June 17, 2025 • 14 mins
With all the hoopla around AI as a hiring tool, we check in with two experts to understand the advantages - and disadvantages - of using machines to make hiring decisions - a fascinating discussion full of tips and insights for job seekers and recruiters alike.
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Episode Transcript
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Speaker 1 (00:05):
Welcome back to on the Job. I'm Avery Thompson. If
you've applied for a job recently, chances are some filtering
software saw your resume before any human did. That is,
if a human saw it at all. Today we're exploring
how artificial intelligence is transforming how and who we hire.
And I've got not one, but two experts in the
(00:27):
field to help us separate fact from fiction on what
happens after we hit submit.
Speaker 2 (00:33):
My nice being e Banks. I'm the CEO and head
of Research for Lighthouse Research and Advisory, also the author
of Artificial Intelligence for HR, among other books.
Speaker 3 (00:41):
So Peter Capelli, I am the George W. Taylor Professor
of Management. I also direct the Center for Human Resources
at the Wharton School.
Speaker 1 (00:51):
So, whether you're on the job hunt or the one
doing the hiring, or like me, just curious what all
this AI hubbub is about. Stick around. When you ask
most little kids what they want to do when they
grow up, you tend to hear some familiar answers, things
like astronaut, fireman, professional athlete. That wasn't the case with
(01:13):
Ben Eubanks.
Speaker 2 (01:14):
I wanted to be an HR and as a kid,
so I watched my parents working in their small business.
They had trouble hiring people, mainteninent benefits, keeping them long term,
all those things, and I thought, I'll figure out to
do that thing, whatever that is.
Speaker 1 (01:24):
For Peter Capelli, well, human resources wasn't exactly his childhood dream.
Like Ben, he also recognized a system that needed fixing.
Speaker 3 (01:33):
It was kind of the discovery of just how bad
it was.
Speaker 1 (01:37):
That discovery led Peter to a career in academia. And
the more he came to learn about how we hire
in this country, the worse it seemed. To get.
Speaker 3 (01:46):
What we're looking for. I think as researchers is something
which is a surprise. That's the big win for us.
And this was a surprise, just unfortunately a band surprise,
but surprising nonetheless.
Speaker 1 (01:57):
And I think it's fair to say Ben agrees the
hiring process has always had issues always to that end,
AI is being heralded as a potential game changer in
the field, able to not only process thousands of applications
and seconds, but also uncover relationships and patterns and data
that humans likely miss and Ben Eubanks says that initially
(02:21):
most people in the field looked at the arrival of
AI with open arms.
Speaker 2 (02:25):
There are some opportunities where AI can play and let
us do things we could have never done unless we
had an unlimited budget and unlimited people on the recruiting team.
There's not a company out there like that. So the
cause of that, everybody's trying to get an infinite amount
of work done with a finite amount of people hours.
Speaker 1 (02:41):
But that doesn't mean these companies necessarily knew what they
were working with.
Speaker 2 (02:45):
I don't expect HR professionals, recruiters, learning leaders. I don't
expect any of them to be experts in technology and
data science and AI. I don't expect that because they
have a full time job already.
Speaker 1 (02:55):
And Peter Capelli says, CEOs, we're guilty as anyone forgetting
caught up in the AI hype.
Speaker 3 (03:03):
The people at the very top of the companies. These
are the CEOs in particular. I think that large language
models and generative AI are just plug and play things.
You just asked chat gbtwo to do your job for you,
and then they're going to do it right. And as
a result, they are pushing lower down in HR in
(03:23):
particular to use more and more AI.
Speaker 1 (03:29):
This plug and play approach. As Peter A. Capelli calls.
It is something Ben Ubanks is all too familiar with,
as human resources personnel try to outsource a lot of
their work.
Speaker 2 (03:39):
Everybody says, letay, I do the work that's kind of repetitive,
and we can put the humans in on the strategic stuff.
We can focus on the higher value work. That's the
big promise of it is we're doing a lot of things.
There's not a single person ever met that got into
the worldoking resources like I can't wait to look at
a stack of resumes, or I can't wait to go
and schedule interviews and then have to schedule inities, and
(04:00):
then that manager is six and then with the reschedule like,
oh gosh, no, they'll pull your hair out. So no
one loves those things. Those are all that the promises.
Is not doing those things anymore.
