The current state of the employment market is quite
interesting. The data on junior employment
companies that are adopting AI and really drinking the kool-aid
of AI is is really only moving in One Direction.
And right now across the S&P 500 headcount fell in 2025 for the
first time since 2016. That's a net reduction of around
400,000 jobs ending 8 consecutive years of just
constant workforce growth. This discussion is going to be
one of the most important discussions to have as a society
over the next few years, and there's some really compelling
arguments against worrying about it.
Some of the people that I'm going to mention in today's
episode have discussed the fact that there's been many waves of
technology that's reshaped, for example, programming and often
thinks the doomsday narrative mysteries, history.
But both things can be true at the same time.
We can be an optimistic and pessimistic view of the future.
Today's episode's going to explore those views, the data
points in the history behind them and why the companies that
are cutting junior headcount are going to make a decade long
mistake. This episode brings together
many forms of the latest research and thinking in this
field to try and present the most up to date.
View this in the loop with Jack Horton.
Hope you enjoy the show. So I guess let's start off with
some of the, the context and the data behind, you know, where
we're at today. And, and I think a really
interesting place to start here is, is a fantastic comparison
that I found online. And I, I fell in love with it.
So in January 1971, a billboard got put up on the, I was going
to say motorway, but in the US they're called highways around
Seattle. And it read with the last person
leaving Seattle, turn off the lights.
And, you know, Boeing, the context behind this is Boeing.
It had started 1969 with 134,000 employees and ended in 1971 with
about less than 80,000. So, you know, about 4445
thousand employees have been let go in 18 months.
They wound on the space program. They had a big supersonic, you
know, transport project that had been completely cancelled.
And Boeing claimed they didn't need engineers anymore.
But you look at today, and the aerospace industry is projecting
a shortage of more than a million engineers by 20.
Thirty, 25% of the current workforce is over 55 years old.
And you know that the cohort that would now say be the senior
experienced employees never ever got hired.
And that really sets the scene for today's discussion.
You know why not hiring junior employees is going to really
hurt the industry in 1015 or 20 years.
And this episode isn't about just whether or not AI is just
destroying jobs, which is often the version of this, I guess,
conversation that generates a lot of big headlines and noise.
It's actually just talking about the realities of talent
pipelines and specifically what happens when the entry point of
a career is closed before people have had the opportunity to
start on their journey. As I said, the S&P 500 across as
a whole in headcount fell in 2025, which was the first since
2016, a net reduction of about 400,000 jobs, which is ending
eight years of basically uninterrupted growth.
And and that's a massive signal. We've had the multiple research
projects released in the last year all pointing broadly in the
same direction, which is a companies that have adopted AI
junior employment drop of between about 7 to 10% within
about a year and a half, while senior employment at those same
places keeps rising. Doesn't take a genius to figure
out that that isn't sustainable. And this decline by the way, in
most places has happened through slower hiring rather than just
redundancies as a whole. So people have stopped bringing
people in at the bottom and and we've had another study for
example that drilled into the specifics of how that's
happening and they found that workers aged 22 to 25 in an AI
exposed job and we can discuss what that is defined as shortly,
are basically down 13% relative to their peers in less exposed
jobs since 2022-2023. For example, 22 to 25 year old,
let's say software engineers, it's around 20%.
So that's like nearly 25% of the developers that would have been
hired and no longer being hired anymore.
That's a huge cohort of people. And a really good telltale here
is that the gap between where employment should be on, let's
say pre ChatGPT employment trends is 500,000 less jobs than
was expected. So there are 500,000 less
developers than there was predicted based on the current
trajectory before ChatGPT released.
And it's a huge mistake for the industry.
Like yes, maybe some job roles are going to be compressed
unless you maybe there are new job roles that will emerge.
But not hiring junior employees and and training people is
obviously unsustainable. So let's explore why this is
happening to junior employees specifically because it's less
obvious than you think. Let's take Luis Garciano, who
who presented a really interesting argument here.
And Garciano is a Spanish economist at the London School
of Economics, and his main work has been exploring how
organization structure knowledge.
So who knows what who does what? You know, how information might
flow through a group of people. And he said a job isn't a list
of tasks that can be chopped up and automated arbitrarily.
It's a bundle, So a bundle of tasks that are entangled into
each other. The question AI pose isn't can
this task be automated? It's can this task be separated
from the rest of the job without unraveling the whole bundle of
tasks. And basically what he's saying
here is that a job isn't just a set checklist.
You know, some parts are completely independent and
freestanding, so you can lift them out, hand them to someone
else and nothing like breaks down.
Whereas some parts are woven into everything else and the
relationships, the judgement calls the accountability.
And if you try to separate those tasks, you make the entire job
role collapse. And junior work is typically
those who have the the most, let's say freestanding
independent tasks that can be automated.
But the problem here is that the only way to become a senior
advanced experienced employee is to spend years doing the junior
work. You know, the the first year
legal associates document review is separable, so you can
automate it. Same with the junior analysts,
you know, financial model or the graduates, I don't know,
research deck who they produce for, you know, leaders of the
company. These tasks can be just lifted
out and done by AI really quickly and easily.
And then the tasks that resist that kind of separation and
automation are those that are senior jobs reading, for
example, a witness in a disposition and deciding whether
to settle on or pricing, let's say a deal and managing the
board and the stakeholders through that deal.
Choosing which pitch to take, for example, into a chief
marketing officer and the person that reads it in the room once
you're there. These are these are very
entangled tasks that are heavily dependent and have a massive
impact on the relationships or contexts or consequences and
accountabilities that hold the whole job role together.
So your junior work doesn't have that protection.
And the pattern isn't unique to just software engineers.
