
safety net in this new era.
Speaker 2 (28:24):
That's the argument, Yeah, in an era of potentially unprecedented
productivity gains driven by machines.
Speaker 1 (28:30):
And then there's the really controversial idea, the robot tax.
Speaker 2 (28:34):
Yeah, this is one of the most hotly debated proposals
out there. The basic idea is that tax companies that
automate heavily, maybe based on the number or value of
the robots or AI systems they deploy.
Speaker 1 (28:44):
What's the logic behind that.
Speaker 2 (28:46):
The logic is, well, since robots displace human workers who
used to pay income tax and payroll taxes, the robots
or the companies using them extensively, should somehow be taxed
to replace that lost public revenue, and that revenue would
be used for rev a new would then, in theory,
be used to fund social support programs like enhanced uninformant
benefits or those retraining initiatives for the workers who are
(29:08):
displaced by the automation itself.
Speaker 1 (29:10):
But the pushback against a robot tax is pretty intense
too write What are the main arguments against it?
Speaker 2 (29:16):
The primary argument against it is that it would stifle innovation.
Critics say taxing technology essentially amounts to taxing productivity gains,
which would make companies, say in the US, less competitive
against companies and countries that don't have such a tax. Plus,
there are huge logistical challenges. How do you even define
a robot for tax purposes. Do sophisticated scheduling software count
(29:39):
Is it only physical machines? Tricky very And if you
tax automation too heavily, companies might just shift their automated
manufacturing or logistics operations over these to avoid the tax,
which kind of defeats the whole purpose of generating domestic funding.
It really highlights this deep tension between wanting to foster
capital efficiency and innovation and ensuring social equid and worker support.
Speaker 1 (30:01):
So ultimately, the big question for these modern mega corporations
like Walmart is how do you balance that relentless pursuit
of efficiency, which, as you said, is almost necessary to
survive competitively, with the responsibility you arguably owe to your
long term employees and the communities that host your operations.
Speaker 2 (30:18):
That balancing act, it really demands an ethical framework, and
that framework has to be rooted in transparency and genuinely
robust support, meaning meaning companies need to be upfront with
workers about exactly what job changes are coming and ideally
when giving people actual time to plan and access programs
like LBU and stressing the need for robust support not
(30:41):
just technically free tuition, but maybe things like stipends to
cover living cost while training, or guaranteed schedule flexibility to
allow for training. That's paramount.
Speaker 1 (30:50):
Looking ahead, then what does the store or maybe the
retail operation of the future look like.
Speaker 2 (30:55):
In this context, it seems inevitably like it's going to
be a hybrid model. The future of work at Walmart
and probably across most of retail will likely combine streamlined
human labor with advanced complex automation.
Speaker 1 (31:07):
Humans and machines working together.
Speaker 2 (31:09):
Yeah, human workers will likely be augmented by the tech,
focusing more on tasks that require creativity, empathy, complex problem solving,
managing the overall customer experience, things machines aren't good at.
Speaker 1 (31:22):
While the machines handle.
Speaker 2 (31:23):
While the machines handle, the vast majority of the time
previously spent on purely repetitive logistics, stocking, data entry, basic transactions.
This requires real agility and serious long term structural planning
from everyone involved, the company, the workers themselves, and the
policy makers who set the rules of the economic environment.
Speaker 1 (31:43):
Okay, so let's try to synthesize this whole deep dive.
We've seen that AI adoption isn't just a choice for
a company the size of Walmart. It feels like an
operational necessity. It's driving efficiency games that were frankly unimaginable
just a decade or two ago.
Speaker 2 (31:57):
The benefits to the operation are.
Speaker 1 (31:59):
Undeniable, but that necessity stands in stark unavoidable contrast to
the huge structural challenge it poses to the traditional entry
level labor market, the market that has historically been the
economic backbone for so many individuals and communities.
Speaker 2 (32:12):
And the core conflict really remains this. The new types
of roles being created, the robot maintenance tech, the data analyst,
the supply chain specialist. They definitely offer higher wages better
job security, which is undeniably a positive outcome for those
who successfully make that transition. But the critical societal task,
(32:34):
the big IF, is ensuring that these new better roles
are genuinely and equitably accessible to the diverse workforce that
currently holds those entry level positions, especially those who might
lack advanced foundational education or the immediate financial means and
time to pursue extensive retraining.
Speaker 1 (32:53):
So the path forward needs more than just massive corporate
investment like LBU, doesn't it It seems to require a much
deeper policy conversation about vital responsibility in the face of
such rapid technological displacement.
Speaker 2 (33:03):
Absolutely, because you know that traditional entry level job it
was always far more than just a paycheck. It was
a first rung on the economic latter. It provided that
initial income, sure, but it also established a work history,
It taught essential professional skills like just showing up on
time teamwork, and it offered at least the possibility of
upward mobility within the company or the economy.
Speaker 1 (33:26):
And as AI accelerates the systematic elimination of these kinds
of roles.
Speaker 2 (33:30):
We have to urgently ask where does the next generation
find that first step or where do people re entering
the workforce after a break, Where do they now gain
their initial experience, their initial income, and maybe most importantly,
that foundational opportunity for advancement.
Speaker 1 (33:46):
This loss of the first rung, the very entry point
to sustained economic activity. That's perhaps the most critical long
term implication we've uncovered in this deep dive. It feels
like the foundation of workforce opportunity is fundamentally shifting under
our feet.
Speaker 2 (34:00):
Definitely something to keep thinking about.
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