Speaker 1 (04:10):
And while AI can sort out that stack of resumes
in the blink of an eye, it's becoming clear that
these systems aren't always doing a good job.
Speaker 3 (04:19):
And the thing that was most prevalent was discrimination. A
fair amount of evidence that women and also minorities of
various kinds got systematically lower scores on performance appraisals.
Speaker 2 (04:33):
There could be a potential bias in there, and so
when I'm seeing that the best companies do their buildings,
technology is thinking about that, planning for it. But that
doesn't completely mitigate that risk.
Speaker 1 (04:42):
It's no surprise that both Ben and Peter brought up biases,
because when it comes to hiring, that's always been the
elephant in the room.
Speaker 3 (04:51):
We are just full of biases, and particularly if you've
got hiring managers who are not trained. You know, one
of the dumber things we've done as a business community
employer community is that we've cut back on recruiters, who
are people who do this for living, you know, hiring
find people, assess them, and we've pushed those tasks onto
(05:12):
line managers, who are people who don't do this for
a living and they've got other full time jobs.
Speaker 1 (05:19):
No matter how equitable and objective a company may strive
to be, humans are inherently prone to biases. So it
makes sense that some in the HR field saw AI
as a potential silver bullet, a purely analytical tool that
could bypass all of our human foibles. No longer would
we need to rely on gut feelings. Instead, we can
(05:42):
just press a button and let the numbers speak for
themselves that there's.
Speaker 2 (05:46):
Some pretty wild data, pretty compelling that's coming out. There's
an experiment done with recruiters that shows that when they're
using algorithms, if they think are very predictive, they're more
likely to take their hands off the wheel and let
the car drive itself. And we generally know that it's
not a good idea.
Speaker 1 (06:02):
And unfortunately, as Peter Capelli explains, these AI systems were
built by humans and rely on historical data that inevitably
reflects the very inequalities we're trying to move away from.
Speaker 3 (06:15):
There is a particular problem that is unique to large
language models and machine learning in particular for hiring. The
data that you're using to train the model is historical, right,
it can't all happen today, and anything strange going on
in the labor market ten years ago will show up
(06:37):
being built into the model. And the thing that was
most prevalent was discrimination.
Speaker 1 (06:45):
As an example, imagine a company builds a screening system
to identify the best applicants, only to realize that system
was filtering out all the women who applied. Unfortunately, that's
not science fiction, but what actually happened. And at Amazon
back in twenty fifteen.
Speaker 3 (07:03):
And even if you took out names and other attributes,
it would still find in the algorithm anything that was
associated with being a woman, like your college courses. You know,
if you took women's studies courses, more likely be a
woman maybe, And so it would still give women lower
(07:23):
scores because historically that was one of the predictors. Any
biases that were in the data earlier are going to
be codified into the algorithm.
Speaker 1 (07:35):
Not sounding like the answer to all our hiring problems now,
is it? In fairness, though, these are the growing pains
of a new technology, one whose powers and limitations were
still discovering.
Speaker 2 (07:48):
See, AI is not a decision engine. It's a recommendation engine,
especially in hiring it should be, and so it's recommending
this path, and it's giving me inputs, and I, as
the HR person or the recruiter, can make a choice
on what to do next.
Speaker 1 (08:01):
But companies aren't the only ones trying to utilize the
power of AI. Job applicants are also giving these new
technologies to go. So when we come back from the break,
we'll explore how AI is helping job seekers land their
next gig.
Speaker 4 (08:17):
Hiring is a lonely business posting jobs sifting through resumes
interviews with unqualified applicants being ghosted. What if you could
link with dozens of qualified candidates who are ready to
work right now with just one connection. That's Express Employment Professionals.
Find us at expresspros dot com. One connection the workforce,
(08:41):
you need problem solved, Express Employment Professionals, expresspros dot Com.
Speaker 1 (08:48):
We're back with our human resources experts, Ben Ubanks and
Peter Cappelli. In the first half of the episode, we
uncovered how HR departments and staffing agencies are incorporating AI
into their hiring processes. But they aren't the only ones
utilizing this powerful new tool. As companies increasingly automated hiring,
(09:08):
job seekers are also turning to AI.
Speaker 2 (09:11):
We're seeing in our research one out of every three
candidate says they're either using AI to help apply for
jobs or they're going to start. So it becomes almost
an arms race. If everybody else is doing it and
I'm not, I'll be shut out the market.