It's happening in journalism, in architecture, basically
anywhere. The entry level work is
primarily about turning information into more structured
output versus having to exercise, you know, really
difficult judgement calls with real consequences attached.
But the problem is this creates a massive pipeline problem.
You know, if AI does the junior work, there's no economic reason
to ever hire a junior employee. And if nobody hires them, how do
they become experienced senior employees of a company?
You know, the pipeline didn't just slow down, it just
completely stops. So I guess that's a very doom
and gloom view. And, and there are arguments for
or against that point of view here.
And and let's say let's start on the optimistic side because it's
it's useful and really important to know.
So Tim O'Reilly, you might recognize his name O'Reilly
publications, but you got his books at school.
But he has some really interesting essays.
Now, he's been a developer in his entire life.
He's a very, very unique and fantastic point of view here.
And his argument is, is that he's been watching programming,
say, transition for, you know, 40 odd years.
And a lot of the time these types of panics misread history.
You know, every new programming wave from assembly to Fortran to
C to consumer BASIC to Windows to cloud look from when you
were, I guess, standing in the middle of it like the end of
programming. Every single one of them turned
out to be just an expansion of programming.
But the expansion wasn't just that the bar got lower, some
more of the same people could get involved.
Each way brought with it an entirely new skill set and
population that previously hadn't been involved.
So Fortran didn't make, for example, assembly programmers
pointless. It brought scientists and
engineers into computing. People would have never got
involved with machine code. Consumer Basic didn't, let's
say, replace C program as it brought in teenagers in the
bedrooms, A demographic who couldn't have ever been involved
in the industry. Operating systems like Windows
didn't stop people building applications.
It meant that an entire industry of applications is born as a
result. So the pyramid got wider every
single level, and the people from the previous way didn't
disappear, they just moved up. Their skills became foundations
that basically others just would build on, rather than suddenly
this new ceiling that meant that they were screwed and they had
to leave the job. Now personally, I used to buy
into this belief and recently I've been wondering whether that
is still the case. Now, one argument is the Javon's
paradox. So I've talked about this a
number of times. And this is when something
becomes so readily available, whether it's affordable or easy
to do, whatever it might be efficient, suddenly there's an
explosion of that thing. At first it looks like the
efficiency gain from the technology that enabled it would
mean that people would lose their jobs.
But just as the as a result of the sheer adoption of a thing,
there is so many more applications and jobs as a
result. So, you know, steam didn't
remove the need for coal, it actually increased it because
efficiency made steam power economically viable in
applications where previously it would have been impossible to
justify the cost. You can apply that to
engineering, which is, you know, making people 1020 more 20 times
more productive might seem like you may need less of them.
But over time the argument is that something there's loads of
new tools that you would build that would never have ever been
justified or or even considered. And as a result, more engineers
needed. Now where I have an issue with
this, to be honest, is the time scale.
This argument is basically about where the industry could be in
2035 or 2040, but the pipeline problem I'm really discussing
here is about 2028 to 2033. You know, if there's no genius
in 20252026, who were the seniors by 20-30?
That requires a specific cohort to have been in the job for
five, 6-7 years, and those people aren't being hired.
You can't suddenly have more software getting built in 2035
and needing more employees and and hires but having less people
to hire because there are just less careers there.
And the Jevons paradox is essentially an aggregate outcome
across the entire economy over a longer period of time.
So it's the culmination of all these things happening over a
10/15/20 year period. But that pipeline problem that
I'm actually discussing is about a very specific set period of
time. To have seniors in the job for
7-8 years, they need to have been hired 7-8 years ago.
Another argument against that I guess positive outlook right
now, and they're doing just seeing somewhat negative here is
that every previous say technology wave, so Fortran
BASIC cloud essentially arrived after the experience of the
previous layer had already kind of been established.
Fortran programmers had spent years working in Fortran before
it then got abstracted away. C programmers broad our
understanding of same memory management into JavaScript and
each abstraction essentially is being built on the last group of
people who had actually done the thing.
The wave would come after the experience form, not before it.
But this current wave is just happening so much faster.
You know something really is happening to entry level work
faster than any previous transition in history.
Although the Chevron's paradox in the long run, 1520 years,
period, period of time might be true, there might be just such
an explosion of things over this next long period that eventually
there's more, there's more jobs, but over the shorter window,
companies are making a massive mistake by not thinking about
their junior pipelines. So the Gevons paradox argument,
that's very optimistic and the pipeline problem that I'm really
discussing here aren't competing.
It's kind of like a sequence of events.
There needs to be an expansion of demand, but the expertise
just isn't there anymore to meet it.
And you eventually get that shortage that I mentioned about
Boeing facing in the aerospace industry.
And that will happen in 2035 rather than in 50 years later,
but it will happen. So I guess to conclude here that
the arguments to say that each transition of new technology and
say programmers and software development really did expand
the surface area the the amount of potential in the space.
And that is a historical analogy that will probably play out to
be true. But the problem right now, and
This is why I think companies are making a massive mistake, is
that the cohort that will become the leaders of a company in 2032
and 2035 aren't being hired and therefore won't be trained.
And there are companies that are doing the right thing here.
IBM tripled its entry level intake in 2026, one of the very
few companies that had done done this.
Publicist, the marketing group tried to restrict its graduate
training program around AI fluency rather than just getting
rid of it. But these are a drop in the
ocean in terms of the general statistics across the the market
right now. And that that billboard that
people put up in Seattle, it's a joke, but it's a reality.
Now, 50 years later, the aerospace companies who made
those cuts are now probably, looking back, really regretting
some stupid decisions. And many of these big, big
companies especially are going to do the same and are going to
make the same mistake in the future.
Anyway, thank you for listening and I'll see you next week.