Speaker 1 (09:24):
But can you blame these applicants. Anyone who's ever applied
for a job knows it's a tedious, time consuming process
and often hitting submit feels like we're just throwing our
resumes into a digital waste paper basket. And unfortunately, there's
actually a fair bit of data to back this up.
Speaker 3 (09:42):
A couple of years ago. The last time I look
the probability that you're going to get hired if you
send your application into one of the big job where
it's like Indeed or one of those, that was about
two percent. So how many people would actually look at it, say,
the short list is double that, so maybe you got
a four percent chance. Don't expect that people are sitting
(10:03):
around at night debating the merits of your resume.
Speaker 1 (10:06):
It seems that job seekers have responded to these incredibly
low success rates by flooding the market. Would you, at
least in theory makes sense.
Speaker 2 (10:15):
Let's say I'm tired of applying for jobs. I can
go to a lazy apply or some other websites I
can pay a little fee, and they're automatically applying to
hundreds or thousands of jobs on my behalf without me
ever lifting another finger to do that. That's happening right now,
and employers are getting flooded by resumes because of this.
I talked to a company the other day that said we
used to get twenty thousand resumes a month. Big global company.
(10:37):
Now we're getting twenty thousand resumes every two and a
half days.
Speaker 1 (10:41):
So, Peter, are we at a state where it's just
computers applying to computers? Yeah?
Speaker 3 (10:46):
I think we probably are now. I guess the slight
caveat to that is it has to start and be
based on something real. That is, you would think any
smart employer, their selection software is ba them what they
really want. And if you're an applicant, your experience really
is a little unique. So it isn't completely standardized at
(11:11):
least if it's sensible. But is it automated? Still? Yeah,
it is.
Speaker 1 (11:18):
I gotta say I find it refreshing to hear two
people more knowledgeable about AI not talking to me about
it like a couple of used car salesmen. Sure, AI
will probably transform things in our world, including the way
we hire, but it doesn't seem like it's going to
happen overnight. In the meanwhile, Ben Ubanks and Peter Cappelli
(11:39):
suggest companies take a more cautious and considerate approach to
how they incorporate these technologies.
Speaker 2 (11:46):
Well, I'm doing a keynote on this topic of AI.
The headline in the Guardian says, job hunting and the
age of AI is grim, frustrating, dehumanizing. When I ask
an audience of HR leaders, hey, how many of you
put your hands up if that's what you want your
job start process to look like at your company? Not
a single hand goes up, Absolutely not. But that's what
we end up with if we're using AI because someone
(12:07):
told us to quote unquote get to an AI.
Speaker 1 (12:09):
And we need to remember that these advanced systems are
only as good as the information and we're feeding them.
So before we look to them for the answers to
all of our future problems, we might want to first
look back at how we fall in short in our
own past.
Speaker 2 (12:24):
Every company out there that takes a job description out
of the old vault blows all the dust off and says, Okay,
let's use the same one from five years ago to
hire this job again. You are doing your company to
service because that job has changed. I can guarantee you
it has changed to some degree. It's an imperfect system, Honestly, Avery,
we're using imperfect job descriptions with imperfect resumes and trying
(12:44):
to make a mess out of that. It's like getting
two boxes of puzzle pieces mixing all together and try
to make one picture, and it's really hard to do.
Speaker 3 (12:50):
What happens now, for the most part, is that we
hire people who we like, and those tend to be
people who are similar to us.
Speaker 1 (13:01):
So, after all this discussion about AI's potential and its pitfalls,
I wanted to end the conversation with some concrete advice
for those of you listening who are struggling to find
a job, and you might be surprised by what they recommended.
Speaker 2 (13:16):
I'm going to tell you something that is going to
upset anyone to know that's in the recruiting world, and
is going to be helpful anyone who's looking for a job.
But the best way to beat the system is to
stay out of the system. Find a human, find a connection,
find a relationship, and play that up.
Speaker 3 (13:34):
One of my kids had a real problem getting a
response from a company he had applied to, and eventually
I told him write them a letter, physical letter. I
don't know whether that's why he heard from them or not,
but I'd like to think it is because they don't
want thousands of people pinging them about jobs. They don't
have time to read them, but a letter. There aren't
(13:55):
very many come in so might very well get to
the person.
Speaker 1 (14:01):
There you have it, folks, what's old is new again.
So maybe instead of spending money in the latest and
greatest AI subscription, we should all go out and buy
some Forever stamps before they go up and price again.
For on the job, I'm Avery Thompson.
Welcome back to on the Job. I'm Avery Thompson. If
you've applied for a job recently, chances are some filtering
software saw your resume before any human did. That is,
if a human saw it at all. Today we're exploring
how artificial intelligence is transforming how and who we hire.
And I've got not one, but two experts in the
(00:27):
field to help us separate fact from fiction on what
happens after we hit submit.
Speaker 2 (00:33):
My nice being e Banks. I'm the CEO and head
of Research for Lighthouse Research and Advisory, also the author
of Artificial Intelligence for HR, among other books.
Speaker 3 (00:41):
So Peter Capelli, I am the George W. Taylor Professor
of Management. I also direct the Center for Human Resources
at the Wharton School.
Speaker 1 (00:51):
So, whether you're on the job hunt or the one
doing the hiring, or like me, just curious what all
this AI hubbub is about. Stick around. When you ask
most little kids what they want to do when they
grow up, you tend to hear some familiar answers, things
like astronaut, fireman, professional athlete. That wasn't the case with
(01:13):
Ben Eubanks.
Speaker 2 (01:14):
I wanted to be an HR and as a kid,
so I watched my parents working in their small business.
They had trouble hiring people, mainteninent benefits, keeping them long term,
all those things, and I thought, I'll figure out to
do that thing, whatever that is.
Speaker 1 (01:24):
For Peter Capelli, well, human resources wasn't exactly his childhood dream.
Like Ben, he also recognized a system that needed fixing.
Speaker 3 (01:33):
It was kind of the discovery of just how bad
it was.
Speaker 1 (01:37):
That discovery led Peter to a career in academia. And
the more he came to learn about how we hire
in this country, the worse it seemed. To get.
Speaker 3 (01:46):
What we're looking for. I think as researchers is something
which is a surprise. That's the big win for us.
And this was a surprise, just unfortunately a band surprise,
but surprising nonetheless.
Speaker 1 (01:57):
And I think it's fair to say Ben agrees the
hiring process has always had issues always to that end,
AI is being heralded as a potential game changer in
the field, able to not only process thousands of applications
and seconds, but also uncover relationships and patterns and data
that humans likely miss and Ben Eubanks says that initially
(02:21):
most people in the field looked at the arrival of
AI with open arms.
Speaker 2 (02:25):
There are some opportunities where AI can play and let
us do things we could have never done unless we
had an unlimited budget and unlimited people on the recruiting team.
There's not a company out there like that. So the
cause of that, everybody's trying to get an infinite amount
of work done with a finite amount of people hours.
Speaker 1 (02:41):
But that doesn't mean these companies necessarily knew what they
were working with.
Speaker 2 (02:45):
I don't expect HR professionals, recruiters, learning leaders. I don't
expect any of them to be experts in technology and
data science and AI. I don't expect that because they
have a full time job already.
Speaker 1 (02:55):
And Peter Capelli says, CEOs, we're guilty as anyone forgetting
caught up in the AI hype.
Speaker 3 (03:03):
The people at the very top of the companies. These
are the CEOs in particular. I think that large language
models and generative AI are just plug and play things.
You just asked chat gbtwo to do your job for you,
and then they're going to do it right. And as
a result, they are pushing lower down in HR in
(03:23):
particular to use more and more AI.
Speaker 1 (03:29):
This plug and play approach. As Peter A. Capelli calls.
It is something Ben Ubanks is all too familiar with,
as human resources personnel try to outsource a lot of
their work.
Speaker 2 (03:39):
Everybody says, letay, I do the work that's kind of repetitive,
and we can put the humans in on the strategic stuff.
We can focus on the higher value work. That's the
big promise of it is we're doing a lot of things.
There's not a single person ever met that got into
the worldoking resources like I can't wait to look at
a stack of resumes, or I can't wait to go
and schedule interviews and then have to schedule inities, and
(04:00):
then that manager is six and then with the reschedule like,
oh gosh, no, they'll pull your hair out. So no
one loves those things. Those are all that the promises.
Is not doing those things anymore.
Speaker 1 (04:10):
And while AI can sort out that stack of resumes
in the blink of an eye, it's becoming clear that
these systems aren't always doing a good job.
Speaker 3 (04:19):
And the thing that was most prevalent was discrimination. A
fair amount of evidence that women and also minorities of
various kinds got systematically lower scores on performance appraisals.
Speaker 2 (04:33):
There could be a potential bias in there, and so
when I'm seeing that the best companies do their buildings,
technology is thinking about that, planning for it. But that
doesn't completely mitigate that risk.
Speaker 1 (04:42):
It's no surprise that both Ben and Peter brought up biases,
because when it comes to hiring, that's always been the
elephant in the room.
Speaker 3 (04:51):
We are just full of biases, and particularly if you've
got hiring managers who are not trained. You know, one
of the dumber things we've done as a business community
employer community is that we've cut back on recruiters, who
are people who do this for living, you know, hiring
find people, assess them, and we've pushed those tasks onto
(05:12):
line managers, who are people who don't do this for
a living and they've got other full time jobs.
Speaker 1 (05:19):
No matter how equitable and objective a company may strive
to be, humans are inherently prone to biases. So it
makes sense that some in the HR field saw AI
as a potential silver bullet, a purely analytical tool that
could bypass all of our human foibles. No longer would
we need to rely on gut feelings. Instead, we can
(05:42):
just press a button and let the numbers speak for
themselves that there's.
Speaker 2 (05:46):
Some pretty wild data, pretty compelling that's coming out. There's
an experiment done with recruiters that shows that when they're
using algorithms, if they think are very predictive, they're more
likely to take their hands off the wheel and let
the car drive itself. And we generally know that it's
not a good idea.
Speaker 1 (06:02):
And unfortunately, as Peter Capelli explains, these AI systems were
built by humans and rely on historical data that inevitably
reflects the very inequalities we're trying to move away from.
Speaker 3 (06:15):
There is a particular problem that is unique to large
language models and machine learning in particular for hiring. The
data that you're using to train the model is historical, right,
it can't all happen today, and anything strange going on
in the labor market ten years ago will show up
(06:37):
being built into the model. And the thing that was
most prevalent was discrimination.
Speaker 1 (06:45):
As an example, imagine a company builds a screening system
to identify the best applicants, only to realize that system
was filtering out all the women who applied. Unfortunately, that's
not science fiction, but what actually happened. And at Amazon
back in twenty fifteen.
Speaker 3 (07:03):
And even if you took out names and other attributes,
it would still find in the algorithm anything that was
associated with being a woman, like your college courses. You know,
if you took women's studies courses, more likely be a
woman maybe, And so it would still give women lower
(07:23):
scores because historically that was one of the predictors. Any
biases that were in the data earlier are going to
be codified into the algorithm.
Speaker 1 (07:35):
Not sounding like the answer to all our hiring problems now,
is it? In fairness, though, these are the growing pains
of a new technology, one whose powers and limitations were
still discovering.
Speaker 2 (07:48):
See, AI is not a decision engine. It's a recommendation engine,
especially in hiring it should be, and so it's recommending
this path, and it's giving me inputs, and I, as
the HR person or the recruiter, can make a choice
on what to do next.
Speaker 1 (08:01):
But companies aren't the only ones trying to utilize the
power of AI. Job applicants are also giving these new
technologies to go. So when we come back from the break,
we'll explore how AI is helping job seekers land their
next gig.
Speaker 4 (08:17):
Hiring is a lonely business posting jobs sifting through resumes
interviews with unqualified applicants being ghosted. What if you could
link with dozens of qualified candidates who are ready to
work right now with just one connection. That's Express Employment Professionals.
Find us at expresspros dot com. One connection the workforce,
(08:41):
you need problem solved, Express Employment Professionals, expresspros dot Com.
Speaker 1 (08:48):
We're back with our human resources experts, Ben Ubanks and
Peter Cappelli. In the first half of the episode, we
uncovered how HR departments and staffing agencies are incorporating AI
into their hiring processes. But they aren't the only ones
utilizing this powerful new tool. As companies increasingly automated hiring,
(09:08):
job seekers are also turning to AI.
Speaker 2 (09:11):
We're seeing in our research one out of every three
candidate says they're either using AI to help apply for
jobs or they're going to start. So it becomes almost
an arms race. If everybody else is doing it and
I'm not, I'll be shut out the market.
Speaker 1 (09:24):
But can you blame these applicants. Anyone who's ever applied
for a job knows it's a tedious, time consuming process
and often hitting submit feels like we're just throwing our
resumes into a digital waste paper basket. And unfortunately, there's
actually a fair bit of data to back this up.
Speaker 3 (09:42):
A couple of years ago. The last time I look
the probability that you're going to get hired if you
send your application into one of the big job where
it's like Indeed or one of those, that was about
two percent. So how many people would actually look at it, say,
the short list is double that, so maybe you got
a four percent chance. Don't expect that people are sitting
(10:03):
around at night debating the merits of your resume.
Speaker 1 (10:06):
It seems that job seekers have responded to these incredibly
low success rates by flooding the market. Would you, at
least in theory makes sense.
Speaker 2 (10:15):
Let's say I'm tired of applying for jobs. I can
go to a lazy apply or some other websites I
can pay a little fee, and they're automatically applying to
hundreds or thousands of jobs on my behalf without me
ever lifting another finger to do that. That's happening right now,
and employers are getting flooded by resumes because of this.
I talked to a company the other day that said we
used to get twenty thousand resumes a month. Big global company.
(10:37):
Now we're getting twenty thousand resumes every two and a
half days.
Speaker 1 (10:41):
So, Peter, are we at a state where it's just
computers applying to computers? Yeah?
Speaker 3 (10:46):
I think we probably are now. I guess the slight
caveat to that is it has to start and be
based on something real. That is, you would think any
smart employer, their selection software is ba them what they
really want. And if you're an applicant, your experience really
is a little unique. So it isn't completely standardized at
(11:11):
least if it's sensible. But is it automated? Still? Yeah,
it is.
Speaker 1 (11:18):
I gotta say I find it refreshing to hear two
people more knowledgeable about AI not talking to me about
it like a couple of used car salesmen. Sure, AI
will probably transform things in our world, including the way
we hire, but it doesn't seem like it's going to
happen overnight. In the meanwhile, Ben Ubanks and Peter Cappelli
(11:39):
suggest companies take a more cautious and considerate approach to
how they incorporate these technologies.
Speaker 2 (11:46):
Well, I'm doing a keynote on this topic of AI.
The headline in the Guardian says, job hunting and the
age of AI is grim, frustrating, dehumanizing. When I ask
an audience of HR leaders, hey, how many of you
put your hands up if that's what you want your
job start process to look like at your company? Not
a single hand goes up, Absolutely not. But that's what
we end up with if we're using AI because someone
(12:07):
told us to quote unquote get to an AI.
Speaker 1 (12:09):
And we need to remember that these advanced systems are
only as good as the information and we're feeding them.
So before we look to them for the answers to
all of our future problems, we might want to first
look back at how we fall in short in our
own past.
Speaker 2 (12:24):
Every company out there that takes a job description out
of the old vault blows all the dust off and says, Okay,
let's use the same one from five years ago to
hire this job again. You are doing your company to
service because that job has changed. I can guarantee you
it has changed to some degree. It's an imperfect system, Honestly, Avery,
we're using imperfect job descriptions with imperfect resumes and trying
(12:44):
to make a mess out of that. It's like getting
two boxes of puzzle pieces mixing all together and try
to make one picture, and it's really hard to do.
Speaker 3 (12:50):
What happens now, for the most part, is that we
hire people who we like, and those tend to be
people who are similar to us.
Speaker 1 (13:01):
So, after all this discussion about AI's potential and its pitfalls,
I wanted to end the conversation with some concrete advice
for those of you listening who are struggling to find
a job, and you might be surprised by what they recommended.
Speaker 2 (13:16):
I'm going to tell you something that is going to
upset anyone to know that's in the recruiting world, and
is going to be helpful anyone who's looking for a job.
But the best way to beat the system is to
stay out of the system. Find a human, find a connection,
find a relationship, and play that up.
Speaker 3 (13:34):
One of my kids had a real problem getting a
response from a company he had applied to, and eventually
I told him write them a letter, physical letter. I
don't know whether that's why he heard from them or not,
but I'd like to think it is because they don't
want thousands of people pinging them about jobs. They don't
have time to read them, but a letter. There aren't
(13:55):
very many come in so might very well get to
the person.
Speaker 1 (14:01):
There you have it, folks, what's old is new again.
So maybe instead of spending money in the latest and
greatest AI subscription, we should all go out and buy
some Forever stamps before they go up and price again.
For on the job, I'm Avery Thompson.
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